Abstract:
A transcript mapping method according to an embodiment of the invention is described hereinafter and combines short tag based (SAGE and MPSS) efficiency with the accuracy of full-length cDNA (flcDNA) for comprehensive characterization of transcriptomes. This method is also referred to as Gene Identification Signature (GIS) analysis. In this method, 5′ and 3′ terminal tags are obtained from a transcript and the terminal tags are for identifying 5′ and 3′ sites located on a genome sequence. Further, occurring segments and thus feasible gene locations are identified along the genome sequence after the 5′ and 3′ sites are identified. A data structure is also generated for indexing the genome sequence so that the terminal tags can be mapped to the genome sequence.

Description:
FIELD OF INVENTION 
     The present invention relates generally to a transcript mapping method. In particular, the invention relates to a transcript mapping method for mapping from a transcript to a compressed suffix array indexed genome sequence. 
     BACKGROUND 
     Since the completion of the genome sequences for human and several other organisms, attention has been drawn towards annotation of genomes for functional elements including gene coding transcript units and regulatory cis-acting elements that modulate gene expression levels. 
     Currently there are three main approaches for genome annotation. The first approach uses existing transcript data to identify gene-coding regions in the genomes, the second approach uses computational algorithms to statistically predict genes and regulatory elements and the third approach compares genomic sequences from other vertebrates for conserved regions based on the view that functional elements in genomes are conserved during evolution. 
     Despite considerable success, these approaches are unsatisfactory for determining the complete and precise content of all functional elements in the human genome. As a result, a complete list of genes in the human genome is still unavailable. In particular, all the low abundant and cell specific genes have not been identified. Many gene models suggest that the current genome annotation is incorrect, particularly regarding where the transcription starts and ends. 
     All the gene predictions have to be validated by experimental means and the prospective genes are required to be cloned in full-length for further functional studies. It is therefore clear that many challenges surround the field of human genome annotation. 
     One of the challenges is the identification of all genes and all transcripts expressed from the genes in human and model organisms. In the annotation of genes, full-length cDNA cloning and sequencing is the most conclusive and is viewed as the gold standard for the analysis of transcripts. However, this approach is expensive and slow when applied to a large number of transcripts across a large number of species and biological conditions. There are short tag based approaches such as SAGE (serial analysis of gene expression) and MPSS (massively parallel signature sequence). These short tag based methods extract a 14-20 bp signature for representing each transcript. Though this approach is efficient in tagging and counting transcripts in a given transcriptome, the specificity of the tags is often poor and the information yielded regarding transcript structures are frequently incomplete and ambiguous. 
     Gene Identification Signature (GIS) ditag sequences, obtained by extracting interlinked 5′ and 3′ ends of full-length cDNA clones into a ditag structure, provide substantial tag specificity. However, there are no existing computer algorithms that are readily applicable for mapping the GISditag sequences to genome. In the past, SAGE and MPSS tags were analyzed using a two-step approach. The tags were first matched to cDNA sequences and then to the genome. In this approach, novel transcripts that did not exist in cDNA databases would not be mapped. The two most often used sequence alignment tools, BLAST (basic local alignment search tool) and BLAT (BLAST-like alignment tool), are not designed for short tag sequences and often leads to poor or incorrect results. 
     Hence, this clearly affirms a need for an improved transcript mapping method. 
     SUMMARY 
     A transcript mapping method according to an embodiment of the invention is described hereinafter and combines short tag based (SAGE and MPSS) efficiency with the accuracy of full-length cDNA (flcDNA) for comprehensive characterization of transcriptomes. This method is also referred to as Gene Identification Signature (GIS) analysis. In this method, the 5′ and 3′ ends of full-length cDNA clones are initially extracted into a ditag structure, with the ditag concatemers of the ditag being subsequently sequenced in an efficient manner, and finally mapped to the genome for defining the gene structure. In the GIS analysis, each sequence read reveals approximately 15 ditags representing  15  transcripts. This approach increases efficiency by at least 30-folds for identifying and quantifying full-length transcripts compared to the current flcDNA cloning and sequencing approaches. Because each GISditag sequence contains 36 base pairs (bp) to represent the beginning and the end of a transcript, the specificity of mapping tag-to-genome is greatly increased compared to the 14-21 bp SAGE and MPSS tags. In addition, as a GISditag represents the 5′ and 3′ ends of a transcript, it is more informative than SAGE and MPSS tags. 
     To accommodate the GISditag data, a Suffix Array based Tag to Genome (SAT2G) algorithm is used for mapping the GISditag sequences to a genome sequence built and indexed on an advanced data structure Compressed Suffix Array (CSA). 
     Therefore, in accordance with a first aspect of the invention, there is disclosed a transcript mapping method implementable using a computer-based system comprising: 
     obtaining a 5′ terminal tag and a 3′ terminal tag from a transcript of a gene; 
     matching the 5′ terminal tag to at least a portion of a genome sequence to thereby identify at least one 5′ site therefrom, each of the at least one 5′ site having a sequence matching the 5′ terminal tag; 
     matching the 3′ terminal tag to at least a portion of the genome sequence to thereby identify at least one 3′ site therefrom, each of the at least one 3′ site having a sequence matching the 3′ terminal tag; 
     identifying quantity disparity between the at least one 5′ site and the at least one 3′ site; 
     limiting the quantity disparity between the at least one 5′ site and the at least one 3′ site; 
     identifying at least one occurring segment, each of the at least one occurring segment being a sequence segment along the genome sequence extending from one of the at least one 5′ site to one of the at least one 3′ site, each of the at least one occurring segment having a sequence length; 
     identifying at least one feasible gene location, each of the feasible gene location being one of the at least one occurring segment having a sequence length not exceeding that of a predefined gene length; and 
     generating a data structure on the computer-based system for indexing the genome sequence, the data structure being accessible to a user, the data structure for enhancing transcript mapping by the computer-based system. 
     In accordance with a second aspect of the invention, there is disclosed a transcript mapping method implementable using a computer-based system, the method comprising the steps of: 
     obtaining a 5′ terminal tag and a 3′ terminal tag from a transcript; 
     matching the 5′ terminal tag to at least a portion of a genome sequence to thereby identify at least one 5′ site therefrom, the 5′ terminal tag obtainable from a transcript, each of the at least one 5′ site having a sequence matching the 5′ terminal tag; 
     matching the 3′ terminal tag to at least a portion of the genome sequence to thereby identify at least one 3′ site therefrom, the 3′ terminal tag obtainable from the transcript, each of the at least one 3′ site having a sequence matching the 3′ terminal tag; 
     identifying quantity disparity between the at least one 5′ site and the at least one 3′ site; 
     limiting the quantity disparity between the at least one 5′ site and the at least one 3′ site; 
     identifying at least one occurring segment, each of the at least one occurring segment being a sequence segment along the genome sequence extending from one of the at least one 5′ site to one of the at least one 3′ site, each of the at least one occurring segment having a sequence length; 
     identifying at least one feasible gene location from the at least one occurring segment, each of the at least one feasible gene location being one of the at least one occurring segment with at least one of the sequence length thereof not exceeding that of the predefined gene length, the sequence order thereof and of the at least one 5′ site and one of the at least one 3′ site corresponding thereto in accordance with a 5′-occurring segment-3′ structure matching the sequence order of the corresponding portion of the genome sequence, the 5′ site and one of the at least one 5′ site and one of the at least one 3′ site corresponding thereto having a 5′-3′ orientation, and one of the at least one 5′ site and one of the at least one 3′ site corresponding to each of the occurring segment being located within the same chromosome; and 
     generating a data structure for indexing the genome sequence on the computer-based system, the data structure being accessible to a user, the data structure enhancing the transcript mapping method implemented by the computer-based system. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the invention are described hereinafter with reference to the following drawings, in which: 
         FIG. 1  shows a schematic diagram of a 5′ and 3′ terminal tags SAGE technique for use in genome annotation; 
         FIG. 2  shows a process flow chart of a transcript mapping method according to an embodiment of the invention; 
         FIG. 3  shows a schematic diagram of a GIS ditag (Sequence ID No. 1) for application of the transcript mapping technique of  FIG. 2  thereto; 
         FIG. 4  shows a pseudo code “Find_Sites” of the transcript mapping method of  FIG. 2  for forward and reverse searching of 5′ sites and 3′ sites from a genome sequence; and 
         FIG. 5  shows a pseudo code “Match_sites — 1” of the transcript mapping method of  FIG. 2  for identifying the sequence length of an occurring segment, the sequence length being subsequently compared with a predefined length for identifying of a feasible gene location therefrom; and 
         FIG. 6  shows a pseudo code “Match_sites — 2” of the transcript mapping method of  FIG. 2  for identifying an occurring segment when a disparity condition is met wherefrom a feasible gene location is subsequently obtained. 
     
    
    
     DETAILED DESCRIPTION 
     A transcript mapping method is described hereinafter for addressing the foregoing problems. 
     Complete genome annotation relies on precise identification of transcription units bounded by a transcription initiation site (TIS) and a polyadenylation site (PAS). To facilitate this, a pair of complementary methods, namely 5′LongSAGE (long serial analysis of gene expression) and 3′LongSAGE, was developed. These methods are based on the original SAGE (serial analysis of gene expression) and LongSAGE methods that utilize typical full-length cDNA cloning technologies to enable high-throughput extraction of the first and the last 20 base pairs (bp) of each transcript. Mapping of 5′ and 3′ LongSAGE tags to the genome allows the localization of the TIS and the PAS. 
     However, matching of 5′ and 3′ tags derived from same transcripts in genome sequences are not always straightforward and can sometimes be very ambiguous. A solution is to clone the 5′ and 3′ tags of the same transcript by inter-linking the 5′ and 3′ tags. To achieve this, a specially designed device comprising cloning adapters and a vector links the 5′ tag and the 3′ tag derived from the same transcript into a ditag. 
     A plurality of ditags can be concatenated for cloning and sequencing, with each ditag representing an individual transcript. Unlike single tag sequences, the paired ditag sequences can be specifically multiplied with a frame of transcripts being precisely definable when being mapped to the genome sequences. This approach, named Gene Identification Signature (GIS) analysis, can accurately map the 5′ and 3′ ends of transcription units encoded by genes as shown in  FIG. 1 . 
     In the GIS analysis, the conventional cap-trapper method is applied to enrich a full-length cDNA and incorporated adapter sequences that bear an MmeI restriction site at each end of the cDNA fragments. The cDNA fragments are then cloned in a cloning vector to construct a GIS flcDNA (full-length cDNA) library. The plasmid prepared from the library is digested by MmeI (a type II restriction enzyme) and cleaved 20 bp downstream of its binding site. After digestion, the flcDNA inserts of the library were dropped from the plasmid to leave 18 bp signatures of 5′ and 3′ ends with the learned cloning vector. Re-circling the vector would create a GIS single ditag library. The ditags of the library were then sliced out and purified for concatenating and cloning to generate the final GIS ditag library for sequence analysis. Typically, each sequence read of the GIS ditag clones reveals  15  ditags. Each unit of the ditag sequence contains 18 bp of 5′ and 18 bp of 3′ signatures with a 12 bp spacer to separate one ditag sequence from another. 
     Mapping ditags to the genome is akin to searching occurrences of a pattern in the genome sequence. Approaches for pattern searching include the conventional BLAST (basic local alignment search tool) and BLAT (BLAST-like alignment tool) method. Both the BLAST and BLAT methods are very slow because each thereof requires a pattern to be searched by scanning through the whole genome. Moreover, conventional full-text indexing is usually employed if exact occurrences of a pattern with a small mismatch margin are required. Efficient full-text indexing data-structures include a suffix tree and a suffix array. 
     A suffix tree is a tree-like data-structure having branches stemming from a root with each branch terminating at a leaf that encodes a suffix of the genome sequence. The suffix array is a sorted sequence of all suffices of the genome according to lexicographic order. The suffix array is represented as an array. SA[i] where i=1 . . . n and that SA[i]=j means that the j-suffix (suffix starting from character j) is the i-th smallest suffix in the lexicographic order. 
     Both the suffix tree and the suffix array allow for fast pattern searching. Given a pattern of length x, its occurrences in the genome G[1 . . . n] can be reported in O(x) time and O(x log n) time for the suffix tree and the suffix array respectively. Although the query time is fast, it is not always feasible to build the suffix tree or the suffix array due to large space requirements thereof. For example, for a mouse genome, the suffix tree and the suffix array require 40 Gigabytes (GB) and 13 GB respectively. Such memory requirement far exceeds the memory space capacity of ordinary computers. To solve the memory space problem, we apply the space-efficient compressed suffix array (CSA) indexing data structure. CSA is a compressed form of the suffix array. It can be built efficiently without need for enormous memory requirements using known algorithms. Also, the built CSA is very small. For example, a CSA for the mouse genome (mm3) occupies approximately 1.3 GB. Additionally, CSA is also able to support searching efficiently. Searching a pattern of length x requires only O(x log n) time. 
     A first embodiment of the invention, a transcript mapping method  20  is described with reference to  FIG. 2 , which shows a process flow chart of the transcript mapping method  100 . The transcript mapping method  100  is for application to a transcript obtained from a gene. The transcript mapping method  100  is preferably implemented using a computer-based system. In a step  110  of the transcript mapping method  100 , a 5′ terminal tag  24  and a 3′ terminal tag  26  are obtained from the transcript. 
     In combination, the 5′ terminal tag  24  and the 3′ terminal tag  26  forms a GIS ditag  30  as described above and as shown in  FIG. 3 . The GIS ditag  30  has a ditag length  32  of 36bp (Sequence ID No. 1) with 18bp nucleotide sequence being derived from the 5′terminal tag  24  and another 18bp of nucleotide sequence being derived from the 3′ terminal tag  26 . Due to some enzymatic variations during molecular cloning, the ditag length  32  of the GIS ditag  30  may vary from 34bp to 38bp. 
     This variation often occurs proximate to the extremities of the 5′ terminal tag  24  and the 3′ terminal tag  26  with the internal nucleotides remaining structurally conserved. In the 3′ terminal tag  26 , two residual nucleotides  34  (AA) are retained during poly-A tail removal therefrom. The AA residual nucleotides  34  are subsequently for use as an orientation indicator. Therefore, only 16 bp of the 3′ terminal tag  26  in the GIS ditag  30  is usuable for mapping to a genome sequence  36 . 
     Following the step  110 , each of the 5′ terminal tag  24  and the 3′ terminal tag  26  is matched to the genome sequence  36  in a step  112 . In the step  112 , 5′ sites  38  and 3′ sites  40  are identified when the 5′ terminal tag  24  and the 3′ terminal tag  26  are respectively matched to the genome sequence  36 . Each of the 5′ sites  38  and each of the 3′ sites  40  is a portion of the genome sequence  36  that has a sequence that substantially matches the 5′terminal tag  24  and the 3′ terminal tag  26  respectively. 
     In a step  114 , at least one occurring segment  42  is identified from the genome sequence  36 . Each of the at least one occurring segment  42  is a sequence segment along the genome sequence  36  situated between one 5′ site  38  and one 3′ site  40 . Each of the at least one occurring segment  42  has a sequence length  44 . 
     Given the GIS ditag  30  (P) for the transcript (R), the computational problem of locating R in the genome sequence  36  (G) is referred to as a transcript location identification problem. Therefore, given G[1 . . . n] and P[1 . . . m], the occurring segment  42  is identified as being a feasible gene location of P when: the sequence length  44  ( j - i ) is smaller than the predefined gene length (maxlength), which is typically less than 1 million base pairs in length for known genes; the 5′ terminal tag  24  and the 3′ terminal tag  26  are longer than predefined minlength 5  and minlength 3  respectively (where minlength 5 =16 bp and minlength 3 =14 bp); and the 5′ terminal tag  24  and the 3′ terminal tag  26  of R are the substrings of P[1 . . . boundary 5 ] and P [boundary 3  . . . m] respectively (where boundary 5 =19 and boundary 3 =18). 
     The genome sequence  36  is preferably indexed using a compressed suffix array (CSA). The 5′ terminal tag  24  and the 3′ terminal tag are matched to the genome sequence  36  preferably by applying binary search to the compressed suffix array. The binary search for matching the 5′ terminal tag  24  and the 3′ terminal tag  26  are dependent on two lemmas, namely, lemma 1 for performing a forward search on the compressed suffix array and lemma 2 for performing a reverse search on the compressed suffix array. 
     Lemma 1 (forward search): given the CSA for the genome G[1..n] and a set of occurrences of a pattern Q in G, for any base cε{adenine (A), cytosine (C), guanine (G), thymine (T)}, a set of occurrences of the pattern Qc is obtainable in O(log n) time. A forward binary search is achieved by modifying a conventional binary search algorithm to use values in the compressed suffix array and suffix array instead of explicit text for the suffixes in the genome sequence  36  when comparing with pattern Q in the binary search. 
     Lemma 2 (reverse search): given the CSA for the genome G [1. . . ] and a set of occurrences of a pattern Q in G, for any base c ε{A,C,G,T,}, we can find the set of occurrences of the pattern cQ using O(log n) time. 
     The pseudo code “Find_Sites” for both the forward search and the reverse search is shown in  FIG. 4 . Instead of applying both the forward search and the reverse search in tandem in the step  114 , an alternative approach is to apply either only the forward search using lemma 1 or only the reverse search using lemma 2 to the genome sequence  36  for identifying the at least one occurring segment  42 . 
     The GIS ditag  30  may appear in the genome sequence  36  in sense or anti-sense. To address this issue, an index is created for each of the sense genome sequence and the anti-sense genome sequence. Instead of creating two separated indexing arrays, an anti-sense GIS ditag is created. The suffix array is searched twice in the step  110  for each of the 5′ terminal tag  24  and the 3′ terminal tag  26 , once using the sense GIS ditag  30  and a second time using the anti-sense GIS ditag (not shown). 
     Additionally, the genome sequence  36  is naturally partitioned into chromosomes. This enables a compressed suffix array to be created for the sequence segment of each chromosome. By doing so, 5′ sites  38  and 3′ sites  40  are obtainable for specific chromosomes instead of the entire genome sequence  36 . 
     Besides the compressed suffix array, a suffix array, a suffix tree, a binary or the like indexing data structure is usable for indexing the genome sequence  36  as abovementioned. 
     Following the step  114 , the 5′ sites  38  and the 3′ sites  40  undergo a series of checks to identify a feasible gene location. The checks comprises of length, locality, orientation and ordering checks. 
     In a step  116 , the length check is performed by comparing the sequence length  44  of each of the at least one occurring segment  42  with a predefined gene length in a step  116 . Initially, the 5′ sites  38  and  3 ′ sites  40  are sorted preferably in an ascending order. Next, each of the at least one occurring segment  42  has a sequence length  44  that does not exceed that of the predefined gene length (maxlength) is identified as a potential feasible gene location. The pseudo code “Match_sites — 1” for step  116  is shown in  FIG. 5 . 
     In a step  118 , the locality check is performed whereby the 5′ site  38  and the 3′ site  40  corresponding to each of the at least one occurring segment  42  are analysed to identify which chromosome each of them are located within. The occurring segment  42  identifies a potential feasible gene location only when the 5′ site  38  and the 3′ site  40  thereof belongs to the same chromosome. 
     In a step  120 , the orientation check is performed by identifying the orientation of the 5′ site  38  and the 3′ site  40  that corresponds to each occurring segment  42 . The orientation of the 5′ site  38  and the 3′ site is identifiable by locating the position of the residual nucleotide  34 . Preferably, the 5′ site  38  and the 3′ site  40  should have a 5′-3′ orientation for the occurring segment  42  thereof to identify a potential feasible gene location. 
     In a step  122 , the ordering check is performed by comparing each of the occurring segments  42  and the corresponding 5′ site  38  and 3′ site  40  to the genome sequence  36 . Preferably, the ordering of each of the occurring segments  42  and its corresponding 5′ site  38  and 3′ site  40  should follow a 5′-occurring segment-3′ structure for it to be a potential feasible site. 
     Steps  116 - 122  of the transcript mapping method can occur in any sequence in combination or independently. 
     In a situation where the feasible gene location is not found from the GIS ditag  30 , the constraints are relaxed to allow at least one mismatch when matching the 3′ terminal tag  26  to the genome sequence  36  in the step  112 . 
     Alternatively, the quantity of the 5′ sites  38  and the quantity of the 3′ sites  40  are initially obtained before the 5′ sites  38  and the 3′ sites  40  are matched to the genome sequence  36  in the step  112 . This enables identification of quantity disparity between the 5′ sites  38  and the 3′ sites  40 , for example, when there only exist less than ten of the 5′ sites  38  and more than tens of thousand of the 3′ sites  40 , or vice versa. 
     When large quantity disparity between the 5′ sites  38  and the 3′ sites  40  exists, the transcript mapping method  20  undergoes multiple iterations of redundant mapping to the genome sequence  36 . Therefore, a modified approach is required for the transcript mapping method  100  when a large quantity disparity arises. To identify the quantity disparity, a disparity condition is established as: 
               1     threshold     5   ,   3         ≤       count   5       count   3       ≤     threshold     5   ,   3             
where count 5  is the quantity of 5′ sites  38 , count 3  is the quantity of 3′ sites  40 , and threshold 5.3  is a pre-defined threshold, for example threshold 5.3  =10,000, for limiting the quantitative disparity between count 5  and count 3 . The CSA enables both count 5  and count 3  to be obtained without enumerating either any of the 5′ sites  38  or any of the 3′ sites.
 
     The method described in the pseudo code “Match_sites — 2” of  FIG. 6  is applied when the above disparity condition is met. In the pseudo code “Match_sites — 2”, the number of iterations required for mapping to the genome sequence  36  is determined by the smaller one of count 5  and count 3 . For example, should there be only two  5 ′ sites  38 , the mapping to or traversal along the genome sequence  36  for obtaining the corresponding one of the 3′ sites  40  is only iterated twice, once for each of the two  5 ′ sites  38 , for obtaining the occurring segments  42  therefrom. 
     However, should the above disparity condition be unmet, the quantity disparity between count 5  and count 3  is not large and therefore the transcript mapping method  100  reverts to the method described in “Match_sites — 1” for obtaining the occurring segments  42 . 
     In the foregoing manner, a transcript mapping method is described according to one embodiment of the invention for addressing the foregoing disadvantages of conventional mapping methods. Although only one embodiment of the invention is disclosed, it will be apparent to one skilled in the art in view of this disclosure that numerous changes and/or modification can be made without departing from the scope and spirit of the invention.