# Patent

## Title
Method of designing siRNAs for gene silencing

## Abstract

The present invention provides a method for identifying siRNA target motifs in a transcript using a position-specific score matrix approach. The invention also provides a method for identifying off-target genes of an siRNA using a position-specific score matrix approach. The invention further provides a method for designing siRNAs with higher silencing efficacy and specificity. The invention also provides a library of siRNAs comprising siRNAs with high silencing efficacy and specificity.

## Background
This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application No. 60/572,314, filed on May 17, 2004, and U.S. Provisional Patent Application No. 60/515,180, filed on Oct. 27, 2003, each of which is incorporated by reference herein in its entirety.


1. FIELD OF THE INVENTION
The present invention relates to methods for identifying siRNA target motifs in a transcript. The invention also relates to methods for identifying off-target genes of an siRNA. The invention further relates to methods for designing siRNAs with higher silencing efficacy and specificity. The invention also relates to a library of siRNAs comprising siRNAs with high silencing efficacy and specificity.
2. BACKGROUND OF THE INVENTION
RNA interference (RNAi) is a potent method to suppress gene expression in mammalian cells, and has generated much excitement in the scientific community (Couzin, 2002, Science 298:2296-2297; McManus et al., 2002, Nat. Rev. Genet. 3, 737-747; Hannon, G. J., 2002, Nature 418, 244-251; Paddison et al., 2002, Cancer Cell 2, 17-23). RNA interference is conserved throughout evolution, from C. elegans to humans, and is believed to function in protecting cells from invasion by RNA viruses. When a cell is infected by a dsRNA virus, the dsRNA is recognized and targeted for cleavage by an RNaseIII-type enzyme termed Dicer. The Dicer enzyme “dices” the RNA into short duplexes of 21 nt, termed siRNAs or short-interfering RNAs, composed of 19 nt of perfectly paired ribonucleotides with two unpaired nucleotides on the 3′ end of each strand. These short duplexes associate with a multiprotein complex termed RISC, and direct this complex to mRNA transcripts with sequence similarity to the siRNA As a result, nucleases present in the RISC complex cleave the mRNA transcript, thereby abolishing expression of the gene product. In the case of viral infection, this mechanism would result in destruction of viral transcripts, thus preventing viral synthesis. Since the siRNAs are double-stranded, either strand has the potential to associate with RISC and direct silencing of transcripts with sequence similarity.
Specific gene silencing promises the potential to harness human genome data to elucidate gene function, identify drug targets, and develop more specific therapeutics. Many of these applications assume a high degree of specificity of siRNAs for their intended targets. Cross-hybridization with transcripts containing partial identity to the siRNA sequence may elicit phenotypes reflecting silencing of unintended transcripts in addition to the target gene. This could confound the identification of the gene implicated in the phenotype. Numerous reports in the literature purport the exquisite specificity of siRNAs, suggesting a requirement for near-perfect identity with the siRNA sequence (Elbashir et al., 2001. EMBO J. 20:6877-6888; Tuschl et al., 1999, Genes Dev. 13:3191-3197; Hutvagner et al., Sciencexpress 297:2056-2060). One recent report suggests that perfect sequence complementarity is required for siRNA-targeted transcript cleavage, while partial complementarity will lead to translational repression without transcript degradation, in the manner of microRNAs (Hutvagner et al., Sciencexpress 297:2056-2060).
The biological function of small regulatory RNAs, including siRNAs and miRNAs is not well understood. One prevailing question regards the mechanism by which the distinct silencing pathways of these two classes of regulatory RNA are determined. miRNAs are regulatory RNAs expressed from the genome, and are processed from precursor stem-loop structures to produce single-stranded nucleic acids that bind to sequences in the 3′ UTR of the target mRNA (Lee et al., 1993, Cell 75:843-854; Reinhart et al., 2000, Nature 403:901-906; Lee et al., 2001, Science 294:862-864; Lau et al., 2001, Science 294:858-862; Hutvagner et al., 2001, Science 293:834-838). miRNAs bind to transcript sequences with only partial complementarity (Zeng et al., 2002, Molec. Cell 9:1327-1333) and repress translation without affecting steady-state RNA levels (Lee et al., 1993, Cell 75:843-854; Wightman et al., 1993, Cell 75:855-862). Both miRNAs and siRNAs are processed by Dicer and associate with components of the RNA-induced silencing complex (Hutvagner et al., 2001, Science 293:834-838; Grishok et al., 2001, Cell 106: 23-34; Ketting et al., 2001, Genes Dev. 15:2654-2659; Williams et al., 2002, Proc. Natl. Acad Sci USA 99:6889-6894; Hammond et al., 2001, Science 293:1146-1150; Mourlatos et al., 2002, Genes Dev. 16:720-728). A recent report (Hutvagner et al., 2002, Sciencexpress 297:2056-2060) hypothesizes that gene regulation through the miRNA pathway versus the siRNA pathway is determined solely by the degree of complementarity to the target transcript. It is speculated that siRNAs with only partial identity to the mRNA target will function in translational repression, similar to an miRNA, rather than triggering RNA degradation.
It has also been shown that siRNA and shRNA can be used to silence genes in vivo. The ability to utilize siRNA and shRNA for gene silencing in vivo has the potential to enable selection and development of siRNAs for therapeutic use. A recent report highlights the potential therapeutic application of siRNAs. Fas-mediated apoptosis is implicated in a broad spectrum of liver diseases, where lives could be saved by inhibiting apoptotic death of hepatocytes. Song (Song et al. 2003, Nat. Medicine 9, 347-351) injected mice intravenously with siRNA targeted to the Fas receptor. The Fas gene was silenced in mouse hepatocytes at the mRNA and protein levels, prevented apoptosis, and protected the mice from hepatitis-induced liver damage. Thus, silencing Fas expression holds therapeutic promise to prevent liver injury by protecting hepatocytes from cytotoxicity. As another example, injected mice intraperitoneally with siRNA targeting TNF-a. Lipopolysaccharide-induced TNF-a gene expression was inhibited, and these mice were protected from sepsis. Collectively, these results suggest that siRNAs can function in vivo, and may hold potential as therapeutic drugs (Sorensen et al., 2003, J. Mol. Biol. 327, 761-766).
Martinez et al. reported that RNA interference can be used to selectively target oncogenic mutations (Martinez et al., 2002, Proc. Natl. Acad Sci. USA 99:14849-14854). In this report, an siRNA that targets the region of the R248W mutant of p53 containing the point mutation was shown to silence the expression of the mutant p53 but not the wild-type p53.
Wilda et al. reported that an siRNA targeting the M-BCR/ABL fusion mRNA can be used to deplete the M-BCR/ABL mRNA and the M-BRC/ABL oncoprotein in leukemic cells (Wilda et al., 2002, Oncogene 21:5716-5724). However, the report also showed that applying the siRNA in combination with Imatinib, a small-molecule ABL kinase tyrosine inhibitor, to leukemic cells did not further increase in the induction of apoptosis.
U.S. Pat. No. 6,506,559 discloses a RNA interference process for inhibiting expression of a target gene in a cell. The process comprises introducing partially or fully doubled-stranded RNA having a sequence in the duplex region that is identical to a sequence in the target gene into the cell or into the extracellular environment. RNA sequences with insertions, deletions, and single point mutations relative to the target sequence are also found as effective for expression inhibition.
U.S. Patent Application Publication No. US 2002/0086356 discloses RNA interference in a Drosophila in vitro system using RNA segments 21-23 nucleotides (nt) in length. The patent application publication teaches that when these 21-23 nt fragments are purified and added back to Drosophila extracts, they mediate sequence-specific RNA interference in the absence of long dsRNA. The patent application publication also teaches that chemically synthesized oligonucleotides of the same or similar nature can also be used to target specific mRNAs for degradation in mammalian cells.
PCT publication WO 02/44321 discloses that double-stranded RNA (dsRNA) 19-23 nt in length induces sequence-specific post-transcriptional gene silencing in a Drosophila in vitro system. The PCT publication teaches that short interfering RNAs (siRNAs) generated by an RNase III-like processing reaction from long dsRNA or chemically synthesized siRNA duplexes with overhanging 3′ ends mediate efficient target RNA cleavage in the lysate, and the cleavage site is located near the center of the region spanned by the guiding siRNA. The PCT publication also provides evidence that the direction of dsRNA processing determines whether sense or antisense-identical target RNA can be cleaved by the produced siRNP complex.
U.S. Patent Application Publication No. US 2002/0162126 discloses a method for attenuating expression of a target gene in cultured cells by introducing double stranded RNA (dsRNA) that comprises a nucleotide sequence that hybridizes under stringent conditions to a nucleotide sequence of the target gene into the cells in an amount sufficient to attenuate expression of the target gene.
PCT publication WO 03/006477 discloses engineered RNA precursors that when expressed in a cell are processed by the cell to produce targeted small interfering RNAs (siRNAs) that selectively silence targeted genes (by cleaving specific mRNAs) using the cell's own RNA interference (RNAi) pathway. The PCT publication teaches that by introducing nucleic acid molecules that encode these engineered RNA precursors into cells in vivo with appropriate regulatory sequences, expression of the engineered RNA precursors can be selectively controlled both temporally and spatially, i.e., at particular times and/or in particular tissues, organs, or cells.
Elbashir et al. disclosed a systematic analysis of the length, secondary structure, sugar backbone and sequence specificity of siRNA for RNAi (Elbashir et al., 2001. EMBO J. 20:6877-6888). Based on the analysis, Elbashir proposed rules for designing siRNAs.
Aza-Blanc et al. reported correlations between silencing efficacy and GC content of the 5′ and 3′ regions of the 19 bp target sequence (Aza-Blanc et al., 2003, Mol. Cell 12:627-637). It was found that siRNAs targeting sequences with a GC rich 5′ and GC poor 3′ perform the best.
Discussion or citation of a reference herein shall not be construed as an admission that such reference is prior art to the present invention.
3. SUMMARY OF THE INVENTION
In one aspect, the invention provides a method for selecting from a plurality of different siRNAs one or more siRNAs for silencing a target gene in an organism, each of the plurality of different siRNAs targeting a different target sequence in a transcript of the target gene, the method comprising (a) ranking the plurality of different siRNAs according to positional base compositions of a corresponding targeted sequence motifs in the transcript, wherein each targeted sequence motif comprises at least a portion of the target sequence of the corresponding siRNA and/or a second sequence in a sequence region flanking the target sequence; and (b) selecting one or more siRNAs from the ranked siRNAs. In a preferred embodiment, each sequence motif comprises the target sequence of the targeting siRNA. In another embodiment, the ranking step is carried out by (a1) determining a score for each different siRNA, wherein the score is calculated using a position-specific score matrix; and (a2) ranking the plurality of different siRNAs according to the score.
In one embodiment, each sequence motif is a nucleotide sequence of L nucleotides, L being an integer, and the position-specific score matrix is {log(eij/pij) }, where eij is the weight of nucleotide i at position j, pij is the weight of nucleotide i at position j in a random sequence, and i=G, C, A, U(T), j=1, . . . , L. In another embodiment, each sequence motif is a nucleotide sequence of L nucleotides, L being an integer, and the position-specific score matrix is {log(eij/pij)}, where eij is the weight of nucleotide i at position j, pij is the weight of nucleotide i at position j in a random sequence, and i=G or C, A, U(T), j=1, . . . , L.
In one embodiment, the score for each siRNA is calculated according to equation




  Score
  =
  
    
      ∑
      
        t
        =
        1
      
      L
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            e
            t
          
          /
          
            p
            t
          
        
        )
      
    
  




wherein et and pt are respectively weights of the nucleotide at position t in the sequence motif as determined according to the position-specific score matrix and in a random sequence.

In another embodiment, each sequence motif comprises the target sequence of the targeting siRNA and at least one flanking sequence. Preferably, each sequence motif comprises the target sequence of the targeting siRNA and a 5′ flanking sequence and a 3′ flanking sequence. In one embodiment, the 5′ flanking sequence and the 3′ flanking sequence are each a sequence of D nucleotides, D being an integer. In a specific embodiment, each target sequence is a sequence of 19 nucleotides, and each 5′ flanking sequence and 3′ flanking sequence are a sequence of 10 nucleotides. In another specific embodiment, each target sequence is a sequence of 19 nucleotides, and each 5′ flanking sequence and 3′ flanking sequence are a sequence of 50 nucleotides.
Preferably, the one or more siRNAs consist of at least 3 siRNAs. In another embodiment, the method further comprises a step of de-overlapping, comprising selecting a plurality of siRNAs among the at least 3 siRNAs such that siRNAs in the plurality are sufficiently different in a sequence diversity measure. In one embodiment, the diversity measure is a quantifiable measure, and the selecting in the de-overlapping step comprises selecting siRNAs having a difference in the sequence diversity measure between different selected siRNAs above a given threshold. In one embodiment, the sequence diversity measure is the overall GC content of the siRNAs. In one embodiment, the given threshold is 5%. In another embodiment, the sequence diversity measure is the distance between siRNAs along the length of the transcript sequence. In one embodiment, the threshold is 100 nucleotides. In still another embodiment, the sequence diversity measure is the identity of the leading dimer of the siRNAs, wherein each of the 16 possible leading dimers is assigned a score of 1-16, respectively. In one embodiment, the threshold is 0.5.
In another embodiment, the method further comprises a step of selecting one or more siRNAs based on silencing specificity, the step of selecting based on silencing specificity comprising, (i) for each of the plurality of siRNAs, predicting off-target genes of the siRNA from among a plurality of genes, wherein the off-target genes are genes other than the target gene and are directly silenced by the siRNA; (ii) ranking the plurality of siRNAs according to their respective numbers of off-target genes; and (iii) selecting one or more siRNAs for which the number of off-target genes is below a given threshold.
In one embodiment, the predicting comprises (i1) evaluating the sequence of each of the plurality of genes based on a predetermined siRNA sequence match pattern; and (i2) predicting the gene as an off-target gene if the gene comprise a sequence that matches the siRNA based on the sequence match patter In one embodiment, the step of evaluating comprises identifying an alignment of the siRNA to a sequence in a gene by a low stringency FastA alignment.
In one embodiment, each siRNA has L nucleotides in its duplex region, and the match pattern is represented by a position match position-specific score matrix (pmPSSM), the position match position-specific score matrix consisting of weights of different positions in an siRNA to match transcript sequence positions in an off-target transcript {Pj}, where j=1, . . . , L, Pj is the weight of a match at position j.
In another embodiment, the step (i1) comprises calculating a position match score pmScore according to equation




  
    p
    ⁢
    
      
    
    ⁢
    m
    ⁢
    
      
    
    ⁢
    S
    ⁢
    
      
    
    ⁢
    c
    ⁢
    
      
    
    ⁢
    o
    ⁢
    
      
    
    ⁢
    r
    ⁢
    
      
    
    ⁢
    e
  
  =
  
    
      ∑
      
        i
        =
        1
      
      L
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            E
            i
          
          /
          0.25
        
        )
      
    
  




where Ei=Pi if position i is a match and Ei=(1-Pi)/3 if position i is a mismatch; and the step (i2) comprises predicting the gene as an off-target gene if the position match score is greater than a given threshold.

In a preferred embodiment, L is 19, and the pmPSSM is given by Table I.
Preferably, the plurality of genes comprises all known unique genes of the organism other than the target gene.
In one embodiment, the position-specific score matrix (PSSM) is determined by a method comprising (aa) identifying a plurality of N siRNAs consisting of siRNAs having 19-nucleotide duplex region and having a silencing efficacy above a chosen threshold; (bb) identifying for each siRNA a functional sequence motif, the functional sequence motif comprising a 19-nucleotide target sequence of the siRNA and a 10-nucleotide 5′ flanking sequence and a 10-nucleotide 3′ flanking sequence; (cc) calculating a frequency matrix {fij}, where i=G, C, A, U(T); j=1, 2 . . . , L, and where fij is the frequency of the ith nucleotide at the jth position, based on the siRNAs functional sequence motifs according to equation




  
    
      f
      ij
    
    =
    
      
        ∑
        
          k
          =
          1
        
        N
      
      ⁢
      
        
          δ
          ik
        
        ⁡
        
          (
          j
          )
        
      
    
  
  ,
  
    
      where
      ⁢
      
        
      
      ⁢
      
        
          δ
          ik
        
        ⁡
        
          (
          j
          )
        
      
    
    =
    
      {
      
        
          
            
              
                1
                ,
                
                  
                    if
                    ⁢
                    
                      
                    
                    ⁢
                    k
                  
                  =
                  i
                
              
            
          
          
            
              
                0
                ,
                
                  
                    if
                    ⁢
                    
                      
                    
                    ⁢
                    k
                  
                  ≠
                  i
                
              
            
          
        
        ,
      
    
  




and (d) determining the PSSM by calculating eij according to equation





  
    e
    ij
  
  =
  
    
      
        f
        ij
      
      N
    
    .
  




In another embodiment, the position-specific score matrix (PSSM) is obtained by a method comprising (aa) initializing the PSSM with random weights; (bb) selecting randomly a weight wij obtained in (aa); (cc) changing the value of the selected weight to generate a test psPSSM comprising the selected weight having the changed value; (dd) calculating a score for each of a plurality of siRNAs functional sequence motifs using the test PSSM according to equation




  Score
  =
  
    
      ∑
      
        k
        =
        1
      
      L
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            w
            k
          
          /
          
            p
            k
          
        
        )
      
    
  




wherein the wk and pk are respectively weights of a nucleotide at position k in the functional sequence motif and in a random sequence; (ee) calculating correlation of the score and a metric of a characteristic of an siRNA among the plurality of siRNAs functional sequence motifs; (ff) repeating steps (cc)-(ee) for a plurality of different values of the selected weight in a given range and retain the value that corresponds to the best correlation for the selected weight; and (gg) repeating steps (bb)-(ff) for a chosen number of times; thereby determining the PSSM.

In one embodiment, the method further comprises selecting the plurality of siRNA functional sequence motifs by a method comprising (i) identifying a plurality of siRNAs consisting of siRNAs having different values in the metric; (ii) identifying a plurality of siRNA functional sequence motifs each corresponding to an siRNA in the plurality of siRNAs. In a preferred embodiment, the characteristic is silencing efficacy.
In one embodiment, the plurality of N siRNAs target a plurality of different genes having different transcript abundances in a cell.
In one embodiment, step (b) is carried out by selecting one or more siRNAs having the highest scores. In another embodiment, step (b) is carried out by selecting one or more siRNAs having a score closest to a predetermined value, wherein the predetermined value is the score value corresponding to the maximum median silencing efficacy of a plurality Of siRNA sequence motifs. In a preferred embodiment, the plurality of siRNA sequence motifs are sequence motifs in transcript having abundance level of less than about 3-5 copies per cell.
In another embodiment, step (b) is carried out by selecting one or more siRNAs having a score within a predetermined range, wherein the predetermined range is a score range corresponding to a plurality of siRNAs sequence motifs having a given level of silencing efficacy. In one embodiment, the silencing efficacy is above 50%, 75%, or 90% at an siRNA dose of about 100 nM.
In a preferred embodiment, the plurality of siRNA sequence motifs are sequence motifs in transcript having abundance level of less than about 3-5 copies per cell.
In another preferred embodiment, the plurality of N siRNAs comprises at least 10, 50, 100, 200, or 500 different siRNAs.
In another embodiment, the position-specific score matrix (PSSM) comprises wk, k=1, . . . , L, wk being a difference in probability of finding nucleotide G or C at sequence position k between a first type of siRNA and a second type of siRNA, and the score for each strand is calculated according to equation




  Score
  =
  
    
      ∑
      
        k
        =
        1
      
      L
    
    ⁢
    
      
        w
        k
      
      .
    
  




In one embodiment, the first type of siRNA consists of one or more siRNAs having silencing efficacy no less than a first threshold and the second type of siRNA consists of one or more siRNAs having silencing efficacy less than a second threshold.
In one embodiment, the difference in probability is described by a sum of Gaussian curves, each of the Gaussian curves representing the difference in probability of finding a G or C at a different sequence position.
In one embodiment, the first and second threshold are both 75% at an siRNA dose of 100 nM.
In another aspect, the invention provides a method for selecting from a plurality of different siRNAs one or more siRNAs for silencing a target gene in an organism, each of the plurality of different siRNAs targeting a different target sequence in a transcript of the target gene, the method comprising (a) ranking the plurality of different siRNAs according to positional base composition of reverse complement sequences of sense strands of the siRNAs; and (b) selecting one or more siRNAs from the ranked siRNAs.
In one embodiment, the ranking step is carried out by (a1) determining a score for each different siRNA, wherein the score is calculated using a position-specific score matrix; and (a2) ranking the plurality of different siRNAs according to the score.
In one embodiment, the siRNA has a nucleotide sequence of L nucleotides in its duplex region, L being an integer, wherein the position-specific score matrix comprises wk, k=1, . . . , L, wk being a difference in probability of finding nucleotide G or C at sequence position k between reverse complement of sense strand of a first type of siRNA and reverse complement of sense strand of a second type of siRNA, and the score for each reverse complement is calculated according to equation




  Score
  =
  
    
      ∑
      
        k
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      
        w
        k
      
      .
    
  




In one embodiment, the first type of siRNA consists of one or more siRNAs having silencing efficacy no less than a first threshold and the second type of siRNA consists of one or more siRNAs having silencing efficacy less than a second threshold.
In another embodiment, the difference in probability is described by a sum of Gaussian curves, each of the Gaussian curves representing the difference in probability of finding a G or C at a different sequence position.
In one embodiment, the first and second threshold are both 75% at an siRNA dose of 100 nM.
In still another aspect, the invention provides a method for selecting from a plurality of different siRNAs one or more siRNAs for silencing a target gene in an organism, each of the plurality of different siRNAs targeting a different target sequence in a transcript of the target gene, the method comprising, (i) for each of the plurality of different siRNAs, predicting off-target genes of the siRNA from among a plurality of genes, wherein the off-target genes are genes other than the target gene and are directly silenced by the siRNA; (ii) ranking the plurality of different siRNAs according to the number of off-target genes; and (iii) selecting one or more siRNAs for which the number of off-target genes is below a given threshold.
In one embodiment, the predicting comprises (i1) evaluating the sequence of each of the plurality of genes based on a predetermined siRNA sequence match pattern; and (i2) predicting a gene as an off-target gene if the gene comprise a sequence that matches the siRNA based on the sequence match pattern.
In one embodiment, each siRNA has L nucleotides in its duplex region, and the sequence match pattern is represented by a position match position-specific score matrix (pmPSSM), the position match position-specific score matrix consisting of weights of different positions in an siRNA to match transcript sequence positions in an off-target transcript {Pj}, where j=1, . . . , L, Pj is the weight of a match at position j.
In another embodiment, the step (i1) comprises calculating a position match score pmScore according to equation




  pmScore
  =
  
    
      ∑
      
        i
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            E
            i
          
          /
          0.25
        
        )
      
    
  




where Ei=Pi if position i is a match and Ei=(1-Pi)/3 if position i is a mismatch; and the step (i2) comprises predicting the gene as an off-target gene if the position match score is greater than a given threshold.

In a preferred embodiment, L is 19, and the pmPSSM is given by Table I.
In one embodiment, the plurality of genes comprises all known unique genes of the organism other than the target gene.
In still another aspect, the invention provides a library of siRNAs, comprising a plurality of siRNAs for each of a plurality of different genes of an organism, wherein each siRNA achieves at least 75%, at least 80%, or at least 90% silencing of its target gene. In one embodiment, the plurality of siNRAs consists of at least 3, at least 5, or at least 10 siRNAs. In another embodiment, the plurality of different genes consists of at least 10, at least 100, at least 500, at least 1,000, at least 10,000, or at least 30,000 different genes.
In still another aspect, the invention provides a method for determining a base composition position-specific score matrix (bsPSSM) {log(eij/pij)} for representing base composition patterns of siRNA functional sequence motifs of L nucleotides in transcripts, wherein i=G, C, A, U(T) and j=1, 2, . . . , L, and each siRNA functional sequence motif comprises at least a portion of the target sequence of the corresponding targeting siRNA and/or a sequence in a sequence region flanking the target sequence, the method comprising (a) identifying a plurality of N different siRNAs consisting of siRNAs having a silencing efficacy above a chosen threshold; (b) identifying a plurality of N corresponding siRNA functional sequence motifs, one for each different siRNA; (c) calculating a frequency matrix {fij}, where i=G, C, A, U(T); j=1, 2, . . . , L, and where fij is the frequency of the ith nucleotide at the jth position, based on the plurality of N siRNAs functional sequence motifs according to equation




  
    
      f
      ij
    
    =
    
      
        ∑
        
          k
          =
          1
        
        N
      
      ⁢
      
        
      
      ⁢
      
        
          δ
          ik
        
        ⁡
        
          (
          j
          )
        
      
    
  
  ,
  
    

  
  ⁢
  
    
      where
      ⁢
      
        
      
      ⁢
      
        
          δ
          ik
        
        ⁡
        
          (
          j
          )
        
      
    
    =
    
      {
      
        
          
            
              
                1
                ,
                
                  
                
                ⁢
                
                  
                    if
                    ⁢
                    
                      
                        
                      
                      ⁢
                      
                        
                      
                    
                    ⁢
                    k
                  
                  =
                  i
                
              
            
          
          
            
              
                0
                ,
                
                  
                
                ⁢
                
                  
                    if
                    ⁢
                    
                      
                    
                    ⁢
                    k
                  
                  ≠
                  i
                
              
            
          
        
        ,
      
    
  




and (d) determining the psPSSM by calculating eij according to equation





  
    e
    ij
  
  =
  
    
      
        f
        ij
      
      N
    
    .
  




In one embodiment, each siRNA functional motif comprises the target sequence of the corresponding targeting siRNA and one or both flanking sequences of the target sequence.
In one embodiment, each siRNA has M nucleotides in its duplex region, and each siRNA functional sequence motif consists of an siRNA target sequence of M nucleotides, a 5′ flanking sequence of D1 nucleotides and a 3′ flanking sequence of D2 nucleotides.
In a specific embodiment, each-siRNA has 19 nucleotides in its duplex region, and each siRNA functional sequence motif consists of an siRNA target sequence of 19 nucleotides, a 5′ flanking sequence of 10 nucleotides and a 3′ flanking sequence of 10 nucleotides. In another specific embodiment, each siRNA has 19 nucleotides in its duplex region, and each siRNA functional sequence motif consists of an siRNA target sequence of 19 nucleotides, a 5′ flanking sequence of 50 nucleotides and a 3′ flanking sequence of 50 nucleotides.
In one embodiment, the plurality of N siRNAs each targets a gene whose transcript abundance is within a given range. In one embodiment, the range is at least about 5, 10, or 100 transcripts per cell. In another embodiment, the range is less than about 3-5 transcripts per cell.
In another embodiment, the silencing threshold is 50%, 75%, or 90% at an siRNA dose of about 100 nM. In still another embodiment, the plurality of N siRNAs comprises 10, 50, 100, 200, or 500 different siRNAs.
In still another aspect, the invention provides a method for determining a base composition position-specific score matrix (bsPSSM) {wij} for representing a base composition pattern representing a plurality of different siRNA functional sequence motifs of L nucleotides, wherein i=G, C, A, U(T) and j=1, 2, . . . , L, and each siRNA functional sequence motif comprises at least a portion of the target sequence of the corresponding targeting siRNA and/or a sequence in a sequence region flanking the siRNA target sequence, the method comprising (a) initializing the bsPSSM with random weights;.(b) selecting randomly a weight wij obtained in (a); (c) changing the value of the selected weight to generate a test psPSSM comprising the selected weight having the changed value; (d) calculating a score for each of the plurality of siRNAs functional sequence motifs using the test psPSSM according to equation




  Score
  =
  
    
      ∑
      
        k
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            w
            k
          
          /
          
            p
            k
          
        
        )
      
    
  




wherein the wk and pk are respectively weights of a nucleotide at position k in the functional sequence motif and in a random sequence; (e) calculating correlation of the score and a metric characterizing an siRNA among the plurality of siRNAs functional sequence motifs; (f) repeating steps (c)-(e) for a plurality of different values of the selected weight in a given range and retain the value that corresponds to the best correlation for the selected weight; and (g) repeating steps (b)-(f) for a chosen number of times; thereby determining the psPSSM.

The invention also provides a method for determining a base composition position-specific score matrix (bsPSSM) {wij} for representing a base composition pattern representing a plurality of different siRNA functional sequence motifs of L nucleotides, wherein i=G/C, A, U(T) and j=1, 2, . . . , L, and each siRNA functional sequence motif comprises a least a portion of the target sequence of the corresponding siRNA and/or a sequence in a sequence region flanking the siRNA target sequence, the method comprising (a) initializing the bsPSSM with random weights; (b) randomly selecting a weight wij obtained in (a); (c) changing the value of the selected weight to generate a test psPSSM comprising the selected weight having the changed value; (d) calculating a score for each of the plurality of siRNA functional sequence motifs using the test psPSSM according to equation




  Score
  =
  
    
      ∑
      
        j
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            w
            k
          
          /
          
            p
            k
          
        
        )
      
    
  




wherein the wk and pk are respectively weights of a nucleotide at position k in the functional sequence motif and in a random sequence; (e) calculating a correlation of the score and a metric of a characteristic of an siRNA among the plurality of siRNAs functional sequence motifs; (f) repeating steps (c)-(e) for a plurality of different values of the selected weight in a given range and retain the value that corresponds to the best correlation for the selected weight; and (g) repeating steps (b)-(f) for a chosen number of times; thereby determining the psPSSM.

In one embodiment, each siRNA functional motif comprises the target sequence of the corresponding targeting siRNA and one or both flanking sequences of the target sequence.
In another embodiment, the method further comprises selecting the plurality of siRNA functional sequence motifs by a method comprising (i) identifying a plurality of siRNAs consisting of siRNAs having different values in the metric; (ii) identifying a plurality of siRNA functional sequence motifs each corresponding to an siRNA in the plurality of siRNAs.
In one embodiment, each siRNA has M nucleotides in its duplex region, and each siRNA functional sequence motif consists of an siRNA target sequence of M nucleotides, a 5′ flanking sequence of D1 nucleotides and a 3′ flanking sequence of D2 nucleotides.
In a specific embodiment, each siRNA has 19 nucleotides in its duplex region, and each siRNA functional sequence motif consists of an siRNA target sequence of 19 nucleotides, a 5′ flanking sequence of 10 nucleotides and a 3′ flanking sequence of 10 nucleotides. In another specific embodiment, each siRNA has 19 nucleotides in its duplex region, and each siRNA functional sequence motif consists of an siRNA target sequence of 19 nucleotides, a 5′ flanking sequence of 50 nucleotides and a 3′ flanking sequence of 50 nucleotides.
In one embodiment, the metric is silencing efficacy.
In one embodiment, the plurality of N siRNAs each targets a gene whose transcript abundance is within a given range. In one embodiment, the range is at least about 5, 10, or 100 transcripts per cell. In another embodiment, the range is less than about 3-5 transcripts per cell. In another embodiment, the threshold is 50%, 75%, or 90% at an siRNA dose of about 100 nM.
In another embodiment, the method further comprises evaluating the psPSSM using an ROC (receiver operating characteristic) curve of the sensitivity of the psPSSM vs. the non-specificity of the psPSSM curve, the sensitivity of the PSSM being the proportion of true positives detected using the psPSSM as a fraction of total true positives, and the non-specificity of the PSSM being the proportion of false positives detected using the psPSSM as a fraction of total false positives.
In one embodiment, the plurality of siRNA functional sequence motifs consists of at least 50, at least 100, or at least 200 different siRNAs functional sequence motifs.
In still another embodiment, the method further comprises testing the psPSSM using another plurality of siRNA functional sequence motifs.
The invention also provides a method for determining a position match position-specific score matrix (pmPSSM) {Ei} for representing position match pattern of an siRNA of L nucleotides with its target sequence in a transcript, wherein Ei is a score of a match at position i, i=1, 2, . . . , L, the method comprising (a) identifying a plurality of N siRNA off-target sequences, wherein each off-target sequence is a sequence on which the siRNA exhibits silencing activity; (b) calculating a position match weight matrix {Pi}, where i=1, 2, . . . , L, based on the plurality of N siRNAs off-target sequences according to equation




  
    
      P
      i
    
    =
    
      
        1
        N
      
      ⁢
      
        
          ∑
          
            k
            =
            1
          
          N
        
        ⁢
        
          
        
        ⁢
        
          
            δ
            k
          
          ⁡
          
            (
            j
            )
          
        
      
    
  
  ,




where δk(j) is 1 if k is a match, and is 0 if k is a mismatch; and (c) determining the psPSSM by calculating Ei such that Ei=Pi if position i is a match and Ei=(1-Pi)/3 if position i is a mismatch.

In a preferred embodiment, L=19.In another preferred embodiment, the position match weight matrix is given by Table I.
The invention also provides a method for evaluating the relative activity of the two strands of an siRNA in off-target gene silencing, comprising comparing position specific base composition of the sense strand of the siRNA and position specific base composition of the antisense strand of the siRNA or reverse complement strand of the sense strand of the siRNA, wherein the antisense strand is the guiding strand for targeting the intended target sequence.
In one embodiment, the comparing is carried out by a method comprising (a) determining a score for the sense strand of the siRNA, wherein the score is calculated using a position-specific score matrix; (b) determining a score for the antisense strand of the siRNA or the reverse complement strand of the sense strand of the siRNA using the position-specific score matrix; and (c) comparing the score for the sense strand and the score for the antisense strand or the reverse complement strand of the sense strand, thereby evaluating strand preference of the siRNA.
In one embodiment, the siRNA has a nucleotide sequence of L nucleotides in its duplex region, L being an integer, wherein the position-specific score matrix is {wij}, where wij is the weight of nucleotide i at position j, i=G, C, A, U(T), j=1, . . . , L.
In another embodiment, the siRNA has a nucleotide sequence of L nucleotides in its duplex region, L being an integer, and the position-specific score matrix is {Wij}, where wij is the weight of nucleotide i at position j, i=G or C, A, U(T), j=1, . . . , L.
In another embodiment, the position-specific score matrix is obtained by a method comprising (a) initializing the position-specific score matrix with random weights; (b) selecting randomly a weight wij obtained in (a); (c) changing the value of the selected weight to generate a test position-specific score matrix comprising the selected weight having the changed value; (d) calculating a score for each of a plurality of siRNAs using the test position-specific score matrix according to equation




  Score
  =
  
    
      ∑
      
        j
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            w
            j
          
          /
          
            p
            j
          
        
        )
      
    
  




wherein the wj and pj are respectively weights of a nucleotide at position j in the siRNA and in a random sequence; (e) calculating correlation of the score with a metric of a characteristic of an siRNA among the plurality of siRNAs; (f) repeating steps (c)-(e) for a plurality of different values of the selected weight in a given range and retain the value that corresponds to the best correlation for the selected weight; and (g) repeating steps (b)-(f) for a chosen number of times; thereby determining the position-specific score matrix.

In one embodiment, the metric is siRNA silencing efficiency.
In one embodiment, the siRNA has 19 nucleotides in its duplex region.
In another embodiment, the siRNA has a nucleotide sequence of L nucleotides in its duplex region, L being an integer, wherein the position-specific score matrix comprises wk, k=1, . . . , L, wk being a difference in probability of finding nucleotide G or C at sequence position k between a first type of siRNA and a second type of siRNA, and the score for each strand is calculated according to equation




  Score
  =
  
    
      ∑
      
        k
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      
        w
        k
      
      .
    
  




In one embodiment, the first type of siRNA consists of one or more siRNAs having silencing efficacy no less than a first threshold and the second type of siRNA consists of one or more siRNAs having silencing efficacy less than a second threshold, and the siRNA is determined as having antisense preference if the score determined in step (a) is greater than the score determined in step (b), or as having sense preference if the score determined in step (b) is greater than the score determined in step (a).
In another embodiment, the difference in probability is described by a sum of Gaussian curves, each of the Gaussian curves representing the difference in probability of finding a G or C at a different sequence position.
In one embodiment, the first and second threshold are both 75% at an siRNA dose of about 100 nM.
In still another aspect, the invention provides a computer system comprising a processor, and a memory coupled to the processor and encoding one or more programs, wherein the one or more programs cause the processor to carry out any one of the method of the invention.
In still another aspect, the invention provides a computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer program mechanism encoded thereon, wherein the computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out any one of the method of the invention.



4. BRIEF DESCRIPTION OF FIGURES
FIGS. 1A-C show that base composition in and around an siRNA target sequence affects the silencing efficacy of the siRNA. A total of 377 siRNAs were tested by Taqman analysis for their ability to silence their target sequences 24 hr following transfection into HeLa cells. Median target silencing was  ̃75%. This dataset was divided into two subsets, one having less than median and one having equal to or greater than median silencing ability (referred to as “bad” and “good” siRNAs, respectively). Shown here are the mean difference within a window of 5 (i.e., averaged over all 5 bases) in GC content (FIG. 1A), A content (FIG. 1B), and U content (FIG. 1C) between good and bad siRNAs at different relative positions on a target sequence.
FIGS. 2A-C (A) GC content of good and bad siRNAs; (B) A content of good and bad siRNAs; (C) U content of good and bad siRNAs. The figures show average compositions of each base. For example, 0.5 on the y-axis corresponds to an average base content of 50%.
FIG. 3 shows the performance of an actual siRNA base composition model used in the siRNA design method of the invention. siRNA efficacy data were subdivided into two pairs of training and test sets. Different PSSMs were optimized on each of the training sets and verified on the test sets. The performance of each PSSM was evaluated by its ability to distinguish good siRNAs (true positives) and bad siRNAs (false positives) as an increasing number of siRNAs were selected from a list ranked by PSSM score. Shown are Receiver Operating Characteristics (ROC) curves demonstrating the performance of two different PSSMs on their respective training and test sets (heavy black and dotted gray lines, respectively). The expected performance of the PSSMs on randomized data is shown for comparison (i.e., no improvement in selection ability, 45° line).
FIG. 4 demonstrates the predictive ability of PSSMs on an independent experimental data set. New siRNAs were designed for five genes by the standard method as described in Elbashir et al., 2001, Nature 411:494-8, with the addition of the specificity prediction method disclosed in this application, and by the PSSM based efficacy and specificity prediction method of the invention. The top free ranked siRNAs per gene were selected for each method and purchased from Dharmacon. All six siRNAs for each of the five genes were then tested for their ability to silence their target sequences. Shown is a histogram of the number of siRNAs that silence their respective target genes by a specified amount. Solid curve, silencing by siRNAs designed by the present method; dashed curve, silencing by siRNAs designed by the standard method; dotted gray curve, silencing by the data set of 377 siRNAs.
FIGS. 5A-C show mean weights of GC, A or U from the two ensembles of base composition PSSM trained and tested with siRNAs in set 1 and set 2, respectively. FIG. 5A mean weights for GC, FIG. 5B mean weights for A, FIG. 5C mean weights for U. siRNAs in set 1 and set 2 are shown in Table II.
FIG. 6 shows an example of alignments of transcripts of off-target genes to the core 19mer of an siRNA oligo sequence. Off-target genes were selected from the Human 25 k v2.2.1 microarray by selecting for kinetic patterns of transcript abundance consistent with direct effects of siRNA oligos. The left hand column lists transcript sequence identifiers. Alignments were generated with FASTA and edited by hand. The black boxes and grey area demonstrate the higher level of sequence similarity in the 3′ half of the alignment.
FIG. 7 shows a position match position-specific scoring matrix for predicting off-target effects. The chart shows the weight associated with each position in a matrix representing the alignment between an siRNA oligo and off-target transcripts. The weight represents the probability that a match will be observed at each position i along an alignment between an siRNA oligo and an observed off-target transcript.
FIG. 8 shows optimization of the threshold score for predicting off-target effects of siRNAs. The R2 values result from the correlation of number of alignments scoring above the threshold with number of observed off-target effects.
FIG. 9 shows a flow chart of an exemplary embodiment of the method for selecting siRNAs for use in silencing a gene.
FIG. 10 illustrates sequence regions that can be used for distinguishing good and bad siRNAs. PSSMs were trained on chunks of sequence 10+ bases in length, from 50 bases upstream to 50 bases downstream of the siRNA 19mer, and tested on independent test sets. The performance of models trained on chunks of interest was compared with models trained on random sequences. Position 1 corresponds to the first 5′ base in the duplex region of a 21 nt siRNA.
FIGS. 11A-B shows curve models for PSSM. 11A: an exemplary set of curve models for PSSM. 11B: the performance of the models on training and test sets.
FIG. 12 illustrates an exemplary embodiment of a computer system useful for implementing the methods of the present invention.
FIG. 13 shows a comparison of the distribution of silencing efficacies of the siRNAs among the 30 siRNAs designed using the method of the invention (solid circles) and siRNAs designed using the standard method (open circles). x-axis: 1, KIF14; 2, PLK; 3, IGF1R; 4, MAPK14; 5, KIF11.y-axis: RNA level. The siRNAs designed using the standard method to the 5 genes exhibited a broad distribution of silencing abilities, while those designed with the method of the invention show more consistent silencing within each gene, as well as across genes. A narrow distribution is very important for functional genomics with siRNAs.
FIGS. 14A-B show a comparison of the GC content of siRNAs and their reverse complements with the GC content of bad siRNAs. The results indicate that bad siRNAs have sense strands similar to good siRNAs, while good siRNAs have sense strands similar to bad siRNAs. RC: reverse complement of the siRNA target sequence.
FIG. 15 shows that less effective siRNAs have active sense strands. Strand bias of 61 siRNAs was predicted from expression profiles by the 3′-biased method, and from comparison of the GC PSSM scores of the siRNAs and their reverse complements. Strand bias predictions were binned by siRNA silencing efficacy.
FIG. 16 shows that silencing efficacy relates to transcript expression level. A total of 222 siRNAs (3 siRNAs per gene for 74 genes) were tested by bDNA or Taqman analysis for their ability to silence their target sequences 24 hr following transfection into HeLa cells. Percent silencing (y-axis) was plotted as a function of transcript abundance (x-axis) measured as intensity on microarray. Shown is the median target silencing observed for 3 siRNAs per gene selected by the previous siRNA design algorithm. The dependence of silencing on gene expression level, as the average of intensities from 2 array types, is shown for 74 genes. TaqMan assays were used for 8 genes. b-DNA data is shown for the remaining 66 genes.
FIG. 17 shows that the silencing efficacy of an siRNA relates to its base composition. siRNAs to poorly-expressed genes were tested by bDNA analysis for their ability to silence their target sequences. Data were divided into subsets having less than 75% silencing and equal to or greater than 75% silencing (bad and good siRNAs, respectively). Shown here is the difference in GC content between good and bad siRNAs (y-axis) at each position in the siRNA sense strand (x-axis.) The dataset includes both poorly-expressed and highly-expressed genes from 570 siRNAs selected to 33 poorly- and 41 highly-expressed genes by Tuschl rules or randomized selection. The siRNA sequences are listed in Table IV. The GC profile for good siRNAs to poorly-expressed genes (gray dotted curve) shows some similar composition preferences to good siRNAs for well-expressed genes (black curve), but also some differences.
FIG. 18 shows the efficacy of newly design siRNAs. siRNAs were designed for 18 poorly-expressed genes by the standard method and by the new algorithm. Standard pipeline: selection for maximum pssm score; minimax filter for long off-target matches. Improved pipeline: selection for 1-3 G+C in sense 19mer bases 2-7, base 1 & 19 asymmetry, −300<pssm score<+200, and blast matches less than 16, 200 bases on either side of the 19mer are not repeat or low-complexity sequences. The top three ranked siRNAs per gene were selected for each method. All six siRNAs for each of the five genes were then tested for their ability to silence their target sequences. Shown is a histogram of the number of siRNAs silencing their target genes by a specified amount. Dotted curve, silencing by siRNAs designed by the new algorithm; solid curve, silencing by siRNAs designed by the standard method. Median silencing improved from 60% (standard algorithm) to 80% (new algorithm).
FIG. 19. Design features of efficacious siRNAs. Studies of design criteria that correlate with siRNA silencing efficacy have revealed a number of features that predict efficacy. These include a base asymmetry at the two termini to direct the antisense (guide) strand into RISC, a U at position 10 for effective cleavage of the transcript, a low GC stretch encompassing the center and 3′ end of the guide strand for enhanced cleavage, and the “seed” region at the 5′ end of the antisense strand implicated in transcript binding. Gray lines above the duplex indicate sequence preferences, light gray lines below the duplex indicate functional attributes.
FIG. 20 shows expression vs. median silencing in 371 siRNAs. These are siRNAs from the original training set of 377 siRNAs. 6 siRNAs were not included in the analysis, as the expression level of their target gene was not available.



5. DETAILED DESCRIPTION OF THE INVENTION
The present invention provides a method for identifying siRNA target motifs in a transcript using a position-specific score matrix approach. The invention also provides a method for identifying off-target genes of an siRNA and for predicting specificity of an siRNA using a position-specific score matrix approach. The invention further provides a method for designing siRNAs with higher silencing efficacy and specificity. The invention also provides a library of siRNAs comprising siRNAs with high silencing efficacy and specificity.
In this application, an siRNA is often said to target a gene. It will be understood that when such a statement is made, it means that the siRNA is designed to target and cause degradation of a transcript of the gene. Such a gene is also referred to as a target gene of the siRNA, and the sequence in the transcript that is acted upon by the siRNA is referred to as the target sequence. For example, a 19-nucleotide sequence in a transcript which is identical to the sequence of the 19-nucleotide sequence in the sense strand of the duplex region of an siRNA is the target sequence of the siRNA. The antisense strand of the siRNA, i.e., the strand that acts upon the target sequence, is also referred to as the guiding strand. In the above example, the antisense strand of the 19-nucleotide duplex region of the siRNA is the guiding strand. In this application, features of an siRNA are often referred to with reference to its sequence, e.g., positional base composition. It will be understood that, unless specifically pointed out otherwise, such a reference is made to the sequence of the sense strand of the siRNA. In this application, a nucleotide or a sequence of nucleotides in an siRNA is often described with reference to the 5′ or 3′ end of the siRNA It will be understood that when such a description is employed, it refers to the 5′ or 3′ end of the sense strand of the siRNA. It will also be understood that, when a reference to the 3′ end of the siRNA is made, it refers to the 3′ duplex region of the siRNA, i.e., the two nucleotides of the 3′ overhang are not included in the numbering of the nucleotides. In the application, an siRNA is also referred to as an oligo.
In this disclosure, design of siRNA is discussed in reference to silencing a sense strand target, i.e., transcript target sequence corresponding to the sense strand of the siRNA. It will be understood by one skilled person in the art that the methods of the invention are also applicable to the design of siRNA for silencing an antisense target (see, e.g., Martinez et al., 2002, Cell 110:563-574).
5.1. Methods of Identifying Sequence Motifs in a Gene for Targeting by a Small Interfering RNA
The invention provides a method of identifying a sequence motif in a transcript which may be targeted by an siRNA for degradation of the transcript, e.g., a sequence motif that is likely to be a highly effective siRNA targeting site. Such a sequence motif is also referred to as an siRNA susceptible motif. The method can also be used for identifying a sequence motif in a transcript which may be less desirable for targeting by an siRNA, e.g., a sequence motif that is likely to be a less effective siRNA targeting site. Such a sequence motif is also referred to as an siRNA resistant motif.
In one embodiment, sequence features characteristic of a functional sequence motif, e.g., an siRNA susceptible sequence motif, are identified and a profile of the functional motif is built using, e.g., a library of siRNAs for which silencing efficacy of has been determined.
In one embodiment, the sequence region of interest is scanned to identify sequences that match the profile of the functional motif.
5.1.1. Sequence Profile and Target Silencing Efficacy
In a preferred embodiment, the profile of a functional sequence motif is represented using a position-specific score matrix (PSSM). A general discussion of PSSM can be found in, e.g., “Biological Sequence Analysis” by R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Cambridge Univ. Press, 1998; and Henikoff et al., 1994, J Mol Biol. 243:574-8.A PSSM is a sequence motif descriptor which captures the characteristics of a functional sequence motif. In this disclosure, a PSSM is used to describe sequence motifs of the invention, e.g., a susceptible or resistant motif. A PSSM of an siRNA susceptible (resistant) motif is also referred to as a susceptible (resistant) PSSM. A skilled person in the art will know that a position-specific score matrix is also termed a position specific scoring matrix, a position weight matrix (PWM), or a Profile.
In the present invention, a functional motif can comprise one or more sequences in an siRNA target sequence. For example, the one or more sequences in an siRNA target sequence may be a sequence at 5′ end of the target sequence, a sequence at 3′ end of the target sequence. The one or more sequences in an siRNA target sequence may also be two stretches of sequences, one at 5′ end of the target sequence and one at 3′ end of the target sequence. A functional motif can also comprise one or more sequences in a sequence region that flanks the siRNA target sequence. Such one or more sequences can be directly adjacent to the siRNA target sequence. Such one or more sequences can also be separated from the siRNA target sequence by an intervening sequence. FIG. 10 illustrates some examples of functional motifs.
In one embodiment, a functional sequence motif, e.g., a susceptible or resistant sequence motif, comprises at least a portion of a sequence targeted by an siRNA. In one embodiment, the functional motif comprises a contiguous stretch of at least 7 nucleotides of the target sequence. In a preferred embodiment, the contiguous stretch is in a 3′ region of the target sequence, e.g., beginning within 3 bases at the 3′ end. In another embodiment, the contiguous stretch is in a 5′ region of the target sequence. In another embodiment, the functional motif comprises a contiguous stretch of at least 3, 4, 5, 6, or 7 nucleotides in a 3′ region of the target sequence and comprises a contiguous stretch of at least 3, 4, 5, 6, or 7 nucleotides in a 5′ region of the target sequence. In still another embodiment, the functional motif comprises a contiguous stretch of at least 11 nucleotides in a central region of the target sequence. Sequence motifs comprise less than the full length of siRNA target sequence can be used for evaluating siRNA target transcripts that exhibit only partial sequence identify to an siRNA (International application No. PCT/US2004/015439 by Jackson et al., filed on May 17, 2004, which is incorporated herein by reference in its entirety). In a preferred embodiment, the functional motif comprises the full length siRNA target sequence.
The functional motif may also comprise a flanking sequence. The inventors have discovered that the sequence of such flanking region plays a role in determining the efficacy of silencing. In one embodiment, a functional sequence motif, e.g., a susceptible or resistant sequence motif, comprises at least a portion of a sequence targeted by an siRNA and one or more sequences in one or both flanking regions. Thus, a sequence motif can include an M nucleotides siRNA target sequence, a flanking sequence of D1 nucleotides at one side of the siRNA target sequence and a flanking sequence of D2 nucleotides at the other side of the siRNA target sequence where M, D1 and D2 are appropriate integers. In one embodiment, D1=D2=D. In one embodiment, M=19.In some preferred embodiments, D1, D2, or D is at least 5, 10, 20, 30, 50 nucleotides in length. In a specific embodiment, a susceptible or resistant sequence motif consists of an siRNA target sequence of 19 nucleotides and a flanking sequence of 10 nucleotides at either side of the siRNA target sequence. In another specific embodiment, a susceptible or resistant sequence motif consists of a 19 nucleotides siRNA target sequence and a 50 nucleotides flanking sequence at either side of the siRNA target sequence.
In another embodiment, a sequence motif can include an M nucleotides siRNA target sequence, and one or more of the following: a contiguous stretch of D1 nucleotides flanking the 5′ end of the target sequence, a contiguous stretch of D2 nucleotides flanking the 3′ end of the target sequence, a contiguous stretch of D3 nucleotides which starts about 35 nucleotides upstream of the 5′ end of the target sequence, a contiguous stretch of D4 nucleotides which starts about 25 nucleotides downstream of the 3′ end of the target sequence, and a contiguous stretch of D5 nucleotides which starts about 60 nucleotides downstream of the 3′ end of the target sequence, where D1, D2, D3, D4, and D5 are appropriate integers. In one embodiment, D1=D2=D. In some preferred embodiments, each of D1, D2, D3, D4, and D5 is at least 5, 10, or 20 nucleotides in length. The length of the functional motif is L=M+D1+D2+D3+D4+D5. In a specific embodiment, the sequence motif include 19 nucleotides siRNA target sequence, a contiguous stretch of about 10 nucleotides flanking the 5′ end of the target sequence, a contiguous stretch of about 10 nucleotides flanking the 3′ end of the target sequence, a contiguous stretch of about 10 nucleotides which starts about 35 nucleotides upstream of the 5′ end of the target sequence, a contiguous stretch of about 10 nucleotides which starts about 25 nucleotides downstream of the 3′ end of the target sequence, and a contiguous stretch of about 10 nucleotides which starts about 60 nucleotides downstream of the 3′ end of the target sequence (see FIG. 10).
In other embodiments, a functional sequence motif, e.g., a susceptible or resistant sequence motif, comprises one or more sequences in one or both flanking regions of an siRNA target sequence but does not comprise any siRNA target sequence. In one embodiment, the functional motif comprises a contiguous stretch of about 10 nucleotides flanking the 5′ end of the target sequence. In another embodiment, the functional motif comprises a contiguous stretch of about 10 nucleotides flanking the 3′ end of the target sequence. In a preferred embodiment, the functional motif comprises a contiguous stretch of about 10 nucleotides flanking the 5′ end of the target sequence and a contiguous stretch of about 10 nucleotides flanking the 3′ end of the target sequence. In one embodiment, the functional motif comprises a contiguous stretch of about 10 nucleotides which starts about 35 nucleotides upstream of the 5′ end of the target sequence. In another embodiment, the functional motif comprises a contiguous stretch of about 10 nucleotides which starts about 25 nucleotides downstream of the 3′ end of the target sequence. In still another embodiment, the functional motif comprises a contiguous stretch of about 10 nucleotides which starts about 60 nucleotides downstream of the 3′ end of the target sequence. In a preferred embodiment, the functional motif comprises a contiguous stretch of about 10 nucleotides flanking the 5′ end of the target sequence, a contiguous stretch of about 10 nucleotides flanking the 3′ end of the target sequence, a contiguous stretch of about 10 nucleotides which starts about 35 nucleotides upstream of the 5′ end of the target sequence, a contiguous stretch of about 10 nucleotides which starts about 25 nucleotides downstream of the 3′ end of the target sequence, and a contiguous stretch of about 10 nucleotides which starts about 60 nucleotides downstream of the 3′ end of the target sequence. Thus, a sequence motif can include a contiguous stretch of D1 nucleotides flanking the 5′ end of the target sequence, a contiguous stretch of D2 nucleotides flanking the 3′ end of the target sequence, a contiguous stretch of D3 nucleotides which starts about 35 nucleotides upstream of the 5′ end of the target sequence, a contiguous stretch of D4 nucleotides which starts about 25 nucleotides downstream of the 3′ end of the target sequence, and a contiguous stretch of D5 nucleotides which starts about 60 nucleotides downstream of the 3′ end of the target sequence, where D1, D2, D3, D4, and D5 are appropriate integers. In some preferred embodiments, each of D1, D2, D3, D4, and D5 is at least 5, 10, or 20 nucleotides in length. The length of the functional motif is L=D1+D2+D3+D4+D5.
In one embodiment, the characteristics of a functional sequence motif are characterized using the frequency of each of G, C, A, U(or T) observed at each position along the sequence motif. In the disclosure, U(or T), or sometimes simply U(T), is used to indicate nucleotide U or T. The set of frequencies forms a frequency matrix, in which each element indicates the number of times that a given nucleotide has been observed at a given position. A frequency matrix representing a sequence motif of length L is a 4·L matrix {fij}, where i=G, C, A, U(T); j=1, 2, . . . , L; where fij is the frequency of the ith nucleotide at the jth position. A frequency matrix of a sequence motif can be derived or built from a set of N siRNA target sequences that exhibit a desired quality, e.g., a chosen level of susceptibility or resistance to siRNA silencing.




  
    
      
        
          f
          ij
        
        =
        
          
            ∑
            
              k
              =
              1
            
            N
          
          ⁢
          
            
          
          ⁢
          
            
              δ
              ik
            
            ⁡
            
              (
              j
              )
            
          
        
      
    
    
      
        (
        1
        )
      
    
  
  
    
      
        
          where
          ⁢
          
            
          
          ⁢
          
            
              δ
              ik
            
            ⁡
            
              (
              j
              )
            
          
        
        =
        
          {
          
            
              
                
                  1
                  ,
                  
                    
                  
                  ⁢
                  
                    
                      if
                      ⁢
                      
                        
                          
                        
                        ⁢
                        
                          
                        
                      
                      ⁢
                      k
                    
                    =
                    i
                  
                
              
            
            
              
                
                  0
                  ,
                  
                    
                  
                  ⁢
                  
                    
                      if
                      ⁢
                      
                        
                      
                      ⁢
                      k
                    
                    ≠
                    i
                  
                
              
            
          
        
      
    
    
      
        (
        2
        )
      
    
  




In embodiments in which a functional sequence motif consists of M nucleotides siRNA target sequence, a flanking sequence of D1 nucleotides at one side of the siRNA target sequence and a flanking sequence of D2 nucleotides at the other side of the siRNA target sequence, L=M+D1+D2. In embodiments in which the functional motif consists of M nucleotides siRNA target sequence,a contiguous stretch of D1 nucleotides flaring the 5′ end of the target sequence, a contiguous stretch of D2 nucleotides flanking the 3′ end of the target sequence, a contiguous stretch of D3 nucleotides which starts about 35 nucleotides upstream of the 5′ end of the target sequence, a contiguous stretch of D4 nucleotides which starts about 25 nucleotides downstream of the 3′ end of the target sequence, and a contiguous stretch of D5 nucleotides which starts about 60 nucleotides downstream of the 3′ end of the target sequence, L=D1+D2+D3+D4+D5.

In another embodiment, the characteristics of a functional sequence motif are characterized using a set of weights, one for each nucleotide occurring at a position in the motif. In such an embodiment, a weight matrix {eij}, where i=G, C, A, U(T); j=1, 2, . . . , L, can be used for representing a functional sequence motif of length L, where eij is the weight of finding the ith nucleotide at the jth position. In one embodiment, the weight eij is the probability of finding the ith nucleotide at the jth position in the functional sequence motif. When a probability is used for the weight, the matrix is also called a probability matrix. A probability matrix of a sequence motif can be derived from a frequency matrix according to equation




  
    
      
        
          e
          ij
        
        =
        
          
            f
            ij
          
          N
        
      
    
    
      
        (
        3
        )
      
    
  




In a preferred embodiment, a position-specific score matrix is used to characterize a functional sequence motif. The PSSM can be constructed using log likelihood values log(eij/pij), where eij is the weight of finding nucleotide i at position j, and pij is the weight of finding nucleotide i at position j in a random sequence. In some embodiments, the probability of finding the ith nucleotide at the jth position in the functional sequence motif is used as eij, the probability of finding nucleotide i at position j in a random sequence is used as pij. The weight or probability pij is an “a prior” weight or probability. In some embodiments, pij=0.25 for each possible nucleotide iε{G, C, A, U(T)} at each position j. Thus, for a given sequence of length L, the sum of log likelihood ratios at all positions can be used as a score for evaluating if the given sequence is more or less likely to match the functional motif than to match a random sequence:




  
    
      
        Score
        =
        
          
            ∑
            
              j
              =
              1
            
            L
          
          ⁢
          
            
          
          ⁢
          
            ln
            ⁡
            
              (
              
                
                  e
                  j
                
                /
                
                  p
                  j
                
              
              )
            
          
        
      
    
    
      
        (
        4
        )
      
    
  




whereinewj and pj are respectively weights of a nucleotide at position j in the functional sequence motif and in a random sequence. For example, if such a score is zero, the sequence has the same probability to match the sequence motif as to that to match a random sequence. A sequence is more likely to match the sequence motif if the ratio is greater than zero.

In another embodiment, when two or more different nucleotides are not to be distinguished, a PSSM with a reduced dimension can be used. For example, if the relative base compositions of G and C in a sequence motif are not to be distinguished, a PSSM can be a 3·L matrix {log(Eij/pij)}, where i=G/C, A, U(T); j=1, 2, . . . , L; where Eij is the weight, e.g., probability, of finding nucleotide i at position j, and pij is the weight, e.g., probability, of finding nucleotide i at position j in a random sequence. Thus, in such cases, a PSSM has 3 sets of weights: GC-specific, A-specific and U-specific, e.g., if the base at a position is a G or a C, the natural logarithm of the ratio of the GC weight and the unbiased probability of finding a G or C at that position is used as the GC-specific weight for the position; and the natural logarithms of the position-specific A and T weights divided by the unbiased probability of respective base are used as the A- and T-specific weights for the position, respectively. The log likelihood ratio score is represented by Eq. (5):




  
    
      
        Score
        =
        
          
            ∑
            
              j
              =
              1
            
            L
          
          ⁢
          
            
          
          ⁢
          
            ln
            ⁡
            
              (
              
                
                  E
                  j
                
                /
                
                  p
                  j
                
              
              )
            
          
        
      
    
    
      
        (
        5
        )
      
    
  




where Ej is the weight assigned to a base—A, U or G/C—at position j, and pj=0.25 for A or U and 0.5 for G/C.

In still another embodiment, when the relative base compositions of G and C in a sequence motif are not to be distinguished and the relative base compositions of A and T in the sequence motif are also not to be distinguished, a PSSM can be a 1·L matrix {log(Eij/pij)}, where i=G/C; j=1, 2, . . . , L; where Eij is the weight, e.g., probability, of finding nucleotide i at position j, and pij is the weight, e.g., probability, of finding nucleotide i at position j in a random sequence. Thus, in such cases, a PSSM has 1 set of GC-specific weights: if the base at a position is a G or a C, the natural logarithm of the ratio of the GC weight and the unbiased probability of finding a G or C at that position is used as the GC-specific weight for the position. The log likelihood ratio score is represented by Eq. (5), except that Ej is the weight assigned to a base—G/C—at position j, and pj=0.50.
5.1.2. Methods of Determining a Profile
The invention provides methods of determining a PSSM of a functional sequence motif based on a plurality of siRNAs for which some quantity or quantities characterizing the siRNAs have been determined. For example, a plurality of siRNAs whose silencing efficacy has been determined can be used for determination of a PSSM of an siRNA susceptible or resistant sequence motif. In the disclosure, for simplicity reasons, efficacy is often used as a measure for classifying siRNAs. Efficacy of an siRNA is measured in the absence of other siRNAs designed to silence the target gene. It will be apparent to one skilled person in the art that the methods of the invention are equally applicable in cases where siRNAs are classified based on another measure. Such a plurality of siRNAs is also referred to as a library of siRNAs. In cases where the functional sequence motif of interest comprises one or more sequences in one or both flanking regions, a plurality of siRNA functional motifs, i.e., a sequence comprising the siRNA target sequence and the sequences in the flanking region(s) in a transcript, can be used to determine the PSSM of the functional motif. In a preferred embodiment, the siRNA functional sequence motif consists of an siRNA target sequence of 19 nucleotides and a flanking sequence of 10 nucleotides at either side of the siRNA target sequence. For simplicity reasons, in this disclosure, unless specified, the term “a library of siRNAs” is often used to referred to both a library of siRNAs and a library of siRNA functional motifs. It will be understood that in the latter cases, when the efficacy of an siRNA is referred to, it refers to the efficacy of the siRNA that targets the motif. Preferably, the plurality of siRNAs or siRNA target motifs comprises at least 10, 50, 100, 200, 500, 1000, or 10,000 different siRNAs or siRNA target motifs.
Each different siRNA in the plurality or library of siRNAs or siRNA functional motifs can have a different level of efficacy. In one embodiment, the plurality or library of siRNAs consists of siRNAs having a chosen level of efficacy. In another embodiment, the plurality or library of siRNAs comprises siRNAs having different levels of efficacy. In such an embodiment, siRNAs may be grouped into subsets, each consisting of siRNAs that have a chosen level of efficacy.
In one embodiment, a PSSM of an siRNA functional motif is determining using a plurality of siRNAs having a given efficacy. In one embodiment, a plurality of N siRNAs consisting of siRNAs having a silencing efficacy above a chosen threshold is used to determine a PSSM of an siRNA susceptible motif. The PSSM is determined based on the frequency of a nucleotide appeared at a position (see Section 5.1.1). The chosen threshold can be 50%, 75%, 80% or 90%. In another embodiment, a plurality of N siRNAs consisting of siRNAs having a silencing efficacy below a chosen threshold is used to determine a PSSM of an siRNA susceptible motif. The chosen threshold can be 5%, 10%, 20%, 50%, 75% or 90%. In a preferred embodiment, the PSSM has a reduced dimension with a weight for G/C.
In preferred embodiments, a PSSM of a susceptible or resistant motif is derived or built using a classifier approach with a set of N sequences. In such embodiments, a library of siRNAs comprising siRNAs having different levels of efficacy are used. In one embodiment, siRNAs in the library may be randomly grouped into subsets, each consisting of siRNAs that have different levels of efficacy, one subset is used as a training set for determining a PSSM and the other is used as a testing set for validating the PSSM. Different criteria can be used to divide the existing siRNA library into training and test sets. For an siRNA library in which a majority of siRNA oligos are designed with the standard method, which requires an AA dimer immediately before the 19mer oligo sequence, several partitions were used and more than one trained PSSMs (rather than single PSSMs) were combined to assign scores to the test oligos. An exemplary siRNA library and divisions of the library into training and test sets are shown in Table II.
In a preferred embodiment, the sequence motif consists of 39 bases in the transcript sequence, beginning 10 bases upstream of the 19mer siRNA target sequence and ending 10 bases downstream of the 19mer. The PSSM characterizing such a sequence motif is described in Section 5.1.1.
In a preferred embodiment, the PSSM is determined by an iterative process. A PSSM is initialized with random weights {eij} or {Eij} within a given search range for all bases at all positions. In another preferred embodiment, PSSM is initialized to the smoothed mean base composition difference between good and bad siRNAs in the training set. As an example, a PSSM describing a 39 nucleotide sequence motif can have 117 elements. In another embodiment, the weights are optimized by comparing the correlation of scores generated to a quantity of interest, e.g., silencing efficacy, and selecting the PSSM whose score best correspond to that quantity. Improvement in PSSM performance is scored by comparing correlation values before and after a change in weights at any one position. In one embodiment, there is no minimum requirement for a change in correlation. Aggregate improvement is calculated as the difference between the final correlation and the initial correlation. In one embodiment, for a PSSM characterizing a 39mer sequence motif, the aggregate improvement threshold after 117 cycles for termination of optimization is a difference of 0.01.
In one embodiment, the weights are optimized to reflect base composition differences between good siRNAs, i.e., siRNAs having at least median efficacy, and bad siRNAs, i.e., siRNAs having below median efficacy, in the range of allowed values for weights. If the PSSM is initialized with a frequency matrix, the range of allowed values corresponds to the frequency matrix elements +/−0.05.If an unbiased search is used, the ranges of the allowed values for weights are 0.45-0.55 for G/C and 0.2-0.3 for A or U. In one embodiment, weights are allowed to vary from initial values by +/−0.05.If an unbiased search is used, the PSSM weights can be set to random initial values within the unbiased search range described above.
In one embodiment, the PSSM is determined by a random hill-climbing mutation optimization procedure. In each step of the process, one base at one position is randomly selected for optimization. For example, for a PSSM describing a 39 nucleotide sequence motif, the 39 bases become a vector of 117 weights: 39 G/C weights, 39 A weights and 39 U weights. One of these 117 weights is selected for optimization in each step, and is run through all values in the search range at that step. For each value in the search range, scores for a training set of siRNAs are calculated. The correlation of these scores with the silencing efficacy of the siRNAs is then calculated. The weight for that position which generate the best correlation between the scores and silencing efficacy is retained as the new weight at that position.
In one embodiment, the metric used to measure the effectiveness of the training and testing is the aggregate false detection rate (FDR) based on the ROC curve, and is computed as the average of the FDR scores of the top 33% oligos sorted by the scores given by the trained PSSM. In computing the FDR scores, those oligos with silencing levels less than the median are considered false, and those with silencing level higher than the median level are considered true. The “false detection rate” is the number of false positives selected divided by the total number of true positives, measured at each ranked position in a list. The false detection rate can be a function of the fraction of all siRNAs selected. In one embodiment, the area under the curve at 33% of the list selected as a single number representing performance. In one embodiment, all at-least-median siRNAs are called as “positives” and all worse-than-median siRNAs are called “negatives.” Thus, half the data are positives and the other half are “false positives.” In an ideal ranking, the area under the curve at 33% or even at 50% of the list selected should be 0.In contrast, a random ranking would cause equal numbers of true positives and false positives to be selected. This corresponds to an area under the curve of 0.17 at 33% of the list selected, or 0.25 at 50% of the list selected.
Correlations between % silencing and PSSM score are calculated according to method known in the art (see, e.g., Applied Multivariate Statistical Analysis, 4th ed., R. A. Johnson & E. W. Wichem, Prentice-hall, 1998).
The process is continued until the aggregate improvement over a plurality of iterations fell below a threshold.
In a preferred embodiment, a plurality of PSSMs are obtained for a functional sequence motif using an siRNA training set. In this disclosure, a plurality of PSSMs is also referred to as an “ensemble” of PSSMs. Each round of optimization may stop at a local optimum distinct from the global optimum. The particular local optimum reached is dependent on the history of random positions selected for optimization. A higher improvement threshold may not bring a PSSM optimized to a local optimum closer to the global optimum. Thus it is more effective to run multiple optimizations than one long optimization. Additional runs (e.g., up to 200) were found to enhance performance. Running more than 200 optimizations was not seen to provide further enhancements in performance. Empirically, scoring siRNAs via the average of multiple runs is less effective than scoring candidate siRNAs on the PSSMs generated by each run and then summing the scores. Thus, in one embodiment, the plurality of PSSMs are used individually or summed to generate a composite score for each sequence match. The plurality of matrices can be tested individually or as a composite on an independent set of siRNA target motifs with known silencing efficacy to evaluate the utility for identifying sequence motifs and in siRNA design. In a preferred embodiment, the plurality of PSSM consists of at least 2, 10, 50, 100, 200, or 500 PSSMs.
In a preferred embodiment, one or more different siRNA training sets are used to obtain one or more ensemble of PSSMS. These different ensembles of PSSMs may be used together in determining the score of a sequence motif.
Sequence weighting methods have been used in the art to reduce redundancy and emphasize diversity in multiple sequence alignment and searching applications. Each of these methods is based on a notion of distance between a sequence and an ancestral or generalized sequence. Here a different approach is presented, in which base weights on the diversity observed at each position in the alignment and the correlation between the base composition and the observed efficacy of the siRNAs, rather than on a sequence distance measure.
In still another embodiment, PSSMs are generated by a method which hypothesized dependency of the base composition of any one position on its neighboring positions, referred to as “curve models.”
In one embodiment, curve models are generated as a sum of normal curves (i.e., Gaussian). It will be apparent to one skilled person in the art that other suitable curve functions, e.g., polynomials, can also be used. Each curve represents the probability of finding a particular base in a particular region. The value at each position in the summed normal curves is the weight given to that position for the base represented by the curve. The weights for each base present at each position in each siRNA and its flanking sequences are then summed to generate an siRNA's score, i.e., the score is Σ wi. The score calculation can also be described as the dot product of the base content in the sequence with the weights in the curve model. As such, it is one way of representing the correlation of the sequence of interest with the model.
Curve models can be initialized to correspond to the major peaks and valleys present in the smoothed base composition difference between good and bad siRNAs, e.g., as described in FIGS. 1A-C and 5A-C. In one embodiment, curve models for G/C, A and U are obtained. In one embodiment, the initial model can be set up for the 3-peak G/C curve model as follows:
Peak 1


    
    
        mean: 1.5
        standard deviation: 2
        amplitude: 0.0455

Peak 1 mean, standard deviation and amplitude are set to correspond to the peak in the mean difference in GC content between good and bad siRNAs occurring within bases—2-5 of the siRNA target site in Set 1 training and test sets.

Peak 2

        mean: 11
        standard deviation: 0.5
        amplitude: 0.0337

Peak 2 mean, standard deviation and amplitude are set to correspond to the peak in the mean difference in GC content between good and bad siRNAs occurring within bases 10-12 of the siRNA target site in Set 1 training and test sets.

Peak 3

        mean: 18.5
        standard deviation: 4
        amplitude: −0.0548

Peak 3 mean, standard deviation and amplitude are set to correspond to the peak in the mean difference in GC content between good and bad siRNAs occurring within bases 12-25 of the siRNA target site in Set 1 training and test sets.

    
    


Peak height (amplitude), center position in the sequence (mean) and width (standard deviation) of a peak in a curve model can be adjusted. Curve models are optimized by adjusting the amplitude, mean and standard deviation of each peak over a preset grid of values. In one embodiment, curve models are optimized on several training sets and tested on several test sets, e.g., training sets and test sets as described in Table II. Each base—G/C, A and U(or T)—is optimized separately, and then combinations of optimized models are screened for best performance.
Preferably, optimization criteria for curve models are: (1) the fraction of good oligos in the top 10%, 15%, 20% and 33% of the scores, (2) the false detection rate at 33% and 50% of the siRNAs selected, and (3) the correlation coefficient of siRNA silencing vs. siRNA scores used as a tiebreaker.
When the model is trained, a grid of possible values for amplitude, mean and standard deviation of each peak is explored. The models with the top value or within the top range of values for any of the above criteria were selected and examined further.
In a preferred embodiment, G/C models are optimized with 3 or 4 peaks, A models are optimized with 3 peaks, and U models are optimized with 5 peaks. Exemplary ranges of parameters optimized for curve models are shown in Example 3, infra.
Preferably, the performance of the obtained PSSM is evaluated. In one embodiment, the PSSM is evaluated using an ROC (receiver operating characteristic) curve. An ROC curve is a plot of the sensitivity of a diagnostic test as a function of non-specificity. An ROC curve indicates the intrinsic properties of a test's diagnostic performance and can be used to compare relative merits of competing procedures. In one embodiment, the sensitivity of a PSSM is calculated as the proportion of true positives detected as a fraction of total true positives, whereas the non-specificity of the PSSM is calculated as the proportion of false positives detected as a fraction of total false positives (see, e.g., Campbell, 1994, Statistics in Medicine 13:499-508; Metz, 1986, Investigative Radiology 21:720-733; Gribskov et al., 1996, Computers Chem. 20:25-33). FIG. 3 shows ROC curves of the two PSSMs selected for the current best practice of the invention.
In another embodiment, the performance of a PSSM is evaluated by comparing a plurality of sequence motifs identified using the PSSM with a plurality of reference sequence motifs. The PSSM is used to obtain the plurality of sequence motifs by, e.g., scanning one or more transcripts and identifying sequence motifs that match the PSSM, e.g., with a score above a threshold. Preferably, the plurality comprises at least 3, 5, 10, 20 or 50 different sequence motifs. The reference sequence motifs can be from any suitable source. In one embodiment, a plurality of reference sequence motifs is obtained using a standard method (e.g., Elbashir et al., 2001, Nature. 411:494-8). The two pluralities are then compared using any standard method known in the art to determine if they are identical.
In a preferred embodiment, the two pluralities are compared using a Wilcoxon rank sum test. A Wilcoxon rank sum test tests if two pluralities of measurements are identical (see, e.g., Snedecor and Cochran, Statistical Methods, Eighth Edition, 1989, Iowa State University Press, pp. 142-144; McClave and Sincich, 2002, Statistics, Ninth Edition, Prentice Hall, Chapter 14). The Wilcoxon rank sum test can be considered a non-parametric equivalent of the unpaired t-test. It is used to test the hypothesis that two independent samples have come from the same population. Because it is non-parametric, it makes only limited assumptions about the distribution of the data. It assumes that the shape of the distribution is similar in the two groups. This is of particular relevance if the test is to be used as evidence that the median is significantly different between the groups.
The test ranks all the data from both groups. The smallest value is given a rank of 1, the second smallest is given a rank of 2, and so on. Where values are tied, they are given an average rank. The ranks for each group are added together (hence the term rank sum test). The sums of the ranks is compared with tabulated critical values to generate a p value. In a Wilkoxon rank sum test, p, a function of X, Y, and α, is the probability of observing a result equal or more extreme than the one using the data (X and Y) if the null hypothesis is true. The value of p indicates the significance for testing the null hypothesis that the populations generating the two independent samples, X and Y, are identical. X and Y are vectors but can have different lengths, i.e., the samples can have different number of elements. The alternative hypothesis is that the median of the X population is shifted from the median of the Y population by a non-zero amount. α is a given level of significance and is a scalar between zero and one. In some embodiment, the default value of α is set to 0.05. If p is near zero, the null hypothesis may be rejected.
In one embodiment, the PSSM approach of the present invention was compared to the standard method (e.g., Elbashir et al., 2001, Nature 411:494-8) for its performance in identifying siRNAs having high efficacy. The results obtained with three siRNAs selected by each method are shown in FIG. 3. siRNAs selected by the method using the PSSM showed better median efficacy (88% as compared to 78% for the standard method siRNA) and were more uniform in their performance. The minimum efficacy was greatly improved (75% as compared to 12% for the standard method). The distribution of silencing efficacies of siRNAs designed using the algorithm based on PSSM was significantly better than that of the siRNAs designed using the standard method for the same genes (p=0.004, Wilcoxon rank sum test).
5.1.3. Alternative Method for Evaluating Silencing Efficacy of siRNAS
Position-specific scoring matrix approaches are the preferred method of representing siRNA functional motifs, e.g., siRNA susceptible and resistant motifs. However the information represented by PSSMs can also be represented by other methods which also provide weights for base-composition at particular positions. This section provides such methods for evaluating siRNA functional motifs.
5.1.3.1. Methods Based on Sequence Windows
A common method of weighting base-composition at positions in a sequence is to tally the number of a particular base or set of bases in a “window” of sequence positions. Alternatively, the tally is represented as a percentage. The number of values of such a score, referred to as a window score, depends on the size of the window. For example, scoring a window of size 5 for G/C content may give values of 0, 1, 2, 3, 4 or 5; or 0%, 20%, 40%, 60%, 80% or 100%.
An alternative method of scoring a window is to calculate the duplex melting temperature or ΔG for the bases in that window. These thermodynamic quantities reflect the composition of all bases in the window as well as their particular order. It is readily apparent to one of skill in the art that these thermodynamic quantities directly depend on the base composition of each window, and are dominated by the G/C content of the window while showing some variation with the order of the bases.
In one embodiment, the information represented by the base-composition differences, e.g., in FIGS. 1A, 1B and 1C, is represented by windows of base-composition corresponding to the positions to the peaks of increased or decreased composition of a particular base(s). These windows can be scored for content of the particular base(s), with increased or decreased base composition corresponding to sequences which are more or less functional or resistant for siRNA targeting. For example, a 5-base window of increased G/C content from base—1 to base 3 relative to the siRNA 19mer duplex, and a 16-base window of decreased G/C content from base 14 to base 29 relative to the siRNA 19mer duplex, can be used to represent some of the siRNA functional motif reflected in FIG. 1A.
The scores may be used directly as a classifier: in the example of a 5-base window, a 5-part classifier is automatically available. Scores can also be compared to a calculated or empirically derived threshold to use the window as a 2-part classifier. Windows can also be used in combination The scores of each sequence over multiple windows can be summed with or without normalization or weighting. In one embodiment, scores for each window are normalized by subtracting the mean score in a set of scores and then dividing by the standard deviation in the set of scores. In another embodiment, scores are weighted by the Pearson correlation coefficient obtained by comparing that window's score with the measured efficacy of a set of siRNAs. In another embodiment, scores are normalized, and then weighted before summation.
As an example of the use of windows to represent siRNA functional motifs, the following list of parameters was considered for prediction of siRNA efficacy:
1. Straight-forward parameters.
ATG_Dist—distance to the start codon.
STOP_Dist—distance to the end of the coding region
Coding_Percent—ATG_Dist as percentage of the length of coding region
End_Dist—distance to the end of the transcript
Total_Percent—start position as a percentage of the length of the transcript sequence.
2. Window-based parameters.
119 bases on the transcript sequence were considered (19mer plus 50 bases downstream and 50 bases upstream). Windows of sizes 3-10 were examined for each position from the beginning to the end of the 119-base chunk. The following items were counted for each window position:
a. Numbers of bases: A, C, G, or U.
b. Numbers of pairs of bases: M (A or C), R (A or G), W (A or U), S (C or G), Y (C or U), and K (G or U).
c. Numbers of various ordered dimers: AC, AT, AG, MM, RY, KM, SW, etc.
d. The longest stretches of the above one base or two-base units.
3. Motif-based parameters.
These parameters are also based on the 119-base chunks. The letters include the bases (A, C, G, U) and pairs of bases (M, R, W, S, Y, K).
(1). Position-Specific one-mer, dimers, or trimers.
(2). Numbers of 1mers to 7mers in four large regions: 50 bases upstream, 19mer proper, 50 bases downstream, and the whole 119mer region.
4. Structural parameters.
The structural parameters are based on the following regions.
the 19mer oligo proper (prefix: proper)
the 20mer immediate upstream the oligo (prefix: up20)
the 40mer immediate upstream the oligo
the 60mer immediate upstream the oligo
the 20mer immediate downstream the oligo (prefix: down20)
the 40mer immediate downstream the oligo
the 60mer immediate downstream the oligo
Base-pairing predicted by RNAStructure was examined and the following parameters were calculated:
the count of bulge loops (parameter bulge)
the total bases in the bulge loops (bulge_b)
the count of internal loops (internal)
the total bases in the internal loops (internal_b)
the count of hairpins (hairpin)
the total bases in the hairpins (hairpin_b)
the count of other motif regions (other)
the total bases in the other motif regions (other_b)
the total paired bases (total_pairs_b)
the total non-paired bases (total_nonpairs_b)
the longest stretch of paired bases (longest_pairs_b)
the longest stretch of non-paired bases (longest_nonpairs_b)
Thus, a total of 12*7=84 parameters were computed about the secondary structure motifs for each siRNA.
5. Parameters on off-target predictions.
10 different parameters were computed using the weighted FASTA score discussed in Section 5.2., the minimax score and the predicted duplex ΔG discussed in Section 5.4, using different conditions.
Parameters were normalized and weighted by the Pearson correlation coefficient of the scores with the silencing efficacy of the siRNAs examined. Various methods were used to select the parameters with the greatest predictive power for siRNA efficacy; the various methods agreed on the selection 1750 parameters. 1190 of these are window-based base composition parameters, 559 are motif-based base composition parameters, and only 1 structural parameter was selected. No other parameters were selected
5.1.3.2. Sequence Family Scoring Methods
Sequence consensus patterns, hidden Markov models and neural networks can also be used to represent siRNA functional motifs, e.g., siRNA susceptible or resistant motifs as an alternative to PSSMS.
First, an siRNA functional motifs, e.g., siRNA susceptible or resistant motif can be understood as a loose consensus sequence for a family of distantly related sequences—e.g. the family of functional siRNA target sites. Scoring sequences for similarity to a family consensus is well known in the art (Gribskov, M., McLachlan, A. D., and Esienberg, D. 1987. Profile analysis: detection of distantly related proteins. PNAS 84:4355-4358; Gribskov, M., Luthy, R., and Eisenberg, D. 1990. Profile analyisis. Meth. Enzymol. 183:146-159). Such scoring methods are most commonly referred to as “profiles”, but may also be referred to as “templates” or “flexible patterns” or similar terms. Such methods are more or less statistical descriptions of the consensus of a multiple sequence alignment, using position-specific scores for particular bases or amino acids as well as for insertions or deletions in the sequence. Weights can be derived from the degree of conservation at each position. A difference between consensus profiles and PSSMs as the term is used in this text is that spacing can be flexible in consensus profiles: discontinuous portions of an siRNA functional motifs, e.g., siRNA susceptible or resistant motif can be found at varying distances to each other, with insertions or deletions permitted and scored as bases are.
Profile hidden Markov models are statistical models which also represent the consensus of a family of sequences. Krogh and colleagues (Krogh, A., Brown, M., Mian, I. S., Sjolander, K. and Haussler, D. 1994. Hidden Markov models in computational biology: Applications to protein modeling. J. Mol Biol.235:1501-1531) applied HMM techniques to modeling sequence profiles, adopting techniques from speech recognition studies (Rabiner, L. R. 1989. A tutorial on hidden Markov models and selected applications to speech recognition. Proc. IEEE 77:257-286). The use of hidden Markov models for analysis of biological sequences is now well known in the art and applications for hidden Markov model calculation are readily available; for example, the program HMMER (hmmer.wustl.edu).
Profile hidden Markov models differ from consensus profiles as described above in that profile hidden Markov models have a formal probabilistic basis for setting the weights for each base, insertion or deletion at each position. Hidden Markov models can also perform the alignment of unknown sequences for discovery of motifs as well as determining position-specific weights for said motifs, while consensus profiles are generally derived from previously aligned sequences.
Consensus profiles and profile hidden Markov models can assume that the base composition at a particular position is independent of the base composition of all other positions. This is similar to the random-hill-climbing PSSMs of this invention but distinct from the windows and curve model PSSMs.
To capture dependency of base composition at a particular position on the composition of neighboring positions, Markov models can be used as fixed-order Markov chains and interpolated Markov models. Salzberg and colleagues applied interpolated Markov models to finding genes in microbial genomes as an improvement over fixed-order Markov chains (Salzberg, S. L., Delcher, A. L., Kasif, S., and White, O. 1998. Nucl. Acids Res. 26:544-548). A fixed-order Markov chain predicts each base of a sequence as a function of a fixed number of bases preceding that position. The number of proceeding bases used to predict the next is known as the order of the Markov chain. Interpolated Markov models use a flexible number of preceeding bases to predict the base composition at a particular position. This permits training on smaller sequence sets. Sufficient predictive data may be available for n-mers of various lengths in a training set such that some predictions of succeeding bases can be made, while insufficient data may be available for all oligomers at any fixed length. Interpolated Markov models thus have more freedom to use preferable longer oligomers for prediction than fixed-order Markov chains, when said long oligomers are sufficiently frequent in the training set. Interpolated Markov models employ a weighted combination of probabilities from a plurality of oligomer lengths for classification of each base.
Fixed-order Markov chains and interpolated Markov models can represent siRNA functional motifs, e.g., siRNA susceptible or resistant motifs in terms of the dependency of the base-composition at a particular position on the composition of the preceding positions. An interpolated Markov model building process will discover the oligomers most predictive of siRNA functional or nonfunctional motifs.
Neural networks are also employed to score sequences for similarity to a family of sequences. A neural network is a statistical analysis tool used to build a model through an iterative leaning process. The trained network will then perform a classification task, dependent upon the desired output and the training input initially associated with that output. Typically a neural network program or computational device is supplied with a training set of sequences and sets up a state representing those sequences. The neural network is then tested for performance on a test set of sequences. Neural networks can be used to predict and model siRNA functional motifs, e.g., siRNA susceptible and resistant motifs. A disadvantage of neural networks is that the actual sequence features of a motif can be difficult or impossible to determine from examination of the state of the trained network.
5.1.4. Methods of Identifying Sequence Motifs in a Gene for Targeting by an siRNA
The invention provides a method for identifying one or more sequence motifs in a transcript which are siRNA-susceptible or -resistant motifs. The corresponding functional or unfunctional siRNAs are thereby also provided by the method. In one embodiment, the sequence region of interest is scanned to identify sequences that match the profile of a functional motif. In one embodiment, a plurality of possible siRNA sequence motifs comprises siRNA sequence motifs tiled across the region at steps of a predetermined base intervals are evaluated to identify sequences that matched the profile. In a preferred embodiment, steps of 1, 5, 10, 15, or 19 base intervals are used. In a preferred embodiment, the entire transcript sequence is scanned. A score is calculated for each different sequence motif using a PSSM as described in Sections 5.1.1.-5.1.3. The sequences are then ranked according to the score. One or more sequences are then selected from the rank list. In one embodiment, siRNA sequence motifs having the highest scores are selected as siRNA-susceptible motifs. In another embodiment, siRNA sequence motifs having the lowest scores are selected as siRNA resistant motifs.
The inventors have discovered that the correlation between silencing efficacy and the base composition profiles of siRNA functional motifs may depend on one or more factors, e.g., the abundance of the target transcript. For example, the inventors have found that for silencing poorly-expressed genes. e.g., genes whose transcript levels are less than about 5 copies per cell, siRNA functional motifs having high GC content asymmetry at the two ends of the target sequence and having high GC content in the sequence regions flanking the target sequence have lower silencing efficiency than siRNA functional motifs having moderate GC content asymmetry at the two ends of the target sequence and low GC content in the flanking regions. The effect of target transcript abundance on silencing efficacy is illustrated in Example 6.
While not to be confirmed by any theory, the inventors reason that the silencing efficacy of a particular siRNA functional motif is a result of the interplay of a number of processes, including RISC formation and siRNA duplex unwinding, diffusion of the RISC and target mRNA, reaction of the RISC/target complex, which may include diffusion of the RISC along the target mRNA, cleavage reaction, and products dissociation, etc. Thus, the abundance of the transcript, the base composition profile of the siRNA, the base composition profile of the target sequence and flanking sequences, and the concentration of the siRNA and RISC in a cell may all affect silencing efficacy. Different processes may involve different sequence regions of an siRNA or siRNA sequence motif, i.e., different sequence regions of an siRNA or siRNA sequence motif may have different functions in transcript recognition, cleavage, and product release, siRNAs may be designed based on criteria that take one or more of such features into account. For example, bases near the 5′ end of the guide strand are implicated in transcript binding (both on- and off-target transcripts), and have been shown to be sufficient for target RNA-binding energy. Weaker base pairing at the 5′ end of the antisense strand (3′ end of the duplex) encourages preferential interaction of the antisense strand with RISC, e.g., by facilitating unwinding of the siRNA duplex by a 5′-3′ helicase component of RISC. A preference for U at position 10 of the sense strand of an siRNA has been associated with improved cleavage efficiency by RISC as it is in most endonucleases. Low GC content sequence flanking the cleavage site may enhance accessibility of the RISC/nuclease complex for cleavage, or release of the cleaved transcript, consistent with recent studies demonstrating that base pairs formed by the central and 3′ regions of the siRNA guide strand provide a helical geometry required for catalysis. Thus, is the invention provides a method of identifying siRNA sequence motifs (and thus siRNAs) by obtaining siRNAs that have an optimal sequence composition in one or more sequence regions such that these siRNAs are optimal in one or more the siRNA functional processes. In one embodiment, the method comprises identifying siRNA sequence motifs whose overall sequence and/or different sequence regions have desired composition profiles. The method can be used to identify siRNAs motifs that have desired sequence composition in a particular region, thus are optimized for one functional process. The method can also be used to identify siRNAs that have desired sequence composition in a number of regions, thus are optimized for a number of functional processes.
In a preferred embodiment, a single siRNA functional profile, e.g., a profile as represented by a set of PSSMS, is obtained, e.g., by training with silencing efficacy data of a plurality of siRNAs that target genes having different transcript abundances using a method described in Section 5.1.2 or Section 5.1.3., and is used to evaluate siRNA sequence motifs in gene transcripts having abundances in all ranges. In one embodiment, the siRNA sequence motifs in gene transcripts having abundances in any range are evaluated based on the degree of similarity of their sequence base composition profiles to the profile or profiles represented by the set of PSSMS. In one embodiment, the PSSM scores of siRNA functional motifs for a gene of interest are obtained by a method described in Section 5.1.1. A predetermined reference value or reference range of values of the PSSM score is determined based on siRNAs that target genes having expression levels in different ranges. Methods for determining the reference value or range of reference value is described below. siRNA functional motifs in a particular gene are then ranked based on the closeness of their scores to the predetermined reference value or within the reference range. One or more siRNAs having scores closest to the predetermined value or within the reference range are then selected. In another embodiment, a predetermined reference value of the PSSM score or a reference range of the PSSM scores is used for genes having expression levels in a given range. The reference value or the reference range is determined based on siRNAs that target genes having expression levels in the range. siRNA functional motifs in a particular gene are then ranked based on the closeness of their scores to the predetermined reference value or within the reference range. One or more siRNAs having scores closest to the predetermined value or within the reference range are then selected.
The reference value or the reference range can be determined in various ways. In a preferred embodiment, correlation of PSSM scores of a plurality of siRNAs having one or more features, e.g., having particular efficiency in one or more siRNA functional processes, with silencing efficacy is evaluated. In a preferred embodiment, the feature is that the plurality of siRNAs targets poorly-expressed genes. The value of the score corresponding to maximum median silencing is used as the reference value. In a specific embodiment, the reference value is 0.One or more siRNAs having PSSM scores the closest to the reference score are selected.
In another embodiment, the range of scores corresponding to siRNAs having a given level of silencing efficacy, e.g., efficacy above 75%, is used as the range for the reference values. In one embodiment, effective siRNAs are found to have scores between −300 and +200 as long as the GC content in bases 2-7 is controlled. In a specific embodiment, a reference value of between −300 and +200 is used. One or more siRNAs having PSSM scores within the range are selected.
In another preferred embodiment, a particular score range within the range of PSSM scores of the plurality of siRNAs having one or more features, e.g., having particular efficiency in one or more siRNA functional processes, is used as the range of the reference value. In a preferred embodiment, the feature is that the plurality of siRNAs targets poorly-expressed genes. In one embodiment, a certain percentile in the range of PSSM scores is used as the range of the reference value, e.g., 90%, 80%, 70%, or 60%. In a specific embodiment, the combined PSSM score range in the training set has a maximum of 200, with 97% of the scores being 0 or below and 60% of the scores are below −300.
In still another preferred embodiment, a sum of scores from a plurality of sets of PSSMs (see Section 5.1.2) is used as the reference score. In a specific embodiment, the plurality of sets consists of the two sets of PSSMs described previously. The two sets of PSSMs differ in the base composition preferred for siRNAs, in particular with respect to the GC content of the 19mer and flanking sequences. With a combined score of 0, the PSSM sets are in balance in their preference for the siRNA.
In another preferred embodiment, in addition to the PSSM scores, the siRNA sequence motifs are also ranked according to GC content at positions corresponding to positions 2-7 of the corresponding siRNAs, and one or more siRNA sequence motifs that have a GC content approximately 0.15 to 0.5 (corresponding to 1-3 G or C) in the region are selected.
In still another preferred embodiment, siRNA sequence motifs having a G or C at the position corresponding to position 1 of the corresponding 19mer siRNA and a A or T at the position corresponding to position 19 of the corresponding 19mer siRNA are selected. In still another preferred embodiment, siRNAs motifs in which 200 bases on either side of the 19mer target region are not repeat or low-complexity sequences are selected.
In a specific embodiment, the siRNA sequence motifs selected in the following manner: (1) they are first ranked according to GC content at positions corresponding to positions 2-7 of the corresponding siRNAs, and one or sore siRNA sequence motifs that have a GC content approximately 0.15 to 0.5 (corresponding to 1-3 G or C) in the region are selected; (2) next, siRNA sequence motifs having a G or C at the position corresponding to position 1 of the corresponding 19mer siRNA and a A or T at the position corresponding to position 19 of the corresponding 19mer siRNA are selected; (3) siRNAs having PSSM scores in the range of −300 to 200 or most close to 0 are then selected; (4) number of off-target BLAST match less than 16 are then selected; and (5) siRNAs motifs in which 200 bases on either side of the 19mer target region are not repeat or low-complexity sequences are selected.
In another embodiment, a reference value or reference range for each of a plurality of different abundance ranges is determined. Selection of siRNA functional motifs in a gene of interest is achieved by using the appropriate reference value or reference range for the abundance range in which the gene of interest falls. In one embodiment, the plurality of different abundance ranges consists of two ranges: below about 3-5 copies per cell, corresponding to poorly-expressed genes, and above 5 copies per cell, corresponding to highly-expressed genes. The reference value or reference range can be determined for each abundance range using any one of the methods described above.
In another embodiment, a plurality of siRNA functional motif profiles are determined for a plurality of different transcript abundance ranges. Each such profile is determined based on silencing efficacy data of siRNAs that target genes having expression levels in a given range, i.e., genes whose transcript abundances fall within a given range, using a method described in Sections 5.1.2 and 5.1.3., supra. In one embodiment, a set of one or more PSSMs for genes having expression levels in a given range are trained as described in Section 5.1.2.using siRNAs that target genes having expression levels in the range. The PSSMs are then used for identifying siRNA functional motifs in a target gene whose expression level falls in the range, e.g., by ranking according to the PSSM scores obtained using a method described in Section 5.1.1. In a preferred embodiment, the transcript abundance ranges are divided into two ranges: below about 3-5 copies per cell, corresponding to poorly-expressed genes, and above 5 copies per cell, corresponding to highly-expressed genes. Two sets of PSSMs are obtained, one for each abundance range. siRNA functional motifs in a gene of interest can be identified using the set of PSSMs that is appropriate for the abundance of the gene of interest.
The invention also provides methods for evaluating the silencing efficacies of siRNA sequence motifs under different siRNA concentrations. For example, the methods described above for evaluating silencing efficacy of siRNA sequence motifs in transcripts having different abundances can be used for such purposes by replacing the abundance parameter with the concentration parameter. In one embodiment, a plurality of siRNA functional motif profiles are determined for a plurality of different siRNA concentration ranges. Each such profile can be determined based on silencing efficacy data of different concentration of siRNAs targeting genes having a different expression level or having an expression level in a different range. In one embodiment, such profiles are determined for transcripts having a given abundance or having a abundance within a range of abundances. Each such profile can be determined based on silencing efficacy data of different concentration of siRNAs targeting genes having the expression level or having an expression level in the range. In one embodiment, one or more PSSMs for a given siRNA concentration range are trained based on silencing efficacy data of siRNAs having a concentration in the range. The PSSMs can then be used for selecting siRNAs that have high efficiency at a concentration that falls in the concentration range. In a preferred embodiment, the transcript abundance ranges is selected to be below 5 copies per cell. In another embodiment, the transcript abundance ranges is selected to be above 5 copies per cell. The invention thus provides a method for selecting one or more siRNA functional motifs for targeting by siRNAs of a given concentration.
The methods can be used for identifying one or more siRNA functional motifs that can be targeted by siRNAs of a given concentration with desired silencing efficacy. The given concentration is preferably in the low nanomolar to sub-nanomolar range, more preferably in the picomolar range. In specific embodiments, the given concentration is 50 nmol, 20 nmol, 10 nmol, 5 nmol, 1 nmol, 0.5 nmol, 0.1 mmol, 0.05 nmol, or 0.01 nmol. The desired silencing efficacy is at least 50%, 75%, 90%, or 99% under a given concentration. Such methods are particularly useful for designing therapeutic siRNAs. For therapeutic uses, it is often desirable to identify siRNAs that can silence a target gene with high efficacy at sub-nanomolar to picomolar concentrations. The invention thus also provides a method for design of therapeutic siRNAs.
The invention also provides a method for determining if a gene is suitable for targeting by a therapeutic siRNA. In one embodiment, the desired siRNA concentration and the desired silencing efficacy are first determined. A plurality of possible siRNA sequence motifs in the transcript of the gene is evaluated using a method of this invention. One or more siRNA sequence motifs that exhibit the highest efficacy, e.g., having PSSM scores satisfying the above described criterion or criteria, are identified. The gene is determined as suitable for targeting by a therapeutic siRNA if the one or more siRNA sequence motifs can be targeted by the corresponding siRNAs with silencing efficacy above or equal to the desired efficacy. In one embodiment, the plurality of possible siRNA sequence motifs comprises siRNA sequence motifs that span or are tiled across a part of or the entire transcript at steps of a predetermined base intervals, e.g. at steps of 1, 5, 10, 15, or 19 base intervals. In a preferred embodiment, successive overlapping siRNA sequence motifs are tiled across the entire transcript sequence. In another preferred embodiment, successive overlapping siRNA sequence motifs tiled across a region of or the entire transcript sequence at steps of 1 base intervals.
5.2. Methods of Identifying Off-Target Genes of an siRNA
The invention also provides a method of identifying off-target genes of an siRNA. As used herein, an “off-target” gene is a gene which is directly silenced by an siRNA that is designed to target another gene (see, International application No. PCT/US2004/015439 by Jackson et al., filed on May 17, 2004, which is incorporated herein by reference in its entirety). An off-target gene can be silenced by either the sense strand or the antisense strand of the siRNA.
5.2.1. Sequence Match Profile and Off-Target Silencing
Microarray experiments suggest that most siRNA oligos result in downregulation of off-target genes through direct interactions between an siRNA and the off-target transcripts. While sequence similarity between dsRNA and transcripts appears to play a role in determining which off-target genes are affected, sequence similarity searches, even combined with thermodynamic models of hybridization, are insufficient to predict off-target effects accurately. However, alignment of off-target transcripts with offending siRNA sequences reveals that some base pairing interactions between the two appear to be more important than others (FIG. 6).
The invention provides a method of identifying potential off-target genes of an siRNA using a PSSM that describes the sequence match pattern between an siRNA and a sequence of an off-target gene (pmPSSM). In one embodiment, the sequence match pattern is represented by weights of different positions in an siRNA to match the corresponding target positions in off-target transcripts {Pi}, where Pi is the weight of a match at position i, i=1, 2, . . . , L, where L is the length of the siRNA. Such a match pattern can be determined based on the frequency with which each position in an siRNA is found to match affected off-target transcripts identified as direct targets of the siRNA by simultaneous downregulation with the intended target through kinetic analysis of expression profiles (see, International application No. PCT/US2004/015439 by Jackson et al., filed on May 17, 2004). A pmPSSM can be {Ei}, where Ei=Pi if position i in the alignment is a match and Ei=(1-Pi)/3 if position i is a mismatch. An exemplary {Pi} for a 19mer siRNA sequence is plotted in FIG. 7 and listed in Table I.







TABLE I





Weights of an exemplary pmPSSM for 21 nt siRNAs having a


19 nt duplex region


















1
0.25



2
0.32



3
0.32



4
0.46



5
0.39



6
0.38



7
0.36



8
0.45



9
0.61



10
0.47



11
0.76



12
0.96



13
0.94



14
0.81



15
0.92



16
0.94



17
0.89



18
0.78



19
0.58










In one embodiment, sequence match pattern of off-target transcripts are used to obtain a pmPSSM. Off-target genes of an siRNA can be identified using a method disclosed in International application No. PCT/US2004/015439 by Jackson et al., filed on May 17, 2004, which is incorporated herein by reference in its entirety. For example, off-target genes of an siRNA are identified based on silencing kinetics (see, e.g., International application No. PCT/US2004/015439 by Jackson et al., filed on May 17, 2004). A pmPSSM can then be generated using the frequency of matches found for each position. In one embodiment, the alignment shown in FIG. 6 and similar data for other siRNAs were combined to generate the exemplary position-specific scoring matrix for use in predicting off-target effects.
The degree of match between an siRNA and a sequence in a transcript can be evaluated with the pmPSSM using a score (also referred to as a position match score, pmScore) according to the following equation




  
    
      
        Score
        =
        
          
            ∑
            
              i
              =
              1
            
            L
          
          ⁢
          
            
          
          ⁢
          
            ln
            ⁡
            
              (
              
                
                  E
                  i
                
                /
                0.25
              
              )
            
          
        
      
    
    
      
        (
        6
        )
      
    
  




where L is the length of the alignment, e.g., 19. A pmScore above a given threshold identifies the sequence as a potential off-target sequence.

The inventors have discovered that for a given siRNA the number of alignments with a score above a threshold is predictive of the number of observed off-target effects. The score threshold can be optimized by maximizing the correlation between predicted and observed numbers of off-target effects (FIG. 8). The optimized threshold can be used to favor selection of siRNAs with relatively small numbers of predicted off-target effects.
5.2.2. Method of Identifying Off-Target Genes of an siRNA
Off-target genes of a given siRNA can be identified by first identifying off-target transcript sequences that align with the siRNA. Any suitable method for pair-wise is alignment, such as but not limited to BLAST and FASTA, can be used. The position-specific scoring matrix is then used to calculate position match scores for these alignments.
In a preferred embodiment, alignments are established with a low-stringency FASTA search and the score for each alignment is calculated according to Eq. 6. A score above a given threshold identifies the transcript comprising the sequence as a potential off-target gene.
The invention thus also provides a method of evaluating the silencing specificity of an siRNA. In one embodiment, potential off-target genes of the siRNA are identified. The total number of such off-target genes in the genome or a portion of the genome is then used as a measure of the silencing specificity of the siRNA.
5.3. Method for Prediction of Strand Preference of siRNAS
The invention provides a method for predicting strand preference and/or the efficacy and specificity of siRNAs based on position specific base composition of the siRNAs. The inventors have discovered that an siRNA whose base composition PSSM score (see Section 5.1.) is greater than the base composition PSSM (G/C PSSM) score of its reverse complement is predicted to have an antisense strand that is more active than its sense strand. In contrast, an siRNA whose base composition PSSM score is less than the base composition PSSM score of its reverse complement is predicted to have a sense strand that is more active than its antisense strand.
It has been shown that increased efficacy of an siRNA in silencing a sense-identical target gene corresponds to greater antisense strand activity and lesser sense strand activity. The inventors have discovered that base composition PSSMs can be used to distinguish siRNAs with strong sense strands as bad siRNAs from siRNAs with weak sense strands as good siRNAs. The reverse complements of bad siRNAs were seen to be even more different from the bad siRNAs themselves than are good siRNAs. On the average, the reverse complements of bad siRNAs had even stronger G/C content at the 5′ end than the good siRNAs did and were similar in G/C content to good siRNAs at the 3′ end. In contrast, the reverse complements of good siRNAs were seen to be substantially more similar to bad siRNAs than the good siRNAs were. On the average, the reverse complements of good siRNAs hardly differed from bad siRNAs in G/C content at the 5′ end and were only slightly less G/C rich than bad siRNAs at the 3′ end. These results indicate that the G/C PSSMS distinguish siRNAs with strong sense strands as bad siRNAs from siRNAs with weak sense strands as good siRNAs.
FIG. 14A shows the difference between the mean G/C content of the reverse complements of bad siRNAs with the mean G/C content of the bad siRNAs themselves, within the 19mer siRNA duplex region. The difference between the mean G/C content of good and bad siRNAs is shown for comparison. The curves are smoothed over a window of 5 (or portion of a window of 5, at the edges of the sequence).
FIG. 14B shows the difference between the mean G/C content of the reverse complements of good siRNAs with the mean G/C content of bad siRNAs, within the 19mer siRNA duplex region. The difference between the mean G/C content of good and bad siRNAs is shown for comparison. The curves are smoothed over a window of 5 (or portion of a window of 5, at the edges of the sequence).
In FIG. 15, siRNAs were binned by measured silencing efficacy, and the frequency of sense-active calls by the 3′-biased method and G/C PSSM method was compared. Although these techniques are based on different analyses, the agreement is quite good. Both show that a higher proportion of low-silencing siRNAs vs. high-silencing siRNAs are predicted to be sense active. The correlation coefficient for (siRNA G/C PSSM score—reverse complement G/C PSSM score) vs. log10(sense-identity score/antisense-identity score) is 0.59 for the set of 61 siRNAs binned in FIG. 15.
Thus, in one embodiment, the invention provides a method for predicting strand preference, i.e., which of the two strands is move active, of siRNAs based on position specific base composition of the siRNAs. In one embodiment, the method comprises evaluating the strand preference of an siRNA in gene silencing by comparing the base compositions of the sense and the antisense strands of the siRNA. In another embodiment, the method comprises evaluating the strand preference of an siRNA in gene silencing by comparing the base compositions of the sense and the reverse complement of the target sequence of the siRNA.
In one embodiment, the sequence of the antisense strand of an siRNA or the reverse complement of the target sequence of the siRNA in a transcript are compared with the target sequence using a PSSM approach (see Section 5.1.). An siRNA and its reverse complement are scored using a PSSM based on a smoothed G/C content difference between good and bad siRNAs within the duplex region as the weight matrix. In one embodiment, a base composition weight matrix as described by FIG. 14A is used as the weight matrix. In a preferred embodiment, the PSSM score of each strand can be calculated as the dot product of the siRNA strand G/C content with the G/C content difference matrix (as the score calculation method of curve model PSSMs). In one embodiment, an siRNA is identified as sense-active if its reverse complement PSSM score exceeded its own PSSM score.
In another embodiment, the 3′-biased method as described in International application No. PCT/US2004/015439 by Jackson et al., filed on May 17, 2004, which is incorporated herein by reference in its entirety, is used in conjunction with the PSSM score to determining the strand preference of an siRNA. In such an embodiment, an siRNA is identified as sense-active by the 3′-biased method of strand preference determination if the antisense-identical score exceeded the sense-identical score.
The method based on comparison of G/C PSSMs of siRNAs and their reverse complements for prediction of strand bias was tested by comparison with estimation of strand bias from siRNA expression profiles by the 3′-biased method.
The invention also provides a method for identifying siRNAs having good silencing efficacy. The method comprises identifying siRNAs having dominant antisense strand activity (“antisense-active” siRNAs) as siRNAs having good silencing efficacy and specificity (for silencing sense-identical target). In one embodiment, the method described in Section 5.1. is used to identify siRNAs having good sense strand (i.e., identifying siRNAs having good silencing efficacy towards an antisense-identical target). Such siRNAs are then eliminated from uses in silencing sense-identical targets. The method can also be used to eliminate siRNAs with dominant sense strand activity (“sense-active” siRNAs) as siRNAs having less efficacy and specificity for silencing sense-identical targets. In one embodiment, the method described in International application No. PCT/US2004/015439 by Jackson et al., filed on May 17, 2004, which is incorporated herein by reference in its entirety, is used to determine strand preference of an siRNA.
The reverse complements of bad siRNAs, on the average, appear to have a GC content profile which differs from that of bad siRNAs in the same manner as the GC content profile of good siRNAs differs from that of bad siRNAs. However, the reverse complements of bad siRNAs show even more extreme differences from bad siRNAs than do the good siRNAs.
This observation is in accord with the evidence in siRNA expression profiles that many bad siRNAs have active sense strands.
The combination of data and analysis thus suggests that the reverse complements of bad siRNAs form an alternative, or perhaps even more advantageous, model for effective siRNAs than the good siRNAs do. Thus, the invention also provides a method for selecting siRNAs based on the base composition of the sequence of a reverse complement of the sense strand of the siRNAs. In one embodiment, a plurality of different siRNAs designed for silencing a target gene in an organism at a different target sequence in a transcript of the target gene is ranked according to positional base composition of the reverse complement sequences of their sense strands. One or more siRNAs whose reverse complement sequences' positional base composition matches the positional base composition of desired siRNAs can then be selected. Preferably, the ranking of siRNAs is carried out by first determining a score for each different siRNA using a position-specific score matrix. The siRNAs are then ranked according to the score. Any method described in Section 5.1., supra, can be used to score reverse complement sequences. In one embodiment, for siRNAs that have a nucleotide sequence of L nucleotides in the duplex region, L being an integer, the position-specific score matrix comprises a difference in probability of finding nucleotide G or C at sequence position k between reverse complement of a first type of siRNA and reverse complement of a second type of siRNA designated as wk, k=1, . . . , L. The score for each reverse complement is calculated according to equation




  
    
      
        Score
        =
        
          
            ∑
            
              k
              =
              1
            
            L
          
          ⁢
          
            
          
          ⁢
          
            w
            k
          
        
      
    
    
      
        (
        7
        )
      
    
  




The first type of siRNA can consist of one or more siRNAs having silencing efficacy no less than a first threshold, e.g., 75%, 80% or 90% at a suitable dose, e.g., 100 nM, and the second type of siRNA can consist of one or more siRNAs having silencing efficacy less than a second threshold, e.g., 25%, 50%, or 75% at a suitable dose, e.g., 100 nM. In a preferred embodiment, the difference in probability is described by a sum of Gaussian curves, each of said Gaussian curves representing the difference in probability of finding a G or C at a different sequence position.

The methods of this invention can also be applied to developing models, e.g., PSSMs, of siRNA functional motifs by training position-specific scoring matrices to distinguish between bad siRNAs and their reverse complements (see, e.g., Section 5.1.). A restriction in this analysis is that the reverse complements of bad siRNAs have no designated targets. Thus, in one embodiment, position-specific scoring matrices of 19mer siRNA duplex sequences are trained to distinguish between bad siRNAs and their reverse complements.
Flanking sequence training can be performed on off-target genes in the case of distinguishing between bad siRNAs and their reverse complements, as well as in the case of distinguishing between any two groups of siRNAs. In other words, the off-target activity of siRNAs can be hypothesized to have the same flanking sequence requirements as the on-target activity, as the same RNA-protein complexes are thought to be involved in both processes.
Thus, if the methods of the off-target application are used to identify genes directly down-regulated by an siRNA (i.e. through kinetic analysis of down-regulation to identify a group of genes down-regulated with the same half-life as the intended target), the regions flanking the alignment of the siRNA with the directly regulated off-target genes can be used to train and test models of flanking sequence requirements. These models can be developed by any of the methods of this invention: random hill-climbing PSSMs, curve-model PSSMs, good-bad difference frequency matrices, good-composition frequency matrices, and/or bad-composition frequency matrices, etc.
5.4. Methods of Designing siRNAS for Gene Silencing
The invention provides a method for designing siRNAs for gene silencing. The method can be used to design siRNAs that have full sequence homology to their respective target sequences in a target gene. The method can also be used to design siRNAs that have only partial sequence homology to a target gene. Methods and compositions for silencing a target gene using an siRNA that has only partial sequence homology to its target sequence in a target gene is disclosed in International application No. PCT/US2004/015439 by Jackson et al., filed on May 17, 2004, which is incorporated herein by reference in its entirety. For example, an siRNA that comprises a sense strand contiguous nucleotide sequence of 11-18 nucleotides that is identical to a sequence of a transcript of the target gene but the siRNA does not have full length homology to any sequences in the transcript may be used to silence the transcript Such contiguous nucleotide sequence is preferably in the central region of the siRNA molecules. A contiguous nucleotide sequence in the central region of an siRNA can be any continuous stretch of nucleotide sequence in the siRNA which does not begin at the 3′ end. For example, a contiguous nucleotide sequence of 11 nucleotides can be the nucleotide sequence 2-12, 3-13, 4-14, 5-15, 6-16, 7-17, 8-18, or 9-19.In preferred embodiments, the contiguous nucleotide sequence is 11-16, 11-15, 14-15, 11, 12, or 13 nucleotides in length. Alternatively, an siRNA that comprises a 3′ sense strand contiguous nucleotide sequence of 9-18 nucleotides which is identical to a sequence of a transcript of the target gene but which siRNA does not have full length sequence identity to any contiguous sequences in the transcript may also be used to silence the transcript. A 3′ 9-18 nucleotide sequence is a continuous stretch of nucleotides that begins at the first paired base, i.e., it does not comprise the two base 3′ overhang. In preferred embodiments, the contiguous nucleotide sequence is 9-16, 9-15, 9-12, 11, 10, or 9 nucleotides in length.
In preferred embodiments, the method of Section 5.1 is used for identifying from among a plurality of siRNAs one or more siRNAs that have high silencing efficacy. In one embodiment, each siRNA in the plurality of siRNAs is evaluated for silencing efficacy by base composition PSSMs. In one embodiment, this step comprises calculating one or more PSSM scores for each siRNA. The plurality of siRNAs are then ranked based on the score, and one or more siRNAs are selected using a method described in Section 5.1.4.
In other preferred embodiments, the method of Section 5.2 is used for identifying from among a plurality of siRNAs one or more siRNAs that have high silencing specificity. In one embodiment, alignments of each siRNA with sequences in each of a plurality of non-target transcripts are identified and evaluated with the pmPSSM approach (see Section 5.2.). A pmScore is calculated for each of the alignments. A pmScore above a given threshold identifies a sequence as a potential off-target sequence. Such a pmScore is also termed an alignment score. For example, when FASTA is used for the alignment, a pmScore can be a weighted FASTA alignment score. The transcript that comprises the potential off-target sequence is identified as a potential off-target transcript The total number of such off-target transcripts in the genome or a portion of the genome is used as a measure of the silencing specificity of the siRNA. One or more siRNAs having less off-target transcripts may then be selected.
The siRNAs having the desired levels of efficacy and specificity for a transcript can be further evaluated for sequence diversity. In this disclosure, sequence diversity is also referred to as “sequence variety” or simply “diversity” or “variety.” Sequence diversity can be represented or measured based on some sequence characteristics. The siRNAs can be selected such that a plurality of siRNAs targeting a gene comprises siRNAs exhibiting sufficient difference in one or more of such diversity characteristics.
Preferably the sequence diversity characteristics used in the method of the invention are quantifiable. For example, sequence diversity can be measured based on GC content, the location of the siRNA target sequence along the length of the target transcript, or the two bases upstream of the siRNA duplex (i.e., the leading dimer, with 16 different possible leading dimers). The difference of two siRNAs can be measured as the difference between values of a sequence diversity measure. The diversity or variety of a plurality of siRNAs can be quantitatively represented by the minimum difference or spacing in a sequence diversity measure between different siRNAs in the plurality.
In the siRNA design method of the invention, the step of selection of siRNAs for diversity or variety is also referred to as a “de-overlap” step. In a preferred embodiment, for a sequence diversity measure that is quantifiable, the de-overlapping selects siRNAs having differences of a sequence diversity measure between two siRNAs above a given threshold. For example, de-overlapping by position establishes a minimum distance between selected oligos along the length of the transcript sequence. In one embodiment, siRNAs positioned at least 100 bases apart in the transcript are selected. De-overlapping by GC content establishes a minimum difference in GC content. In one embodiment, the minimum difference in GC content is 1%, 2% or 5%. De-overlapping by leading dimers establishes the probability of all or a portion of the 16 possible leading dimers among the selected siRNAs. In one embodiment, each of the 16 possible dimers is assigned a score of 1-16, and a 0.5 is used to selected all possible leading primer with equal probability.
In some embodiments, the candidates are preferably de-overlapped on GC content, with a minimum spacing of 5%, a maximum number of duplicates of each value of GC % of 100 and at least 200 candidates selected; more preferably they are de-overlapped on GC content with a minimum spacing of 5%, a maximum number of duplicates of each value of GC % of 80 and at least 200 candidates selected; and still more preferably they are de-overlapped on GC content with a minimum spacing of 5%, a maximum number of duplicates of each value of GC % of 60 and at least 200 candidates selected.
siRNAs can be further selected based additional selection criteria.
In one embodiment, siRNAs targeting sequences not common to all documented splice forms are eliminated.
In another embodiment, siRNAs targeting sequences overlapping with simple or interspersed repeat elements are eliminated.
In still another embodiment, siRNAs targeting sequences positioned at least 75 bases downstream of the translation start codon are selected.
In another embodiment, siRNAs targeting sequences overlapping or downstream of the stop codon are eliminated. This avoids targeting sequences absent in undocumented alternative polyadenylation forms.
In still another embodiment, siRNAs with GC content close to 50% are selected. In one embodiment, siRNAs with GC % <20% and >70% are eliminated. In another embodiment, 10% <GC % <90%, 20% <GC % <80%, 25% <GC % <75%, 30% <GC % <70% are retained.
In still another embodiment, siRNAs targeting sequence containing 4 consecutive guanosine, cytosine, adenine or uracil residues are eliminated. In still another embodiment, siRNAs targeting a sequence with a guanine or cytosine residue at the first position in the 19mer duplex region at the 5′ end are selected. Such siRNAs target sequences that are effectively transcribed by RNA polymerase III.
In still another embodiment, siRNAs targeting a sequence containing recognition sites for one or more given restriction endonucleases, e.g., XhoI or EcoRI restriction endonucleases, are eliminated. This embodiment may be used to select siRNAs sequences for construction of the shRNA vectors.
In still another embodiment, the siRNAs are evaluated for binding energy. See WO 01/05935 for an exemplary method of determining binding energy. In a preferred embodiment, the binding energy is evaluated by calculating the nearest-neighbor 21 mer ΔG.
In still another embodiment, the siRNAs are evaluated for binding specificity. See WO 01/05935 for an exemplary method of determining binding specificity of a 21mer. In a preferred embodiment, the binding specificity is evaluated by calculating a 21mer minimax score against the set of unique sequence representatives of genes of an organism, e.g., the set of unique sequences representatives for each cluster of Homo sapiens Unigene build 161 (ncbi website with the extension “nlm.nih.gov/entrez/query.fcgi?db=unigene”)
In still another embodiment, the method for predicting strand preference and/or the efficacy and specificity of siRNAs based on position specific base composition of the siRNAs as described in Section 5.3. can be used to evaluate the siRNA candidates.
A flow chart of an exemplary embodiment of the method used to select the siRNAs is shown in FIG. 9.
In step 101, siRNA sequences that target a transcript are selected. In one embodiment, all 19mer subsequences of the transcript are considered. The appropriate flanking sequences for each siRNA sequence are also obtained and considered. The siRNAs are evaluated against the following filters: (1) eliminating siRNAs targeting sequences not common to all documented splice forms; (2) eliminating siRNAs targeting sequences overlapping with simple or interspersed repeat elements; (3) eliminating siRNAs targeting sequences positioned within 75 bases downstream of the translation start codon; and (4) eliminating siRNAs overlapping or downstream of the stop codon.
For shRNA selection, the following steps are also taken: (5) eliminating siRNAs targeting sequence containing 4 consecutive guanosine, cytosine, adenine or uracil residues; (6) retaining siRNAs targeting a sequence with a guanine or cytosine residue at the first position in the 19mer duplex region at the 5′ end; and (7) eliminating siRNAs targeting a sequence containing recognition sites for one or more given restriction enzymes, e.g., XhoI or EcoRI restriction endonucleases, if siRNAs sequences used in construction of the shRNA vectors.
In step 102, the siRNA is evaluated for silencing efficacy by base composition PSSMs. In one embodiment, step 102 comprises calculating a first PSSM score, i.e., the PSSM-1 score, and a second PSSM score, i.e., the PSSM-2 score, for an siRNA. The two scores are sum to calculate the combined PSSM-1+PSSM-2 score for the siRNA. In one embodiment, the PSSMs used are those whose performance is shown in FIG. 2. The siRNA is retained if the combined score is above a given threshold.
The siRNA is then evaluated for its binding energy by calculating the nearest-neighbor 21mer ΔG. The siRNA is then evaluated for binding specificity by calculating a 21mer minimax score against the set of unique sequence representatives of genes of an organism, e.g., the set of unique sequences representatives for each cluster of Homo sapiens Unigene build 161. See WO 01/05935 for methods of calculating the ΔG and the minimax score. In one embodiment, the parameters for the BLAST alignments and nearest-neighbor delta-G calculations based on the BLAST alignments, which are used to compute minimax scores, are as follows: -p blastn -e 100 -F F -W 11 -b 200 -v 10000 -S 3; and delta-G: temperature 66°; salt 1M; concentration 1 pM; type of nucleic acid, RNA. In one embodiment, the siRNA is eliminated if the (21mer ΔG−21mer minimax)≦0.5.
In step 103, siRNAs are screened for overall GC content. In one embodiment, siRNAs with GC content significantly deviated from 50%, e.g., GC % <20% and >70%, are eliminated.
In step 104, siRNAs are screened for diversity or variety. Position simply refers to the position of the oligo in the transcript sequence and is automatically provided by identifying the oligo. Variety is enforced in one or more “de-overlap” steps in the method. Briefly, de-overlapping selects for above-threshold spacing between selected oligos in some calculable parameter. To de-overlap, oligos are first ranked according to some parameter thought to distinguish better from poorer performers and then selected for spacing between oligos according to some other parameter. To begin, the top ranked oligo is selected. Then the ranked list is examined, and the next-best oligo with at least the minimum required spacing from the selected oligo is selected. Then the next-best oligo with at least the minimum spacing from the two selected oligos is also selected. The process continues until the desired number of oligos is selected. In one embodiment, multiple oligos may share the same value if a parameter is few-valued, and the number of oligos sharing the same value is limited by a set threshold. In one embodiment, if an insufficient number of oligos is selected in a first pass of de-overlapping, the spacing requirement can be relaxed until the desired number, or the set of all remaining available oligos, is selected.
For example, de-overlapping by position establishes a minimum distance between selected oligos along the length of the transcript sequence. In one embodiment, siRNAs are ranked by a PSSM score and the ranked siRNAs positioned at least 100 bases apart in the transcript are selected. De-overlapping by GC content establishes a minimum difference in GC content. In one embodiment, the minimum difference in GC content is 1%, 2% or 5%. Duplicates are allowed for few-valued parameters such as the GC % of a 19mer. De-overlapping by leading dimers establishes the probability of all or a portion of the 16 possible leading dimers among the selected siRNAs. In one embodiment, each of the 16 possible dimers is assigned a score of 1-16, and a 0.5 is used to selected all possible leading primer with equal probability, i.e., to distribute candidate siRNAs over all possible leading dimer values.
De-overlapping with different parameters may be combined.
In step 105, off-target activity of an siRNA is evaluated according to the method described in Section 5.2. Alignments of each siRNA with sequences in each of a plurality of non-target transcripts are identified and evaluated with a pmPSSM using a pmScore calculated according to equation (6). A pmScore above a given threshold identifies the sequence as a potential off-target sequence. The transcript that comprises the potential off-target sequence is identified as a potential off-target transcript The total number of such off-target transcripts in the genome or a portion of the genome is used as a measure of the silencing specificity of the siRNA. One or more siRNAs having less off-target transcripts are selected.
In one embodiment, transcripts of genes are scanned using FASTA with the parameters: KTUP 6 -r 3/-7 -g -6 -f -6 -d 14000 -b 14000 -E 7000.A pmScore is determined for each alignment as described in Section 5.2. The FASTA weighted score is used to: (1) quantify the nearest sequence match to the candidate siRNA; and (2) count the total matches to the candidate siRNA with weighted scores over a threshold. The total number of such off-target genes in the genome or a portion of the genome is then used as a measure of the silencing specificity of the siRNA.
In a preferred embodiment, the selected siRNAs are subjected to a second round of selection for variety (step 106), and re-ranked by their base composition PSSM scores (step 107). The desired number of siRNAs is retained from the top of this final ranking (step 108).
The invention also provides a method for selecting a plurality of siRNAs for each of a plurality of different genes, each siRNA achieving at least 75%, at least 80%, or at least 90% silencing of its target gene. The method described above is used to select a plurality of siRNAs for each of a plurality of genes. Preferably, the plurality of siNRAs consists of at least 3, 5, or 10 siRNAs. Preferably, the plurality of different genes consists of at least 100, 500, 1,000, 5,000, 10,000 or 30,000 different genes.
The invention also provides a library of siRNAs which comprises a plurality of siRNAs for each of a plurality of different genes, each siRNA achieves at least 75%, at least 80%, or at least 90% silencing of its target gene. The standard conditions are 100 nM siRNA, silencing assayed by TaqMan 24 hours post-transfection. Preferably, the plurality of siNRAs consists of at least 3, at least 5, or at least 10 siRNAs. Preferably, the plurality of different genes consists of at least 10, 100, 500, 1,000, 5,000, 10,000 or 30,000 different genes.
5.5. Methods and Compositions for RNA Interference and Cell Assays
Any standard method for gene silencing can be used in conjunction with the present invention, e.g., to carry our gene silencing using siRNAs designed by a method described in the present invention (see, e.g., Guo et al., 1995, Cell 81:611-620; Fire et al., 1998, Nature 391:806-811; Grant, 1999, Cell 96:303-306; Tabara et al., 1999, Cell 99:123-132; Zamore et al., 2000, Cell 101:25-33; Bass, 2000, Cell 101:235-238; Petcherski et al., 2000, Nature 405:364-368; Elbashir et al., Nature 411:494-498; Paddison et al., Proc. Natl. Acad. Sci. USA 99:1443-1448). In one embodiment, gene silencing is induced by presenting the cell with the siRNA, mimicking the product of Dicer cleavage (see, e.g., Elbashir et al., 2001, Nature 411, 494-498; Elbashir et al., 2001, Genes Dev. 15, 188-200, all of which are incorporated by reference herein in their entirety). Synthetic siRMA duplexes maintain the ability to associate with RISC and direct silencing of mRNA transcripts. siRNAs can be chemically synthesized, or derived from cleavage of double-stranded RNA by recombinant Dicer. Cells can be transfected with the siRNA using standard method known in the art.
In one embodiment, siRNA transfection is carried out as follows: one day prior to transfection, 100 microliters of chosen cells, e.g., cervical cancer HeLa cells (ATCC, Cat. No. CCL-2), grown in DMEM/10% fetal bovine serum (Invitrogen, Carlsbad, Calif.) to approximately 90% confluency are seeded in a 96-well tissue culture plate (Corning, Corning, N.Y.) at 1500 cells/well. For each transfection 85 microliters of OptiMEM (Invitrogen) is mixed with 5 microliter of serially diluted siRNA (Dharma on, Denver) from a 20 micro molar stock. For each transfection 5 microliter OptiMEM is mixed with 5 microliter Oligofectamine reagent (Invitrogen) and incubated 5 minutes at room temperature. The 10 microliter OptiMEM/Oligofectamine mixture is dispensed into each tube with the OptiMEM/siRNA mixture, mixed and incubated 15-20 minutes at room temperature. 10 microliter of the transfection mixture is aliquoted into each well of the 96-well plate and incubated for 4 hours at 37° C. and 5% CO2.
In one embodiment, RNA interference is carried out using pool of siRNAs. In a preferred embodiment, an siRNA pool containing at least k k=2, 3, 4, 5, 6 or 10) different siRNAs targeting a target gene at different sequence regions is used to transfect the cells. In another preferred embodiment, an siRNA pool containing at least k k=2, 3, 4, 5, 6 or 10) different siRNAs targeting two or more different target genes is used to supertransfect the cells. In a preferred embodiment, the total siRNA concentration of the pool is about the same as the concentration of a single siRNA when used individually, e.g., 100 nM. Preferably, the total concentration of the pool of siRNAs is an optimal concentration for silencing the intended target gene. An optimal concentration is a concentration further increase of which does not increase the level of silencing substantially. In one embodiment, the optimal concentration is a concentration further increase of which does not increase the level of silencing by more than 5%, 10% or 20%. In a preferred embodiment, the composition of the pool, including the number of different siRNAs in the pool and the concentration of each different siRNA, is chosen such that the pool of siRNAs causes less than 30%, 20%, 10% or 5%, 1%, 0.1% or 0.01% of silencing of any off-target genes. In another preferred embodiment, the concentration of each different siRNA in the pool of different siRNAs is about the same. In still another preferred embodiment, the respective concentrations of different siRNAs in the pool are different from each other by less than 5%, 10%, 20% or 50%. In still another preferred embodiment, at least one siRNA in the pool of different siRNAs constitutes more than 90%, 80%, 70%, 50%, or 20% of the total siRNA concentration in the pool. In still another preferred embodiment, none of the siRNAs in the pool of different siRNAs constitutes more than 90%, 80%, 70%, 50%, or 20% of the total siRNA concentration in the pool. In other embodiments, each siRNA in the pool has an concentration that is lower than the optimal concentration when used individually. In a preferred embodiment, each different siRNA in the pool has an concentration that is lower than the concentration of the siRNA that is effective to achieve at least 30%, 50%, 75%, 80%, 85%, 90% or 95% silencing when used in the absence of other siRNAs or in the absence of other siRNAs designed to silence the gene. In another preferred embodiment, each different siRNA in the pool has a concentration that causes less than 30%, 20%, 10% or 5% of silencing of the gene when used in the absence of other siRNAs or in the absence of other siRNAs designed to silence the gene. In a preferred embodiment, each siRNA has a concentration that causes less than 30%, 20%, 10% or 5% of silencing of the target gene when used alone, while the plurality of siRNAs causes at least 80% or 90% of silencing of the target gene.
Another method for gene silencing is to introduce into a cell an shRNA, for short hairpin RNA (see, e.g., Paddison et al., 2002, Genes Dev. 16, 948-958; Brummelknp et al., 2002, Science 296, 550-553; Sui, G. et al. 2002, Proc. Natl. Acad. Sci. USA 99, 5515-5520, all of which are incorporated by reference herein in their entirety), which can be processed in the cells into siRNA. In this method, a desired siRNA sequence is expressed from a plasmid (or virus) as an inverted repeat with an intervening loop sequence to form a hairpin structure. The resulting RNA transcript containing the hairpin is subsequently processed by Dicer to produce siRNAs for silencing. Plasmid-based shRNAs can be expressed stably in cells, allowing long-term gene silencing in cells both in vitro and in vivo, e.g., in animals (see, McCaffrey et al. 2002, Nature 418, 38-39; Xia et al., 2002, Nat. Biotech. 20, 1006-1010; Lewis et al., 2002, Nat. Genetics 32, 107-108; Rubinson et al., 2003, Nat. Genetics 33, 401-406; Tiscornia et al., 2003, Proc. Natl. Acad. Sci USA 100, 1844-1848, all of which are incorporated by reference herein in their entirety). Thus, in one embodiment, a plasmid-based shRNA is used.
In a preferred embodiment, shRNAs are expressed from recombinant vectors introduced either transiently or stably integrated into the genome (see, e.g., Paddison et al, 2002, Genes Dev 16:948-958; Sui et al., 2002, Proc Natl Acad Sci U S A 99:5515-5520; Yu et al., 2002, Proc Natl Acad Sci U S A 99:6047-6052; Miyagishi et al., 2002, Nat Biotechnol 20:497-500; Paul et al., 2002, Nat Biotechnol 20:505-508; Kwak et al., 2003, J Pharmacol Sci 93:214-217; Brummelkamp et al., 2002, Science 296:550-553; Boden et al., 2003, Nucleic Acids Res 31:5033-5038; Kawasaki et al., 2003, Nucleic Acids Res 31:700-707). The siRNA that disrupts the target gene can be expressed (via an shRNA) by any suitable vector which encodes the shRNA. The vector can also encode a marker which can be used for selecting clones in which the vector or a sufficient portion thereof is integrated in the host genome such that the shRNA is expressed. Any standard method known in the art can be used to deliver the vector into the cells. In one embodiment, cells expressing the shRNA are generated by transfecting suitable cells with a plasmid containing the vector. Cells can then be selected by the appropriate marker. Clones are then picked, and tested for knockdown. In a preferred embodiment, a plurality of recombinant vectors are introduced into the genome such that the expression level of the siRNA can be above a given value. Such an embodiment is particular useful for silencing genes whose transcript level is low in the cell.
In a preferred embodiment, the expression of the shRNA is under the control of an inducible promoter such that the silencing of its target gene can be turned on when desired. Inducible expression of an siRNA is particularly useful for targeting essential genes. In one embodiment, the expression of the shRNA is under the control of a regulated promoter that allows tuning of the silencing level of the target gene. This allows screening against cells in which the target gene is partially knocked out As used herein, a “regulated promoter” refers to a promoter that can be activated when an appropriate inducing agent is present. An “inducing agent” can be any molecule that can be used to activate transcription by activating the regulated promoter. An inducing agent can be, but is not limited to, a peptide or polypeptide, a hormone, or an organic small molecule. An analogue of an inducing agent, i.e., a molecule that activates the regulated promoter as the inducing agent does, can also be used. The level of activity of the regulated promoter induced by different analogues may be different, thus allowing more flexibility in tuning the activity level of the regulated promoter. The regulated promoter in the vector can be any mammalian transcription regulation system known in the art (see, e.g., Gossen et al, 1995, Science 268:1766-1769; Lucas et al, 1992, Annu. Rev. Biochem. 61:1131; Li et al., 1996, Cell 85:319-329; Saez et al., 2000, Proc. Natl. Acad. Sci. USA 97:14512-14517; and Pollock et al., 2000, Proc. Natl. Acad. Sci. USA 97:13221-13226). In preferred embodiments, the regulated promoter is regulated in a dosage and/or analogue dependent manner. In one embodiment, the level of activity of the regulated promoter is tuned to a desired level by a method comprising adjusting the concentration of the inducing agent to which the regulated promoter is responsive. The desired level of activity of the regulated promoter, as obtained by applying a particular concentration of the inducing agent, can be determined based on the desired silencing level of the target gene.
In one embodiment, a tetracycline regulated gene expression system is used (see, e.g., Gossen et al, 1995, Science 268: 1766-1769; U.S. Pat. No. 6,004,941). A tet regulated system utilizes components of the tet repressor/operator/inducer system of prokaryotes to regulate gene expression in eukaryotic cells. Thus, the invention provides methods for using the tet regulatory system for regulating the expression of an shRNA linked to one or more tet operator sequences. The methods involve introducing into a cell a vector encoding a fusion protein that activates transcription. The fusion protein comprises a first polypeptide that binds to a tet operator sequence in the presence of tetracycline or a tetracycline analogue operatively linked to a second polypeptide that activates transcription in cells. By modulating the concentration of a tetracycline, or a tetracycline analogue, expression of the tet operator-inked shRNA is regulated.
In other embodiments, an ecdyson regulated gene expression system (see, e.g., Saez et al., 2000, Proc. Natl. Acad. Sci. USA 97:14512-14517), or an MMTV glucocorticoid response element regulated gene expression system (see, e.g., Lucas et al, 1992, Annu. Rev. Biochem. 61:1131) may be used to regulate the expression of the shRNA.
In one embodiment, the pRETRO-SUPER (pRS) vector which encodes a puromycin-resistance marker and drives shRNA expression from an H1 (RNA Pol III) promoter is used The pRS-shRNA plasmid can be generated by any standard method known in the art. In one embodiment, the pRS-shRNA is deconvoluted from the library plasmid pool for a chosen gene by transforming bacteria with the pool and looking for clones containing only the plasmid of interest. Preferably, a 19mer siRNA sequence is used along with suitable forward and reverse primers for sequence specific PCR. Plasmids are identified by sequence specific PCR, and confirmed by sequencing. Cells expressing the shRNA are generated by transfecting suitable cells with the pRS-shRNA plasmid. Cells are selected by the appropriate marker, e.g., puromycin, and maintained until colonies are evident. Clones are then picked, and tested for knockdown. In another embodiment, an shRNA is expressed by a plasmid, e.g., a pRS-shRNA. The knockdown by the pRS-shRNA plasmid, can be achieved by transfecting cells using Lipofectanine 2000 (Invitrogen).
In yet another method, siRNAs can be delivered to an organ or tissue in an animal, such a human, in vivo (see, e.g., Song et al. 2003, Nat. Medicine 9, 347-351; Sorensen et al., 2003, J. Mol. Biol. 327, 761-766; Lewis et al., 2002, Nat. Genetics 32, 107-108, all of which are incorporated by reference herein in their entirety). In this method, a solution of siRNA is injected intravenously into the animal. The siRNA can then reach an organ or tissue of interest and effectively reduce the expression of the target gene in the organ or tissue of the animal.
The siRNAs can also be delivered to an organ or tissue using a gene therapy approach. Any of the methods for gene therapy available in the art can be used to deliver the siRNA. For general reviews of the methods of gene therapy, see Goldspiel et al., 1993, Clinical Pharmacy 12:488-505; Wu and Wu, 1991, Biotherapy 3:87-95; Tolstoshev, 1993, Ann. Rev. Pharmacol. Toxicol. 32:573-596; Mulligan, 1993, Science 260:926-932; and Morgan and Anderson, 1993, Ann. Rev. Biochem. 62:191-217; Robinson, 1993, TIBTECH 11(5):155-215). In a preferred embodiment, the therapeutic comprises a nucleic acid encoding the siRNA as a part of an expression vector. In particular, such a nucleic acid has a promoter operably linked to the siRNA coding region, in which the promoter being inducible or constitutive, and, optionally, tissue-specific. In another particular embodiment, a nucleic acid molecule in which the siRNA coding sequence is flanked by regions that promote homologous recombination at a desired site in the genome is used (see e.g., Koller and Smithies, 1989, Proc. Natl. Acad. Sci. U.S.A. 86:8932-8935; Zijlstra et al., 1989, Nature 342:435-438).
In a specific embodiment, the nucleic acid is directly administered in vivo. This can be accomplished by any of numerous methods known in the art, e.g., by constructing it as part of an appropriate nucleic acid expression vector and administering it so that it becomes intracellular, e.g., by infection using a defective or attenuated retroviral or other viral vector (see U.S. Pat. No. 4,980,286), or by direct injection of naked DNA, or by use of microparticle bombardment (e.g., a gene gun; Biolistic, Dupont), or coating with lipids or cell-surface receptors or transfecting agents, encapsulation in liposomes, microparticles, or microcapsules, or by administering it in linkage to a peptide which is known to enter the nucleus, by administering it in linkage to a ligand subject to receptor-mediated endocytosis (see e.g., Wu and Wu, 1987, J. Biol. Chem. 262:4429-4432) (which can be used to target cell types specifically expressing the receptors), etc. In another embodiment, a nucleic acid-ligand complex can be formed in which the ligand comprises a fusogenic viral peptide to disrupt endosomes, allowing the nucleic acid to avoid lysosomal degradation. In yet another embodiment, the nucleic acid can be targeted in vivo for cell specific uptake and expression, by targeting a specific receptor (see, e.g., PCT Publications WO 92/06180 dated Apr. 16, 1992 (Wu et al.); WO 92/22635 dated Dec. 23, 1992 (Wilson et al.); WO92/20316 dated Nov. 26, 1992 (Findeis et al.); WO93/14188 dated Jul. 22, 1993 (Clarke et al.), WO 93/20221 dated Oct. 14, 1993 (Young)). Alternatively, the nucleic acid can be introduced intracellularly and incorporated within host cell DNA for expression, by homologous recombination (Koller and Smithies, 1989, Proc. Natl. Acad. Sci. U.S.A. 86:8932-8935; Zijlstra et al., 1989, Nature 342:435-438).
In a specific embodiment, a viral vector that contains the siRNA coding nucleic acid is used. For example, a retroviral vector can be used (see Miller et al., 1993, Meth. Enzymol. 217:581-599). These retroviral vectors have been modified to delete retroviral sequences that are not necessary for packaging of the viral genome and integration into host cell DNA. The siRNA coding nucleic acid to be used in gene therapy is cloned into the vector, which facilitates delivery of the gene into a patient. More detail about retroviral vectors can be found in Boesen et al., 1994, Biotherapy 6:291-302, which describes the use of a retroviral vector to deliver the mdr1 gene to hematopoietic stem cells in order to make the stem cells more resistant to chemotherapy. Other references illustrating the use of retroviral vectors in gene therapy are: Clowes et al., 1994, J. Clin. Invest. 93:644-651; Kiem et al., 1994, Blood 83:1467-1473; Salmons and Gunzberg, 1993, Human Gene Therapy 4:129-141; and Grossman and Wilson, 1993, Curr. Opin. Genet. and Devel. 3:110-114.
Adenoviruses are other viral vectors that can be used in gene therapy. Adenovizuses are especially attractive vehicles for delivering genes to respiratory epithelia. Adenoviruses naturally infect respiratory epithelia where they cause a mild disease. Other targets for adenovirus-based delivery systems are liver, the central nervous system, endothelial cells, and muscle. Adenoviruses have the advantage of being capable of infecting non-dividing cells. Kozarsky and Wilson (1993, Current Opinion in Genetics and Development 3:499-503) present a review of adenovirus-based gene therapy. Bout et al. (1994, Human Gene Therapy 5:3-10) demonstrated the use of adenovirus vectors to transfer genes to the respiratory epithelia of rhesus monkeys. Other instances of the use of adenoviruses in gene therapy can be found in Rosenfeld et al., 1991, Science 252:431-434; Rosenfeld et al., 1992, Cell 68:143-155; and Mastrangeli et al., 1993, J. Clin. Invest. 91:225-234.Adeno-associated virus (AAV) may also been used in gene therapy (Walsh et al., 1993, Proc. Soc. Exp. Biol. Med. 204:289-300).
Degree of silencing can be determined using any standard RNA or protein quantification method known in the art. For example, RNA quantification can be performed using Real-time PCR, e.g., using AP Biosystems TaqMan pre-developed assay reagent (#4319442). Primer probe for the appropriate gene can be designed using any standard method known in the art, e.g. using Primer Express software. RNA values can be normalized to RNA for actin (#4326315). Protein levels can be quantified by flow cytometry following staining with appropriate antibody and labeled secondary antibody. Protein levels can also be quantified by western blot of cell lysates with appropriate monoclonal antibodies followed by Kodak image analysis of chemiluminescent immunoblot. Protein levels can also be normalized to actin levels.
Effects of gene silencing on a cell can be evaluated by any known assay. For example, cell growth can be assayed using any suitable proliferation or growth inhibition assays known in the art. In a preferred embodiment, an MTT proliferation assay (see, e.g., van de Loosdrechet, et al., 1994, J. Immunol. Methods 174: 311-320; Ohno et al., 1991, J. Immunol. Methods 145:199-203; Ferrari et al., 1990, J. Immunol. Methods 131: 165-172; Alley et al., 1988, Cancer Res. 48: 589-601; Carmichael et al., 1987, Cancer Res. 47:936-942; Gerlier et al., 1986, J. Immunol. Methods 94:57-63; Mosmann, 1983, J. Immunological Methods 65:55-63) is used to assay the effect of one or more agents in inhibiting the growth of cells. The cells are treated with chosen concentrations of one or more candidate agents for a chosen period of time, e.g., for 4 to 72 hours. The cells are then incubated with a suitable amount of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) for a chosen period of time, e.g., 1-8 hours, such that viable cells convert MTT into an intracellular deposit of insoluble formazan. After removing the excess MTT contained in the supernatant, a suitable MTT solvent, e.g., a DMSO solution, is added to dissolved the formazan. The concentration of MTT, which is proportional to the number of viable cells, is then measured by determining the optical density at e.g., 570 nm. A plurality of different concentrations of the candidate agent can be assayed to allow the determination of the concentrations of the candidate agent or agents which causes 50% inhibition.
In another preferred embodiment, an alamarBlueTM Assay for cell proliferation is used to screen for one or more candidate agents that can be used to inhibit the growth of cells (see, e.g., Page et al., 1993, Int. J. Oncol. 3:473-476). An alamarBlueTM assay measures cellular respiration and uses it as a measure of the number of living cells. The internal environment of proliferating cells is more reduced than that of non-proliferating cells. For example, the ratios of NADPH/NADP, FADH/FAD, FMNH/FMN, and NADH/NAF increase during proliferation. AlamarBlue can be reduced by these metabolic intermediates and, therefore, can be used to monitor cell proliferation. The cell number of a treated sample as measured by alamarBlue can be expressed in percent relative to that of an untreated control sample. alamarBlue reduction can be measured by either absorption or fluorescence spectroscopy. In one embodiment, the alamarBlue reduction is determined by absorbance and calculated as percent reduced using the equation:




  
    
      
        
          %
          ⁢
          
            
          
          ⁢
          Reduced
        
        =
        
          
            
              
                
                  (
                  
                    
                      ɛ
                      ox
                    
                    ⁢
                    
                      λ
                      2
                    
                  
                  )
                
                ⁢
                
                  (
                  
                    A
                    ⁢
                    
                      
                    
                    ⁢
                    
                      λ
                      1
                    
                  
                  )
                
              
              -
              
                
                  (
                  
                    
                      ɛ
                      ox
                    
                    ⁢
                    
                      λ
                      1
                    
                  
                  )
                
                ⁢
                
                  (
                  
                    A
                    ⁢
                    
                      
                    
                    ⁢
                    
                      λ
                      2
                    
                  
                  )
                
              
            
            
              
                
                  (
                  
                    
                      ɛ
                      red
                    
                    ⁢
                    
                      λ
                      1
                    
                  
                  )
                
                ⁢
                
                  (
                  
                    
                      A
                      ′
                    
                    ⁢
                    
                      λ
                      2
                    
                  
                  )
                
              
              -
              
                
                  (
                  
                    
                      ɛ
                      red
                    
                    ⁢
                    
                      λ
                      2
                    
                  
                  )
                
                ⁢
                
                  (
                  
                    
                      A
                      ′
                    
                    ⁢
                    
                      λ
                      1
                    
                  
                  )
                
              
            
          
          ×
          100
        
      
    
    
      
        (
        8
        )
      
    
  




where:

    λ1=570 nm
    λ2=600 nm
    (εred λ1)=155,677 (Molar extinction coefficient of reduced alamarBlue at 570 nm)
    (εred λ2)=14,652 (Molar extinction coefficient of reduced alamarBlue at 600 nm)
    (ε0X λ1)=80,586 (Molar extinction coefficient of oxidized alamarBlue at 570 nm)
    (ε0X λ2)=117,216 (Molar extinction coefficient of oxidized alamarBlue at 600 nm)
    (A λ1)=Absorbance of test wells at 570 nm
    (A λ2)=Absorbance of test wells at 600 nm
    (A′λ1)=Absorbance of negative control wells which contain medium plus alamar Blue but to which no cells have been added at 570 nm.
    (A′λ2)=Absorbance of negative control wells which contain medium plus alamar Blue but to which no cells have been added at 600 nm. Preferably, the % Reduced of wells containing no cell was subtracted from the % Reduced of wells containing samples to determine the % Reduced above background.


Cell cycle analysis can be carried out using standard method known in the art. In one embodiment, the supernatant from each well is combined with the cells that have been harvested by trypsinization. The mixture is then centrifuged at a suitable speed. The cells are then fixed with, e.g., ice cold 70% ethanol for a suitable period of time, e.g.,  ̃30 minutes. Fixed cells can be washed once with PBS and resuspended, e.g., in 0.5 ml of PBS containing Propidium Iodide (10 microgram/ml) and RNase A (1 mg/ml), and incubated at a suitable temperature, e.g., 37° C., for a suitable period of time, e.g., 30 min. Flow cytometric analysis is then carried out using a flow cytometer. In one embodiment, the Sub-G1 cell population is used as a measure of cell death. For example, the cells are said to have been sensitized to an agent if the Sub-G1 population from the sample treated with the agent is larger than the Sub-G1 population of sample not treated with the agent.
5.6. Implementation Systems and Methods
The analytical methods of the present invention can preferably be implemented using a computer system, such as the computer system described in this section, according to the following programs and methods. Such a computer system can also preferably store and manipulate measured signals obtained in various experiments that can be used by a computer system implemented with the analytical methods of this invention. Accordingly, such computer systems are also considered part of the present invention.
An exemplary computer system suitable from implementing the analytic methods of this invention is illustrated in FIG. 12. Computer system 1201 is illustrated here as comprising internal components and as being linked to external components. The internal components of this computer system include one or more processor elements 1202 interconnected with a main memory 1203. For example, computer system 1201 can be an Intel Pentium IV®-based processor of 2 GHZ or greater clock rate and with 256 MB or more main memory. In a preferred embodiment, computer system 1201 is a cluster of a plurality of computers comprising a head “node” and eight sibling “nodes,” with each node having a central processing unit (“CPU”). In addition, the cluster also comprises at least 128 MB of random access memory (“RAM”) on the head node and at least 256 MB of RAM on each of the eight sibling nodes. Therefore, the computer systems of the present invention are not limited to those consisting of a single memory unit or a single processor unit.
The external components can include a mass storage 1204. This mass storage can be one or more hard disks that are typically packaged together with the processor and memory. Such hard disk are typically of 10 GB or greater storage capacity and more preferably have at least 40 GB of storage capacity. For example, in a preferred embodiment, described above, wherein a computer system of the invention comprises several nodes, each node can have its own hard drive. The head node preferably has a hard drive with at least 10 GB of storage capacity whereas each sibling node preferably has a hard drive with at least 40 GB of storage capacity. A computer system of the invention can further comprise other mass storage units including, for example, one or more floppy drives, one more CD-ROM drives, one or more DVD drives or one or more DAT drives.
Other external components typically include a user interface device 1205, which is most typically a monitor and a keyboard together with a graphical input device 1206 such as a “mouse.” The computer system is also typically linked to a network link 1207 which can be, e.g., part of a local area network (“LAN”) to other, local computer systems and/or part of a wide area network (“WAN”), such as the Internet, that is connected to other, remote computer systems. For example, in the preferred embodiment, discussed above, wherein the computer system comprises a plurality of nodes, each node is preferably connected to a network, preferably an NFS network, so that the nodes of the computer system communicate with each other and, optionally, with other computer systems by means of the network and can thereby share data and processing tasks with one another.
Loaded into memory during operation of such a computer system are several software components that are also shown schematically in FIG. 12. The software components comprise both software components that are standard in the art and components that are special to the present invention. These software components are typically stored on mass storage such as the hard drive 1204, but can be stored on other computer readable media as well including, for example, one or more floppy disks, one or more CD-ROMs, one or more DVDs or one or more DATs. Software component 1210 represents an operating system which is responsible for managing the computer system and its network interconnections.
The operating system can be, for example, of the Microsoft WindowsTM family such as Windows 95, Window 98, Windows NT, Windows 2000 or Windows XP. Alternatively, the operating software can be a Macintosh operating system, a UNIX operating system or a LINUX operating system. Software components 1211 comprises common languages and functions that are preferably present in the system to assist programs implementing methods specific to the present invention. Languages that can be used to program the analytic methods of the invention include, for example, C and C++, FORTRAN, PERL, HTML, JAVA, and any of the UNIX or LINUX shell command languages such as C shell script language. The methods of the invention can also be programmed or modeled in mathematical software packages that allow symbolic entry of equations and high-level specification of processing, including specific algorithms to be used, thereby freeing a user of the need to procedurally program individual equations and algorithms. Such packages include, e.g., Matiab from Mathworks (Natick, Mass.), Mathematica from Wolfram Research (Champaign, Ill.) or S-Plus from MathSoft (Seattle, Wash.).
Software component 1212 comprises any analytic methods of the present invention described supra, preferably programmed in a procedural language or symbolic package. For example, software component 1212 preferably includes programs that cause the processor to implement steps of accepting a plurality of measured signals and storing the measured signals in the memory. For example, the computer system can accept measured signals that are manually entered by a user (e.g., by means of the user interface). More preferably, however, the programs cause the computer system to retrieve measured signals from a database. Such a database can be stored on a mass storage (e.g., a hard drive) or other computer readable medium and loaded into the memory of the computer, or the compendium can be accessed by the computer system by means of the network 1207.
In addition to the exemplary program structures and computer systems described herein, other, alternative program structures and computer systems will be readily apparent to the skilled artisan. Such alternative systems, which do not depart from the above described computer system and programs structures either in spirit or in scope, are therefore intended to be comprehended within the accompanying claims.
6. EXAMPLES
The following examples are presented by way of illustration of the present invention, and are not intended to limit the present invention in any way.
6.1. Example 1
Designing siRNA for High Silencing Efficacy
A library of siRNAs targeting more than 700 genes was constructed. The siRNAs in the library were designed by use of a “standard” approach, based on a combination of limited design principles available from the scientific literature (Elbashir et al., 2001, Nature 411:494-8) and a method for predicting off target effects by sequence similarity scoring as described in Section 5.2. A set of 377 siRNAs was tested by Taqman analysis for their ability to silence their respective target genes. The set of 377 siRNAs are listed in Table II. Table II lists the following information for the 377 siRNAs: the ID number of the siRNA, the accession number of the target gene, start position of the target sequence, target sequence, % silencing, the set it belongs (i.e., training or test) in Set 1, the set it belongs in Set 2, and the SEQ ID NO. The results of this test showed that most siRNAs successfully silenced their target genes (median silencing,  ̃75%), but individual siRNAs still showed a wide range of silencing performance. Good (or poor) silencing ability was not consistently associated with any particular base at any position, overall GC content, the position of the siRNA sequence within the target transcript, or with alternative splicing of target transcripts.
The potential relationship between target gene silencing and the base-composition, thermodynamics and secondary structure of the siRNA and target sequences was explored using a classifier approach. siRNAs were divided into groups containing those with less than median silencing ability (“bad” siRNAs) and those with median or better silencing ability (“good” siRNAs). A number of metrics were evaluated for their ability to distinguish good and bad siRNAs, including base composition in windows of the 19mer siRNA duplex sequence and the flanking target region, secondary structure predictions by various programs and thermodynamic properties. These tests revealed that siRNA efficacy correlated well with siRNA and target gene base composition, but poorly with secondary structure predictions and thermodynamic properties. In particular, the GC content of good siRNAs differed substantially from that of bad siRNAs in a position-specific manner (FIGS. 1-3). For example, good siRNA duplexes were not observed to be associated with any particular sequence, but tended to be GC rich at the 5′ end and GC poor at the 3′ end. The data indicate that a good siRNA duplex encourages preferential interaction of the antisense strand by being GC poor at its 3′ end and discourages interaction of the sense strand by being GC rich at its 5′ end. The data further demonstrate that position-specific sequence preferences extend beyond the boundaries of the siRNA target sequence into the adjacent sequence(s). This suggests that steps during RNA silencing other than unwinding of the siRNA duplex are affected by position-specific base composition preferences.
The GC-content difference between good and bad siRNAs shown in FIGS. 1 and 2 was used to develop methods for selecting good siRNAs. Best results were obtained with a position-specific scoring matrix (PSSM) approach. The PSSM provides weights for GC, A or U at every position on the sense strand of the target gene sequence from 10 bases upstream of the start to 10 bases downstream of the end of the siRNA duplex. The siRNA efficacy data were divided into two sets, one to be used for training and the other for an independent test. A random-mutation hill-climbing search algorithm was used to optimize the weights for each base at each position of the PSSM simultaneously. The optimization criterion was the correlation coefficient between the target silencing of the siRNA and its PSSM score. Multiple runs of optimization on the training data set were averaged to complete each PSSM. Each PSSM was then tested on the independent (test) set of siRNAs. The performance of two PSSMs on their training and test data sets is demonstrated in FIG. 2.
An siRNA design method was developed based on a position-specific score matrix (PSSM). A scoring scheme is used to predict the efficacy of siRNA oligos. The score is a weighted sum of 39 bases (10 bases upstream of the 19mer, 19 bases on the siRNA proper, and 10 bases downstream) computed as follows:




  Score
  =
  
    
      ∑
      
        i
        =
        1
      
      39
    
    ⁢
    
      
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            E
            i
          
          /
          
            p
            i
          
        
        )
      
    
  




where Pi equals the random probability of any base, i.e., 0.25, and Ei the weight assigned to the base A, U, G or C at position i. Therefore, a total of 117 weights (39 positions times 3 base types—G or C, A, U) need to be assigned and optimized.

A random-mutation hill climbing (RMHC) search algorithm was utilized to optimize the weights based on a training oligo set and the resulting profiles applied to a test set, with the optimizing criteria being the correlation coefficient between the knock-down (KD) levels of the oligos and the computed PSSM scores. The metric to measure the effectiveness of the training and testing is the aggregate false detection rate (FDR) based on the ROC curve, and is computed as the average of the FDR scores of the top 33% oligos sorted by the scores given by the trained predictor. In computing the FDR scores, those oligos with silencing levels less than the median are considered false, and those more than the median silencing levels considered true.
Different criteria were used to divide the existing siRNA performance data into training and test sets. The greatest obstacle to an ideal partition is that the vast majority of siRNA oligos are designed with the standard method, which requires an AA dimer immediately before the 19mer oligo sequence. This limitation was found later to be detrimental rather than helpful to the design process and was abolished. To limit the influence of this on the training procedure, several partitions were used and more than one trained predictors, i.e., PSSMs, (rather than single predictors) were combined to assign scores to the test oligos.
Finally, a state-of-the-art siRNA oligo design procedure (also referred to as “pipeline”) was constructed. It incorporates the off-target prediction procedure and two ensembles of siRNA oligo efficacy predictors trained and tested on different data sets. A total of 30 siRNA oligos (6 oligos for each of 5 genes) were selected and tested. The results were significantly better than any of the previously existing pipelines.
The initial training and testing results showed that the PSSM is very effective in predicting the on-target efficacy of siRNA oligos. Typically the aggregate FDR scores for training are between 0.02 and 0.08, and those for testing between 0.05 and 0.10.As a reference, random predictions have a mean aggregate FDR of 0.17, with the standard deviation being 0.02 (data computed with 10,000 randomly generated predictions). FIG. 3 illustrates typical ROC curves, generated by an ensemble of about 200 randomly optimized predictors. It could be seen that the performance of the training is better than the test set, which is hardly surprising. Both curves are significantly better than random.
FIG. 5 illustrates the resulting sequence profiles from training and testing on several different oligo sets. This profile illustrates that G or C bases are strongly preferred at the beginning, i.e., the 5′ end, and strongly disfavored at the end, i.e., the 3′ end, of the 19mer sequence. To confirm this observation, the average knock-down levels for oligos starting and ending with G/C or A/U are computed, and those oligos starting with G/C and ending with A/U have the best performance, far superior to the other three categories. Simply by comparing the weights at different positions, a 19mer oligo having a sequence of GCGTTAATGTGATAATATA (SEQ ID NO: 1), and the oligos that are most similar to this sequence are identified as an siRNA that may have high silencing efficacy.
The design method incorporated both PSSMs shown in FIG. 3 because the combination gave better performance as compared to using either one PSSM alone. The improved siRNA design method selected oligonucleotides based on 4 principles: base composition, off-target identity, position in the transcript, and sequence variety. Certain oligonucleotides containing sequence from features such as untranslated regions, repeats or homopolymeric runs were eliminated. Remaining oligonucleotides were ranked by their PSSM scores. Top-ranking oligonucleotides were selected for variety in GC content, in start position, and in the two bases upstream of the siRNA 19mer duplex. Selected oligonucleotides were then filtered for predicted off-target activity, which was calculated as a position-weighted FASTA alignment score. Remaining oligonucleotides were ranked by PSSM scores, subjected to a second round of selection for variety and finally re-ranked by their PSSM scores. The desired number of siRNAs was retained from the top of this final is ranking.
The improved method was compared to the standard method by side-by-side testing of new siRNAs selected by each. The results obtained with three siRNAs selected by each method are shown in FIG. 3. siRNAs designed by the improved algorithm showed better median efficacy (88%, compared to 78% for the standard method siRNA) and were more uniform in their performance. The distribution of silencing efficacies of the improved algorithm siRNAs was significantly better than that of the standard method siRNAs for the same genes (p=0.004, Wilcoxon rank sum test).
The test results of 30 experimental oligos using the new pipeline proved to be successful. Table III lists the 30 siRNAs. In the past, an siRNA design with the standard method had a median silencing level of 75%. Of the 30 experimental oligos, 28 had silencing levels equal to or better than 75%, 26 better than or equal to 80%, and 37% better than 90%, comparing with only 10% better than 90% using the standard method Two target genes (KIF14 and IGF1R) had been very difficult to silence by siRNAs, with previous oligos achieving only 40% to 70% and no more than 80% silencing levels in the past. The 12 new oligos targeting these gene all achieved silencing of at least 80% and 6 achieved 90% levels. The two oligos among the 30 oligos which had less than 75% silencing level turned out to be targeting an exon that is unique to one target transcript sequence, but absent in all other alternative splice forms of the same gene. Therefore, the failure of these two oligos was due to improper input sequence rather than the PSSM method. Therefore, when given proper input sequences, the pipeline appears to be able to pick oligos that can knock down target genes by at least 75% for 100% of the target genes.







TABLE II







A library of 377 siRNAs





















SEQ



accession
start

%


ID


BioID
number
position
19 mer sequence
silencing
Set 1
Set 2
NO

















  31
NM_000075
437
TGTTGTCCGGCTGATGGAC
27.0
Training
Training
2


 


  36
NM_001813
1036
ACTCTTACTGCTCTCCAGT
86.1
Test
Training
3


 


  37
NM_001813
1278
CTTAACACGGATGCTGGTG
60.1
Test
Training
4


 


  38
NM_001813
3427
GGAGAGCTTTCTAGGACCT
88.0
Test
Training
5


 


  39
NM_004073
192
AGTCATCCCGCAGAGCCGC
55.0
Training
Training
6


 


  40
NM_004073
1745
ATCGTAGTGCTTGTACTTA
70.0
Training
Training
7


 


  41
NM_004073
717
GGAGACGTACCGCTGCATC
65.0
Training
Training
8


 


  42
AK092024
437
GCAGTGATTGCTCAGCAGC
93.0
Training
Training
9


 


  43
NM_030932
935
GAGTTTACCGACCACCAAG
81.0
Training
Training
10


 


  44
NM_030932
1186
TGCGGATGCCATTCAGTGG
35.0
Training
Training
11


 


  45
NM_030932
1620
CACGGTTGGCAGAGTCTAT
73.0
Training
Training
12


 


  49
U53530
169
GCAAGTTGAGCTCTACCGC
59.0
Training
Training
13


 


  50
U53530
190
TGGCCAGCGCTTACTGGAA
75.0
Training
Training
14


 


  64
NM_006101
1623
GTTCAAAAGCTGGATGATC
79.0
Test
Training
15


 


  65
NM_006101
186
GGCCTCTATACCCCTCAAA
74.4
Test
Training
16


 


  66
NM_006101
968
AGAACCGAATCGTCTAGAG
80.3
Test
Training
17


 


  67
NM_000859
253
CACGATGCATAGCCATCCT
25.0
Training
Training
18


 


  68
NM_000859
1075
CAGAGACAGAATCTACACT
45.0
Training
Training
19


 


  69
NM_000859
1720
CAACAGAAGGTTGTCTTGT
50.0
Training
Training
20


 


  70
NM_000859
2572
TTGTGTGTGGGACCGTAAT
80.0
Training
Training
21


 


  71
NM_000875
276
GCTCACGGTCATTACCGAG
63.9
Training
Training
22


 


  72
NM_000875
441
CCTGAGGAACATTACTCGG
0.0
Training
Training
23


 


  73
NM_000875
483
TGCTGACCTCTGTTACCTC
50.0
Training
Training
24


 


  74
NM_000875
777
CGACACGGCCTGTGTAGCT
58.0
Training
Training
25


 


  75
NM_000875
987
CGGCAGCCAGAGCATGTAC
63.0
Training
Training
26


 


  76
NM_000875
1320
CCAGAACTTGCAGCAACTG
70.0
Training
Training
27


 


  81
NM_000875
351
CCTCACGGTCATCCGCGGC
0.0
Training
Training
28


 


  83
NM_000875
387
CTACGCCCTGGTCATCTTC
32.0
Training
Training
29


 


  84
NM_000875
417
TCTCAAGGATATTGGGCTT
54.0
Training
Training
30


 


  85
NM_000875
423
GGATATTGGGCTTTACAAC
71.0
Training
Training
31


 


  86
NM_000875
450
CATTACTCGGGGGGCCATC
53.0
Training
Training
32


 


  87
NM_000875
481
AATGCTGACCTCTGTTACC
54.6
Training
Training
33


 


 117
NM_004523
1689
CTGGATCGTAAGAAGGCAG
74.7
Training
Test
34


 


 118
NM_004523
484
TGGAAGGTGAAAGGTCACC
16.0
Training
Test
35


 


 119
NM_004523
802
GGACAACTGCAGCTACTCT
84.1
Training
Test
36


 


 139
NM_002358
219
TACGGACTCACCTTGCTTG
83.0
Training
Training
37


 


 144
NM_001315
779
GTATATACATTCAGCTGAC
78.5
Training

38


 


 145
NM_001315
1080
GGAACACCCCCCGCTTATC
27.2
Training

39


 


 146
NM_001315
1317
GTGGCCGATCCTTATGATC
81.3
Training

40


 


 152
NM_001315
607
ATGTGATTGGTCTGTTGGA
95.0
Training

41


 


 153
NM_001315
1395
GTCATCAGCTTTGTGCCAC
92.0
Training

42


 


 154
NM_001315
799
TAATTCACAGGGACCTAAA
82.0
Training

43


 


 155
NM_001315
1277
TGCCTACTTTGCTCAGTAC
95.0
Training

44


 


 193
NM_001315
565
CCTACAGAGAACTGCGGTT
90.0
Training

45


 


 190
NM_001315
763
TTCTCCGAGGTCTAAAGTA
87.0
Training

46


 


 192
NM_001315
1314
CCAGTGGCCGATCCTTATG
89.0
Training

47


 


 194
NM_001315
1491
GGCCTTTTCACGGGAACTC
97.0
Training

48


 


 201
NM_016195
2044
CTGAAGAAGCTACTGCTTG
80.3
Test
Training
49


 


 202
NM_016195
4053
GACATGCGAATGACACTAG
75.9
Test
Training
50


 


 203
NM_016195
3710
AGAGGAACTCTCTGCAAGC
84.7
Test
Training
51


 


 204
NM_014875
4478
AAACTGGGAGGCTACTTAC
93.0
Test
Training
52


 


 205
NM_014875
1297
ACTGACAACAAAGTGCAGC
37.0
Test
Training
53


 


 206
NM_014875
5130
CTCACATTGTCCACCAGGA
91.6
Test
Training
54


 


 210
NM_004523
4394
GACCTGTGCCTTTTAGAGA
63.7
Training
Test
55


 


 211
NM_004523
2117
GACTTCATTGACAGTGGCC
71.0
Training
Test
56


 


 212
NM_004523
799
AAAGGACAACTGCAGCTAC
49.0
Training
Test
57


 


 213
NM_000314
2753
TGGAGGGGAATGCTCAGAA
40.0
Training
Training
58


 


 214
NM_000314
2510
TAAAGATGGCACTTTCCCG
79.0
Training
Training
59


 


 215
NM_000314
2935
AAGGCAGCTAAAGGAAGTG
55.0
Training
Training
60


 


 234
NM_007054
963
TATTGGGCCAGCAGATTAC
76.9
Training
Training
61


 


 235
NM_007054
593
TTATGACGCTAGGCCACAA
74.4
Training
Training
62


 


 236
NM_007054
1926
GGAGAAAGATCCCTTTGAG
78.3
Training
Training
63


 


 237
NM_006845
324
ACAAAAACGGAGATCCGTC
72.2
Training
Training
64


 


 238
NM_006845
2206
ATAAGCAGCAAGAAACGGC
30.9
Training
Training
65


 


 239
NM_006845
766
GAATTTCGGGCTACTTTGG
65.8
Training
Training
66


 


 240
NM_005163
454
CGCACCTTCCATGTGGAGA
86.8
Training
Training
67


 


 241
NM_005163
1777
AGACGTTTTTGTGCTGTGG
76.0
Training
Training
68


 


 242
NM_005163
1026
GCTGGAGAACCTCATGCTG
87.8
Training
Training
69


 


 243
NM_005733
2139
CTCTACCACTGAAGAGTTG
90.7
Training
Training
70


 


 244
NM_005733
1106
AAGTGGGTCGTAAGAACCA
82.5
Training
Training
71


 


 245
NM_005733
696
GAAGCTGTCCCTGCTAAAT
93.4
Training
Training
72


 


 246
NM_001813
3928
GAAGAGATCCCAGTGCTTC
86.8
Test
Training
73


 


 247
NM_001813
4456
TCTGAAAGTGACCAGCTCA
82.5
Test
Training
74


 


 248
NM_001813
2293
GAAAATGAAGCTTTGCGGG
78.4
Test
Training
75


 


 249
NM_005030
1135
AAGAAGAACCAGTGGTTCG
83.0
Test
Test
76


 


 250
NM_005030
572
CCGAGTTATTCATCGAGAC
93.6
Test
Test
77


 


 251
NM_005030
832
AAGAGACCTACCTCCGGAT
85.0
Test
Test
78


 


 255
NM_001315
3050
AATATCCTCAGGGGTGGAG
36.0
Training

79


 


 256
NM_001315
1526
GTGCCTCTTGTTGCAGAGA
88.0
Training

80


 


 257
NM_001315
521
GAAGCTCTCCAGACCATTT
96.0
Training

81


 


 261
NM_006218
456
AGAAGCTGTGGATCTTAGG
65.3
Test
Training
82


 


 262
NM_006218
3144
TGATGCACATCATGGTGGC
68.9
Test
Training
83


 


 263
NM_006218
2293
CTAGGAAACCTCAGGCTTA
94.7
Test
Training
84


 


 264
NM_000075
1073
GCGAATCTCTGCCTTTCGA
79.0
Training
Training
85


 


 265
NM_000075
685
CAGTCAAGCTGGCTGACTT
78.0
Training
Training
86


 


 266
NM_000075
581
GGATCTGATGCGCCAGTTT
77.0
Training
Training
87


 


 288
NM_020242
1829
GCACAACTCCTGCAAATTC
87.4
Training
Training
88


 


 289
NM_020242
3566
GATGGAAGAGCCTCTAAGA
82.7
Training
Training
89


 


 290
NM_020242
2631
ACGAAAAGCTGCTTGAGAG
73.4
Training
Training
90


 


 291
NM_004073
570
GAAGACCATCTGTGGCACC
65.0
Training
Training
91


 


 292
NM_004073
1977
TCAGGGACCAGCTTTACTG
60.0
Training
Training
92


 


 293
NM_004073
958
GTTACCAAGAGCCTCTTTG
75.0
Training
Training
93


 


 294
NM_005026
3279
AACCAAAGTGAACTGGCTG
56.3
Training
Training
94


 


 295
NM_005026
2121
GATCGGCCACTTCCTTTTC
70.9
Training
Training
95


 


 296
NM_005026
4004
AGAGATCTGGGCCTCATGT
67.3
Training
Training
96


 


 303
NM_000051
5373
AGTTCGATCAGCAGCTGTT
60.9
Training
Training
97


 


 304
NM_000051
3471
TAGATTGTTCCAGGACACG
71.2
Training
Training
98


 


 305
NM_000051
7140
GAAGTTGGATGCCAGCTGT
56.3
Training
Training
99


 


 309
NM_004064
1755
TGGTGATCACTCCAGGTAG
25.3
Training
Training
100


 


 310
NM_004064
1505
TGTCCCTTTCAGAGACAGC
5.0
Training
Training
101


 


 311
NM_004064
1049
GACGTCAAACGTAAACAGC
50.2
Training
Training
102


 


 312
NM_006219
1049
AAGTTCATGTCAGGGCTGG
76.6
Test
Training
103


 


 313
NM_006219
2631
CAAAGATGCCCTTCTGAAC
88.9
Test
Training
104


 


 314
NM_006219
453
AATGCGCAAATTCAGCGAG
32.9
Test
Training
105


 


 339
NM_003600
437
GCACAAAAGCTTGTCTCCA
96.0
Test
Training
106


 


 340
NM_003600
1071
TTGCAGATTTTGGGTGGTC
37.0
Test
Training
107


 


 341
NM_003600
1459
ACAGTCTTAGGAATCGTGC
61.1
Test
Training
108


 


 342
NM_004958
1476
AGGACTTCGCCCATAAGAG
61.8
Test
Training
109


 


 343
NM_004958
5773
CAACCTCCAGGATACACTC
80.9
Test
Training
110


 


 344
NM_004958
7886
CCAACTTTCTAGCTGCTGT
71.1
Test
Training
111


 


 348
NM_004856
1999
GAATGTGAGCGTAGAGTGG
92.2
Training
Training
112


 


 349
NM_004856
1516
CCATTGGTTACTGACGTGG
87.7
Training
Training
113


 


 350
NM_004856
845
AACCCAAACCTCCACAATC
71.8
Training
Training
114


 


 369
XM_294563
117
GAAAGAAGCAGTTGACCTC
59.9
Training
Training
115


 


 370
XM_294563
2006
CTAAAAGCTGGGTGGACTC
69.4
Training
Training
116


 


 371
XM_294563
389
GAAAGCACCTCTTTGTGTG
64.2
Training
Training
117


 


 399
NM_000546
1286
TGAGGCCTTGGAACTCAAG
17.8


118


 


 400
NM_000546
2066
CCTCTTGGTCGACCTTAGT
74.5


119


 


 401
NM_000546
1546
GCACCCAGGACTTCCATTT
93.2


120


 


 417
NM_001184
3790
GAAACTGCAGCTATCTTCC
75.8
Training
Training
121


 


 418
NM_001184
7717
GTTACAATGAGGCTGATGC
73.0
Training
Training
122


 


 419
NM_001184
5953
TCACGACTCGCTGAACTGT
78.8
Training
Training
123


 


 453
NM_005978
323
GACCGACCCTGAAGCAGAA
91.3
Test
Test
124


 


 454
NM_005978
254
TTCCAGGAGTATGCTGTTT
74.4
Test
Test
125


 


 455
NM_005978
145
GGAACTTCTGCACAAGGAG
96.5
Test
Test
126


 


 465
NM_000551
495
TGTTGACGGACAGCCTATT
75.5
Test
Training
127


 


 466
NM_000551
1056
GGCATTGGCATCTGCTTTT
89.7
Test
Training
128


 


 467
NM_000551
3147
GTGAATGAGACACTCCAGT
82.2
Test
Training
129


 


 468
NM_002658
1944
GAGCTGGTGTCTGATTGTT
82.8
Test
Training
130


 


 469
NM_002658
1765
GTGTAAGCAGCTGAGGTCT
44.4
Test
Training
131


 


 470
NM_002658
232
CTGCCCAAAGAAATTCGGA
47.8
Test
Training
132


 


 507
NM_003391
792
ATTTGCCCGCGCATTTGTG
27.2
Test
Training
133


 


 508
NM_003391
2171
AGAAGATGAATGGTCTGGC
69.4
Test
Training
134


 


 509
NM_003391
981
AACGGGCGATTATCTCTGG
43.3
Test
Training
135


 


 540
NM_002387
3490
GACTTAGAGCTGGGAATCT
83.7
Test
Training
136


 


 541
NM_002387
4098
AGTTGAGGAGGTTTCTGCA
86.1
Test
Training
137


 


 542
NM_002387
1930
GGATTATATCCAGCAGCTC
82.3
Test
Training
138


 


 585
NM_014885
509
GTGGCTGGATTCATGTTCC
81.5
Training
Training
139


 


 586
NM_014885
798
CAAGGCATCCGTTATATCT
84.7
Training
Training
140


 


 587
NM_014885
270
ACCAGGATTTGGAGTGGAT
84.7
Training
Training
141


 


 639
NM_001274
250
CTGAAGAAGCAGTCGCAGT
77.7


142


 


 640
NM_001274
858
ATCGATTCTGCTCCTCTAG
86.2


143


 


 641
NM_001274
1332
TGCCTGAAAGAGACTTGTG
85.4


144


 


 651
NM_001259
807
TCTTGGACGTGATTGGACT
89.8
Training
Training
145


 


 652
NM_001259
1036
AGAAAACCTGGATTCCCAC
88.9
Training
Training
146


 


 653
NM_001259
556
ACCACAGAACATTCTGGTG
89.3
Training
Training
147


 


 672
NM_003161
2211
GAAAGCCAGACAACTTCTG
87.1
Test
Training
148


 


 673
NM_003161
1223
CTCTCAGTGAAAGTGCCAA
91.2
Test
Training
149


 


 674
NM_003161
604
GACACTGCCTGCTTTTACT
98.1
Test
Training
150


 


 678
NM_004972
3526
AAGAACCTGGTGAAAGTCC
57.2
Training
Training
151


 


 679
NM_004972
4877
GAAGTGCAGCAGGTTAAGA
54.8
Training
Training
152


 


 680
NM_004972
1509
AGCCGAGTTGTAACTATCC
74.9
Training
Training
153


 


 684
NM_007194
1245
GATCACAGTGGCAATGGAA
80.9


154


 


 685
NM_007194
1432
AAACTCTTGGAAGTGGTGC
39.2


155


 


 686
NM_007194
2269
ATGAATCCACAGCTCTACC
44.6


156


 


 687
NM_007313
3866
GAATGGAAGCCTGAACTGA
92.4
Test
Training
157


 


 688
NM_007313
2451
AGACATCATGGAGTCCAGC
5.0
Test
Training
158


 


 689
NM_007313
1296
CAAGTTCTCCATCAAGTCC
91.1
Test
Training
159


 


 711
NM_139049
129
GGAATAGTATGCGCAGCTT
92.5
Test
Training
160


 


 712
NM_139049
369
GTGATTCAGATGGAGCTAG
89.0
Test
Training
161


 


 713
NM_139049
969
CACCCGTACATCAATGTCT
77.0
Test
Training
162


 


 858
NM_001253
522
TCATTGGAAGAACAGCGGC
0.0
Test
Training
163


 


 859
NM_001253
2571
AAGAAGACGTTCAGCGACA
93.5
Test
Training
164


 


 860
NM_001253
911
AAAAAGCCTGCCCTTGGTT
88.1
Test
Training
165


 


1110
NM_006101
1847
CTTGCAACGTCTGTTAGAG
72.3
Test
Training
166


 


1111
NM_006101
999
CTGAAGGCTTCCTTACAAG
82.9
Test
Training
167


 


1112
NM_006101
1278
CAGAAGTTGTGGAATGAGG
79.1
Test
Training
168


 


1182
NM_016231
1302
GCAATGAGGACAGCTTGTG
79.8
Test
Training
169


 


1183
NM_016231
1829
TGTAGCTTTCCACTGGAGT
79.3
Test
Training
170


 


1184
NM_016231
1019
TCTCCTTGTGAACAGCAAC
62.5
Test
Training
171


 


1212
NM_001654
1072
AGTGAAGAACCTGGGGTAC
79.3
Test
Training
172


 


1213
NM_001654
595
GTTCCACCAGCATTGTTCC
86.2
Test
Training
173


 


1214
NM_001654
1258
GAATGAGATGCAGGTGCTC
86.9
Test
Training
174


 


1287
NM_005417
2425
CAATTCGTCGGAGGCATCA
73.9
Test
Training
175


 


1288
NM_005417
1077
GGGGAGTTTGCTGGACTTT
66.4
Test
Training
176


 


1289
NM_005417
3338
GCAGTGCCTGCCTATGAAA
68.2
Test
Training
177


 


1290
NM_001982
3223
CTAGACCTAGACCTAGACT
63.5
Test
Training
178


 


1291
NM_001982
3658
GAGGATGTCAACGGTTATG
49.4
Test
Training
179


 


1292
NM_001982
2289
CAAAGTCTTGGCCAGAATC
45.3
Test
Training
180


 


1293
NM_005400
249
GATCGAGCTGGCTGTCTTT
85.4
Test
Training
181


 


1294
NM_005400
1326
GGTCTTAAAGAAGGACGTC
63.4
Test
Training
182


 


1295
NM_005400
1848
TGAGGACGACCTATTTGAG
0.0
Test
Training
183


 


1317
NM_002086
465
TGAGCTGGTGGATTATCAC
85.5
Test
Test
184


 


1318
NM_002086
183
CTGGTACAAGGCAGAGCTT
95.5
Test
Test
185


 


1319
NM_002086
720
CCGGAACGTCTAAGAGTCA
92.3
Test
Test
186


 


1332
NM_006219
2925
TACAGAAAAGTTTGGCCGG
20.1
Test
Training
187


 


1333
NM_006219
2346
AATGAAGCCTTTGTGGCTG
22.4
Test
Training
188


 


1334
NM_006219
2044
GTGCACATTCCTGCTGTCT
79.0
Test
Training
189


 


1335
NM_003600
1618
CCTCCCTATTCAGAAAGCT
84.2
Test
Training
190


 


1336
NM_003600
650
GACTTTGAAATTGGTCGCC
52.1
Test
Training
191


 


1337
NM_003600
538
CACCCAAAAGAGCAAGCAG
96.3
Test
Training
192


 


1338
XM_294563
2703
TAAGCCTGGTGGTGATCTT
78.1
Training
Training
193


 


1339
XM_294563
1701
AAGGTCTTTACGCCAGTAC
29.5
Training
Training
194


 


1340
XM_294563
789
GGAATGTATCCGAGCACTG
73.5
Training
Training
195


 


1386
NM_033360
493
GGACTCTGAAGATGTACCT
91.0
Test
Training
196


 


1387
NM_033360
897
GGCATACTAGTACAAGTGG
84.8
Test
Training
197


 


1388
NM_033360
704
GAAAAGACTCCTGGCTGTG
0.0
Test
Training
198


 


1389
NM_024408
4735
CTTTGAATGCCAGGGGAAC
91.6
Test
Training
199


 


1390
NM_024408
2674
CCAAGGAACCTGCTTTGAT
96.4
Test
Training
200


 


1391
NM_024408
5159
GACTCAGACCACTGCTTCA
95.8
Test
Training
201


 


1392
NM_000435
6045
GCTGCTGTTGGACCACTTT
0.0
Test
Training
202


 


1393
NM_000435
5495
TGCCAACTGAAGAGGATGA
0.0
Test
Training
203


 


1394
NM_000435
4869
TGATCACTGCTTCCCCGAT
0.0
Test
Training
204


 


1410
AF308602
770
ATATCGACGATTGTCCAGG
36.7
Test
Training
205


 


1411
AF308602
3939
AGGCAAGCCCTGCAAGAAT
81.3
Test
Training
206


 


1412
AF308602
1644
CACTTACACCTGTGTGTGC
81.3
Test
Training
207


 


1581
NM_005633
3593
TATCAGACCGGACCTCTAT
70.8
Test
Training
208


 


1582
NM_005633
364
ATTGACCACCAGGTTTCTG
1.4
Test
Training
209


 


1583
NM_005633
3926
CTTACAAAAGGGAGCACAC
66.9
Test
Training
210


 


1620
NM_002388
1097
GTCTCAGCTTCTGCGGTAT
95.0
Test
Training
211


 


1621
NM_002388
286
AGGATTTTGTGGCCTCCAT
94.6
Test
Training
212


 


1622
NM_002388
2268
TCCAGGTTGAAGGCATTCA
92.5
Test
Training
213


 


1629
NM_012193
3191
TTGGCAAAGGCTCCTTGTA
80.0
Test
Test
214


 


1630
NM_012193
5335
CCATCTGCTTGAGCTACTT
85.0
Test
Test
215


 


1631
NM_012193
2781
GTTGACTTACCTGACGGAC
43.1
Test
Test
216


 


1632
NM_004380
3708
GACATCCCGAGTCTATAAG
85.3
Test
Training
217


 


1633
NM_004380
339
TGGAGGAGAATTAGGCCTT
81.1
Test
Training
218


 


1634
NM_004380
5079
GCACAAGGAGGTCTTCTTC
79.0
Test
Training
219


 


1641
NM_017412
2331
CAGATCACTCCAGGCATAG
97.3
Test
Training
220


 


1643
NM_017412
2783
ATGTGTGGTGACTGCTTTG
95.7
Test
Training
221


 


1695
NM_001903
2137
TGACATCATTGTGCTGGCC
38.4
Test
Training
222


 


1696
NM_001903
655
CGTTCCGATCCTCTATACT
97.9
Test
Training
223


 


1697
NM_001903
3117
TGACCAAAGATGACCTGTG
40.1
Test
Training
224


 


1815
NM_020168
3064
GAGAAAGAATGGGGTCGGT
85.0
Training
Training
225


 


1816
NM_020168
681
CGACATCCAGAAGTTGTCA
86.1
Training
Training
226


 


1817
NM_020168
1917
TGAGGAGCAGATTGCCACT
72.1
Training
Training
227


 


2502
NM_000271
237
GAGGTACAATTGCGAATAT
87.0
Training
Training
228


 


2503
NM_000271
559
TACTACGTCGGACAGAGTT
76.0
Training
Training
229


 


2504
NM_000271
1783
AACTACAATAACGCCACTG
39.0
Training
Training
230


 


2505
NM_000271
2976
GCCACAGTCGTCTTGCTGT
84.0
Training
Training
231


 


2512
NM_005030
245
GGGCGGCTTTGCCAAGTGC
88.6
Test
Test
232


 


2513
NM_005030
1381
CACGCCTCATCCTCTACAA
90.5
Test
Test
233


 


2514
NM_005030
834
GAGACCTACCTCCGGATCA
91.0
Test
Test
234


 


2521
NM_000314
1316
CCCACCACAGCTAGAACTT
93.0
Training
Training
235


 


2522
NM_000314
1534
CTATTCCCAGTCAGAGGCG
89.0
Training
Training
236


 


2523
NM_000314
2083
CAGTAGAGGAGCCGTCAAA
90.0
Training
Training
237


 


2524
NM_006622
1928
CAGTTCACTATTACGCAGA
65.0
Training
Training
238


 


2525
NM_006622
586
TGTTACGAGATGACAGATT
73.0
Training
Training
239


 


2526
NM_006622
1252
AACCCAGAGGATCGTCCCA
70.0
Training
Training
240


 


2527
NM_139164
200
CTGTTTGGAGAAAACCCTC
79.0
Training
Training
241


 


2528
NM_139164
568
GACAACCCAAACCAGAGTC
71.0
Training
Training
242


 


2529
NM_139164
488
GTCTTGACTGGGATGAAAA
66.0
Training
Training
243


 


2530
NM_139164
578
ACCAGAGTCTTTTGACAGG
82.0
Training
Training
244


 


2546
NM_014875
1090
TAGACCACCCATTGCTTCC
63.5
Test
Training
245


 


2547
NM_014875
1739
AGAGCCTTCGAAGGCTTCA
73.2
Test
Training
246


 


2548
NM_014875
3563
GACCATAGCATCCGCCATG
87.1
Test
Training
247


 


2602
NM_002387
2655
TAGCTCTGCTAGAGGAGGA
71.0
Test
Training
248


 


2603
NM_002387
1418
ACAGAACGGCTGAATAGCC
43.5
Test
Training
249


 


2604
NM_002387
941
GAGAATGAGAGCCTGACTG
81.0
Test
Training
250


 


2605
NM_016231
1683
GGAAACAGAGTGCCTCTCT
55.3
Test
Training
251


 


2606
NM_016231
915
CCACTCAGCTCAGATCATG
82.3
Test
Training
252


 


2607
NM_016231
737
TCTGGTCTCTTGCAAAAGG
30.3
Test
Training
253


 


2611
NM_004380
4230
ATTTTTGCGGCGCCAGAAT
79.0
Test
Training
254


 


2612
NM_004380
2197
GAAAAACGGAGGTCGCGTT
85.9
Test
Training
255


 


2613
NM_004380
5701
GAAAACAAATGCCCCGTGC
55.4
Test
Training
256


 


2614
NM_005978
276
TGGCACTCATCACTGTCAT
91.8
Test
Test
257


 


2615
NM_005978
229
TGAGAACAGTGACCAGCAG
91.9
Test
Test
258


 


2616
NM_005978
369
GGGCCCAGGACTGTTGATG
94.5
Test
Test
259


 


2617
NM_017412
3128
AGAGATGGGCATTGTTTCC
94.3
Test
Training
260


 


2618
NM_017412
814
GCTCATGGAGATGTTTGGT
88.7
Test
Training
261


 


2619
NM_017412
1459
AGCATTGCTGTTTCACGCC
93.1
Test
Training
262


 


2620
NM_001654
1902
TTGAGCTGCTGCCACGGTC
67.2
Test
Training
263


 


2621
NM_001654
1006
GTCCCCACATTCCAAGTCA
90.0
Test
Training
264


 


2622
NM_001654
2327
CCTCTCTGGAATTTGTGCC
85.7
Test
Training
265


 


2623
NM_002658
202
CAAGTACTTCTCCAACATT
87.2
Test
Training
266


 


2624
NM_002658
181
TGGAGGAACATGTGTGTCC
0.0
Test
Training
267


 


2625
NM_002658
436
TTACTGCAGGAACCCAGAC
0.0
Test
Training
268


 


2629
NM_006218
1334
TGGCTTTGAATCTTTGGCC
3.5
Test
Training
269


 


2630
NM_006218
2613
AGGTGCACTGCAGTTCAAC
53.8
Test
Training
270


 


2631
NM_006218
1910
TTCAGCTAGTACAGGTCCT
78.0
Test
Training
271


 


2632
NM_003161
1834
TTGATTCCTCGCGACATCT
88.3
Test
Training
272


 


2633
NM_003161
1555
GCTTTTCCCATGATCTCCA
90.7
Test
Training
273


 


2634
NM_003161
217
CTTGGCATGGAACATTGTG
61.4
Test
Training
274


 


2635
NM_003391
2072
GCCTCAGAAAGGGATTGCT
79.1
Test
Training
275


 


2636
NM_003391
1318
GCTCTGGATGTGCACACAT
60.5
Test
Training
276


 


2637
NM_003391
1734
GTGTCTCAAAGGAGCTTTC
87.1
Test
Training
277


 


2641
AF308602
4260
ATTCAACGGGCTCTTGTGC
0.0
Test
Training
278


 


2642
AF308602
1974
GATCGATGGCTACGAGTGT
84.0
Test
Training
279


 


2643
AF308602
5142
CATCCCCTACAAGATCGAG
41.6
Test
Training
280


 


2644
NM_024408
8232
GCAACTTTGGTCTCCTTTC
91.0
Test
Training
281


 


2645
NM_024408
10503
GCAATTGGCTGTGATGCTC
86.6
Test
Training
282


 


2646
NM_024408
8643
GAGACAAGTTAACTCGTGC
89.4
Test
Training
283


 


2647
NM_007313
4222
TCCTGGCAAGAAAGCTTGA
65.6
Test
Training
284


 


2648
NM_007313
3237
AAACCTCTACACGTTCTGC
53.5
Test
Training
285


 


2649
NM_007313
302
CTAAAGGTGAAAAGCTCCG
67.8
Test
Training
286


 


2650
NM_000551
631
GATCTGGAAGACCACCCAA
70.9
Test
Training
287


 


2651
NM_000551
4678
CAGAACCCAAAAGGGTAAG
0.0
Test
Training
288


 


2652
NM_000551
4382
AGGAAATAGGCAGGGTGTG
4.3
Test
Training
289


 


2653
NM_001903
1888
AGCAGTGCTGATGATAAGG
89.1
Test
Training
290


 


2654
NM_001903
2606
AAGCCATTGGTGAAGAGAG
91.9
Test
Training
291


 


2655
NM_001903
1583
TGTGTCATTGCTCTCCAAG
90.3
Test
Training
292


 


2656
NM_002388
842
GCAGATGAGCAAGGATGCT
86.8
Test
Training
293


 


2657
NM_002388
1754
GTACATCCATGTGGCCAAA
94.6
Test
Training
294


 


2658
NM_002388
2642
TGGGTCATGAAAGCTGCCA
93.1
Test
Training
295


 


2662
NM_005633
3251
GAACACCGTTAACACCTCC
31.2
Test
Training
296


 


2663
NM_005633
2899
ATAACAGGAGAGATCCAGC
21.7
Test
Training
297


 


2664
NM_005633
2607
TGGTGTCCTTGAGGTTGTC
75.1
Test
Training
298


 


2665
NM_033360
329
ACCTGTCTCTTGGATATTC
81.4
Test
Training
299


 


2666
NM_033360
529
TAAATGTGATTTGCCTTCT
47.8
Test
Training
300


 


2667
NM_033360
585
GAAGTTATGGAATTCCTTT
94.2
Test
Training
301


 


2668
NM_139049
745
CACCATGTCCTGAATTCAT
80.7
Test
Training
302


 


2669
NM_139049
433
TCAAGCACCTTCATTCTGC
42.6
Test
Training
303


 


2670
NM_139049
550
CGAGTTTTATGATGACGCC
79.9
Test
Training
304


 


2671
NM_002086
555
ATACGTCCAGGCCCTCTTT
87.9
Test
Test
305


 


2672
NM_002086
392
TGCAGCACTTCAAGGTGCT
36.9
Test
Test
306


 


2673
NM_002086
675
CGGGCAGACCGGCATGTTT
92.6
Test
Test
307


 


2674
NM_004958
5024
GACATGAGAACCTGGCTCA
77.8
Test
Training
308


 


2675
NM_004958
2155
CTTGCAGGCCTTGTTTGTG
83.2
Test
Training
309


 


2676
NM_004958
6955
TAATACAGCTGGGGACGAC
52.3
Test
Training
310


 


2677
NM_012193
467
AGAACCTCGGCTACAACGT
71.5
Test
Test
311


 


2678
NM_012193
473
TCGGCTACAACGTGACCAA
51.3
Test
Test
312


 


2679
NM_012193
449
TCCGCATCTCCATGTGCCA
37.5
Test
Test
313


 


2680
NM_005400
665
TCACAAAGTGTGCTGGGTT
43.9
Test
Training
314


 


2681
NM_005400
2178
CCAGGAGGAATTCAAAGGT
41.6
Test
Training
315


 


2682
NM_005400
1022
GCTCACCATCTGAGGAAGA
64.2
Test
Training
316


 


2686
NM_001982
948
TGACAGTGGAGCCTGTGTA
65.8
Test
Training
317


 


2687
NM_001982
1800
CTTTCTGAATGGGGAGCCT
61.7
Test
Training
318


 


2688
NM_001982
2860
TACACACACCAGAGTGATG
0.0
Test
Training
319


 


2692
NM_016195
5331
ATGAAGGAGAGTGATCACC
10.5
Test
Training
320


 


2693
NM_016195
4829
AATGGCAGTGAAACACCCT
67.3
Test
Training
321


 


2694
NM_016195
1480
AAGTTTGTGTCCCAGACAC
80.5
Test
Training
322


 


2695
NM_000435
2107
AATGGCTTCCGCTGCCTCT
0.0
Test
Training
323


 


2696
NM_000435
5193
GAACATGGCCAAGGGTGAG
15.5
Test
Training
324


 


2697
NM_000435
7273
GAGTCTGGGACCTCCTTCT
0.0
Test
Training
325


 


2802
NM_004523
46
CCAGGGAGACTCCGGCCCC
6.7
Training
Test
326


 


2803
NM_004523
132
GGGACCGTCATGGCGTCGC
8.2
Training
Test
327


 


2804
NM_004523
221
ATTTAATTTGGCAGAGCGG
0.0
Training
Test
328


 


2805
NM_004523
322
GCTCAAGGAAAACATACAC
76.2
Training
Test
329


 


2806
NM_004523
365
TACTAAACAGATTGATGTT
77.9
Training
Test
330


 


2807
NM_004523
581
TACTGATAATGGTACTGAA
93.8
Training
Test
331


 


2808
NM_004523
716
AGGAGTGATAATTAAAGGT
84.8
Training
Test
332


 


2809
NM_004523
852
GTTTTCTCTGTTACAATAC
85.4
Training
Test
333


 


2810
NM_004523
995
TGGAAATATAAATCAATCC
0.0
Training
Test
334


 


2811
NM_004523
1085
ACTAACTAGAATCCTCCAG
0.0
Training
Test
335


 


2812
NM_004523
1174
AAACTCTGAGTACATTGGA
81.9
Training
Test
336


 


2813
NM_004523
1375
TAACTGTTCAAGAAGAGCA
14.1
Training
Test
337


 


2814
NM_004523
1570
AAGAAGAATATATCACATC
0.0
Training
Test
338


 


2815
NM_004523
1706
AGTTGACCAACACAATGCA
86.0
Training
Test
339


 


2816
NM_004523
2197
TACATGAACTACAAGAAAA
90.0
Training
Test
340


 


2817
NM_004523
2858
GACTAAGCTTAATTGCTTT
87.0
Training
Test
341


 


2818
NM_004523
3089
GGGGCAGTATACTGAAGAA
64.5
Training
Test
342


 


2819
NM_004523
3878
TTCTTGTATATTATTAAGT
0.0
Training
Test
343


 


2820
NM_004523
4455
TCTATAATTTATATTCTTT
9.3
Training
Test
344


 


2821
NM_004523
4648
TACAAAGAATAAATTTTCT
23.5
Training
Test
345


 


2823
NM_005030
45
CAGCGCAGCTTCGGGAGCA
72.1
Training
Test
346


 


2824
NM_005030
131
CGGAGTTGCAGCTCCCGGA
85.7
Training
Test
347


 


2825
NM_005030
303
GGCAAGATTGTGCCTAAGT
80.3
Training
Test
348


 


2826
NM_005030
346
GGGAGAAGATGTCCATGGA
100.0
Training
Test
349


 


2827
NM_005030
432
GACTTCGTGTTCGTGGTGT
89.3
Training
Test
350


 


2828
NM_005030
519
GCCCGATACTACCTACGGC
86.2
Training
Test
351


 


2829
NM_005030
648
GGACTGGCAACCAAAGTCG
86.7
Training
Test
352


 


2830
NM_005030
777
TGTATCATGTATACCTTGT
84.3
Training
Test
353


 


2831
NM_005030
821
TTCTTGCCTAAAAGAGACC
26.8
Training
Test
354


 


2832
NM_005030
907
TCCAGAAGATGCTTCAGAC
90.8
Training
Test
355


 


2833
NM_005030
952
ACGAGCTGCTTAATGACGA
87.7
Training
Test
356


 


2834
NM_005030
1038
TCGATTGCTCCCAGCAGCC
31.4
Training
Test
357


 


2835
NM_005030
1082
CACAGTCCTCAATAAAGGC
62.9
Training
Test
358


 


2836
NM_005030
1214
CAATGCCTCCAAGCCCTCG
0.0
Training
Test
359


 


2837
NM_005030
1300
AGTGGGTGGACTATTCGGA
84.9
Training
Test
360


 


2838
NM_005030
1515
TACATGAGCGAGCACTTGC
20.3
Training
Test
361


 


2839
NM_005030
1860
CTCAAGGCCTCCTAATAGC
74.2
Training
Test
362


 


2840
NM_005030
1946
CCGCGGTGCCATGTCTGCA
79.7
Training
Test
363


 


2841
NM_005030
2075
CCCCTCCCCCTCAACCCCA
34.6
Training
Test
364


 


3041
NM_014875
4629
ATTTTCTAGAAAACGGTAA
91.8


365


 


3042
NM_014875
77
GAGGGGCGAAGTTTCGGCA
71.2


366


 


3043
NM_014875
243
CTGGGACCGGGAAGCCGGA
0.0


367


 


3044
NM_014875
5094
CTTCTACTTCTGTTGGCAG
85.9


368


 


3045
NM_014875
4354
ACTTACTATTCAGACTGCA
85.7


369


 


3046
NM_014875
524
GCCCTCACCCACAGTAGCC
68.1


370


 


3047
NM_014875
5349
CAGAGGAATGCACACCCAG
73.6


371


 


3048
NM_014875
4824
GATTGATTAGATCTCTTGA
91.3


372


 


3049
NM_014875
3014
GTGAGTATTATCCCAGT7G
41.5


373


 


3050
NM_014875
2959
ATCTGGGGTGCTGATTGCT
46.3


374


 


3051
NM_014875
1514
GTGACAGTGGCAGTACGCG
67.7


375


 


3052
NM_014875
1114
TCAGACTGAAGTTGTTAGA
80.8


376


 


3053
NM_014875
2079
GTTGGCTAGAATTGGGAAA
91.8


377


 


3054
NM_014875
3560
GAAGACCATAGCATCCGCC
74.8


378
















TABLE III







30 siRNAs designed using the method of this example

















SEQ




Gene
Sequence
%
ID


BioID
Accession
name
(sense strand)
Silencing
NO





3844
NM_014875
KIF14
CAGGTAAAGTCAGAGACAT
87
379


 


3845
NM_014875
KIF14
GGGATTGACGGCAGTAAGA
89
380


 


3846
NM_014875
KIF14
CACTGAATGTGGGAGGTGA
92
381


 


3847
NM_014875
KIF14
GTCTGGGTGGAAATTCAAA
93
382


 


3848
NM_014875
KIF14
CATCTTTGCTGAATCGAAA
86
383


 


3849
NM_014875
KIF14
CAGGGATGCTGTTTGGATA
95
384


 


3850
NM_005030
PLK
CCCTGTGTGGGACTCCTAA
87
385


 


3851
NM_005030
PLK
GGTGTTCGCGGGCAAGATT
86
386


 


3852
NM_005030
PLK
CGCCTCATCCTCTACAATG
88
387


 


3853
NM_005030
PLK
GTTCTTTACTTCTGGCTAT
97
388


 


3854
NM_005030
PLK
CTCCTTAAATATTTCCGCA
92
389


 


3855
NM_005030
PLK
CTGAGCCTGAGGCCCGATA
75
390


 


3856
NM_000875
IGF1R
CAAATTATGTGTTTCCGAA
90
391


 


3857
NM_000875
IGF1R
CGCATGTGCTGGCAGTATA
84
392


 


3858
NM_000875
IGF1R
CCGAAGATTTCACAGTCAA
79
393


 


3859
NM_000875
IGF1R
ACCATTGATTCTGTTACTT
86
394


 


3860
NM_000875
IGF1R
ACCGCAAAGTCTTTGAGAA
88
395


 


3861
NM_000875
IGF1R
GTCCTGACATGCTGTTTGA
79
396


 


3862
NM_001315
MAPK14
GGAATTCAATGATGTGTAT
85
397


 


3863
NM_001315
MAPK14
GCTGTTGACTGGAAGAACA
84
398


 


3864
NM_001315
MAPK14
CTCCTGAGATCATGCTGAA
81
399


 


3865
NM_001315
MAPK14
CCATTTCAGTCCATCATTC
88
400


 


3866
NM_001315
MAPK14
CAGATTATGCGTCTGACAG
25
401


 


3867
NM_001315
MAPK14
CGCTTATCTCATTAACAGG
14
402


 


3871
NM_004523
KIF11
GAGCCCAGATCAACCTTTA
87
403


 


3872
NM_004523
KIF11
CTGACAAGAGCTCAAGGAA
89
404


 


3873
NM_004523
KIF11
GGCATTAACACACTGGAGA
92
405


 


3874
NM_004523
KIF11
GATGGCAGCTCAAAGCAAA
93
406


 


3875
NM_004523
KIF11
CAGCAGAAATCTAAGGATA
86
407


 


3876
NM_004523
KIF11
CGTTCTGGAGCTGTTGATA
95
408









6.2. Example 2
Selection of siRNAS for Silencing Specificity
The importance of off-target effects of siRNA and sbRNA sequences have been shown. Microarray experiments suggest that most siRNA oligos result in downregulation of off-target genes through direct interactions between dsRNA and the off-target transcripts. While sequence similarity between dsRNA and transcripts appears to play a role in determining which off-target genes will be affected, sequence similarity searches, even combined with thermodynamic models of hybridization, are insufficient to predict off-target effects accurately. However, alignment of off-target transcripts with offending siRNA sequences reveals that some base pairing interactions between the two appear to be more important than others (FIG. 6).
FIG. 6 shows an example of alignments of transcripts of off-target genes to the core 19mer of an siRNA oligo sequence. Off-target genes were selected from the Human 25 k v2.2.1 microarray by selecting for kinetic patterns of transcript abundance consistent with direct effects of siRNA oligos. Alignments were generated with FASTA and edited by hand. The black boxes and grey area demonstrate the higher level of sequence similarity in the 3′ half of the alignment.
The alignment shown in FIG. 6 and similar data for other siRNAs were combined to generate a position-specific scoring matrix for use in predicting off-target effects. The matrix, which reflects the frequency with which each position in the oligo is found to match affected off-target transcripts, is represented in FIG. 7.
The position-specific scoring matrix is used to calculate scores for alignments between a candidate RNAi sequence and off-target transcript sequences. Alignments of interest are established with a low-stringency FASTA search and the score for each alignment is calculated with the Eq. 6




  Score
  =
  
    
      ∑
      
        i
        =
        1
      
      n
    
    ⁢
    
      
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            E
            i
          
          /
          0.25
        
        )
      
    
  




where: n is the length of the alignment (generally 19); Ei=Pi from FIG. 7 if position i in the alignment is a match and Ei=(1-Pi)/3 if position i is a mismatch. It was observed that the number of alignments for a given siRNA which score above a threshold is predictive of the number of observed off-target effects. The threshold of the score was optimized to maximize the correlation between predicted and observed numbers of effects (FIG. 8). The selection pipeline uses the optimized threshold to favor sequences with relatively small numbers of predicted off-target effects.

6.3. Example 3
Curve Model PSSMS
PSSMs were also generated by a method which hypothesized dependency of the base composition of any one position on its neighboring positions, referred to as “curve models”.
The curve models were generated as a sum of normal curves. Each curve represents the probability of finding a particular base in a particular region. The value at each position in the summed normal curves is the weight given to that position for the base represented by the curve. The weights for each base present at each position in each siRNA and its flanking sequences were summed to generate an siRNA's score, i.e., the score is Σ wi. The score calculation can also be described as the dot product of the base content in the sequence with the weights in the curve model. As such, it is one way of representing the correlation of the sequence of interest with the model.
Curve models can be initialized to correspond to the major peaks and valleys present in the smoothed base composition difference between good and bad siRNAs, e.g., as described in FIGS. 1A-C and 5A-C. The initial model can be set up for the 3-peak G/C curve model as follows:
Peak 1


    
    
        mean: 1.5
        standard deviation: 2
        amplitude: 0.0455

Peak 1 mean, standard deviation and amplitude are set to correspond to the peak in the mean difference in GC content between good and bad siRNAs occurring within bases—2-5 of the siRNA target site in Set 1 training and test sets.

Peak 2

        mean: 11
        standard deviation: 0.5
        amplitude: 0.0337

Peak 2 mean, standard deviation and amplitude are set to correspond to the peak in the mean difference in GC content between good and bad siRNAs occurring within bases 10-12 of the siRNA target site in Set 1 training and test sets.

Peak 3

        mean: 18.5
        standard deviation: 4
        amplitude: −0.0548
    
    


Peak 3 mean, standard deviation and amplitude are set to correspond to the peak in the mean difference in GC content between good and bad siRNAs occurring within bases 12-25 of the siRNA target site in Set 1 training and test sets.
Peak height (amplitude), center position in the sequence (mean) and width (standard deviation) of a peak in a curve model can be adjusted. Curve models were optimized by adjusting the amplitude, mean and standard deviation of each peak over a preset grid of values. Curve models were optimized on several training sets and tested on several test sets, e.g., training sets and test sets as described in Table II. Each base—G/C, A or U—was optimized separately, and then combinations of optimized models were screened for best performance.
The optimization criteria for curve models were: (1) the fraction of good oligos in the top 10%, 15%, 20% and 33% of the scores, (2) the false detection rate at 33% and 50% of the siRNAs selected, and (3) the correlation coefficient of siRNA silencing vs. siRNA scores used as a tiebreaker.
When the model is trained, a grid of possible values for amplitude, mean and standard deviation of each peak is explored. The models with the top value or within the top range of values for any of the above criteria were selected and examined further.
G/C models were optimized with 3 or 4 peaks. A models were optimized with 3 peaks. U models were optimized with 5 peaks.
Exemplary optimization ranges for the models are listed below:
3 Peak G/C Models:


    
    
        peak 1:
    
    
    amplitudes: gc1=0-0.091
    means: gc1=−2.5-1.5
    standard deviations: gc1=2.5-4
    
        peak 2:
    
    
    amplitudes: gc2=0.0337-0.1011
    means: gc2=11-11.5
    standard deviations: gc2=0.5-0.9
    
        peak 3:
    
    
    amplitudes: gc3=−0.1644-−0.0822
    means: gc3=18.75-20.75
    standard deviations: gc3=2.5-3.5

4 Peak G/C models:
    
        peak 0:
    
    
    amplitudes: gc0=0-0.091
    means: gc0=−5.5-−3.5
    standard deviations: gc0=1-2.5
    
        peak 1:
    
    
    amplitudes: gc1=0-0.091
    means: gc1=−2.5-1.5
    standard deviations: gc1=2.5-4
    
        peak 2:
    
    
    amplitudes: gc2=0.0337-0.1011
    means: gc2=11-11.5
    standard deviations: gc2=0.5-0.9
    
        peak 3:
    
    
    amplitudes: gc3=−0.1644-−0.0822
    means: gc3=18.75-20.75
    standard deviations: gc3=2.5-3.5

5 Peak U models:
    
        U peak 1:
    
    
    amplitudes: u1=−0.2-0.0
    means: u1=1-2
    standard deviations: u1=0.75-1.5
    
        U peak 2:
    
    
    amplitudes: u2=0.0-0.16
    means: u2=5-6
    standard deviations: u2=0.75-1.5
    
        U peak 3:
    
    
    amplitudes: u3=0.0-0.1
    means: u3=10-11
    standard deviations: u3=1-2
    
        U peak 4:
    
    
    amplitudes: u4=0.0-0.16
    means: u4=13-14
    standard deviations: u4=0.75-1.5
    
        U peak 5:
    
    
    amplitudes: u5=0.0-0.16
    means: u5=17-18
    standard deviations: u5=1-3
    3 Peak A model:
    
        A peak 1:
    
    
    amplitudes: a1=0.0442-0.2210
    means: a1=5.5-6.5
    standard deviations: a1=1-2
    
        A peak 2:
    
    
    amplitudes: a2=−0.05-0
    means: a2=10-12.5
    standard deviations: a2=25-4.5
    
        A peak 3:
    
    
    amplitudes: a3=0.0442-0.2210
    means: a3=18-20
    standard deviations: a3=4-6


An exemplary set of curve models for PSSM is shown in FIG. 11A. FIG. 11B shows the performance of the models on training and test sets.
6.4. Example 4
Base Composition Models for Prediction of Strand Preference of siRNAS
The mean difference in G/C content between good and bad siRNAs provides a model for G/C PSSMs which can be used to classify siRNA functional and resistant motifs. As it is known that both strands of the siRNA can be active (see, e.g., Elbashir et al., 2001, Genes Dev. 15:188-200), it was of interest to discover how well the G/C contents of both sense and antisense strands of siRNAs fit the model of siRNA functional target motif G/C content derived from the mean difference in G/C content between good and bad siRNAs. To this end, the reverse complements of good and bad siRNAs were examined. These reverse complements correspond to the hypothetical perfect match target sites for the sense strands of the siRNA duplexes. The reverse complements were compared to the actual good and bad siRNAs, represented by the actual perfect match target sites of the antisense strands of the siRNA duplexes.
FIG. 14A shows the difference between the mean G/C content of the reverse complements of bad siRNAs with the mean G/C content of the bad siRNAs themselves, within the 19mer siRNA duplex region. The difference between the mean G/C content of good and bad siRNAs is shown for comparison. The curves were smoothed over a window of 5 (or portion of a window of 5, at the edges of the sequence).
FIG. 14B shows the difference between the mean G/C content of the reverse complements of good siRNAs with the mean G/C content of bad siRNAs, within the 19mer siRNA duplex region. The difference between the mean G/C content of good and bad siRNAs is shown for comparison. The curves were smoothed over a window of 5 (or portion of a window of 5, at the edges of the sequence).
The reverse complements of bad siRNAs were seen to be even more different from the bad siRNAs themselves than are good siRNAs. On the average, the reverse complements of bad siRNAs had even stronger G/C content at the 5′ end than the good siRNAs did and were similar in G/C content to good siRNAs at the 3′ end. In contrast, the reverse complements of good siRNAs were seen to be substantially more similar to bad siRNAs than the good siRNAs were. On average, the reverse complements of good siRNAs hardly differed from bad siRNAs in G/C content at the 5′ end and were only slightly less G/C rich than bad siRNAs at the 3′ end.
These results appear to imply that the G/C PSSMs are distinguishing siRNAs with strong sense strands as bad siRNAs from siRNAs with weak sense strands as good siRNAs. An siRNA whose G/C PSSM score is greater than the G/C PSSM score of its reverse complement is predicted to have an antisense strand that is more active than its sense strand. In contrast, an siRNA whose G/C PSSM score is less than the G/C PSSM score of its reverse complement is predicted to have a sense strand that is more active than its antisense strand.
It has been shown that increased efficacy corresponds to greater antisense strand activity and lesser sense strand activity. Thus the G/C PSSMs of this invention would appear to distinguish good siRNAs with greater efficacy due to dominant antisense strand activity (“antisense-active” siRNAs) from siRNAs with dominant sense strand activity (“sense-active” siRNAs).
The relevance of comparison of G/C PSSMs of siRNAs and their reverse complements for prediction of strand bias was tested by comparison with estimation of strand bias from siRNA expression profiles by the 3′-biased method.
siRNAs and their reverse complements were scored using the smoothed G/C content difference between good and bad siRNAs within the 19mer, shown in FIG. 14A, as the weight matrix. The G/C PSSM score of each strand was the dot product of the siRNA strand G/C content with the G/C content difference matrix, following the score calculation method of curve model PSSMs.
siRNAs were called sense-active by the 3′-biased method of expression profile analysis if the antisense-identical score exceeded the sense-identical score. siRNAs were called sense-active by the G/C PSSM method if their reverse complement G/C PSSM score exceeded their own G/C PSSM score.
In FIG. 15, siRNAs were binned by measured silencing efficacy, and the frequency of sense-active calls by the expression profile and G/C PSSM methods was compared. Although these techniques are based on distinct analyses, the agreement is quite good. Both show that a higher proportion of low-silencing siRNAs vs. high-silencing siRNAs are predicted to be sense active. The correlation coefficient for (siRNA G/C PSSM score—reverse complement G/C PSSM score) vs. log10(sense-identity score/antisense-identity score) is 0.59 for the set of 61 siRNAs binned in FIG. 15.
6.5. Example 5
Designing siRNAS For Silencing Genes Having Low Transcript Levels
In the previous examples, an improved siRNA design algorithm that permits selection of siRNAs with greater and more uniform silencing ability was described. Despite this dramatic improvement, some genes remain difficult to silence with high efficacy. A general trend toward poorer silencing for poorly-expressed genes (less than −0.5 intensity on microarray; <5 copies per cell; FIG. 16) was observed. This example describes identification of parameters affecting silencing efficacy of siRNAs to poorly expressed genes.
Twenty-four poorly-expressed genes were selected for detailed analysis of parameters affecting siRNA silencing efficacy. A number of criteria were evaluated for their ability to distinguish good and bad siRNAs, including base composition of the 19mer siRNA duplex sequence and the flanking target region. In addition, the contribution of the GC content of the target transcript was considered. These tests revealed that siRNA efficacy correlated well with siRNA and target gene base composition. In particular, the GC content of good siRNAs differed substantially from that of bad siRNAs in a region-specific manner (FIG. 17). The sequences of siRNAs used in generating FIG. 17 are listed in Table IV. Good siRNA duplexes tended to be GC poor at positions 2-7 of the 5′ end of the sense strand, and GC poor at the 3′ end (positions 18-19). Furthermore, siRNA efficacy correlated with low GC content in the transcript sequence flanking the siRNA binding site. The requirement for low GC content as a determinant of siRNA efficacy may explain the difficulty in silencing the poorly-expressed transcripts, as these transcripts tend to be GC rich overall. Base composition of the siRNA duplex also affected silencing of poorly expressed genes. In particular, the GC content of good siRNAs differed substantially from that of bad siRNAs in a region-specific manner (FIG. 17). Good siRNA duplexes tended to be GC rich at the first position, GC poor at positions 2-7 of the 5′ end of the sense strand, and GC poor at the 3′ end (positions 18-19.) Of the criteria examined, low GC content in positions 2-7 of the sense strand (FIG. 17, dotted line) produced the greatest improvement in silencing efficacy. This is consistent with the region of the siRNA implicated in the catalysis step of transcript silencing. Low GC content in this region may provide accessibility or optimal helical geometry for enhanced cleavage. Requiring low GC content in this region of the siRNA may also select for target sites that contain low GC content flanking the binding site, which also correlated with silencing efficacy.
The base composition for good siRNAs to poorly-expressed genes diverges somewhat from our previously-derived base composition criteria for good siRNAs to well-expressed genes (FIG. 17, solid line). Good siRNAs to both types of genes show a preference for high GC at position 1, and low GC at the 3′ end. However, siRNAs for well-expressed genes show an extreme asymmetry in GC content between the two termini, while siRNAs for poorly-expressed genes prefer a more moderate asymmetry. Our previous design algorithm seeks to maximize asymmetry, in accordance with the features seen in good siRNAs to well-expressed genes. Our current results indicate that base composition of more than one region of the siRNA can influence efficacy. Different regions of the siRNA may be more critical for silencing of different targets, perhaps depending on target transcript features such as expression level or overall GC content. Consistent with this idea, different commercially available design algorithms work well on different subsets of genes (data not shown).
A new siRNA design algorithm was developed based on the GC composition derived for poorly-expressed genes. The new algorithm includes the following adjustments to the previous algorithm:
(1) selection for 1-3 G+C in sense 19mer bases 2-7,
(2) sense 19mer base 1 & 19 asymmetry (position 1, G or C; position 19, A or T),
(3) −300<pssm score<+200,
(4) greatest off-target BLAST match no more than 16, and
(5) 200 bases on either side of the 19mer are not repeat or low-complexity sequences. The new algorithm was compared to the algorithm described in previous examples, by side-by-side testing of new siRNAs selected by each. The results obtained with three siRNAs selected by each method are shown in FIG. 18. siRNAs designed by the new algorithm of the present example showed better median efficacy (80%, compared to 60% for the standard method siRNA) and were more uniform in their performance. The distribution of silencing efficacies of siRNAs obtained by the new algorithm was significantly better than that of the previous algorithm for the same genes (p=10−5, Wilcoxon rank-sum). siRNAs designed using the new design algorithm also appear effective at silencing more highly-expressed transcripts, based on an examination of 12 highly-expressed genes.
The new design criteria may capture features important to siRNA functionality in general (FIG. 19), and emphasize that different regions of siRNAs have different functions in transcript recognition, cleavage, and product release. Bases near the 5′ end of the guide strand are implicated in transcript binding (both on- and off-target transcripts), and have recently been shown to be sufficient for target RNA-binding energy. The design criteria are also consistent with available data on how siRNAs interact with RISC, the protein-RNA complex that mediates RNA silencing. These studies show that weaker base pairing at the 5′ end of the antisense strand (3′ end of the duplex) encourages preferential interaction of the antisense strand with RISC, perhaps by facilitating unwinding of the siRNA duplex by a 5′-3′ helicase component of RISC. As in the previous design, our new design maintains the base composition asymmetry that encourages preferential interaction of the antisense strand. This suggests that the previous inefficiency of silencing poorly-expressed transcripts is not due to inefficient association with RISC, but rather is likely due to inefficient targeting of the RISC complex to the target transcript, or inefficient cleavage and release of the target transcript. The designs described in these examples include a preference for U at position 10 of the sense strand, which has been associated with improved cleavage efficiency by RISC as it is in most endonucleases. The observed preference for low GC content flanking the cleavage site may enhance accessibility of the RISC/nuclease complex for cleavage, or release of the cleaved transcript, consistent with recent studies demonstrating that base pairs formed by the central and 3′ regions of the siRNA guide strand provide a helical geometry required for catalysis. The new design criteria may increase the efficiency of these and additional steps in the RNAi pathway, thereby providing efficient silencing of transcripts at different levels of expression.







TABLE IV







siRNAs for FIG. 17













SEQ





ID


ACCESSION NUMBER
GENE
sIRNA sequence
NO





AK092024_NM_030932
DIAPH3
GCAGTGATTGCTCAGCAGC
409


 


AK092024_NM_030932
DIAPH3
GAGTTTACCGACCACCAAG
410


 


AK092024_NM_030932
DIAPH3
CACGGTTGGCAGAGTCTAT
411


 


AK092024_NM_030932
DIAPH3
TGCGGATGCCATTCAGTGG
412


 


NM_014875
KIF14
AAACTGGGAGGCTACTTAC
413


 


NM_014875
KIF14
CTCACATTGTCCACCAGGA
414


 


NM_014875
KIF14
GACCATAGCATCCGCCATG
415


 


NM_014875
KIF14
AGAGCCTTCGAAGGCTTCA
416


 


NM_014875
KIF14
TAGACCACCCATTGCTTCC
417


 


NM_014875
KIF14
ACTGACAACAAAGTGCAGC
418


 


U53530
DNCH1
TGGCCAGCGCTTACTGGAA
419


 


U53530
DNCH1
GCAAGTTGAGCTCTACCGC
420


 


NM_000859
HMGCR
TTGTGTGTGGGACCGTAAT
421


 


NM_000859
HMGCR
CAACAGAAGGTTGTCTTGT
422


 


NM_000859
HMGCR
CAGAGACAGAATCTACACT
423


 


NM_000859
HMGCR
CACGATGCATAGCCATCCT
424


 


NM_000271
NPC1
GAGGTACAATTCCGAATAT
425


 


NM_000271
NPC1
GCCACAGTCGTCTTGCTGT
426


 


NM_000271
NPC1
TACTACGTCGGACAGAGTT
427


 


NM_000271
NPC1
AACTACAATAACGCCACTG
428


 


NM_004523
KNSL1
TACTGATAATCGTACTGAA
429


 


NM_004523
KNSL1
TACATGAACTACAAGAAAA
430


 


NM_004523
KNSL1
GACTAAGCTTAATTGCTTT
431


 


NM_004523
KNSL1
AGTTGACCAACACAATGCA
432


 


NM_004523
KNSL1
GTTTTCTCTGTTACAATAC
433


 


NM_004523
KNSL1
AGGAGTGATAATTAAAGGT
434


 


NM_004523
KNSL1
AAACTCTGAGTACATTGGA
435


 


NM_004523
KNSL1
TACTAAACAGATTGATGTT
436


 


NM_004523
KNSL1
GCTCAAGGAAAACATACAC
437


 


NM_004523
KNSL1
CTGGATCGTAAGAAGGCAG
438


 


NM_004523
KNSL1
GACTTCATTGACAGTGGCC
439


 


NM_004523
KNSL1
GGACAACTGCAGCTACTCT
440


 


NM_004523
KNSL1
GGGGCAGTATACTGAAGAA
441


 


NM_004523
KNSL1
GACCTGTGCCTTTTAGAGA
442


 


NM_004523
KNSL1
AAAGGACAACTGCAGCTAC
443


 


NM_004523
KNSL1
TACAAAGAATAAATTTTCT
444


 


NM_004523
KNSL1
TGGAAGGTGAAAGGTCACC
445


 


NM_004523
KNSL1
TAACTGTTCAAGAAGAGCA
446


 


NM_004523
KNSL1
TCTATAATTTATATTCTTT
447


 


NM_004523
KNSL1
GGGACCGTCATGGCGTCGC
448


 


NM_004523
KNSL1
CCAGGGAGACTCCGGCCCC
449


 


NM_004523
KNSL1
ATTTAATTTGGCAGAGCGG
450


 


NM_004523
KNSL1
TGGAAATATAAATCAATCC
451


 


NM_004523
KNSL1
ACTAACTAGAATCCTCCAG
452


 


NM_004523
KNSL1
AAGAAGAATATATCACATC
453


 


NM_004523
KNSL1
TTCTTGTATATTATTAAGT
454


 


NM_004064
CDKN1B
GACGTCAAACGTAAACAGC
455


 


NM_004064
CDKN1B
TGGTGATCACTCCAGGTAG
456


 


NM_004064
CDKN1B
TGTCCCTTTCAGAGACAGC
457


 


NM_004073
CNK
GTTACCAAGAGCCTCTTTG
458


 


NM_004073
CNK
ATCGTAGTGCTTGTACTTA
459


 


NM_004073
CNK
GAAGACCATCTGTGGCACC
460


 


NM_004073
CNK
GGAGACGTACCGCTGCATC
461


 


NM_004073
CNK
TCAGGGACCAGCTTTACTG
462


 


NM_004073
CNK
AGTCATCCCGCAGAGCCGC
463


 


NM_001315
MAPK14
GGCCTTTTCACGGGAACTC
464


 


NM_001315
MAPK14
GAAGCTCTCCAGACCATTT
465


 


NM_001315
MAPK14
TGCCTACTTTGCTCAGTAC
466


 


NM_001315
MAPK14
ATGTGATTGGTCTGTTGGA
467


 


NM_001315
MAPK14
GTCATCAGCTTTGTGCCAC
468


 


NM_001315
MAPK14
CCTACAGAGAACTGCGGTT
469


 


NM_001315
MAPK14
CCAGTGGCCGATCCTTATG
470


 


NM_001315
MAPK14
GTGCCTCTTGTTGCAGAGA
471


 


NM_001315
MAPK14
TTCTCCGAGGTCTAAAGTA
472


 


NM_001315
MAPK14
TAATTCACAGGGACCTAAA
473


 


NM_001315
MAPK14
GTGGCCGATCCTTATGATC
474


 


NM_001315
MAPK14
GTATATACATTCAGCTGAC
475


 


NM_001315
MAPK14
AATATCCTCAGGGGTGGAG
476


 


NM_001315
MAPK14
GGAACACCCCCCGCTTATC
477


 


NM_006101
HEC
CTGAAGGCTTCCTTACAAG
478


 


NM_006101
HEC
AGAACCGAATCGTCTAGAG
479


 


NM_006101
HEC
CAGAAGTTGTGGAATGAGG
480


 


NM_006101
HEC
GTTCAAAAGCTGGATGATC
481


 


NM_006101
HEC
GGCCTCTATACCCCTCAAA
482


 


NM_006101
HEC
CTTGCAACGTCTGTTAGAG
483


 


NM_000314
PTEN
CCCACCACAGCTAGAACTT
484


 


NM_000314
PTEN
CAGTAGAGGAGCCGTCAAA
485


 


NM_000314
PTEN
CTATTCCCAGTCAGAGGCG
486


 


NM_000314
PTEN
TAAAGATGGCACTTTCCCG
487


 


NM_000314
PTEN
AAGGCAGCTAAAGGAAGTG
488


 


NM_000314
PTEN
TGGAGGGGAATGCTCAGAA
489


 


NM_000075
CDK4
GCGAATCTCTGCCTTTCGA
490


 


NM_000075
CDK4
CAGTCAAGCTGGCTGACTT
491


 


NM_000075
CDK4
GGATCTGATGCGCCAGTTT
492


 


NM_000075
CDK4
TGTTGTCCGGCTGATGGAC
493


 


NM_006622
SNK
TGTTACGAGATGACAGATT
494


 


NM_006622
SNK
AACCCAGAGGATCGTCCCA
495


 


NM_006622
SNK
CAGTTCACTATTACGCAGA
496


 


NM_139164
STARD4
ACCAGAGTCTTTTGACAGG
497


 


NM_139164
STARD4
CTGTTTGGAGAAAACCCTC
498


 


NM_139164
STARD4
GACAACCCAAACCAGAGTC
499


 


NM_139164
STARD4
GTCTTGACTGGGATGAAAA
500


 


NM_005030
PLK
GGGAGAAGATGTCCATGGA
501


 


NM_005030
PLK
CCGAGTTATTCATCGAGAC
502


 


NM_005030
PLK
GAGACCTACCTCCGGATCA
503


 


NM_005030
PLK
TCCAGAAGATGCTTCAGAC
504


 


NM_005030
PLK
CACGCCTCATCCTCTACAA
505


 


NM_005030
PLK
GACTTCGTGTTCGTGGTGT
506


 


NM_005030
PLK
GGGCGGCTTTGCCAAGTGC
507


 


NM_005030
PLK
ACGAGCTGCTTAATGACGA
508


 


NM_005030
PLK
GGACTGGCAACCAAAGTCG
509


 


NM_005030
PLK
GCCCGATACTACCTACGGC
510


 


NM_005030
PLK
CGGAGTTGCAGCTCCCGGA
511


 


NM_005030
PLK
AAGAGACCTACCTCCGGAT
512


 


NM_005030
PLK
AGTGGGTGGACTATTCGGA
513


 


NM_005030
PLK
TGTATCATGTATACCTTGT
514


 


NM_005030
PLK
AAGAAGAACCAGTGGTTCG
515


 


NM_005030
PLK
GGCAAGATTGTGCCTAAGT
516


 


NM_005030
PLK
CCGCGGTGCCATGTCTGCA
517


 


NM_005030
PLK
CTCAAGGCCTCCTAATAGC
518


 


NM_005030
PLK
CAGCGCAGCTTCGGGAGCA
519


 


NM_005030
PLK
CACAGTCCTCAATAAAGGC
520


 


NM_005030
PLK
CCCCTCCCCCTCAACCCCA
521


 


NM_005030
PLK
TCGATTGCTCCCAGCAGCC
522


 


NM_005030
PLK
TTCTTGCCTAAAAGAGACC
523


 


NM_005030
PLK
TACATGAGCGAGCACTTGC
524


 


NM_005030
PLK
CAATGCCTCCAAGCCCTCG
525


 


NM_000875
IGF1R
GGATATTGGGCTTTACAAC
526


 


NM_000875
IGF1R
CTTGCAGCAACTGTGGGAC
527


 


NM_000875
IGF1R
GCTCACGGTCATTACCGAG
528


 


NM_000875
IGF1R
GATGATTCAGATGGCCGGA
529


 


NM_000875
IGF1R
CGACACGGCCTGTGTAGCT
530


 


NM_000875
IGF1R
AATGCTGACCTCTGTTACC
531


 


NM_000875
IGF1R
TCTCAAGGATATTGGGCTT
532


 


NM_000875
IGF1R
CATTACTCGGGGGGCCATC
533


 


NM_000875
IGF1R
TGCTGACCTCTGTTACCTC
534


 


NM_000875
IGF1R
CTACGCCCTGGTCATCTTC
535


 


NM_000875
IGF1R
CCTCACGGTCATCCGCGGC
536


 


NM_000875
IGF1R
CCTGAGGAACATTACTCGG
537


 


NM_001813
CENPE
GGAGAGCTTTCTAGGACCT
538


 


NM_001813
CENPE
GAAGAGATCCCAGTGCTTC
539


 


NM_001813
CENPE
ACTCTTACTGCTCTCCAGT
540


 


NM_001813
CENPE
TCTGAAAGTGACCAGCTCA
541


 


NM_001813
CENPE
GAAAATGAAGCTTTGCGGG
542


 


NM_001813
CENPE
CTTAACACGGATGCTGGTG
543


 


NM_004958
FRAP1
CTTGCAGGCCTTGTTTGTG
544


 


NM_004958
FRAP1
CAACCTCCAGGATACACTC
545


 


NM_004958
FRAP1
GACATGAGAACCTGGCTCA
546


 


NM_004958
FRAP1
CCAACTTTCTAGCTGCTGT
547


 


NM_004958
FRAP1
AGGACTTCGCCCATAAGAG
548


 


NM_004958
FRAP1
TAATACAGCTGGGGACGAC
549


 


NM_005163
AKTI
GCTGGAGAACCTCATGCTG
550


 


NM_005163
AKT1
CGCACCTTCCATGTGGAGA
551


 


NM_005163
AKT1
AGACGTTTTTGTGCTGTGG
552


 


NM_002358
MAD2L1
TACGGACTCACCTTGCTTG
553


 


NM_000551
VHL
GGCATTGGCATCTGCTTTT
554


 


NM_000551
VHL
GTGAATGAGACACTCCAGT
555


 


NM_000551
VHL
TGTTGACGGACAGCCTATT
556


 


NM_000551
VHL
GATCTGGAAGACCACCCAA
557


 


NM_000551
VHL
AGGAAATAGGCAGGGTGTG
558


 


NM_000551
VHL
CAGAACCCAAAAGGGTAAG
559


 


NM_001654
ARAF1
GTCCCCACATTCCAAGTCA
560


 


NM_001654
ARAF1
GAATGAGATGCAGGTGCTC
561


 


NM_001654
ARAF1
GTTCCACCAGCATTGTTCC
562


 


NM_001654
ARAF1
CCTCTCTGGAATTTGTGCC
563


 


NM_001654
ARAF1
AGTGAAGAACCTGGGGTAC
564


 


NM_001654
ARAF1
TTGAGCTGCTGCAACGGTC
565


 


NM_000435
NOTCH3
GAACATGGCCAAGGGTGAG
566


 


NM_000435
NOTCH3
GAGTCTGGGACCTCCTTCT
567


 


NM_000435
NOTCH3
AATGGCTTCCGCTGCCTCT
568


 


NM_000435
NOTCH3
TGATCACTGCTTCCCCGAT
569


 


NM_000435
NOTCH3
TGCCAACTGAAGAGGATGA
570


 


NM_000435
NOTCH3
GCTGCTGTTGGACCACTTT
571


 


NM_024408
NOTCH2
CCAAGGAACCTGCTTTGAT
572


 


NM_024408
NOTCH2
GACTCAGACCACTGCTTCA
573


 


NM_024408
NOTCH2
CTTTGAATGCCAGGGGAAC
574


 


NM_024408
NOTCH2
GCAACTTTGGTCTCCTTTC
575


 


NM_024408
NOTCH2
GAGACAAGTTAACTCGTGC
576


 


NM_024408
NOTCH2
GCAATTGGCTGTGATGCTC
577


 


NM_012193
FZD4
CCATCTGCTTGAGCTACTT
578


 


NM_012193
FZD4
TTGGCAAAGGCTCCTTGTA
579


 


NM_012193
FZD4
AGAACCTCGGCTACAACGT
580


 


NM_012193
FZD4
TCGGCTACAACGTGACCAA
581


 


NM_012193
FZD4
GTTGACTTACCTGACGGAC
582


 


NM_012193
FZD4
TCCGCATCTCCATGTGCCA
583


 


NM_007313
ABL1
GAATGGAAGCCTGAACTGA
584


 


NM_007313
ABL1
CAAGTTCTCCATCAAGTCC
585


 


NM_007313
ABL1
CTAAAGGTGAAAAGCTCCG
586


 


NM_007313
ABL1
TCCTGGCAAGAAAGCTTGA
587


 


NM_007313
ABL1
AAACCTCTACACGTTCTGC
588


 


NM_007313
ABL1
AGACATCATGGAGTCCAGC
589


 


NM_017412
FZD3
CAGATCACTCCAGGCATAG
590


 


NM_017412
FZD3
ATGTGTGGTGACTGCTTTG
591


 


NM_017412
FZD3
AGAGATGGGCATTGTTTCC
592


 


NM_017412
FZD3
AGCATTGCTGTTTCACGCC
593


 


NM_017412
FZD3
GCTCATGGAGATGTTTGGT
594


 


NM_005633
SOS1
TGGTGTCCTTGAGGTTGTC
595


 


NM_005633
SOS1
TATCAGACCGGACCTCTAT
596


 


NM_005633
SOS1
CTTACAAAAGGGAGCACAC
597


 


NM_005633
SOS1
GAACACCGTTAACACCTCC
598


 


NM_005633
SOS1
ATAACAGGAGAGATCCAGC
599


 


NM_005633
SOS1
ATTGACCACCAGGTTTCTG
600


 


NM_005417
SRC
CAATTCGTCGGAGGCATCA
601


 


NM_005417
SRC
GCAGTGCCTGCCTATGAAA
602


 


NM_005417
SRC
GGGGAGTTTGCTGGACTTT
603


 


NM_005400
PRKCE
GATCGAGCTGGCTGTCTTT
604


 


NM_005400
PRKCE
GCTCACCATCTGAGGAAGA
605


 


NM_005400
PRKCE
GGTCTTAAAGAAGGACGTC
606


 


NM_005400
PRKCE
TCACAAAGTGTGCTGGGTT
607


 


NM_005400
PRKCE
CCAGGAGGAATTCAAAGGT
608


 


NM_005400
PRKCE
TGAGGACGACCTATTTGAG
609


 


NM_002388
MCM3
GTCTCAGCTTCTGCGGTAT
610


 


NM_002388
MCM3
GTACATCCATGTGGCCAAA
611


 


NM_002388
MCM3
AGGATTTTGTGGCCTCCAT
612


 


NM_002388
MCM3
TGGGTCATGAAAGCTGCCA
613


 


NM_002388
MCM3
TCCAGGTTGAAGGCATTCA
614


 


NM_002388
MCM3
GCAGATGAGCAAGGATGCT
615


 


NM_004380
CREBBP
GAAAAACGGAGGTCGCGTT
616


 


NM_004380
CREBBP
GACATCCCGAGTCTATAAG
617


 


NM_004380
CREBBP
TGGAGGAGAATTAGGCCTT
618


 


NM_004380
CREBBP
ATTTTTGCGGCGCCAGAAT
619


 


NM_004380
CREBBP
GCACAAGGAGGTCTTCTTC
620


 


NM_004380
CREBBP
GAAAACAAATGCCCCGTGC
621


 


NM_006219
PIK3CB
CAAAGATGCCCTTCTGAAC
622


 


NM_006219
PIK3CB
GTGCACATTCCTGCTGTCT
623


 


NM_006219
PIK3CB
AAGTTCATGTCAGGGCTGG
624


 


NM_006219
PIK3CB
AATGCGCAAATTCAGCGAG
625


 


NM_006219
PIK3CB
AATGAAGCCTTTGTGGCTG
626


 


NM_006219
PIK3CB
TACAGAAAAGTTTGGCCGG
627


 


NM_006218
PIK3CA
CTAGGAAACCTCAGGCTTA
628


 


NM_006218
PIK3CA
TTCAGCTAGTACAGGTCCT
629


 


NM_006218
PIK3CA
TGATGCACATCATGGTGGC
630


 


NM_006218
PIK3CA
AGAAGCTGTGGATCTTAGG
631


 


NM_006218
PIK3CA
AGGTGCACTGCAGTTCAAC
632


 


NM_006218
PIK3CA
TGGCTTTGAATCTTTGGCC
633


 


NM_002086
GRB2
CTGGTACAAGGCAGAGCTT
634


 


NM_002086
GRB2
CGGGCAGACCGGCATGTTT
635


 


NM_002086
GRB2
CCGGAACGTCTAAGAGTCA
636


 


NM_002086
GRB2
ATACGTCCAGGCCCTCTTT
637


 


NM_002086
GRB2
TGAGCTGGTGGATTATCAC
638


 


NM_002086
GRB2
TGCAGCACTTCAAGGTGCT
639


 


NM_001982
ERBB3
TGACAGTGGAGCCTGTGTA
640


 


NM_001982
ERBB3
CTAGACCTAGACCTAGACT
641


 


NM_001982
ERBB3
CTTTCTGAATGGGGAGCCT
642


 


NM_001982
ERBB3
GAGGATGTCAACGGTTATG
643


 


NM_001982
ERBB3
CAAAGTCTTGGCCAGAATC
644


 


NM_001982
ERBB3
TACACACACCAGAGTGATG
645


 


NM_001903
CTNNAI
CGTTCCGATCCTCTATACT
646


 


NM_001903
CTNNAI
AAGCCATTGGTGAAGAGAG
647


 


NM_001903
CTNNAI
TGTGTCATTGCTCTCCAAG
648


 


NM_001903
CTNNAI
AGCAGTGCTGATGATAAGG
649


 


NM_001903
CTNNAI
TGACCAAAGATGACCTGTG
650


 


NM_001903
CTNNAI
TGACATCATTGTGCTGGCC
651


 


NM_003600
STK6
CACCCAAAAGAGCAAGCAG
652


 


NM_003600
STK6
GCACAAAAGCTTGTCTCCA
653


 


NM_003600
STK6
CCTCCCTATTCAGAAAGCT
654


 


NM_003600
STK6
ACAGTCTTAGGAATCGTGC
655


 


NM_003600
STK6
GACTTTGAAATTGGTCGCC
656


 


NM_003600
STK6
TTGCAGATTTTGGGTGGTC
657


 


NM_003161
RPS6KB1
GACACTGCCTGCTTTTACT
658


 


NM_003161
RPS6KB1
CTCTCAGTGAAAGTGCCAA
659


 


NM_003161
RPS6KB1
GCTTTTCCCATGATCTCCA
660


 


NM_003161
RPS6KB1
TTGATTCCTCGCGACATCT
661


 


NM_003161
RPS6KB1
GAAAGCCAGACAACTTCTG
662


 


NM_003161
RPS6KB1
CTTGGCATGGAACATTGTG
663


 


AF308602
NOTCH1
GATCGATGGCTACGAGTGT
664


 


AF308602
NOTCH1
CACTTACACCTGTGTGTGC
665


 


AF308602
NOTCH1
AGGCAAGCCCTGCAAGAAT
666


 


AF308602
NOTCH1
CATCCCCTACAAGATCGAG
667


 


AF308602
NOTCH1
ATATCGACGATTGTCCAGG
668


 


AF308602
NOTCH1
ATTCAACGGGCTCTTGTGC
669


 


NM_016231
NLK
CCACTCAGCTCAGATCATG
670


 


NM_016231
NLK
GCAATGAGGACAGCTTGTG
671


 


NM_016231
NLK
TGTAGCTTTCCACTGGAGT
672


 


NM_016231
NLK
TCTCCTTGTGAACAGCAAC
673


 


NM_016231
NLK
GGAAACAGAGTGCCTCTCT
674


 


NM_016231
NLK
TCTGGTCTCTTGCAAAAGG
675


 


NM_001253
CDC5L
AAGAAGACGTTCAGCGACA
676


 


NM_001253
CDC5L
AAAAAGCCTGCCCTTGGTT
677


 


NM_001253
CDC5L
TCATTGGAAGAACAGCGGC
678


 


NM_003391
WNT2
GTGTCTCAAAGGAGCTTTC
679


 


NM_003391
WNT2
GCCTCAGAAAGGGATTGCT
680


 


NM_003391
WNT2
AGAAGATGAATGGTCTGGC
681


 


NM_003391
WNT2
GCTCTGGATGTGCACACAT
682


 


NM_003391
WNT2
AACGGGCGATTATCTCTGG
683


 


NM_003391
WNT2
ATTTGCCCGCGCATTTGTG
684


 


NM_002387
MCC
AGTTGAGGAGGTTTCTGCA
685


 


NM_002387
MCC
GACTTAGAGCTGGGAATCT
686


 


NM_002387
MCC
GGATTATATCCAGCAGCTC
687


 


NM_002387
MCC
GAGAATGAGAGCCTGACTG
688


 


NM_002387
MCC
TAGCTCTGCTAGAGGAGGA
689


 


NM_002387
MCC
ACAGAACGGCTGAATAGCC
690


 


NM_005978
S100A2
GGAACTTCTGCACAAGGAG
691


 


NM_005978
S100A2
GGGCCCAGGACTGTTGATG
692


 


NM_005978
S100A2
TGAGAACAGTGACCAGCAG
693


 


NM_005978
S100A2
TGGCACTCATCACTGTCAT
694


 


NM_005978
S100A2
GACCGACCCTGAAGCAGAA
695


 


NM_005978
S100A2
TTCCAGGAGTATGCTGTTT
696


 


NM_033360
KRAS2
GAAGTTATGGAATTCCTTT
697


 


NM_033360
KRAS2
GGACTCTGAAGATGTACCT
698


 


NM_033360
KRAS2
GGCATACTAGTACAAGTGG
699


 


NM_033360
KRAS2
ACCTGTCTCTTGGATATTC
700


 


NM_033360
KRAS2
TAAATGTGATTTGCCTTCT
701


 


NM_033360
KRAS2
GAAAAGACTCCTGGCTGTG
702


 


NM_139049
MAPK8
GGAATAGTATGCGCAGCTT
703


 


NM_139049
MAPK8
GTGATTCAGATGGAGCTAG
704


 


NM_139049
MAPK8
CACCATGTCCTGAATTCAT
705


 


NM_139049
MAPK8
CGAGTTTTATGATGACGCC
706


 


NM_139049
MAPK8
CACCCGTACATCAATGTCT
707


 


NM_139049
MAPK8
TCAAGCACCTTCATTCTGC
708


 


NM_002658
PLAU
CAAGTACTTCTCCAACATT
709


 


NM_002658
PLAU
GAGCTGGTGTCTGATTGTT
710


 


NM_002658
PLAU
CTGCCCAAAGAAATTCGGA
711


 


NM_002658
PLAU
GTGTAAGCAGCTGAGGTCT
712


 


NM_002658
PLAU
TGGAGGAACATGTGTGTCC
713


 


NM_002658
PLAU
TTACTGCAGGAACCCAGAC
714


 


NM_016195
MPHOSPH1
AGAGGAACTCTCTGCAAGC
715


 


NM_016195
MPHOSPH1
AAGTTTGTGTCCCAGACAC
716


 


NM_016195
MPHOSPH1
CTGAAGAAGCTACTGCTTG
717


 


NM_016195
MPHOSPH1
GACATGCGAATGACACTAG
718


 


NM_016195
MPHOSPH1
AATGGCAGTGAAACACCCT
719


 


NM_016195
MPHOSPH1
ATGAAGGAGAGTGATCACC
720


 


NM_020168
PAK6
CGACATCCAGAAGTTGTCA
721


 


NM_020168
PAK6
GAGAAAGAATGGGGTCGGT
722


 


NM_020168
PAK6
TGAGGAGCAGATTGCCACT
723


 


NM_000051
ATM
TAGATTGTTCCAGGACACG
724


 


NM_000051
ATM
AGTTCGATCAGCAGCTGTT
725


 


NM_000051
ATM
GAAGTTGGATGCCAGCTGT
726


 


NM_001259
CDK6
TCTTGGACGTGATTGGACT
727


 


NM_001259
CDK6
ACCACAGAACATTCTGGTG
728


 


NM_001259
CDK6
AGAAAACCTGGATTCCCAC
729


 


NM_004856
KNSL5
GAATGTGAGCGTAGAGTGG
730


 


NM_004856
KNSL5
CCATTGGTTACTGACGTGG
731


 


NM_004856
KNSL5
AACCCAAACCTCCACAATC
732


 


NM_006845
KNSL6
ACAAAAACGGAGATCCGTC
733


 


NM_006845
KNSL6
GAATTTCGGGCTACTTTGG
734


 


NM_006845
KNSL6
ATAAGCAGCAAGAAACGGC
735


 


NM_004972
JAK2
AGCCGAGTTGTAACTATCC
736


 


NM_004972
JAK2
AAGAACCTGGTGAAAGTCC
737


 


NM_004972
JAK2
GAAGTGCAGCAGGTTAAGA
738


 


NM_005026
PIK3CD
GATCGGCCACTTCCTTTTC
739


 


NM_005026
PIK3CD
AGAGATCTGGGCCTCATGT
740


 


NM_005026
PIK3CD
AACCAAAGTGAACTGGCTG
741


 


NM_014885
APC10
CAAGGCATCCGTTATATCT
742


 


NM_014885
APC10
ACCAGGATTTGGAGTGGAT
743


 


NM_014885
APC10
GTGGCTGGATTCATGTTCC
744


 


NM_005733
RAB6KIFL
GAAGCTGTCCCTGCTAAAT
745


 


NM_005733
RAB6KIFL
CTCTACCACTGAAGAGTTG
746


 


NM_005733
RAB6KIFL
AAGTGGGTCGTAAGAACCA
747


 


NM_007054
KIF3A
GGAGAAAGATCCCTTTGAG
748


 


NM_007054
KIF3A
TATTGGGCCAGCAGATTAC
749


 


NM_007054
KIF3A
TTATGACGCTAGGCCACAA
750


 


NM_020242
KNSL7
GCACAACTCCTGCAAATTC
751


 


NM_020242
KNSL7
GATGGAAGAGCCTCTAAGA
752


 


NM_020242
KNSL7
ACGAAAAGCTGCTTGAGAG
753


 


NM_001184
ATR
TCACGACTCGCTGAACTGT
754


 


NM_001184
ATR
GAAACTGCAGCTATCTTCC
755


 


NM_001184
ATR
GTTACAATGAGGCTGATGC
756


 


NM_014875
KIF14
ATTTTCTAGAAAACGGTAA
757


 


NM_014875
KIF14
GAGGCGCGAAGTTTCGGCA
758


 


NM_014875
KIF14
CTGGGACCGGGAAGCCGGA
759


 


NM_014875
KIF14
CTTCTACTTCTGTTGGCAG
760


 


NM_014875
KIF14
ACTTACTATTCAGACTGCA
761


 


NM_014875
KIF14
GCCCTCACCCACAGTAGCC
762


 


NM_014875
KIF14
CAGAGGAATGCACACCCAG
763


 


NM_014875
KIF14
GATTGATTAGATCTCTTGA
764


 


NM_014875
KIF14
GTGAGTATTATCCCAGTTG
765


 


NM_014875
KIF14
ATCTGGGGTGCTGATTGCT
766


 


NM_014875
KIF14
GTGACAGTGGCAGTACGCG
767


 


NM_014875
KIF14
TCAGACTGAAGTTGTTAGA
768


 


NM_014875
KIF14
GTTGGCTAGAATTGGGAAA
769


 


NM_014875
KIF14
GAAGACCATAGCATCCGCC
770


 


NM_001274
CHEK1
TGCCTGAAAGAGACTTGTG
771


 


NM_001274
CHEK1
ATCGATTCTGCTCCTCTAG
772


 


NM_001274
CHEK1
CTGAAGAAGCAGTCGCAGT
773


 


NM_007194
CHEK2
GATCACAGTGGCAATGGAA
774


 


NM_007194
CHEK2
ATGAATCCACAGCTCTACC
775


 


NM_007194
CHEK2
AAACTCTTGGAAGTGGTGC
776


 


NM_000546
TP53
GCACCCAGGACTTCCATTT
777


 


NM_000546
TP53
CCTCTTGGTCGACCTTAGT
778


 


NM_000546
TP53
TGAGGCCTTGGAACTCAAG
779


 


NM_005400
PRKCE
AGCGCCTGGGCCTGGATGA
780


 


NM_005400
PRKCE
ACCGGGCAGCATCGTCTCC
781


 


NM_005400
PRKCE
CAGCGGCCAGAGAAGGAAA
782


 


NM_005400
PRKCE
CAGAAGGAAGAGTGTATGT
783


 


NM_0054O0
PRKCE
TGCAGTGTAAAGTCTGCAA
784


 


NM_005400
PRKCE
GCGCATCGGCCAAACGGCC
785


 


NM_005400
PRKCE
ATTGCAGAGACTTCATCTG
786


 


NM_005400
PRKCE
GAAGAGCCGGTACTCACCC
787


 


NM_005400
PRKCE
AGTACTGGCCGACCTGGGC
788


 


NM_005400
PRKCE
GGATGCAGAAGGTCACTGC
789


 


NM_005400
PRKCE
CGTGAGCTTGAAGCCCACA
790


 


NM_005400
PRKCE
CACAAAGTGTGCTGGGTTA
791


 


NM_005400
PRKCE
GACGAAGCAATTGTAAAGC
792


 


NM_005400
PRKCE
CACCCTTCAAACCACGCAT
793


 


NM_005400
PRKCE
GTCAGCATCTTGAAAGCTT
794


 


NM_005400
PRKCE
CAACCGAGGAGAGGAGCAC
795


 


NM_005400
PRKCE
TACATTGCCCTCAATGTGG
796


 


NM_005400
PRKCE
GAGGAATCGCCAAAGTACT
797


 


NM_005400
PRKCE
GGGATTTGAAACTGGACAA
798


 


NM_006218
PIK3CA
TTACACGTTCATGTGCTGG
799


 


NM_006218
PIK3CA
CACAATCCATGAACAGCAT
800


 


NM_006218
PIK3CA
CAATCAAACCTGAACAGGC
801


 


NM_006218
PIF3CA
CAGTTCAACAGCCACACAC
802


 


NM_006218
PIK3CA
GTGTTACAAGGCTTATCTA
803


 


NM_006218
PIK3CA
GATCCTATGGTTCGAGGTT
804


 


NM_006218
PIK3CA
CTCCAAATAATGACAAGCA
805


 


NM_006218
PIK3CA
ACTTTGCCTTTCCATTTGC
806


 


NM_006218
PIK3CA
AGAATATCAGGGCAAGTAC
807


 


NM_006218
PIK3CA
TTGGATCTTCCACACAATT
808


 


NM_006218
PIK3CA
AGTAGGCAACCGTGAAGAA
809


 


NM_006218
PIK3CA
CAGGGCTTGCTGTCTCCTC
810


 


NM_006218
PIK3CA
GAGCCCAAGAATGCACAAA
811


 


NM_006218
PIK3CA
GCCAGAACAAGTAATTGCT
812


 


NM_006218
PIK3CA
GGATGCCCTACAGGGCTTG
833


 


NM_006218
PIK3CA
TCAAATTATTCGTATTATG
814


 


NM_006218
PIK3CA
GAATTGGAGATCGTCACAA
815


 


NM_006218
PIK3CA
TGAGGTGGTGCGAAATTCT
816


 


NM_006218
PIK3CA
GATTTACGGCAAGATATGC
817


 


NM_006218
PIK3CA
TGATGAATACTTCCTAGAA
818


 


NM_001982
ERBB3
GCTGCTGGGACTATGCCCA
819


 


NM_001982
ERBB3
ATCTGCACAATTGATGTCT
820


 


NM_001982
ERBB3
CTTTGAACTGGACCAAGGT
821


 


NM_001982
ERBB3
CATCATGCCCACTGCAGGC
822


 


NM_001982
ERBB3
AACTTTCCAGCTGGAACCC
823


 


NM_001982
ERBB3
TGAAGGAAATTAGTGCTGG
824


 


NM_001982
ERBB3
AATTCGCCAGCGGTTCAGG
825


 


NM_001982
ERBB3
ACCAGAGCTTCAAGACTGT
826


 


NM_001982
ERBB3
GAGGCTACAGACTCTGCCT
827


 


NM_001982
ERBB3
TGGAGCCAGAACTAGACCT
828


 


NM_001982
ERBB3
ACACTGTACAAGCTCTACG
829


 


NM_001982
ERBB3
TAATGGTCACTGCTTTGGG
830


 


NM_001982
ERBB3
ACAGGCACTCCTGGAGATA
831


 


NM_001982
ERBB3
GTTTAGGACAAACACTGGT
832


 


NM_001982
ERBB3
GATTACTGGCATAGCAGGC
833


 


NM_001982
ERBB3
ATGAATACATGAACCGGAG
834


 


NM_001982
ERBB3
CACTTAATCGGCCACGTGG
835


 


NM_001982
ERBB3
GGCCTGTCCTCCTGACAAG
836


 


NM_001982
ERBB3
TCTGCGGAGTCATGAGGGC
837


 


NM_001982
ERBB3
TAGACCTAGACTTGGAAGC
838


 


NM_004283
RAB3D
GATTTCAGGTCTCCCTGTC
839


 


NM_004283
RAB3D
GCCACAGTGGTTATCTCCA
840


 


NM_004283
RAB3D
GCAATCCCTTCCCTCCTGT
841


 


NM_004283
RAB3D
TCTCTGATCCTGAAGTGAA
842


 


NM_004283
RAB3D
CATCAATGTGAAGCAGGTC
843


 


NM_004283
RAB3D
CATGAGCTTGCTGCTTTCC
844


 


NM_004283
RAB3D
AACGTGTTGTGCCTGCTGA
845


 


NM_004283
RAB3D
CTGCTTTCCAGGGTGTGTT
846


 


NM_004283
RAB3D
GCGGCCAGGGCCAAGCCGC
847


 


NM_004283
RAB3D
CTTCTAGCTTAGAACCATT
848


 


NM_004283
RAB3D
CAGGGTGTGTTGAGGGTGG
849


 


NM_004283
RAB3D
CTCTTTCTCAGGTCCTGCA
850


 


NM_004283
RAB3D
CTTGTGCCAAGATGGCATC
851


 


NM_004283
RAB3D
GCACCATCACCACGGCCTA
852


 


NM_004283
RAB3D
CGCGGACGACTCCTTCACT
853


 


NM_004283
RAB3D
TCATCCAGGGAAGGCGGCG
854


 


NM_004283
RAB3D
GACACTGACGTGCATCAGC
855


 


NM_004283
RAB3D
CCCTCCCAGGCCCTGTTTA
856


 


NM_004283
RAB3D
AGGTCTTCGAGCGCCTGGT
857


 


NM_004283
RAB3D
CCTCTTTCTCAGGTCCTGC
858


 


NM_003620
PPM1D
TTGCCCGGGAGCACTTGTG
859


 


NM_003620
PPM1D
CGTGTGCGACGGGCACGGC
860


 


NM_003620
PPM1D
ATTAGGTCTTAAAGTAGTT
861


 


NM_003620
PPM1D
AGCCCTGACTTTAAGGATA
862


 


NM_003620
PPM1D
TGTGGAGCCCGAACCGACG
863


 


NM_003620
PPM1D
GCGACGGGCACGGCGGGCG
864


 


NM_003620
PPM1D
GATTATATGGGTATATATT
865


 


NM_003620
PPM1D
TTAGAAGGAGCACAGTTAT
866


 


NM_003620
PPM1D
CCGGCCAGCCGGCCATGGC
867


 


NM_003620
PPM1D
GAGCAGATAACACTAGTGC
868


 


NM_003620
PPM1D
AGATCCCATCTCAATGTGC
869


 


NM_003620
PPM1D
GCGGCACAGTTTGCCCGGG
870


 


NM_003620
PPM1D
CGTAGCAATGCCTTCTCAG
871


 


NM_003620
PPM1D
TATATGGGTATATATTCAT
872


 


NM_003620
PPM1D
GCTGCTAATTCCCAACATT
873


 


NM_003620
PPM1D
ACAACTGCCAGTGTGGTCA
874


 


NM_003620
PPM1D
TTGACCCTCAGAAGCACAA
875


 


NM_003620
PPM1D
GTCTTAAAGTAGTTACTCC
876


 


NM_003620
PPM1D
ATGCTCCGAGCAGATAACA
877


 


NM_003620
PPM1D
GCGCCTAGTGTGTCTCCCG
878


 


NM_022048
CSNKIG1
TAGCCATCCAGCTGCTTTC
879


 


NM_022048
CSNKIG1
TTCTCATTGGAAGGGACTC
880


 


NM_022048
CSNKIG1
CACGCATCTTGGCAAAGAG
881


 


NM_022048
CSNKIG1
TAGCTTGGAGGACTTGTTT
882


 


NM_022048
CSNKIG1
ACTCAATTGTACCTGCAGC
883


 


NM_022048
CSNKIG1
CTAAGTGCTGCTGTTTCTT
884


 


NM_022048
CSNKIG1
GCAAAGCCGGAGAGATGAT
885


 


NM_022048
CSNKIG1
CCTCTTCACAGACCTCTTT
886


 


NM_022048
CSNKIG1
GAAGGGACTCCTCTTTGGG
887


 


NM_022048
CSNKIG1
GAGAGCTCAGATTAGGTAA
888


 


NM_022048
CSNKIG1
CACGTAGATTCTGGTGCAT
889


 


NM_022048
CSNKIG1
ATGAGTATTTACGGACCCT
890


 


NM_022048
CSNKIG1
GGTGGGACCCAACTTCAGG
891


 


NM_022048
CSNKIG1
AGAGCTGAATGTTGATGAT
892


 


NM_022048
CSNKIG1
GATTCTGGTGCATCTGCAA
893


 


NM_022048
CSNKIG1
AACTTCAGGGTTGGCAAGA
894


 


NM_022048
CSNKIG1
TCTCGAATGGAATACGTGC
895


 


NM_022048
CSNKIG1
CCGAGGAGAGTGGGAAATT
896


 


NM_022048
CSNKIG1
GGGAGCCCACTCCAATGCA
897


 


NM_022048
CSNKIG1
GTCAAGCCAGAGAACTTCC
898


 


NM_000082
CKN1
TTAGCAGTTTCCTGGTCTC
899


 


NM_000082
CKN1
ATGTGAGAAGAGCATCAGG
900


 


NM_000082
CKN1
AGCAGTGTGTTCCATTGGC
901


 


NM_000082
CKN1
GGATCCTGTTCTCACATTC
902


 


NM_000082
CKN1
CAGCAGTGATGAAGAAGGA
903


 


NM_000082
CKN1
GATAACTATGCTTAAGGGA
904


 


NM_000082
CKN1
TGGACTTCACCTCCTCACT
905


 


NM_000082
CKN1
TTGAAGTCTGGATCCTGTT
906


 


NM_000082
CKN1
AGGAACTTTATAGTGGTAG
907


 


NM_000082
CKN1
AAGTGATGGACTTCACCTC
908


 


NM_000082
CKN1
TGTTTATACAGTTTACTCA
909


 


NM_000082
CKN1
GAAGGGAGATACATGTTAT
910


 


NM_000082
CKN1
GGGTTTGGAGGACCCTCTT
911


 


NM_000082
CKN1
ATATGTCTCCAGTCTCCAC
912


 


NM_000082
CKN1
GATGGACTTCACCTCCTCA
913


 


NM_000082
CKN1
TGAAAGTATGGGATACAAA
914


 


NM_000082
CKN1
ATGTAAAGCAGTGTGTTCC
915


 


NM_000082
CKN1
TCTACAGGGTCACAGACAA
916


 


NM_000082
CKN1
GAGGCCATCAGTATTGACT
917


 


NM_000082
CKN1
ACTGTTTGGTAGCAGTTGG
918


 


NM_002843
PTPRJ
AGGAGGAGGCGAAGGAGAC
919


 


NM_002843
PTPRJ
CTACGTCACCACCACGGAG
920


 


NM_002843
PTPRJ
TCGCCTAATTCCAAAGGAA
921


 


NM_002843
PTPRJ
CAAGTATGTAGTAAAGCAT
922


 


NM_002843
PTPRJ
AAGCTGGTCACCCTTCTGC
923


 


NM_002843
PTPRJ
CACAGAAGGTGGCTTGGAT
924


 


NM_002843
PTPRJ
TGGAATCTAGCCGATGGAA
925


 


NM_002843
PTPRJ
ATAAACAGAATGGAACTGG
926


 


NM_002843
PTPRJ
CCTGGAGAGCTGCTCCTCT
927


 


NM_002843
PTPRJ
AACTTTAAGTTGGCAGAAC
928


 


NM_002843
PTPRJ
ACACAGTGGAGATCTTTGC
929


 


NM_002843
PTPRJ
CAGTACACACGGCCCAGCA
930


 


NM_002843
PTPRJ
TTGAACAGGGAAGAACCAA
931


 


NM_002843
PTPRJ
ATTATGTTGACTAAATGTG
932


 


NM_002843
PTPRJ
TGACTCAAGACTCAAGACT
933


 


NM_002843
PTPRJ
AACTTTCGGTCCAGACCCA
934


 


NM_002843
PTPRJ
GGCCAGACCACGGTGTTCC
935


 


NM_002843
PTPRJ
TCACTGGAACCTGGCCGGA
936


 


NM_002843
PTPRJ
ACACAGGAGGGAGCTGGCA
937


 


NM_002843
PTPRJ
TGTTCTCATTTGATCAGGG
938


 


NM_004037
AMPD2
TCATCCGGGAGAAGTACAT
939


 


NM_004037
AMPD2
ACCCAACTATACCAAGGAA
940


 


NM_004037
AMPD2
CCTGCATGAACCAGAAGCA
941


 


NM_004037
AMPD2
CTGCGGGAGGTCTTTGAGA
942


 


NM_004037
AMPD2
GCCTCTTTGATGTGTACCG
943


 


NM_004037
AMPD2
GACAACATGAGAAATCGTG
944


 


NM_004037
AMPD2
GCCACCCAGTGAAAGCAAA
945


 


NM_004037
AMPD2
CAGGAACACTTTCCATCGC
946


 


NM_004037
AMPD2
TGTGGGAGAGGCAGCTGCC
947


 


NM_004037
AMPD2
GCCGTGAACAGACGCTGCG
948


 


NM_004037
AMPD2
AAATATCCCTTTAAGAAGC
949


 


NM_004037
AMPD2
GTAAAGAGCCACTGGCTGG
950


 


NM_004037
AMPD2
CGTCCTGCATGAACCAGAA
951


 


NM_004037
AMPD2
GCTCAGCAACAACAGCCTC
952


 


NM_004037
AMPD2
CACATCATCAAGGAGGTGA
953


 


NM_004037
AMPD2
CTCATTGTTGTTTGGGCTC
954


 


NM_004037
AMPD2
AAGCTCAGCTCCTGCGATA
955


 


NM_004037
AMPD2
TGCGATATGTGTGAGCTGG
956


 


NM_004037
AMPD2
CTGGGCCCATCCACCACCT
957


 


NM_004037
AMPD2
GAAGGACCAGCTAGCCTGG
958


 


NM_016218
POLK
TATTTCATTTCTTGTCAAT
959


 


NM_016218
POLK
GACGAGGGATGGAGAGAGG
960


 


NM_016218
POLK
AGTAGATTGTATAGCTTTA
961


 


NM_016218
POLK
TATAGATAACTCATCTAAA
962


 


NM_016218
POLK
AAGAACTTTGCAGTGAGCT
963


 


NM_016218
POLK
GAATTAGAACAAAGCCGAA
964


 


NM_016218
POLK
TGTGCTATCAATGAGTTCT
965


 


NM_016218
POLK
ACACCTGACGAGGGATGGA
966


 


NM_016218
POLK
TGCATCTACAGTTTCATCT
967


 


NM_016218
POLK
ACACACCTGACCAGGGATG
968


 


NM_016218
POLK
TGGATAGCACAAAGGAGAA
969


 


NM_016218
POLK
AGGGTGCATCAGTCTGGAA
970


 


NM_016218
POLK
TATAGCTTTAGTAGATACT
971


 


NM_016218
POLK
TGTTTCTACTGCAGAAGAA
972


 


NM_016218
POLK
GTTGTTTCTACTGCAGAAG
973


 


NM_016218
POLK
CTGACAAAGATAAGTTTGT
974


 


NM_016218
POLK
GCATCAGTCTGGAAGCCTT
975


 


NM_016218
POLK
CTCAGGATCTACAGAAAGA
976


 


NM_016218
POLK
AAGGAGATTTGGTGTTCGT
977


 


NM_016218
POLK
TAGTGCACATTGACATGGA
978









This application includes a Sequence Listing submitted on compact disc, recorded on two compact discs, including one duplicate, containing file name NEW SEQLIST 9301-244-999.TXT, of size 205 kilo-bytes, created Apr. 21, 2008. The Sequence Listing on the compact discs is incorporated by reference herein in its entirety.
7. REFERENCES CITED
All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.
Many modifications and variations of the present invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only, and the invention is to be limited only by the terms of the appended claims along with the full scope of equivalents to which such claims are entitled.

## Claims

1. A method for selecting from a plurality of different siRNAs one or more siRNAs for silencing a target gene in an organism, each different siRNA in said plurality of different siRNAs targeting a different target sequence in a transcript of said target gene, said method comprising
(a) calculating a score for a corresponding targeted sequence motif in said transcript, for each said different siRNA in said plurality of different siRNAs, wherein said score is calculated using a position-specific score matrix (PSSM), wherein each said sequence motif is a nucleotide sequence of L nucleotides, L being an integer, and wherein said PSSM is {log(eij/pij)}, where eij is the weight of nucleotide i at position j, pij is the weight of nucleotide i at position j in a random sequence, and i=G or C, A, U(T), j=1, . . . L, wherein each of said targeted sequence motifs comprises at least a portion of the target sequence of the corresponding siRNA and/or a second sequence in a sequence region flanking said target sequence;
(b) ranking said plurality of different siRNAs according to said scores; and
(c) selecting one or more siRNAs from said ranked siRNAs, wherein at least one of steps (a), (b) or (c) is performed by a suitably programmed computer.

2. The method of claim 1, wherein each said sequence motif comprises said target sequence of said targeting siRNA.

3. The method of claim 1, wherein said score for each said siRNA is calculated according to equation




  Score
  =
  
    
      ∑
      
        t
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            e
            t
          
          /
          
            p
            t
          
        
        )
      
    
  




wherein said et and pt are respectively weights of the nucleotide at position t in said sequence motif as determined according to said position-specific score matrix and in a random sequence.

4. The method of claim 3, wherein each said sequence motif comprises said target sequence of said targeting siRNA and at least one flanking sequence.

5. The method of claim 4, wherein each said sequence motif comprises said target sequence of said targeting siRNA and a 5′ flanking sequence and a 3′ flanking sequence.

6. The method of claim 5, wherein said 5′ flanking sequence and said 3′ flanking sequence are each a sequence of D nucleotides, D being an integer.

7. The method of claim 6, wherein each said target sequence is a sequence of 19 nucleotides, and each said 5′ flanking sequence and 3′ flanking sequence are a sequence of 10 nucleotides.

8. The method of claim 5, wherein each said target sequence is a sequence of 19 nucleotides, and each said 5′ flanking sequence and 3′ flanking sequence are a sequence of 50 nucleotides.

9. The method of claim 1, further comprising a step of selecting one or more siRNAs based on silencing specificity, said step of selecting based on silencing specificity comprising, (i) for each of said plurality of siRNAs, predicting off-target genes of said siRNA from among a plurality of genes, wherein said off-target genes are genes other than said target gene and are directly silenced by said siRNA; (ii) ranking said plurality of siRNAs according to the number of off-target genes; and (iii) selecting one or more siRNAs for which said number of off-target genes is below a given threshold.

10. The method of claim 9, wherein said predicting comprises (il) evaluating sequence of each of said plurality of genes based on a predetermined siRNA sequence match pattern; and (i2) predicting said gene as an off-target gene if said gene comprise a sequence that matches said siRNA based on said sequence match pattern.

11. The method of claim 10, wherein said step of evaluating comprises identifying an alignment of said siRNA to a sequence in a gene by a low stringency FASTA alignment.

12. The method of claim 11, wherein each said siRNA has L nucleotides in its duplex region, and wherein said match pattern is represented by a position match position-specific score matrix (pmPSSM), said position match position-specific score matrix consisting of weights of different positions in an siRNA to match transcript sequence positions in an off-target transcript {Pj}, where j=1, . . . , L, Pj is the weight of a match at position j.

13. The method of claim 12, wherein said step (i1) comprises calculating a position match score pmScore according to equation




  pmScore
  =
  
    
      ∑
      
        i
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      ln
      ⁡
      
        (
        
          
            E
            i
          
          /
          0.25
        
        )
      
    
  




where Ei=Pi if position i is a match and Ei=(1-Pi)/3 if position i is a mismatch; and said step (i2) comprises predicting said gene as an off-target gene if said position match score is greater than a given threshold.

14. The method of claim 13, wherein L is 19, and wherein said pmPSSM is given by Table I.

15. The method of claim 14, wherein said plurality of genes comprises all known unique genes of said organism other than said target gene.

16. The method of claim 7, wherein said position-specific score matrix (PSSM) is obtained by a method comprising
(aa) identifying a plurality of N siRNAs consisting of siRNAs having 19-nucleotide duplex region and having a silencing efficacy above a chosen threshold;
(bb) identifying for each said siRNA a functional sequence motif, said functional sequence motif comprising a 19-nucleotide target sequence of said siRNA and a 10-nucleotide 5′ flanking sequence and a 10-nucleotide 3′ flanking sequence;
(cc) calculating a frequency matrix {fij}, where i=G, C, A, U(T); j=1, 2,  . . . , L, and where f ij is the frequency of the ith nucleotide at the jth position, based on said siRNAs functional sequence motifs according to equation





  
    
      f
      ij
    
    =
    
      
        ∑
        
          k
          =
          1
        
        N
      
      ⁢
      
        
      
      ⁢
      
        
          δ
          ik
        
        ⁡
        
          (
          j
          )
        
      
    
  
  ,
  
    
      where
      ⁢
      
        
      
      ⁢
      
        
          δ
          ik
        
        ⁡
        
          (
          j
          )
        
      
    
    =
    
      {
      
        
          
            
              
                1
                ,
                
                  
                    if
                    ⁢
                    
                      
                    
                    ⁢
                    k
                  
                  =
                  i
                
              
            
          
          
            
              
                0
                ,
                
                  
                    if
                    ⁢
                    
                      
                    
                    ⁢
                    k
                  
                  ≠
                  i
                
              
            
          
        
        ,
        and
      
    
  



(dd) determining said PSSM by calculating eij according to equation





  
    e
    ij
  
  =
  
    
      
        f
        ij
      
      N
    
    .

17. The method of claim 16, wherein said plurality of N siRNAs target a plurality of different genes having different transcript abundances in a cell.

18. The method of claim 17, wherein said step (c) is carried out by selecting one or more siRNAs having the highest scores.

19. The method of claim 17, wherein said step (c) is carried out by selecting one or more siRNAs having a score closest to a predetermined value, wherein said predetermined value is the score value corresponding to the maximum median silencing efficacy of a plurality of siRNA sequence motifs.

20. The method of claim 19, wherein said plurality of siRNA sequence motifs are sequence motifs in transcript having abundance level of less than about 3-5 copies per cell.

21. The method of claim 17, wherein said step (c) is carried out by selecting one or more siRNAs having a score within a predetermined range, wherein said predetermined range is a score range corresponding to a plurality of siRNAs sequence motifs having a given level of silencing efficacy.

22. The method of claim 21, wherein said silencing efficacy is above 50% at an siRNA dose of about 100 nM.

23. The method of claim 22, wherein said plurality of siRNA sequence motifs are sequence motifs in transcript having abundance level of less than about 3-5 copies per cell.

24. The method of claim 1, wherein said position-specific score matrix (PSSM) is obtained by a method comprising
(aa) initializing said PSSM with random weights;
(bb) selecting randomly a weight wij obtained in (aa);
(cc) changing the value of said selected weight to generate a test psPSSM comprising said selected weight having said changed value;
(dd) calculating a score for each of a plurality of siRNAs functional sequence motifs using said test PSSM according to equation





  
    Score
    =
    
      
        ∑
        
          k
          =
          1
        
        L
      
      ⁢
      
        
      
      ⁢
      
        ln
        ⁡
        
          (
          
            
              w
              k
            
            /
            
              p
              k
            
          
          )
        
      
    
  
  ⁢
  
    
  




wherein said wk and pk are respectively weights of a nucleotide at position k in said functional sequence motif and in a random sequence;
(ee) calculating correlation of said score and a metric of a characteristic of an siRNA among said plurality of siRNAs functional sequence motifs;
(ff) repeating steps (cc)-(ee) for a plurality of different values of said selected weight in a given range and retain the value that corresponds to the best correlation for said selected weight; and
(gg) repeating steps (bb)-(ff) for a chosen number of times; thereby determining said PSSM.

25. The method of claim 24, further comprising selecting said plurality of siRNA functional sequence motifs by a method comprising
(i) identifying a plurality of siRNAs consisting of siRNAs having different values in said metric;
(ii) identifying a plurality of siRNA functional sequence motifs each corresponding to an siRNA in said plurality of siRNAs.

26. The method of claim 25, wherein said characteristic is silencing efficacy.

27. The method of claim 26, wherein said plurality of N siRNAs target a plurality of different genes having different transcript abundances in a cell.

28. The method of claim 27, wherein said step (c) is carried out by selecting one or more siRNAs having the highest scores.

29. The method of claim 27, wherein said step (bb) is carried out by selecting one or more siRNAs having a score closest to a predetermined value, wherein said predetermined value is the score value corresponding to the maximum median silencing efficacy of a plurality of siRNA sequence motifs.

30. The method of claim 29, wherein said plurality of siRNA sequence motifs are sequence motifs in transcript having abundance level of less than about 3-5 copies per cell.

31. The method of claim 27, wherein said step (bb) is carried out by selecting one or more siRNAs having a score within a predetermined range, wherein said predetermined range is a score range corresponding to a plurality of siRNAs sequence motifs having a given level of silencing efficacy.

32. The method of claim 31, wherein said silencing efficacy is above 50% at an siRNA dose of about 100 nM.

33. The method of claim 32, wherein said plurality of siRNA sequence motifs are sequence motifs in transcript having abundance level of less than about 3-5 copies per cell.

34. The method of claim 24, wherein said position-specific score matrix (PSSM) comprises wk, k=1, . . . , L, wk being a difference in probability of finding nucleotide G or C at sequence position k between a first type of siRNA and a second type of siRNA, and wherein said score for each said strand is calculated according to equation




  Score
  =
  
    
      ∑
      
        k
        =
        1
      
      L
    
    ⁢
    
      
    
    ⁢
    
      
        w
        k
      
      .

35. The method of claim 34, wherein said first type of siRNA consists of one or more siRNAs having silencing efficacy no less than a first threshold and said second type of siRNA consists of one or more siRNAs having silencing efficacy less than a second threshold.

36. The method of claim 35, wherein said difference in probability is described by a sum of Gaussian curves, each of said Gaussian curves representing the difference in probability of finding a G or C at a different sequence position.

37. The method of claim 36, wherein said first and second thresholds are both 75% at an siRNA dose of 100 nM.