Abstract:
The present invention relates generally to the field of bioinformatics and its applications to functional genomics and advanced genetic engineering. More particularly, the present invention contemplates a method for identifying effector molecules capable of modulating gene network integration and which facilitate genetic multi-tasking and the regulation of complex suites of programmed responses within, on and between eukaryotic cells. The present invention permits, therefore, the identification of a new generation of proteome and nucleome modulators useful in a range of therapeutic and trait-modifying protocols. The ability to manipulate genetic networks within a cell and within whole organisms also provides a sophisticated genetic engineering approach of introducing new traits and to influencing the genetic architecture and, hence, to enable cell and organismal programming or re-programming. The identification of effector molecules and their target or receiver sites, further enables the development of diagnostic protocols for a range of conditions or physiological or genetic states of an organism useful, for example, in modulating stem cell differentiation, quantitative traits, aging or the development of pathological conditions.

Description:
FIELD OF THE INVENTION  
         [0001]    The present invention relates generally to the field of bioinformatics and its applications to functional genomics and advanced genetic engineering. More particularly, the present invention contemplates a method for identifying effector molecules capable of modulating gene network integration and which facilitate genetic multi-tasking and the regulation of complex suites of programmed responses within, on and between eukaryotic cells. The present invention permits, therefore, the identification of a new generation of proteome and nucleome modulators useful in a range of therapeutic and trait-modifying protocols. The ability to manipulate genetic networks within a cell and within whole organisms also provides a sophisticated genetic engineering approach of introducing new traits and to influencing the genetic architecture and, hence, to enable cell and organismal programming or re-programming. The identification of effector molecules and their target or receiver sites, further enables the development of diagnostic protocols for a range of conditions or physiological or genetic states of an organism, for example, in modulating stem cell differentiation, quantitative traits, aging or the development of pathological conditions.  
         BACKGROUND OF THE INVENTION  
         [0002]    Bibliographic details of references provided in the subject specification are listed at the end of the specification.  
           [0003]    Reference to any prior art in this specification is not, and should not be taken as, an acknowledgment or any form of suggestion that this prior art forms part of the common general knowledge in any country.  
           [0004]    The current understanding of the relationship between genetic information and biological function is predicated in the one gene-one protein hypothesis and in the classical studies of the lac operon and the “genetic code”, i.e. the triplet code specifying amino acids in protein coding sequences. The concept of DNA as a relatively stable, heritable source of template information for proteins, transduced through a temporary and discrete RNA readout has influenced ideas on the structure of genetic systems. Accordingly, cells and organisms are thought of as being built from a myriad of structural and catalytic proteins, whose expression is generally controlled by other regulatory proteins which bind to DNA. This is a biochemical rather than an informatic perspective, which, apart from local analysis of promoter function, gives little thought to the problem of how complex programs of gene activity in the higher organisms might be integrated and regulated in four dimensions.  
           [0005]    Genome sequencing projects have shown that the core proteome sizes of  Caenorhabditis elegans  and  Drosophila melanogaster  are of similar size and each only about twice the size of yeast and some bacteria, despite these animals&#39; every appearance of possessing more than twice the complexity of microorganisms (Chervitz et al.,  Science  282: 2022-2028, 1998; Rubin et al.,  Science  287: 2204-2215, 2000), leading to the conclusion that “the evolution of additional complex attributes is essentially an organizational one; a matter of novel interactions that derive from the temporal and spatial segregation of fairly similar components” (Rubin et al.,  Science  287: 2204-2215, 2000). This conclusion is reinforced by the finding that the human genome has only about 30,000 protein coding genes (Roest Crollius et al.,  Nature Genet.  25: 235-238, 2000; Consortium,  Nature  409: 860-921, 2001; Venter et al.,  Science  291: 1304-1351, 2001), the vast majority of which are shared in common with the mouse. The increased complexity of the higher eukaryotes is related, at least in part, to the production of different protein isoforms from the same gene by alternative splicing (Croft et al.,  Nature Genet.  24: 340-341, 2000). However, perhaps the most surprising and yet so far least considered feature of the genomes of the complex organisms, relative to simpler organisms, is the huge increase in the output of non-protein-coding RNA sequences, which have been estimated to account for around 97-98% of all transcriptional output from the human genome (Mattick,  EMBO Reports  2: 986-991, 2001) (see below).  
           [0006]    The view that phenotypic variation in complex organisms results from the differential use of a set of core components is becoming common (Duboule and Wilkins,  Trends. Genet.  14: 54-59, 1998) and includes such concepts as “synexpression groups” (Niehrs and Pollet,  Nature  402: 483-487, 1999), “syntagms” of interacting genes (Huang,  Int. J. Dev. Biol.  42: 487-494, 1998) and gene cassettes (Jan and Jan,  Proc. Natl. Acad. Sci. USA  90: 8305-8307, 1993), the re-use of modules in signaling pathways (Pawson,  Nature  373: 573-580, 1995; Hunter,  Cell  100: 113-127, 2000a) and enhanced rates of evolution by varying connections between modular network components (Hartwell et al.,  Nature  402: C47-52, 1999; Holland  Nature  402: C-41-44, 1999). These concepts have been drawn primarily from electrical circuit design and have focussed principally on the modules rather than on the interconnecting control architecture of the system.  
           [0007]    Particular network models, which range in size from single regulated circuits (Mestl et al.,  J. Theor. Biol.  176: 291-300, 1995; Mendoza and Alvarez-Buylla,  J. Theor. Biol.  193: 307-319, 1998; Yuh et al.,  Science  279: 1896-1902, 1998) to complete genomes (Thieffry et al.,  Bioessays  20: 433-440, 1998) have demonstrated that feedback subnetworks can exhibit computational behaviors including “learned behavior” (Bhalla and Iyengar,  Science  283: 381-387, 1999) that switching networks and transcriptional control networks can exhibit dynamical stability (Wolf and Eeckman,  J. Theor. Biol.  195: 167-186, 1998; Smolen et al.,  Am. J. Physiol.  277: C777-790, 1999) and that feedback circuits can implement oscillators governing cell cycles and circadian clocks (Dano et al.,  Nature  402: 320-322, 1999; Haase and Reed,  Nature  401: 394-397, 1999; Shearman et al.,  Science  288: 1013-1019, 2000). Stochastic noise and time delays allowing feedback, molecular memory and oscillations can be incorporated into such circuit models (Smolen et al.,  Am. J. Physiol.  277: C777-790, 1999) generating probabilistic phenotypic variation (McAdams and Arkin,  Proc. Natl. Acad. Sci. USA  94: 814-819, 1997) and amplification of signals (Hasty t al.,  Proc. Natl. Acad. Sci. USA  97: 2075-2080, 2000). Some of these models have been verified by synthesizing circuits in cells to feature bistability, oscillations and stochastic destruction of temporal correlations (Becskei and Serrano,  Nature  405: 590-593, 2000; Elowitz and Leibler,  Nature  403: 335-338, 2000; Gardner et al.,  Nature  403: 339-342, 2000).  
           [0008]    However, such models are unsuited to the analysis of global cellular connectivity and dynamics as they cannot be scaled up to large network sizes, since linear increases in the number of interconnected circuit nodes requires quadratic increases in the number of interconnecting molecules. This leads to an explosive increase in model size which severely constrains numerical simulations using current computing technologies (see e.g. Weng et al.,  Science  284: 92-96, 1999). A number of alternate approaches have sought to avoid this size explosion by treating sub-networks as active integrated logic components which are interconnected into larger networks (McAdams and Shapiro,  Science  269: 650-656, 1995) or by exploiting hierarchically organized control systems to significantly decrease analytical complexity (van der Gugten and Westerhoff,  Biosystems  44: 79-106, 1997).  
           [0009]    In work leading up to the present invention, the inventors reasoned that biology has solved this problem differentily, and that the types of network control architecture which are used to integrate and multi-task computers and which are used in the brain to coordinate complex activities such as motor coordination and cognition, may also be employed by molecular biological networks to generate phenotypic complexity and variability.  
           [0010]    Multi-tasking is employed in every computer where control codes (program instructions) of n bits set the central processing circuit to process one of 2 n  different operations. Sequences of control codes (a program) can be internally stored in memory creating a self-contained programmed response network—a computer—as originally defined by von Neumann in 1945 (von Neumann, First Draft of a report on the EDVAC. In: B. Randall, ed. The origins of digital computers: selected papers. Spring, Berlin, 1982). Prior to the arrival of the von Neumann computing architecture, a computer could only be reprogrammed by laborious re-wiring of the central processing unit, while subsequently re-programming simply required loading new control codes into memory. In all computing networks, processing requires not only stored program instructions, but also communication between nodes to synchronize and integrate network activity. The present inventors propose, in accordance with the present invention, that gene networks could exploit similar technology using internal controls based on RNA to multi-task components and sub-networks to generate a wide range of programmed responses, such as in differentiation and development. This system has interesting and perhaps mutually informative analogies with small world networks and dataflow computing.  
           [0011]    Existing genetic circuit models, although sophisticated, ignore endogenous controlled multi-tasking and consider each molecular sub-network (involving a few genes for instance) to be sparsely interconnected, and either off or on to express only one dynamical output (see e.g. McAdams and Shapiro,  Science  269: 650-656 1995; Bhalla and Iyengar,  Science  283: 381-387 1999; Weng et al.,  Science  284: 92-96 1999). Such models require more complex genetic programs to be built from many sub-networks encoded by exponentially large numbers of genes, a severe constraint, both in theory and in practice. In contrast, multi-tasking via n controls (single molecules suffice) can, in theory, achieve exponential (2 n ) multi-tasking of sub-network dynamical outputs, and allow a wide range of programmed responses to be obtained from limited numbers of sub-networks (and genetic coding information). The imbalance between the exponential benefit of controlled multi-tasking and the small linear cost of control molecules makes it likely that evolution will have explored this option. Indeed, this may have been the only feasible way to lift the constraints on the complexity and sophistication of genetic programming.  
           [0012]    Complex organisms require two levels of genetic programming for their autopoeitic development from a fertilised embryo. The genomes of these organisms must specify the functional components of the system, mainly proteins, which have been the primary focus of genetic and genomic research to date. Damage to these components (by mutation) is also very obvious (as in monogenic diseases), just as damaging the components of any structure is obvious. The genomes of these organisms must also specify the control architecture which deploys these components in sophisticated suites of differentiation and development. Damage to this architecture is much more subtle, because of the nature and complexity of this information (which primarily affects quantitative trait variation). Traditionally it has been assumed that this architecture is embedded in the cis-acting control sequences which regulate gene expression in conjunction with trans-acting proteins acting at a variety of levels. However, as noted above, the vast majority of the transcriptional output of the genomes of the higher organisms, up to 97-98% in humans, is noncoding RNA. This noncoding RNA is derived from the introns of both protein-encoding and non-protein-encoding (noncoding RNA) genes, and the exons of noncoding RNA genes, which appear to comprise at least half of all transcripts from the human genome. Putting together the extent of introns in protein coding genes with the estimate of the number of non-coding RNA genes suggests that at least 50% of the human genome is actively transcribed into non-coding RNAs. Thus, either that the human genome is replete with useless transcription or these RNAs are fulfilling some unexpected function(s).  
         SUMMARY OF THE INVENTION  
         [0013]    Throughout this specification, unless the context requires otherwise, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element or integer or group of elements or integers but not the exclusion of any other element or integer or group of elements or integers.  
           [0014]    Nucleotide and amino acid sequences are referred to by a sequence identifier number (SEQ ID NO:). The SEQ ID NOs: correspond numerically to the sequence identifiers &lt;400&gt;1 (SEQ ID NO:1), &lt;400&gt;2 (SEQ ID NO:2), etc. A summary of the sequence identifiers is provided in Table 1. A sequence listing is provided after the claims.  
           [0015]    The present invention is predicated in part on the proposal that non-coding RNAs have evolved to form a second tier of gene expression in the eukaryotes, and that these molecules (or their processed derivatives) act as endogenous controls for genetic multitasking and regulating complex suites of gene expression. Since intronic RNAs are produced in parallel with protein encoding sequences, their most logical (general) function would be networking, i.e. a molecular memory of recent transcription events which allows activity at one locus to be communicated directly to others. If this is the case, then it can be predicted that these RNAs are further processed into multiple species, each one capable of transmitting information independently to different targets. This is similar to the types of networks that exist in other complex information systems such as the brain, where secondary outputs (termed efference signals) underlie sensory awareness, motor coordination, and cognition, and wherein the patterns of neural activation depend on the flux of “hidden units”, collectively referred to as the “hidden layer” (Mattick and Gagen.  Molec. Biol. Evol.  18: 1611-1630, 2001). At face value, such efference RNAs (eRNAs) would enable an enormous increase in network connectivity and functionality over the situation where system activity is solely regulated through protein-based feedback loops which relay metabolic and environmental state information. They would also allow a much more sophisticated and genomically compact regulatory system than would be possible using proteins alone, especially for integrating the complex subroutines that operate during embryonic differentiation and development. Moreover, if a system utilizing an RNA communication network has evolved, it is also predicted that many genes have evolved solely to express RNA, as higher order regulators in the network. These noncoding RNAs would be expected to interact with, and to transmit signals to, a variety of cellular targets, including other RNAs, genes (DNA/chromatin), and proteins. It would also be predicted that a significant proportion of these interactions, perhaps the majority, would occur via sequence-specific interactions between the eRNAs (transmitters) and homologous target sequences in other RNAs or the genome (receivers), i.e. that the specificity of signalling is embedded in the primary sequence of the RNA transmitter and the RNA or DNA receiver as a kind of “bit string” or “zip code”. In both cases these transmitter and receiver sequences are encoded in the genome and potential interacting pairs within this regulatory network will be recognisable by sequence homology using rules that apply to duplex or higher order DNA-RNA or RNA-RNA interactions. In the case of RNA-protein interactions, the interacting partners will be identified by direct experimental procedures and/or ab initio from sequence analysis when the algorithms for this become available.  
           [0016]    In accordance with the present invention, it is proposed that efference RNA signals integrate and regulate gene activity in eukaryotes at a variety of levels. It is also proposed that this RNA network was a fundamental advance in the genetic operating system of the eukaryotes, which lies at the heart of the programmed responses which direct cellular and differentiation and organismal development. At face value such a system has enormous advantages over a regulatory circuitry that relies simply on protein feedback loops, especially when attempting to integrate large sets and different levels of gene activity. If this is so, it further suggests that the evolution of a more advanced genetic operating system based on a highly parallel RNA-based communication network may have been the fundamental prerequisite for the emergence of complex organisms. It also implies that the basis of species diversity and quantitative trait variation in complex organisms is primarily embedded in the control architecture of the system, rather than structural variation in the protein components themselves (although this will also contribute). This in turn has considerable implications for understanding and modifying the genetic programming of the higher organisms and the genetic factors underpinning complex traits.  
           [0017]    In accordance with the present invention therefore, it is proposed that RNA sequences derived from introns of protein-encoding genes and from introns and exons of non-protein-encoding transcripts have evolved to function as network control molecules in higher organisms, freeing such organisms from the constraints of a simple single-output protein-based genetic operating system. The recognition that such RNA sequences, referred to herein as efference or eRNAs, are genetic signalling modifiers permits the rational design of a range of signal modifiers including the identification of corresponding receiver DNA, RNA and protein molecules and permits rational modification of physiological, biochemical and genetic output to alter inter alia organismal differentiation and development to modify quantiative traits and to alter physiological parameters underlying disease and disease susceptibility. The recognition of the importance of eRNAs in defining the genetic architecture of a cell further enables cell and organismal programming or re-programming. This includes the identification and modification of eRNA transmitter sequences or their target sequences to alter the epigenetic status and accessiblity of genomic loci, gene transcription, alternative splicing, RNA turnover, mRNA translation and signal transduction systems. This is useful in directing the differentiation and development, for example of stem cells. It also enables the development of novel diagnostic and therapeutic protocols.  
           [0018]    In addition, the present invention further enables the identification of embedded structural motifs which are involved in protein/RNA complex interaction.  
           [0019]    The recognition that eRNAs and their receiver targets are involved in genetic network signalling permits the rational design of eRNAs and their analogs and to identify target sequences to thereby modulate genetic signalling pathways. The present invention enables, therefore, genetic engineering of cells at a highly sophisticated level. The present invention further provides a computer system for identifying eRNAs or DNA sequences encoding same as well as receiver DNA, RNA and proteins. Such a computer system includes software, hardware, computer codes, user interfaces and databases acquiring storing and retrieving genetic data and/or physiological or other biological data associated with eRNAs or DNAs encoding same.  
           [0020]    Furthermore, the recognition of the role of eRNAs in determining the genetic architecture of a cell or group or family of cells, enables the design of protocols and genetic and chemical agents which can influence this architecture. Accordingly, agents can now be identified which can program a cell to differentiate, proliferate and/or re-new or re-program an already differentiated or partially differentiated cell to exhibit characteristics of another cell type.  
           [0021]    The present invention provides, therefore, a method for modulating the genetic make up of a cell or the phenotype of a cell as well as agents useful for same. The present invention further enables high throughput screening protocols for agents which act via eRNAs or their receiver targets. Such agents include enogenous molecules such as RNA&#39;s or products identified by natural products screening or the screening of chemical libraries.  
           [0022]    An example of eRNA is the shared intronic sequence of GRIA2, GRIA3 and GRIA4 genes shown in FIG. 6. The present invention extends to homologous eRNAs having at least 70% identity to the nucleotide sequence shown in FIG. 6 and to nucleotide sequences capable of hybridzing to the sequence shown in FIG. 6 or its complementary form under low stringency conditions.  
           [0023]    The present invention is further useful in manipulating stem cells to differentiate along a particular pathway and, hence, be involved in tissue repair, regeneration and/or augmentation.  
                         TAABLE 1                           SUMMARY OF SEQUENCE IDENTIFIERS (SEQ ID Nos.)            Seq ID No.   Description                1   Nucleotide sequence of intron from human Chr19 be-           tween nucleotides 38234 and 167860        2-43   Olgonucleotide human sequence enquiries       44   Nucleotide sequence of intron from human Chr12 be-           tween 156966 and 180225       45-52   Olgonucleotide human sequence enquiries       53   Nucleotide sequence of intron on human Chr12 between           nucleotide 156966 and 180225       54-81   Oligonucleotide sequence enquiries        82-121   Putative eRNA sequences for  S. cerevisiae                    
 
       
    
    
     BRIEF DESCRIPTION OF THE FIGURES  
       [0024]    [0024]FIG. 1 is a schematic representation of sub-network, an uncontrolled regulated network and a controlled multi-tasked network. Panel (a) shows an uncontrolled sub-network wherein nodes take limited numbers of regulatory inputs r k  and generate limited numbers of protein outputs g k . Here, g 1  regulates n 2  while being subject to feedback interactions from g 2  (dotted line). Panel (b) shows the same sub-network with each node expressing a multiplex output of protein product g k  and many control molecules c k  each capable of targeted interactions to multi-task the sub-network. A sample interactions (shown as dot-dash lines) include control c 1  determining the alternative splicing of the node n 3  output giving g 3  or g 3 , the latter of which regulates node n 2  when expressed, while nodes n 1  and n 3  each feedback controls onto the other. It is evident that controls increase interconnectivity which increases network dynamical output complexity.  
         [0025]    [0025]FIG. 2 is a diagrammatic representation showing (A) a simple network involved in particular cellular functions and (B) a complex network involved in cellular differentiation and development.  
         [0026]    [0026]FIG. 3 is a diagrammatic representation of a system used to carry out the instructions encoded by the storage medium of FIGS. 4 and 5.  
         [0027]    [0027]FIG. 4 is a diagrammatic representation of a cross-section of a magnetic storage medium.  
         [0028]    [0028]FIG. 5 is a diagrammatic representation of a cross-section of an optically readable data storage system.  
         [0029]    [0029]FIG. 6 is a diagrammatic representation of an eRNA network centred around the GRIA2, GRIA3 and GRIA4 genes. The eRNA comprises the nucleotide sequence which is a shared intronic sequence of the GRIA genes. The sequence is shown in the figure.  
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0030]    The present invention is predicated in part on the recognition that eukaryotic cells have evolved a complex network of genetic signals which facilitates integration of gene activity and multi-tasking of the cellular proteome. It is proposed, in accordance with the present invention, that integration and multi-tasking of this sophisticated and complex genetic network is mediated at least in part by trans-acting, non-protein coding RNA molecules corresponding to introns or other non-coding RNA sequences of protein-encoding nucleotide sequences or introns and/or exons from RNA sequences of non-protein-encoding nucleotide sequences. The identification of these RNA molecules, referred to herein as efference RNAs or eRNAs, permits the development of a further level of functional genomics and advanced genetic engineering. In particular, eRNAs and/or their target or associated molecules or homologs, analogs, functional equivalents or synthetic forms are now obtainable and have utility as therapeutic agents and trait-modifying agents in eukaryotic cells such as vertebrate and invertebrate animal cells and plant cells. The eRNAs and their targets influence, therefore, the genetic architecture of the cell and, hence, these molecules were as well as analogs and homologs thereof have trait-modification potential. Reference to a “target” includes a “receiver” and includes nucleotide sequences in genomic DNA or RNA, including introns, exons 5′ or 3′ untranslated regions of genes or their transcripts (UTRs), as well as 5′ or 3′ flanking regions of genes and intergenic regions, which act as receivers of the eRNAs. Such targets are referred to herein as “receiver DNAs” or “receiver RNAs”. The targets may also be proteins with which eRNAs interact (i.e. “receiver proteins”). The eRNAs are regarded as “transmitters”.  
         [0031]    Accordingly, one aspect of the present invention contemplates a method for identifying an eRNA or a DNA sequence comprising an eRNA-encoding sequence in the nucleome of a eukaryotic cell, said method comprising identifying non-protein-encoding nucleotide sequences within an RNA transcript or a DNA sequence encoding same in said nucleome, determining the nucleotide sequence of said non-protein-encoding nucleotide sequence and subjecting said sequence to phenotyping to determine its effect on one or more biological events within a cell or an organism and/or determining the degree to which said sequence is conserved or is variant in the organism&#39;s genome or in the genome of other species or genera of eukaryotic cells wherein a non-protein-encoding nucleotide sequence having a biological effect in a cell or a nucleotide sequence conserved within the genome or between different cells&#39; nucleomes is deemed to be an eRNA or DNA sequence comprising a nucleotide sequence encoding same.  
         [0032]    In a related embodiment, there is provided a method for identifying a receiver DNA or RNA, said method comprising identifying an eRNA by the method comprising identifying non-protein-encoding nucleotide sequences within an RNA transcript or a DNA sequence encoding same in said nucleome, determining the nucleotide sequence of said non-protein-encoding nucleotide sequence and subjecting said sequence to phenotyping to determine its effect on one or more biological events within a cell or an organism and/or determining the degree to which said sequence is conserved or is variant in the organism&#39;s genome or in the genome of other species or genera of eukaryotic cells wherein a non-protein-encoding nucleotide sequence having a biological effect in a cell or a nucleotide sequence conserved within the genome or between different cells&#39; nucleomes is deemed to be an eRNA or DNA sequence comprising a nucleotide sequence encoding same and then contacting said eRNA with nucleome material and screening for interaction between the eRNA and a DNA or RNA wherein the detection of such interaction is indicative of a receiver molecule.  
         [0033]    In a further related embodiment, the present invention provides a method for identifying a receiver protein, said method comprising identifying an eRNA by the method comprising identifying non-protein-encoding nucleotide sequences within an RNA transcript or a DNA sequence encoding same in said nucleome, determining the nucleotide sequence of said non-protein-encoding nucleotide sequence and subjecting said sequence to phenotyping to determine its effect on one or more biological events within a cell or an organism and/or determining the degree to which said sequence is conserved or is variant in the organism&#39;s genome or in the genome of other species or genera of eukaryotic cells wherein a non-protein-encoding nucleotide sequence having a biological effect in a cell or a nucleotide sequence conserved within the genome or between different cells&#39; nucleomes is deemed to be an eRNA or DNA sequence comprising a nucleotide sequence encoding same and then contacting said eRNA with proteome material and screening for interaction between the eRNA and a protein wherein the detection of such interaction is indicative of a receiver protein.  
         [0034]    In an alternative embodiment, bioinformatics is used to identify conserved nucleotide sequences of putative eRNAs or receiver sequences. An example of a non-bioinformatic method to detect eRNAs and/or receiver molecules is by gel retardation assays.  
         [0035]    An “eRNA” means an “efference RNA” and corresponds to an RNA derived from intronic sequences of protein-encoding genes or derived from intronic and/or exonic sequences of non-protein-encoding transcripts which are involved in endogenous control of a genetic network within eukaryotic cells, including modulation of signalling and genetic, events within and between eukaryotic cells to alter differentiation and development and to alter gene expression patterns that may be useful in advanced genetic engineering of plants, animals and other eukaryotes and in the treatment of imbalances that underlie common diseases including cancer. An eRNA is regarded herein as a transmitter. A non-protein-encoding transcript means an RNA sequence transcribed from a gene but which is not translated into a protein sequence. Reference to a “genetic network” includes the genetic signals required to inter alia induce expression of a suite of genes, induce physiological changes within, on or between cells or facilitate multi-tasking of a cell&#39;s proteome. The genetic network may also be regarded as the genetic architecture of the cell. Such networking may involve the facilitation of RNA-DNA, RNA-RNA and RNA-protein interactions and may readily be observed by parameters such as alterations to gene expression, RNA splicing, DNA methylation, remodelling of chromatin, other signal transduction systems and cellular physiology, including responses to environmental variables. eRNAs act inter alia via receiver DNA, RNA or protein sequences.  
         [0036]    Reference to an “intron” includes any RNA sequence which is capable of being excised from a primary RNA transcript (e.g. a pre-messenger RNA transcript). An “exon” includes any RNA sequence which is re-assembled to form a contiguous RNA after the removal of introns by splicing, which may form a messenger RNA (mRNA) containing protein-coding sequence, or a non-protein-coding RNA without protein-coding capacity. “Non-protein-encoding RNA sequences” also includes introns as well as RNA sequences 5′ of the authentic translation initiation site or 3′ of the translation termination codon. The latter two sites are generally referred to 5′ untranslated regions (UTR) or 3′ UTR of mRNA. The term “untranslated region” or “UTR” is a term of the art referring to the particular location of a genetic sequence relative to the translation initiation site. However, the use of these terms is not to exclude the possibility that some partial translation may occur in this region. For convenience, reference to a “protein” includes reference to a peptide or polypeptide. In a particularly preferred embodiment, the 3′ and 5′ UTRs or parts thereof act as receiver molecules for eRNAs.  
         [0037]    An “RNA transcript” represents the sequence of ribonucleotides transcribed from a deoxyribonucleotide sequence of a gene. Thus, an RNA transcript includes and encompasses a primary gene transcript or pre-messenger RNA (pre-mRNA), which may contain one or more introns, as well as a messenger RNA (mRNA) in which any introns of the pre-mRNA have been excised and the exons spliced together. It is proposed, in accordance with the present invention, that some of the excised RNA introns in protein-coding transcripts or introns and exons in non-protein-coding transcripts act as eRNA molecules and modulate genetic signalling within a cell.  
         [0038]    The “proteome” is regarded as the total protein within and on a cell. The “nucleome” is the total nucleic acid complement and includes the genome and all RNA molecules such as mRNA, heterogenous nuclear RNA (hnRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), small cytoplasmic RNA (scRNA), ribosomal RNA (rRNA), translational control RNA (tcRNA), transfer RNA (tRNA), eRNA, messenger-RNA-interfering complementary RNA (micRNA) or interference RNA (iRNA) and mitochondrial RNA (mtRNA).  
         [0039]    It is particularly useful to identify eRNAs on the basis of conserved ribonucleotide sequences in intronic RNA sequences of protein-encoding nucleotide sequences or intronic and/or exonic sequences of non-protein-encoding nucleotide sequences or their corresponding deoxyribonucleotide sequences. Reference to “conserved” includes any polyribonucleotide or polydeoxyribonucleotide sequence sharing at least about 80% nucleotide complementarity to another sequence in the nucleome. Conserved sequences in the genome including 3′ and 5′ regions of genes is suggestive of a putative receiver molecule.  
         [0040]    The term “similarity” as used herein includes partial or exact sequence identity or complementarity between compared sequences at the nucleotide level. In a preferred embodiment, nucleotide and sequence comparisons are made at the level of exact complimentarity or identity rather than partial identity or complementarity.  
         [0041]    Terms used to describe sequence relationships between two or more polynucleotides include “reference sequence”, “comparison window”, “sequence similarity”, “sequence identity”, “sequence complementarity”, “percentage of sequence similarity”, “percentage of sequence identity”, “percentage of sequence complementarity”, “substantial similarity”, “substantial complementarity” and “substantial identity”. A “reference sequence” is at least 12 but frequently 15 to 18 and often at least 25 or above, such as 30 monomer units, inclusive of nucleotides, in length. Because two polynucleotides may each comprise (1) a sequence (i.e. only a portion of the complete polynucleotide sequence) that is similar between the two polynucleotides, and (2) a sequence that is divergent between the two polynucleotides, sequence comparisons between two (or more) polynucleotides are typically performed by comparing sequences of the two polynucleotides over a “comparison window” to identify and compare local regions of sequence similarity or complementarity. A “comparison window” refers to a conceptual segment of typically 12 contiguous residues that is compared to a reference sequence. The comparison window may comprise additions or deletions (i.e. gaps) of about 20% or less as compared to the reference sequence (which does not comprise additions or deletions) for optimal alignment of the two sequences. Optimal alignment of sequences for aligning a comparison window may be conducted by computerised implementations of algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package Release 7.0, Genetics Computer Group, 575 Science Drive Madison, Wis., USA) or by inspection and the best alignment (i.e. resulting in the highest percentage homology over the comparison window) generated by any of the various methods selected. Reference also may be made to the BLAST family of programs as, for example, disclosed by Altschul et al.  Nucl. Acids Res.  25: 3389 1997. A detailed discussion of sequence analysis can be found in Unit 19.3 of Ausubel et al. (1998).  
         [0042]    The terms “sequence similarity”, “sequence identity” and “sequence complementarity” as used herein refers to the extent that sequences are identical or functionally or structurally similar or complementary on a nucleotide-by-nucleotide basis over a window of comparison using standard rules for DNA-DNA, RNA-RNA and RNA-DNA base pairing. Thus, a “percentage of sequence identity”, for example, is calculated by comparing two optimally aligned sequences over the window of comparison, determining the number of positions at which the identical nucleic acid base (e.g. A, T, C, G, I, U) occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the window of comparison (i.e., the window size), and multiplying the result by 100 to yield the percentage of sequence identity or complementarity. For the purposes of the present invention, “sequence identity” between DNA sequences will be understood to mean the “match percentage” calculated by the DNASIS computer program (Version 2.5 for windows; available from Hitachi Software engineering Co., Ltd., South San Francisco, Calif., USA) using standard defaults as used in the reference manual accompanying the software. Similar comments apply in relation to DNA sequence similarity. Sequence complementarity in duplex and higher order RNA-RNA, RNA-DNA and RNA-protein interactions will be assessed by rules as described in Hermann. et al.,  Chem Biol,  6: R335-43. 1999; Masquida et al.  Rna,  6: 9-15. 2000; Praseuth et al.,  Biochim Biophys Acta,  1489: 181-206 1999; Varani et al.,  EMBO Rep,  1: 18-23 2000.  
         [0043]    Conveniently, an intronic or other protein-non-encoding sequence at the RNA or DNA level to a database of DNA or RNA sequences in the genome or nucleome and the identification of at least 80% similar sequences (e.g. determined by BLAST analysis) after optimal alignment is determined. The presence of one or more other homologous or complementary sequences in the database or between databases for different species, genera or families of invertebrate or non-invertebrate animals or plants is indicative of a candidate sequence involved in genetic network signal modulation.  
         [0044]    Sequence similarity and complementarity provides one of a number of features or identifiers useful for analyzing the likelihood of a target RNA sequence being an eRNA. Other identifiers include the participation of the gene from which the potential eRNA is derived in a pathway or its involvement in multiple pathways such as part of the physiological or genetic networks contained within a cell. Furthermore, putative eRNA sequences may also share common secondary or tertiary structures. This may occur, for example, when the eRNA interacts with certain RNAses or ribosomes or nucleic acid binding proteins. Partly as a result of these features, apart from sequence determination, putative eRNA sequences may be detected by conventional genetic techniques such as deletional analysis, transgenesis, genetic silencing procedures (e.g. co-suppression, antisense techniques, RNAi induction) and the physiological effects of such procedures observed. Such physiological effects are referred to herein as a nucleotide sequence having a “biological effect”. Furthermore, the effect of eRNA may be demonstrated by ectopic expression studies. For example, intronic sequences from protein-coding sequences may be expressed on non-protein-coding sequences to determine the function of the eRNA in the absence of exon sequences or cis-acting elements in the transcript from which the eRNA is obtained. Transgenic animals and cells obtained therefrom in which genomic sequences have been replaced by cDNA sequences which do not contain the introns of the genetic sequences can also be employed.  
         [0045]    The main advantage of RNA as a regulatory molecule is its compact size and sequence specificity. The likelihood is that most RNA signals will be transmitted through primary sequence-specific interactions with other RNAs and with DNA, forming complexes that are recognized by proteins containing particular types of domains. This provides an opportunity to identify both the potential transmitters and receivers (targets) in such networks, as well as the types of interacting proteins. Importantly, most of these interactions would be expected to involve RNA-RNA and RNA-DNA interactions (potentially including triplexes and other higher-order structures) that do not obey canonical Watson-Crick base-pairing rules. Thus, the present invention extends to algorithms which allow genomic sequence to be searched for these different types of interactions. Complete search algorithms, such as those based on suffix arrays and suffix trees are particularly useful to analyse this properly.  
         [0046]    The ability of RNA to form strong interactions with other RNAs suggests that RNA-RNA and (to a lesser extent) RNA-DNA base pairing is stronger than DNA-DNA base pairing, and can allow for stable mismatches and the formation of particular secondary structures such as bulges, stems and loops, which, rather than being seen as mismatch errors (as in DNA repair), may also in fact contain embedded structural motifs that can be recognized by particular proteins. For example, perfect versus imperfect matching of microRNAs to their targets determines whether the mRNA target is actively degraded by the RNAi pathway or is translationally repressed.  
         [0047]    Accordingly, it is proposed that the prediction can be made that different types of RNA signals and the different structures of the resulting complexes are recognized and acted on by particular classes of nucleic-acid-binding proteins. An understanding these secondary structural and mismatch rules enables the bioinformatic approaches to dissecting these networks at the genomic level. It also allows better prediction of the regulatory consequences of different types of RNA signals, by the development of specific algorithms to identify particular subsets that obey different sets of rules for the combination of sequence specificity and the type of secondary structure that is created by the interaction, bearing in mind that parts of the network will be silent in any given cell or lineage because an RNA transmitter or target is not expressed, or a DNA target has been made inaccessible by chromatin modification.  
         [0048]    The present invention is predicated in part on the proposal that in order for a molecular genetic network to be capable of complex programming and multi-tasking, each of the gene sub-networks within a cell must produce numerous control molecules in parallel with their primary gene products, which dynamically communicate with other sub-networks (via transcriptional, splicing and translational controls, among others). Such a system would be expected to display an exponential increase in its ability to manage and integrate larger genetic datasets, and in its functionality and phenotypic range. In addition, because modulation of system dynamics can be readily achieved by mutation of control molecules, such a system should be able to explore new expression space at fast evolutionary rates over short evolutionary timescales.  
         [0049]    An example of eRNA is the shared intronic sequence of GRIA2, GRIA3 and GRIA4 genes shown in FIG. 6. The present invention extends to homologous eRNAs having at least 70% identity to the nucleotide sequence shown in FIG. 6 and to nucleotide sequences capable of hybridzing to the sequence shown in FIG. 6 or its complementary form under low stringency conditions.  
         [0050]    A controlled multi-tasked molecular network is schematically shown in FIG. 1, in contrast to an uncontrolled regulated network. This network architecture can be equally applied to computer networks, neural networks and cellular networks. An example of simple and complex genetic networks is shown in FIG. 2.  
         [0051]    The nodes of a controlled multi-tasked network must be capable of generating and integrating multiple inputs and outputs. Such networks are generally stable and scale-free, with some nodes having high connectivity and others low connectivity, similar to most communication and social networks, including the Internet (Albert et al.,  Nature  406: 378-382, 2000). Multiply connected networks are widely employed in other complex information processing systems, including in neurobiology where secondary networking signals, termed “efference” signals, underlie sensory awareness and motor coordination (Bridgeman,  Ann. Biomed. Eng.  23: 409-422 1995; Andersen et al.,  Annu. Rev. Neurosci  20: 303-330 1997). The concept of multiple inputs and outputs is also a well established feature of neural networks in cognition, language and memory (Plunkett et al.,  J. Child Psychol. Psychiatry  38: 53-80 1997; Elman, A Companion to Cognitive Science, Basil Blackwood Bechtel and Graham, Eds 1998). These networks involve densely connected webs of processing units that propagate and transform complex patterns of activity, and are capable of self-organization. They operate by a form of parallel distributed processing, whereby information is distributed across the system such that patterns of activation across sets of “hidden units” (i.e. controls), which define the state of the network, then determine the pattern of activation across output nodes (McClelland and Rumelhart,  J. Exp. Psychol. Gen  114: 159-197 1985; McClelland and Plaut,  Curr. Opin. Neurohol  3: 209-216 1993; Plunkett et al.,  J. Child Psychol. Psychiatry  38: 53-80 1997).  
         [0052]    The assessment of the presence of similar nucleotide sequences in a genome or nucleome database is suitably facilitated with the assistance of a computer programmed with software, which inter alia adds or weighs index values (I V ) for each feature associated with the candidate sequences to provide a predictive value (P V ) corresponding to the likelihood of the candidate sequences being involved in modulating genetic network signalling. The features are selected from:— 
         [0053]    (a) the transmitter sequence is derived from an intron in a protein-coding RNA transcript or an intron or an exon in a non-protein-coding RNA transcript or their DNA equivalents;  
         [0054]    (b) the target receiver sequence lies in an intron or an exon in an RNA transcript or its DNA equivalent;  
         [0055]    (c) the target receiver sequence lies in an intergenic genomic DNA sequence, such as a promoter or enhancer region;  
         [0056]    (d) the target receiver is a DNA or RNA sequence capable of interaction with an eRNA;  
         [0057]    (e) the target receiver sequence lies in a 5′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0058]    (f) the target receiver sequence lies in a 3′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0059]    (g) the target receiver is a protein capable of sequence-specific recognition of an eRNA and/or its target recognition sequences;  
         [0060]    (h) the sequence is a DNA or RNA which recognizes and/or interacts with an eRNA;  
         [0061]    (i) the sequence comprises at least 12 nucleotides;  
         [0062]    (j) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence of the same genome or nucleome;  
         [0063]    (k) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence in a genome or nucleome of a different species, genus or family of animal or plant cells;  
         [0064]    (l) The sequence associates by its position to a feature from available databases, for example, Genbank, the Gene Ontology databse or SWISSPORT; and  
         [0065]    (m) The sequence associates by its position to a protein (ie. falls within the transcript) and that protein&#39;s expression profile, as determined by microarray analysis, is modulated in a specific way during a phenomona of interest, for example, highly up or down regulated in the initial phase of meiosis.  
         [0066]    In a preferred embodiment of the features (j) and (k), the sequence preferably has at least 90% and more preferably at least 95% nucleotide identity or complementarity to said at least one sequence (e.g. as determined by BLAST analysis) such as at least about 96%, 97%, 98%, 99% or 100%.  
         [0067]    With respect to feature (i), the preferred number of nucleotides is from about 12 to about 100, more preferably from about 12 to about 50 and even more preferably from about 12 to about 30 such as about 22.  
         [0068]    Preferably, the features are further selected from:— 
         [0069]    (1) expression of the sequences mentioned in (e) is associated with the modulation of the same phenotype.  
         [0070]    In accordance with the present invention, index values for such features are stored in a machine-readable storage medium which is capable of being processed by the processing means of the computer to provide a predictive value for a candidate sequence being involved in genetic regulation.  
         [0071]    Thus, in another aspect, the invention contemplates a computer program product for assessing the likelihood of a candidate nucleotide sequence or group of nucleotide sequences being an eRNA or a receiver for an eRNA involved in network genetic signalling, said product comprising:— 
         [0072]    (1) code that receives as input index values for one or more of features wherein said features are selected from:  
         [0073]    (a) the transmitter sequence is derived from an intron in a protein-coding RNA transcript or an intron or an exon in a non-protein-coding RNA transcript or their DNA equivalent;  
         [0074]    (b) the target receiver sequence lies in an intron or an exon in an RNA transcript or its DNA equivalent;  
         [0075]    (c) the target receiver sequence lies in an intergenic genomic DNA sequence, such as a promoter or enhancer region;  
         [0076]    (d) the target receiver is a DNA or RNA sequence capable of interaction with an eRNA;  
         [0077]    (e) the target receiver sequence lies in a 5′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0078]    (f) the target receiver sequence lies in a 3′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0079]    (g) the target receiver is a protein capable of sequence-specific recognition of an eRNA and/or its target recognition sequences;  
         [0080]    (h) the sequence is a DNA or RNA which recognizes and/or interacts with an eRNA;  
         [0081]    (i) the sequence comprises at least 12 nucleotides;  
         [0082]    (j) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence of the same genome or nucleome;  
         [0083]    (k) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence in a genome or nucleome of a different species, genus or family of animal or plant cells;  
         [0084]    (l) the sequence associates by its position to a feature from available databases, for example, Genbank, the Gene Ontology database, SWISSPORT  
         [0085]    (m) The sequence associates by its position to a protein (ie. falls within the transcript) and that protein&#39;s expression profile, as determined by microarray analysis, is modulated in a specific way during a phenomona of interest, for example highly up or down regulated in the initial phase of meiosis.  
         [0086]    (2) code that adds said index values to provide a sum corresponding to a predictive value for said candidate sequences; and  
         [0087]    (3) a computer readable medium that stores the codes.  
         [0088]    In a related embodiment, the present invention is directed to a computer program product for assessing the likelihood of a candidate nucleotide sequence or group of nucleotide sequences being a receiver molecule involved in network signalling via an eRNA, said product comprising:— 
         [0089]    (1) code that receives as input index values for one or more of features wherein said features are selected from:— 
         [0090]    (a) the target receiver sequence lies in an intergenic genomic DNA sequence, such as a promoter or enhancer region;  
         [0091]    (b) the target receiver is a DNA or RNA sequence capable of interaction with an eRNA;  
         [0092]    (c) the target receiver sequence lies in a 5′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0093]    (d) the target receiver sequence lies in a 3′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0094]    (e) the target receiver is a protein capable of sequence-specific recognition of an eRNA and/or its target recognition sequences;  
         [0095]    (f) the sequence is a DNA or RNA which recognizes and/or interacts with an eRNA;  
         [0096]    (g) the sequence comprises at least 12 nucleotides;  
         [0097]    (h) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence of the same genome or nucleome;  
         [0098]    (i) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence in a genome or nucleome of a different species, genus or family of animal or plant cells;  
         [0099]    (j) The sequence associates by its position to a feature from available databases, for example, Genbank, the Gene Ontology database, SWISSPORT;  
         [0100]    (k) The sequence associates by its position to a protein (ie. falls within the transcript) and that proteins expression profile, as determined by microarray analysis, is modulated in a specific way during a phenomona of interest, for example highly up or down regulated in the initial phase of meiosis.  
         [0101]    (2) code that adds said index values to provide a sum corresponding to a predictive value for said candidate sequences; and  
         [0102]    (3) a computer readable medium that stores the codes.  
         [0103]    In a preferred embodiment, the computer program product comprises codes which assign an index value for each feature of a candidate sequence.  
         [0104]    In a related aspect, the invention extends to a computer system for assessing the likelihood of a candidate sequence or group of candidate sequences being an eRNA involved in network genetic signalling wherein said computer system comprises:— 
         [0105]    (1) a machine-readable data storage medium comprising a data storage material encoded with machine-readable data, wherein said machine-readable data comprise index values for one or more features, wherein said features are selected from:— 
         [0106]    (a) the transmitter eRNA sequence is derived from an intron in a protein-coding RNA transcript or an intron or an exon in a non-protein-coding RNA transcript, or their DNA equivalent;  
         [0107]    (b) the sequence comprises at least 12 nucleotides;  
         [0108]    (c) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence of the same genome or nucleome;  
         [0109]    (d) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence in a genome or nucleome of a different species, genus or family of animal or plant cells;  
         [0110]    (e) the sequence comprises a secondary or tertiary structure having an activity; and  
         [0111]    (f) the sequence exhibits catalytic activity;  
         [0112]    (2) a working memory for storing instructions for processing said machine-readable data;  
         [0113]    (3) a central-processing unit coupled to said working memory and to said machine-readable data storage medium, for processing said machine readable data to provide a sum of said index values corresponding to a predictive value for said candidate sequences; and  
         [0114]    (4) an output hardware coupled to said central processing unit for receiving said predictive value.  
         [0115]    Even yet another aspect of the invention extends to a computer system for assessing the likelihood of a candidate sequence or group of candidate sequences being a receiver RNA, DNA or protein involved in network genetic signalling wherein said computer system comprises:— 
         [0116]    (1) a machine-readable data storage medium comprising a data storage material encoded with machine-readable data, wherein said machine-readable data comprise index values for one or more features, wherein said features are selected from:— 
         [0117]    (a) the sequence is located in an intron or an exon in an RNA transcript or its DNA equivalent;  
         [0118]    (b) the target receiver sequence lies in an intergenic genomic DNA sequence, such as a promoter or enhancer region;  
         [0119]    (c) the sequence is located in a 5′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0120]    (d) the sequence is located in a 3′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0121]    (e) the sequence is a protein capable of sequence-specific recognition of an eRNA and/or its target recognition sequence;  
         [0122]    (f) the sequence is an RNA or DNA which recognizes and/or interacts with an eRNA;  
         [0123]    (g) the sequence comprises at least 12 nucleotides;  
         [0124]    (h) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence of the same genome or nucleome;  
         [0125]    (i) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence in a genome or nucleome of a different species, genus or family of animal or plant cells;  
         [0126]    (j) the sequence comprises a secondary or tertiary structure having an activity; and  
         [0127]    (k) the sequence exhibits catalytic activity;  
         [0128]    (2) a working memory for storing instructions for processing said machine-readable data;  
         [0129]    (3) a central-processing unit coupled to said working memory and to said machine-readable data storage medium, for processing said machine readable data to provide a sum of said index values corresponding to a predictive value for said candidate sequences; and  
         [0130]    (4) an output hardware coupled to said central processing unit for receiving said predictive value.  
         [0131]    A version of these embodiments is presented in FIG. 3, which shows a system  10  including a computer  11  comprising a central processing unit (“CPU”)  20 , a working memory  22  which may be, e.g. RAM (random-access memory) or “core” memory, mass storage memory  24  (such as one or more disk drives or CD-ROM drives), one or more cathode-ray tube (“CRT”) display terminals  26 , one or more keyboards  28 , one or more input lines  30 , and one or more output lines  40 , all of which are interconnected by a conventional bidirectional system bus  50 .  
         [0132]    Input hardware  36 , coupled to computer  11  by input lines  30 , may be implemented in a variety of ways. For example, machine-readable data of this invention may be inputted via the use of a modem or modems  32  connected by a telephone line or dedicated data line  34 . Alternatively or additionally, the input hardware  36  may comprise CD. Alternatively, ROM drives or disk drives  24  in conjunction with display terminal  26 , keyboard  28  may also be used as an input device.  
         [0133]    Output hardware  46 , coupled to computer  11  by output lines  40 , may similarly be implemented by conventional devices. By way of example, output hardware  46  may include CRT display terminal  26  for displaying a synthetic polynucleotide sequence or a synthetic polypeptide sequence as described herein. Output hardware might also include a printer  42 , so that hard copy output may be produced, or a disk drive  24 , to store system output for later use.  
         [0134]    In operation, CPU  20  coordinates the use of the various input and output devices  36 , 46  coordinates data accesses from mass storage  24  and accesses to and from working memory  22 , and determines the sequence of data processing steps. A number of programs may be used to process the machine readable data of this invention. Exemplary programs may use for example the following steps:— 
         [0135]    (1) inputting index values for at least one feature associated with a candidate sequence, wherein said features are selected from:— 
         [0136]    (a) the sequence is an intron or exon in an RNA transcript or its DNA equivalent;  
         [0137]    (b) the sequence is a 5′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0138]    (c) the sequence is a 3′ untranslated region of an RNA transcript or its DNA equivalent;  
         [0139]    (d) the sequence is a DNA, RNA or protein which is capable of interaction with an eRNA;  
         [0140]    (e) the sequence comprises at least 12 nucleotides;  
         [0141]    (f) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence of the same genome or nucleome;  
         [0142]    (g) the sequence has at least 80% nucleotide identity or complementarity to at least one sequence in a genome or nucleome of a different species, genus or family of animal or plant cells;  
         [0143]    (h) the sequence comprises a secondary or tertiary structure having an activity; and  
         [0144]    (i) the sequence exhibits catalytic activity;  
         [0145]    (2) adding the index values for said features to provide a predictive value for said sequence; and (3) outputting said predictive value.  
         [0146]    [0146]FIG. 4 shows a cross section of a magnetic data storage medium  100  which can be encoded with machine readable data, or set of instructions, for designing a synthetic molecule of the invention, which can be carried out by a system such as system  10  of FIG. 5. Medium  100  can be a conventional floppy diskette or hard disk, having a suitable substrate  101 , which may be conventional, and a suitable coating  102 , which may be conventional, on one or both sides, containing magnetic domains (not visible) whose polarity or orientation can be altered magnetically. Medium  100  may also have an opening (not shown) for receiving the spindle of a disk drive or other data storage device  24 . The magnetic domains of coating  102  of medium  100  are polarized or oriented so as to encode in manner which may be conventional, machine readable data such as that described herein, for execution by a system such as system  10  of FIG. 3.  
         [0147]    [0147]FIG. 4 shows a cross section of an optically readable data storage medium  110  which also can be encoded with such a machine-readable data, or set of instructions, for screening a candidate molecule of the present invention, which can be carried out by a system such as system  10  of FIG. 3. Medium  110  can be a conventional compact disk read only memory (CD-ROM) or a rewritable medium such as a magneto-optical disk, which is optically readable and magneto-optically writable. Medium  100  preferably has a suitable substrate  111 , which may be conventional, and a suitable coating  112 , which may be conventional, usually of one side of substrate  111 .  
         [0148]    In the case of CD-ROM, as is well known, coating  112  is reflective and is impressed with a plurality of pits  113  to encode the machine-readable data. The arrangement of pits is read by reflecting laser light off the surface of coating  112 . A protective coating  114 , which preferably is substantially transparent, is provided on top of coating  112 .  
         [0149]    In the case of a magneto-optical disk, as is well known, coating  112  has no pits  113 , but has a plurality of magnetic domains whose polarity or orientation can be changed magnetically when heated above a certain temperature, as by a laser (not shown). The orientation of the domains can be read by measuring the polarisation of laser light reflected from coating  112 . The arrangement of the domains encodes the data as described above.  
         [0150]    In essence, the subject computer software analyzes genomic or nucleomic databases for the presence of particular sequences which have one or more features as defined above. Each of these features carries a certain weight as to the importance in establishing that a target sequence is an eRNA or is a DNA sequence encoding an eRNA. Multiple features may be created by combining the features with certain biological effects as discussed above. For example, a conserved intron between species may combine with certain biological phenomena associated with a conserved deletion of this sequence. The resulting features, sub-features and multiple features and combinations thereof combine to produce a “fingerprint” or “descriptor” of not only an individual eRNA but also families of eRNAs and this may also provide a fingerprint of the gene expression status of a cell or animal or plant comprising cells at any given time.  
         [0151]    The present system retrieves features and forms composite features from them. More than one feature can be combined in a variety of different ways to form these composite features. In particular, the composite feature can be any function or combination of a simple feature and other composite features. The function can be algebraic, logical, sinusoidal, logarithmic, linear, hyperbolic, statistical and the like. Alternatively, more than one feature can be obtained in a functional manner (e.g. arithmetic, algebraic). By way of example, a composite feature may equal the sum of two or more features or a composite feature may correspond to a sub-fraction of overlap of one or more features from another feature. Alternatively, a composite feature may equal a constant times one or more features. Of course, there are many other ways composite features can be defined.  
         [0152]    The genome/nucleome databases may be from any eukaryotic cell such as from a vertebrate or invertebrate, including mammalian, avian, reptilian and amphibian animals, as well as from plants. The term “plants” includes monocotyledonous and dicotyledonous plants. It is particularly useful to employ the analysis function aspect of the present invention to human genome databases.  
         [0153]    Computer programs may also be designed to screen nucleic acid molecule similarity at the secondary or tertiary levels. Furthermore, epidemiological studies together with polymorphism mapping may identify conserved polymorphisms in otherwise non-homologous nucleotide sequences. This would suggest an eRNA which is active at the secondary or tertiary levels.  
         [0154]    Although not intending to limit the present invention to any one theory or mode of action, it is proposed that the eRNA molecules are “eRNA senders” or “eRNA transmitters” in the sense that they function as trans-acting networking molecules. eRNA senders have target molecules in the form of DNA, RNA and protein receivers. The receiver molecules may be located anywhere in the proteome, genome or nucleome. The identification of an eRNA permits the identification of these receiver molecules. Furthermore, again not intending to limit the present invention to any one theory or mode of action, it is proposed that there may be a connection between interference RNA (RNAi) and eRNA. RNAi is induced by, for example, double standard RNA generally corresponding to at least part of a coding strand of a gene. It is proposed, herein, that eRNAs may also induce RNAi and in fact be the true inducer of RNAi.  
         [0155]    Consequently, another aspect of the present invention contemplates a method of inducing post transcription gene silencing (PTGS) of a gene carrying a nucleotide receiver sequence, said method comprising expressing an eRNA having said receiver nucleotide sequence which induces an RNAi capable of targeting said receiver sequence in an mRNA transcript of said gene. The ability to induce specific RNAi mediated PTGS or transcriptional gene silencing (TGS) using eRNAs or their homologs or analogs will greatly enhance the ability to modify traits in plant and animal cells.  
         [0156]    RNAi, both in therapeutic and experimental usage, is complicated by an effect known as RNAi transitivity. When a gene is silenced by a RNAi signal, if the transcript of the gene has within it a sequence exactly homologous to the transcript of another gene it is possible for the second gene to be silenced as well, an effect which could lead to invalid experimental results or side-effects in therapy.  
         [0157]    Thus, another aspect of the present invention is the utilization of eRNA networks to predict the scope and effect of transitive RNAi, by analysing the sequence of the targeted gene and comparing it to known effectors in the gene regulatory network.  
         [0158]    Another aspect of the present invention provides an eRNA molecule identified by the method comprising identifying non-protein-encoding nucleotide sequences within an RNA transcript or a DNA sequence encoding same in said nucleome, determining the nucleotide sequence of said non-protein-encoding nucleotide sequence and subjecting said sequence to phenotyping to determine its effect on one or more biological events within a cell and/or determining the degree to which said sequence is conserved in the cell&#39;s genome or in the genome of other species or genera of eukaryotic cells wherein a non-protein-encoding nucleotide sequence having a biological effect in a cell or a nucleotide sequence conserved within the genome or between different cells&#39; nucleomes is deemed to be an eRNA or DNA sequence comprising a nucleotide sequence encoding same.  
         [0159]    Yet another aspect of the present invention is directed to a receiver DNA or RNA identified by the method comprising identifying non-protein-encoding nucleotide sequences within an RNA transcript or a DNA sequence encoding same in said nucleome, determining the nucleotide sequence of said non-protein-encoding nucleotide sequence and subjecting said sequence to phenotyping to determine its effect on one or more biological events within a cell and/or determining the degree to which said sequence is conserved in the cell&#39;s genome or in the genome of other species or genera of eukaryotic cells wherein a non-protein-encoding nucleotide sequence having a biological effect in a cell or a nucleotide sequence conserved within the genome or between different cells&#39; nucleomes is deemed to be an eRNA or DNA sequence comprising a nucleotide sequence encoding same and then contacting said eRNA with nucleome material and screening for interaction between the eRNA and a DNA, RNA or protein wherein the detection of such interaction is indicative of a receiver molecule.  
         [0160]    Still another aspect of the present invention provides a receiver protein identified by the method comprising identifying non-protein-encoding nucleotide sequences within an RNA transcript or a DNA sequence encoding same in said nucleome, determining the nucleotide sequence of said non-protein-encoding nucleotide sequence and subjecting said sequence to phenotyping to determine its effect on one or more biological events within a cell and/or determining the degree to which said sequence is conserved in the cell&#39;s genome or in the genome of other species or genera of eukaryotic cells wherein a non-protein-encoding nucleotide sequence having a biological effect in a cell or a nucleotide sequence conserved within the genome or between different cells&#39; nucleomes is deemed to be an eRNA or DNA sequence comprising a nucleotide sequence encoding same and then contacting said eRNA with proteome material and screening for interaction between the eRNA and a protein wherein the detection of such interaction is indicative of a receiver protein.  
         [0161]    Determination of methylation profiles within a cell and more particularly changing profiles in differentiating, aging or mutating cells is a convenient way of identifying epigenetic signatures in the genome and therefore identifying putative genetic targets for the presence of putative eRNAs or their corresponding receiver sequences.  
         [0162]    One convenient method is described in an International Application filed 14 Sep. 2002 in the name of The University of Queensland and involves an amplification-based assay procedure to determine the methylation profile of nucleotides in the genome of a cell or group of cells. More particularly, the nucleotides are in the form of CpG or CpNpG sites. The ability to determine genomic and transgene methylomes in a cell or group of cells is an important tool in functional genomics and in developing the next generation of gene-expression modulating agents. Combining methylation profile with mapping enables a determination of the epigenetic consequences of internal and external stimuli. For example, methylation profiles may correlate with disease conditions or a propensity for a disease condition to develop or monitoring the aging process or the development process of cells. Furthermore, the methylation profile can be used to determine genes which either are expressed or are not expressed in certain disease states or with certain phenotypic traits. The identification of a condition or predisposition for development of a condition leads to the selection of targets for the identification of eRNAs or receiver sequences for eRNAs.  
         [0163]    The amplification-based technology is referred to as amplified methylation polymorphisms (AMP). The AMP technology determines the methylation profile of many thousands of CpG or CpNpG sites around the genome and provides a genetic profile of the methylation status of these sites. This genetic signature is the methylome fingerprint of a cell&#39;s or group of cells&#39; genome.  
         [0164]    The AMP technology involves amplification of DNA markers in the form of small inverted repeats comprising the CpG or CpNpG sites but where amplification depends on the methylation status of the cytosines within the amplicon or nearby.  
         [0165]    The protocol uses, in one form, a single arbitrary decamer oligonucleotide primer containing the recognition sequences of a methylation-sensitive restriction enzyme. These short oligonucleotide primers containing such recognition sequences are referred to herein as AMP primers. The recognition sequences for the methylation-sensitive restriction enzyme are located in the middle of the primer followed by up to four selective nucleotides, extending to the 3′ end. AMP profiles are generated from both undigested genomic DNA and genomic DNA digested with the methylation sensitive enzyme. Comparison of the profiles from digested and undigested genomic DNA reveals three classes of AMP markers: digestion resistant (Class I) indicative of methylation, digestion sensitive (Class II) indicative of non-methylation, and digestion dependent (Class III). The nature of the last class of AMP markers is proposed to represent physically-linked cis-acting inhibitory sequences which suppress amplification of Class III markers from undigested template. Digestion with the enzyme removes the inhibitor from the amplicon, thereby allowing amplification. The digestion-dependent (Class III) markers are proposed to encompass a methylated restriction site or sites in the amplicon sequence flanked by a non-methylated restriction site and then the putative inhibitory sequence. Digestion-dependent markers represent, therefore, junctions between methylated and non-methylated DNA in the genome. Cloning, sequencing and mapping AMP markers shows that they often correspond to CpG islands, features known to be landmarks for genes in genomes. These are then proposed to be sites of eRNA or eRNA receiver systems.  
         [0166]    Methylation enzymes contemplated herein include AatII, AciI, AclI, AgeI, AscI, AvaI, BamHI, BsaA1, BsaH1, BsiE, BsiW, BsrF, BssHII, BstBI, BstUI, Cla1, EagI, HaeII, HgaI, HhaI, HinPI, HpaII, MloI, MspI, NaeI, NarI, NotI, NruI and PmlI. HpaII is particularly preferred in accordance with the present invention.  
         [0167]    Accordingly, another aspect of the present invention provides a method for identifying a gene having encoding a putative eRNA or comprising a receiver sequence for an eRNA said method comprising determining the methylation profile of one or more CpG or CpNpG nucleotides at one or more sites within the genome of a eukaryotic cell or group of cells by obtaining a sample of genomic DNA from the cell or group of cells, digesting a sub-sample of the sample of genomic DNA with HpaII which has a recognition nucleotide sequence corresponding to or within the sites, subjecting the digested DNA to an amplification means such as polymerase chain reaction (PCR) using primers comprising a nucleotide sequence capable of annealing to a non-cleaved form of a HpaII cleavable nucleotide sequence and subjecting the products of the PCR to separation or other detection means relative to a control, said control comprising another sub-sample of the sample of genomic DNA not subjected to digestion by HpaII but subjected to an amplification reaction using the same primers as for the digested DNA sample and then subjecting the products to the amplification reaction to the separation or detection means wherein the presence of PCR products in enzyme digested and non-digested samples is indicative of a HpaII-digestion-resistant marker (Hr), the absence and presence of PCR products in enzyme digested and undigested samples, respectively, is indicative of a HpaII-digestion-sensitive marker (HS) and the presence and absence of PCR products in enzyme digested and undigested samples, respectively, is indicative of a HpaII-digestion-dependent marker (H d ) wherein these sites are proposed to comprise genes or intergenic regions which are then screened for the presence of eRNAs or receive sequences.  
         [0168]    The present invention is further described by the following non-limiting Examples.  
       EXAMPLE 1  
     A Role for Introns and Other Non-Coding RNAs in Dynamical Gene-Gene Communication, Genetic Multi-Tasking and Systems Integration  
       [0169]    Potential cellular control molecules enabling multi-tasking and system integration must be capable of specifically targeted interactions with other molecules, must be plentiful (as limited numbers impair connectivity and adaptation in real and evolutionary time), and must carry information about the dynamical state of cellular gene expression. These goals are most directly or economically achieved by spatially and temporally synchronizing control molecule production with gene expression. Most protein-coding genes of higher eukaryotes are mosaics containing one or more intervening sequences (introns) of generally high sequence complexity, which are spliced out during pre-mRNA processing to generate a nuclear population of intronic RNA with concentration profiles linked to that of the exons, which are reassembled during this process to form mRNA, and which are subsequently translated into protein. The numbers of protein coding genes do not increase exponentially in complex organisms and hence cannot provide large scale cellular connectivity (which does increase exponentially). The genomes of higher organisms are, nevertheless, much larger than those of single celled organisms, with the vast majority of this size increase (after accounting for variable amounts of repetitive DNA) occurring within intron sequences and other non-protein-coding RNAs. Introns, therefore, fulfil the essential conditions for system connectivity and multi-tasking—(i) multiple output in parallel with gene expression; (ii) large numbers, especially if, as is likely (see below), they are further processed to smaller molecules after excision from the primary transcript; and (iii) the potential for specifically targeted interactions as a function of their sequence complexity. Sequences of just 20-30 nucleotides should generally have sufficient specificity for homology-dependent or structure-specific interactions. Introns are, therefore, excellent candidates for, and perhaps the only source of, possible control molecules for multi-tasking eukaryotic molecular networks, which relieve the problems associated with protein-based systems as genetic output can be multiplexed and target specificity can be efficiently encoded, assuming a receptive infrastructure.  
       EXAMPLE 2  
     Introns have Populated the Eukaryotic Lineage Late in Evolution  
       [0170]    Modern nuclear introns are not ancient remnants of the prebiotic assembly of genes but the evolutionary descendants of self catalytic group II introns, which have similar splicing mechanisms (Lambowitz et al.,  Annu. Rev. Biochem.  62: 587-6221993; Eickbush,  Nature  404: 940-941 2000). These elements appear to have penetrated the eukaryotic lineage late in evolution (Cavalier-Smith,  Trends Genet.  7: 145-148 1991; Palmer et al.,  Curr. Opin. Genet. Dev.  1: 470-477, 1991; Mattick,  Curr. Opin. Genet. Dev.  4: 823-831 1994; Stoltzfus et al.,  Science  265: 202-207 1994; Cho and Doolittle,  J. Mol. Evol.  44: 573-584 1997; Wolf et al.,  J. Theor. Biol.  195: 167-186 1998) and to have expanded initially by retrotransposition (Cousineau et al., 2000; Eickbush, 2000) and later (after their sequence constraints were reduced by the evolution of the spliceosome) by other mutational, recombinational and insertional processes (Tarrio et al.,  Proc. Natl. Acad. Sci. USA  95: 1658-1662 1998). Self-catalytic group II introns do occur in bacteria, usually in tRNA genes (Ferat et al.,  Nature  364: 358-361 1993; Martinez-Abarca et al.,  Mol. Microbiol.  38: 917-926 2000) and the likely reason that introns are generally absent from prokaryotic protein coding sequences is the intimate coupling of transcription and translation in these cells, which does not allow time for intron excision (Mattick,  Curr. Opin. Genet. Dev.  4: 823-831 1994).  
         [0171]    The evolution of the nucleus and the separation of transcription and translation in the eukaryotes provided the opportunity for these introns to invade protein coding genes, as long as their removal by self splicing was efficient enough not to interfere with mRNA and protein production. The subsequent evolution of the spliceosome (involving the devolution of internal cis-acting catalytic RNAs into trans-acting spliceosomal RNAs and recruitment of accessory proteins) (Lambowitz et al.  Annu. Rev. Biochem.  62: 587-622, 1993; Mattick,  Curr. Opin. Genet. Dev.  4: 823-831 1994; Newman,  Curr. Opin. Genet. Dev.  4: 298-304 1994; Stoltzfus,  J. Mol. Evol.  49: 169-181 1999; Yean et al.,  Nature  408: 881-884 2000) made intron processing easier, which reduced the negative selection against them and allowed them more latitude. It also relaxed their internal sequence requirements, leaving them free to evolve and to explore new evolutionary space, based on RNA molecules produced in parallel with protein coding sequences (Mattick,  Curr. Opin. Genet. Dev.  4: 823-831 1994). This would have been accelerated by the co-evolution of receptor systems for these molecules, involving RNA-protein, RNA-RNA and RNA-DNA/chromatin interactions, in the same way as other complex systems such as the ribosome and the spliceosome have evolved (Stoltzfus,  J. Mol. Evol.  49: 169-181 1999). It is proposed, therefore, that intron-derived RNAs may have evolved trans-acting functions.  
       EXAMPLE 3  
     Intron Density Correlates with Developmental Complexity  
       [0172]    Intron size and sequence complexity correlates well with developmental complexity, and introns comprise the majority of pre-mRNA sequences in the higher organisms. In developmentally simple eukaryotes like  Schizosaccharomyces pombe, Aspergillus  and  Dictyostelium , introns comprise only 10-20% of the primary transcript, and are generally small with an average length of less than 100 bases and density about 1-3 introns per kilobase of protein coding sequence. These data are consistent with hybridization kinetic analyses of the relative sequence complexity of hnRNA (“heterogeneous nuclear RNA”) versus mRNA in lower eukaryotes (Davidson, 1976). In the higher plants there are 2-4 introns per gene of average length about 250 bases comprising about 50% of the primary transcript. In animals the average intron size rises to about 500 bases in  Drosophila  and  C. elegans , and to about 3400 in human (6-7 introns per gene, average over 95% of the primary transcript) (Palmer et al.,  Curr. Opin. Genet. Dev.  1: 470-477, 1991; Deutsch et al.  Nucleic Acids Res.  27: 3219-3228, 1999; Consortium,  Nature  409: 860-921 2001; Venter et al.,  Science  291: 1304-1351 2001).  
       EXAMPLE 4  
     Introns have the Signatures of Information  
       [0173]    Introns (and other non-protein coding RNAs, see below) of higher organisms exhibit all the signatures of information. They generally have high sequence complexity (Tautz et al.,  Nature  322: 652-656 1986) although one must distinguish between introns that may have evolved function and those that have not (which will be more degenerate) and take account of the differing proportions of functional and non-functional introns in lineages of different developmental complexity. While introns generally show less conservation than adjacent protein coding sequences, which are subject to strong constraints, so also do adjacent promoters and 5′ and 3′ untranslated regions of mRNA. The plasticity and more rapid evolution of these regulatory sequences does not mean they are non-functional and the present inventors suggest the same holds, in general, for introns.  
       EXAMPLE 5  
     Non-Coding RNAs Comprise the Majority of Genomic Output  
       [0174]    Many (if not most, see below) transcripts from the genomes of higher organisms do not encode proteins at all (Eddy,  Curr. Opin. Genet. Dev.  9: 695-699 1999; Erdmann et al.,  Nucleic Acids Res.  27: 192-195 1999). Where they have been examined these non-protein-coding transcripts are conserved and clearly functional. Well documented examples include XIST (involved in female X chromosome inactivation) (Brockdorff,  Curr. Opin. GEnet. Dev.  8: 328-333 1998; Lee et al.,  Cell  75: 843-854 1999; Hong et al.,  Mamm, Genome  11: 220-224 2000) and H19 (mutants of which promote tumor development) (Wrana,  Bioessays  16: 89-90 1994; Hurst et al.  Trends Genet.  15: 134-135, 1999), both of which are imprinted and differentially spliced without encoding any protein. Others include roX1 and roX2 RNAs involved in dosage response (male X-chromosome activation) in  Drosophila , heat shock response RNA in  Drosophila , oxidative stress response RNAs in mammals, His-1 RNA involved in viral response/carcinogenesis in human and mouse, SCA8 RNA involved in spinocerebellar ataxia type 8 which is antisense to an actin-binding protein, and ENOD40 RNA in legumes and other plants (Eddy,  Curr. Opin. Genet. Dev.  9: 695-699 1999; Erdmann et al.,  Nucleic Acids Res.  27: 192-195 1999; Nemes et al.,  Hum. Mol. Genet.  9: 1543-1551 2000). The 200 kb bithorax-abdominal A/B locus of  Drosophila  produces seven major transcripts (there may be minor ones as well), only three of which encode proteins, but all of which have phenotypic signatures and are developmentally regulated (Akam et al.,  Quant. Biol.  50: 195-200 1985; Hogness et al.,  Quant. Biol.  50: 181-194 1985; Lipshitz et al.,  Genes Dev.  1: 307-322 1987; Sanchez-Herrero et al.,  Drosophila. Development  107: 321-329 1989). These are not isolated examples. Many loci, including imprinted loci, express non-coding antisense and intergenic transcripts, some of which are alternatively spliced and developmentally regulated (Ashe et al.,  Genes Dev.  11: 2494-2509 1997; Lipman,  Nucleic Acids Res.  25: 3580-3583 1997; Potter et al.,  Mamm. Genome  9: 799-806 1998; Lee et al.,  Nature Genet.  21: 400-404 1999; Filipowicz,  Acta. Biochim. Pol.  46: 377-389 2000; Hastings et al.,  J. Biol. Chem.  275: 11507-11513 2000; Nemes et al.,  Hum. Mol. Genet.  9: 1543-1551 2000), as well as being stably detectable in the nucleus (Ashe et al.,  Genes Dev.  11: 2494-2509 1997).  
       EXAMPLE 6  
     Examples of Gene Regulation and Communication by Introns and Non-Coding RNAs  
       [0175]    The activity of the heterochronic genes lin-14 and lin-41, which regulate developmental timing in  C. elegans , are controlled by lin-4 and let-7 gene products encoding small RNAs that are antisense to repeated elements in the 3′ untranslated region of target mRNAs, and which appear to inhibit translation by RNA-RNA interactions (Lee et al.,  Cell  75: 843-854 1993; Wightman et al.,  C. elegans. Cell  75: 855-862 1993; Feinbaum et al.,  Caenorhabditis elegans . Dev. Biol. 210: 87-95 1999; Reinhart et al.,  Caenorhabditis elegans. Nature  403: 901-906 2000) possibly by targeting the mRNA for endoribonuclease attack (Nashimoto,  FEBS Lett.  472: 179-186 2000). Lin-4 and let-7 do not contain obvious protein coding sequences, and the surrounding genomic sequences suggests that both are derived from functional introns surrounded by vestigial exons (Lee et al.,  Cell  75: 843-854 1993; Reinhart et al.,  Caenorhabditis elegans. Nature  403: 901-906 2000). Moreover, let-7 is functionally conserved in other bilaterian animals, from mollusks to mammals (Pasquinelli et al.,  Nature  408: 86-89 2000). Interestingly, the size of these RNAs (21-22 nt) is similar to that produced by the RNA interference (RNAi) pathway (Bass,  Cell  101: 235-238 2000; Parrish et al.,  Mol. Cell.  6: 1077-1087 2000; Yang et al.,  Curr. Biol.  10: 1191-1200 2000; Zamore et al.,  Cell  101: 25-33 2000; Sharp,  Genes Dev  15: 485-490 2001) (see below).  
         [0176]    It has also been discovered that most small nucleolar RNAs (a group of more than 100 stable RNA molecules concentrated in the nucleolus) derive from processed introns of other genes, which encode various ribosomal proteins (e.g. L1, L5, L7, L13, S1, S3, S7, S8, S13 and others), ribosome-associated proteins (e.g. eIF-4A), nucleolar proteins (e.g. nucleolin, laminin, fibrillarin), the heat shock protein hsc70 and the cell-cycle regulated protein RCC1, among others (Prislei et al.,  Gene  163: 221-226 1993; Sollner-Webb,  Cell  75: 403-405 1993; Bachellerie et al.,  Biochem. Cell. Biol.  73: 835-843 1995; Maxwell et al.,  Annu. Rev. Biochem.  64: 897-934, 1995; Nicoloso et al.,  J. Mol. Biol.  260: 178-195 1996; Rebane et al.,  Gene  210: 255-263 1998; Filipowicz et al.,  Acta. Biochim, Pol.  46: 377-389 1999; Filipowicz,  Proc. Natl. Acad. Sci. USA  97: 14035-14037 2000). These provide both clear examples of dual gene outputs, and potential instances of coordinate regulation (efference control) involving intronic sequences, in this case of ribosomal biogenesis and cell growth (Pelczar et al.,  Mol. Cell. Biol.  18: 4509-4518 1998; Smith et al.,  Mol. Cell. Biol.  18: 6897-6909 1998; Tanaka et al.,  Genes Cells  5: 277-287 2000). More tellingly, some genes have so evolved that their protein coding capacity no longer exists, and their primary product is intron-derived small nucleolar RNAs (Tycowski et al.,  Nature  379: 464-466 1996; Bortolin et al.,  RNA  4: 445-454 1998; Pelczar et al.,  Mol. Cell. Biol.  18: 4509-4518 1998; Smith Smith et al.,  Mol. Cell. Biol.  18: 6897-6909 1998; Tanaka et al.,  Genes Cells  5: 277-287 2000) leading to the statement that “genes generating functionally important RNAs exclusively from their intron regions are probably more frequent than has been anticipated” (Bortolin et al.,  RNA  4: 445-454 1998).  
         [0177]    These nucleolar RNAs are processed from introns by specific mechanisms involving endonucleolytic cleavage by double stranded RNase III-related enzymes (Caffarelli et al.,  X laevis. Biochem. Biophys. Res. Commun.  233: 514-517 1997; Chanfreau et al.,  EMBO J.  17: 3726-3737 1998; Qu et al.,  Mol. Cell. Biol.  19: 1144-1158 1999) (also implicated in RNAi, transgene silencing and methylation (Mette et al.,  EMBO J.  19: 5194-5201 2000)—see below), exonucleolytic trimming (Cecconi et al.,  Nucleic Acids Res.  23: 4670-4676 1995; Mitchell et al.,  Nature Struct. Biol.  7: 843-8461997; Allmang et al.,  EMBO J.  18: 5399-5410 1999a; Allmang et al.,  Genes Dev.  13: 2148-2158 1999b; van Hoof et al.,  Cell  99: 347-350 1999; van Hoof et al.,  EMBO J.  19: 1357-1365 2000) and possibly even adjacent RNA sequences that have self cleaving activity (Prislei et al.,  Gene  163: 221-226 1995). This processing occurs in large RNA processing complexes called exosomes, which are also involved in processing rRNA and small nuclear RNAs, and which contain at least 10 3′-5′ exonucleases, helicases and RNA binding proteins and which are found in both the nucleus and the cytoplasm (Mitchell, et al.,  Cell  91: 457-466 1997; Allmang et al.,  EMBO J.  18: 5399-5410 1999a,b; van Hoof et al.  Cell  99: 347-350, 1999; Mitchell et al.,  Nature Struct. Biol.  7: 843-846 2000).  
       EXAMPLE 7  
     Intron Processing, Stability, Decay and Memory  
       [0178]    After splicing, introns (initially in lariat form) are debranched (Ruskin et al.,  Science  229: 135-140 1985), a process that is itself subject to regulation (Ruskin et al.,  Science  229: 135-140 1985; Qian et al.,  Nucleic Acids Res.  20: 5345-5350 1992), but subsequent events are unknown. The inventors suggest that it is likely that excised introns are processed by specific pathways similar to those used to produce small nucleolar RNAs, and which generate multiple smaller species which can function independently as transacting signals in the network, affecting the metabolism of other RNAs and the modulation of chromatin structure, among other things (see below).  
         [0179]    There are other documented examples of small transacting functional RNAs processed from longer transcripts (Sit et al.,  Science  281: 829-832 1998; Cavaille et al.,  Proc. Natl. Acad. Sci. USA  97: 14311-14316 2000). There are also large numbers of ribonucleases and other RNA-related proteins in plants and animals (see below), most of whose functions and substrates are not well defined. Such processing may also involve other splicing pathways (Santoro et al.,  Mol. Cell. Biol.  14: 6975-6982 1994; Kreivi et al.,  Curr. Biol.  6: 802-805 1996) and guide RNAs, possibly derived from introns or other non-protein-coding RNAs. These have been described as “riboregulators” (in relation to antisense RNAs) (Delihas,  Mol. Microbiol.  15: 411-414 1995) and the “ribotype” (in relation to alternatively spliced mRNAs) (Herbert et al.,  Nature Genet.  21: 265-269 1999a), and may be considered to be part of the “soft wiring” of the cell (Herbert et al.,  Acad. Sci.  870: 119-132 1999b; Mattick,  Curr. Opin. Genet. Dev.  4: 823-831 1994).  
         [0180]    The decay characteristics of eRNAs are likely to be important to their function. Both short- and long-lived eRNAs provide a molecular memory of prior gene activation status, a significant efficiency gain over using bistable regulated gene networks as memories (Gardner et al.,  Escherichia coli. Nature  403: 339-342 2000). Differential eRNA decay (Qian et al.,  Nucleic Cids Res.  20: 5345-5350 1992) and diffusion rates would create spatially and temporally complex signal pulses that enable specific communication speeds, half lives and maximal communication radii for eRNA information transfer, allowing fine control of cellular activities.  
       EXAMPLE 8  
     Transvection and Chromatic Structure  
       [0181]    The inventors propose predict that if eRNAs do have an important function in regulating gene expression, there should be genetic clues from intensively studied systems. A good candidate is the  Drosophila  bithorax complex, which is the archetypal developmental control locus, and which has been subjected to a considerable amount of genetic and molecular scrutiny. The bithorax region of this complex locus covers over 100 kb and contains 3 transcription units, one of which (Ubx) contains large introns and is differentially spliced to produce several variants of the morphogenetic homeobox protein UBX (Hogness et al.,  Quant. Biol.  50: 181-194 1985; Duncan,  Annu. Rev. Genet.  21: 285-319 1987). The others are located upstream and are referred to as the early and late bxd units, and do not appear to encode proteins. Mutants of this locus can be classified into Ubx alleles, which disrupt the protein coding sequence and the abx, bx, pbx, and bxd alleles, which are located either within the introns of the Ubx unit (abx, bx) or in the 40 kb upstream region (pbx, bxd) and which affect the spatial pattern of UBX expression. The latter alleles are thought to represent cis-acting regulatory sequences controlling Ubx expression and are usually interpreted in terms of conventional enhancer elements, despite the fact that they are themselves transcribed. The bxd transcription unit produces a 27 kb transcript early in embryogenesis, which has a number of large introns, and is subject to differential splicing to give various small (˜1.2 kb) polyA+RNAs which do not contain any significant open reading frame (Akam et al.,  Quant. Biol.  50: 195-200 1985; Hogness et al.,  Quant. Biol.  50: 181-194 1985; Lipshitz et al.,  Genes. Dev.  1: 307-322 1987). The expression of this transcript is highly regulated during embryogenesis, in a pattern that is partially reflexive of Ubx transcript (Akam et al.,  Quant. Biol.  50: 195-200 1985; Irish et al.,  EMBO J.  8: 1527-1537 1989). A number of bxd insertional mutations have no effect on the amount or the size of the bxd polyA+RNA, suggesting that this species is irrelevant to the observed phenotypes and that the real import of the transcription and processing of this gene is to produce intronic RNAs (Hogness et al.,  Quant. Biol.  50: 181-194 1985). The “cis-regulatory” elements in this region also appear to be able to regulate the expression of Ubx in trans, since defective elements can be complemented by wild-type sequences on the other chromosome.  
         [0182]    This phenomenon (partial complementation, or “allelic cross-talk”, between a mutation in a “cis-regulator” on one chromosome and one in the coding region of the adjacent gene on the other chromosome) has been known for many years, and is termed “transvection” (Judd,  Cell  53: 841-843 1988; Pirrotta,  Bioessays  12: 409-414 1990). Transvection has been observed in a number of different loci, and appears to be synapsis-dependent, since translocation of the “regulatory” sequences to other chromosomal sites normally diminishes or eliminates this trans-complementation of gene expression patterns (Judd,  Cell  53: 841-843 1988; Pirrotta,  Bioessays  12: 409-414 1990; Wu et al.,  Curr. Opin. Genet. Dev.  9: 237-246 1999). Mechanistically this has been interpreted in terms of enhancer elements from one copy of the gene being able to interact directly with its homolog on the other chromosome (i.e. to influence both promoters) because of their close alignment (Geyer et al.,  Drosophila. EMBO J.  9: 2247-2256 1990), although there are other propositions, mostly based on the same theme of chromosome pairing (Wu et al.,  Curr. Opin. Genet. Dev.  9: 237-246 1999). However, translocation of these regulatory sequences can in fact lead to a spectrum of transvection effects, ranging from weak to strong, suggesting that remote action is possible (Micol et al.,  Genetics  126: 365-373 1990) and that a simple model of chromosome pairing and transcriptional crossover is incorrect (Goldsborough et al.,  Nature  381: 807-810 1996). Moreover, these effects may be simply interpreted by regarding the “cis-acting regulatory regions” as encoding separate (non-coding RNA) genes.  
         [0183]    Transvection at distance is accentuated in the presence of mutant alleles of the Polycomb gene (which normally acts to maintain repression of transcription of Ubx and other genes in cells where it was not initially activated) and at many loci is dependent on the zeste gene product, which acts in opposition to polycomb-group proteins to enhance transcription (Wu et al.,  Trends Genet.  5: 189-194 1989; Laney et al,  Genes Dev.  6: 1531-1541 1992; Pirrotta,  Biochim. Biophys. Acta  1424: M1-8 1999), indicating that factors other than chromosome pairing are involved in this process (Castelli-Gair et al.,  EMBO J.  9: 4267-4275 1990; Castelli-Gair et al.,  Genetics  126: 177-184 1990). Zeste null mutants do not affect chromosome pairing, even though transvection at some loci is entirely dependent on zeste (Gemkow et al.,  Drosophila melanogaster. Development  125: 4541-4552 1998; Pirrotta,  Biochim. Biophys. Acta  1424: M1-8 1999). Moreover it has been shown that a region in the vicinity of the late bxd transcript which can attenuate Ubx expression can exert its action independent of its position (Castelli-Gair et al.,  Development  114: 877-184 1992a; Castelli-Gair et al.,  Mol. Gen. Genet.  234: 117-184 1992b). To explain such observations one has either to invoke DNA looping over enormous (interchromosomal) distances to bring regulatory proteins into contact with the Ubx promoter, or a (diffusible) substance expressed from these sequences, i.e. RNA.  
         [0184]    Similar observations have been made at the downstream abdA-AbdB region of the bithorax complex which also encode homeotic proteins controlling segment identity. As in the case of bithorax itself, the sequences upstream of abdA and AbdB, which are referred to as the infrabdominal (iab) region, are thought to function as cis-acting regulatory elements, despite the fact that this region, like bxd, is also itself transcribed. Transvection (involving iab and abdA/AbdB alleles) at this locus is synapsis (pairing) independent and relatively insensitive to location, again suggesting that a trans-acting RNA may be involved (Hendrickson et al.,  Drosophila melangaster, Genetics  139: 835-848 1995; Hopmann et al.,  Genetics  139: 815-833 1995; Sipos et al.,  Genetics  149: 1031-1050 1998). The efficiency of this transvection is also different in different tissues, indicating that the state of differentiation has an effect on this process (Sipos et al.,  Genetics  149: 1031-1050 1998). Another (small, 800 bp) “element” in this region (Mcp) has also been shown to be capable of “trans-silencing”, independent of homology or homology pairing in the immediate vicinity of Mcp transgene inserts. The inventors propose that Mcp encodes a trans-acting RNA, whose ability to communicate with its target loci is affected by spatial separation and by polycomb/zeste mediated effects on chromatin architecture.  
         [0185]    These genetic phenomena are connected, with common features being non-protein-coding RNAs and dynamic interactions and remodeling of chromatin involving DNA methylation and trithorax- and polycomb-group proteins, occurring in large complexes with a variety of other proteins, including histone modifying factors and transcription factors. The influence on transvection and other phenomena of complexes containing trithorax- and polycomb-group proteins may, therefore, be interpreted more easily in terms of maintaining, enhancing or inhibiting accessibility of these sites to trans-acting RNAs and/or executing signals from such RNAs.  
       EXAMPLE 9  
     Genetic Programming and the Evolution of Complex Organisms  
       [0186]    The evolution of complex phenotypes is usually understood to proceed by a sequence from cells that were entirely unregulated and whose dynamics were governed by rate processes and input constraints. The existence of these cells provided the preconditions for the appearance of regulatory mechanisms which fine tuned rate processes. The inventors propose that these regulated networks, following a change in gene structure and output in the eukaryotic lineage, provided the necessary precondition for the appearance of controlled multi-tasked networks, which in turn, led to the appearance of programmed response networks capable of implementing stored sequences of dynamical activities in response to internal and external stimuli. Further, the inventors suggest that there is only one plausible mechanism for the evolution and control of multi-tasking in cell and developmental biology and that far from being evolutionary junk, nuclear introns and other non-protein-coding RNAs have evolved this function.  
         [0187]    The majority of information in a multi-tasked network is held in control sequences. Non-protein-coding RNAs comprise the majority of the genomic output and unique sequence information in the higher eukaryotes and the evidence is growing that these RNAs are functional, as is the realization that RNA metabolism in these organisms is much more complex than previously realized.  
         [0188]    The three critical steps in the evolution of this system were (i) the entry of introns into protein coding genes in the eukaryotic lineage, (ii) the subsequent relaxation of internal sequence constraints by the evolution of the spliceosome and the exploration of new sequence space, and (iii) the co-evolution of processing and receiver mechanisms for transacting RNAs, which are not yet well characterized but which are likely to involve the dynamic modeling and re-modeling of chromatin and DNA, as well as RNA-RNA and RNA-protein interactions in other parts of the cell. Steps (ii) and (iii) probably occurred, at least initially, by constructive neutral evolution (Stoltzfus, 1999), involving biased variation, epistatic interactions and excess capacities underlying a complex series of steps giving rise to novel structures and operations, and later by molecular co-evolution (Dover et al.,  Biol. Sci.  312: 275-289 1986). Once this system of RNA communication began to be established, the rate of evolution of functional introns would have accelerated (by positive selection), and led also to the evolution of other non-protein-coding RNAs, which are also usually spliced and are probably derived from genes that had lost their protein coding capacity, as appears to have occurred in the case of transcripts producing small nucleolar RNAs.  
         [0189]    In practical terms then, the inventors propose that functional introns provide a cellular memory of recent transcriptional events and underpin a multiple output parallel processing system where gene activity at one locus can connect to others in real time, allowing integration and multi-tasking of a sophisticated network of cellular activity. In this scheme, non-protein-coding RNAs are control molecules in the network that do not require concomitant production of protein. Thus, there are two levels of information produced by gene expression in the higher organisms—mRNA and eRNA—allowing the concomitant expression of both structural (i.e. protein-coding) and networking information, the latter involving multiplex contacts between different genes and gene products via RNA signals that are implicit in primary transcripts. As some genes have evolved to express only eRNA and some genes lack introns, there are three types of genes in the higher organisms—those that encode only protein (which are rare), those that encode only eRNA, and those that encode both.  
         [0190]    One prediction of this model is that many core proteins in the higher eukaryotes will be multi-tasked, i.e. have different roles in different sub-networks to produce different phenotypic outcomes. This appears to occur. For example, it has been shown that glycogen synthase kinase-3β participates both in the specification of the vertebrate embryonic dorsoventral axis (via the Wnt/wingless signaling pathway) and in the NF-ηB-mediated cell survival response following TNF activation (Hoeflich et al.,  Nature  406: 86-90 2000). Both cytochrome c and a flavoprotein (apoptosis-inducing factor) have redox functions in mitochondria as well as specific apoptogenic functions (Chinnaiyan,  Neoplasia  1: 5-15 1999; Daugas et al.,  FEBS Lett.  476: 118-123 2000; Loeffler et al.,  Exp. Cell Res.  256: 19-26 2000). The XPD gene product functions in both transcription and excision repair of DNA (Lehmann,  Genes Dev.  15: 15-23 2001). There are many other documented examples of proteins that participate in more than one developmental and signalling pathway (sub-network) (see e.g. Boutros et al.,  Mech. Dev.  83: 27-37 1999; Szebenyi et al.,  Int. Rev. Cytol.  185: 45-106 1999; Coffey et al.,  J. Neurosci.  20: 7602-7613 2000; O&#39;Brien et al.,  Proc. Natl. Acad. Sci. USA  97: 12074-12078 2000). There are also examples of proteins having different, even antagonistic, functions in different settings, often as a result of alternative splicing (Jiang et al.,  Proc. Soc. Exp. Biol. Med.  220: 64-72 1999; Lopez,  Annu. Rev. Genet.  32: 279-305 1998; Hastings et al.,  J. Biol. Chem.  275: 11507-11513 2000), a process that we predict will turn out to be regulated and guided not simply by tissue-specific RNA binding proteins/splicing factors but also by trans-acting RNAs produced by the activity of other genes (see, e.g. Hastings et al.,  J. Biol. Chem.  275: 11507-11513 2000). Consequently, developmental and phylogenetic profiling efforts will need to assign a range of biological, in addition to biochemical, functions to individual proteins and their splice variants in the network.  
         [0191]    A multi-tasked network allows the rapid exploration of exponentially many protein expression profiles without equivalent increase in the size of the controlled parent network. The model therefore also predicts that the core proteome will be relatively stable in the higher organisms, which appears to be the case (Duboule et al.,  Trends Genet.  14: 54-59 1998; Rubin et al.,  Science  287: 2204-2215 2000) and that phenotypic variation will result primarily and quite easily from variation in the control architecture, rather than duplication and mutation of gene sub-networks. Once in place, therefore, a controlled multitasked network enables not only the efficient programming of different cellular phenotypes in the differentiation and development of multicellular organisms, but also rapid evolutionary radiation during expansions into uncontested environments, such as initially observed in the Cambrian explosion and as seen after major extinction events.  
         [0192]    The corollary is that prokaryotes and simpler eukaryotes operating on simple protein control circuitry are limited in their phenotypic range, genome size and complexity not by the available diversity of polypeptide structures and chemistry, but by a primitive genetic operating system incapable of supporting integrated multi-tasking of gene networks. This would also explain why the Earth was restricted to simpler unicellular and colonial life forms for over 3 billion years, and the rapid evolution of complex life forms after the conditions for feasible parallel outputs were satisfied by the entry of introns into the eukaryotic lineage around 1.2 billion years ago, and the subsequent evolution of the necessary infrastructure for sending and receiving intronic and other non-protein-coding RNA signals.  
         [0193]    Genomes are datasets with controls. The present invention examines, therefore, biology and genomes from the viewpoint of information and network theory and unifies a wide range of evolutionary and molecular genetic observations, including the long lag then sudden appearance of developmentally sophisticated multicellular organisms, the plasticity of phenotypic diversity despite the relative conservation of the core proteome and a wide range of unexplained molecular genetic phenomena that all intersect with RNA, the enabling molecule.  
       EXAMPLE 10  
     eRNA Regulators of HOX, ets-Domain Transcription Factor and Immunoglobulin Gene Expression  
       [0194]    A method to identify eRNA elements and potential eRNA elements and/or their targets has been developed. The method searches the database of choice for known and predicted introns. The sequences of the known and predicted introns may then be compared in a BlastN search to identify from the non-redundant genome databases genes that are homologous to eRNA elements. eRNA elements may be embedded within introns or other non-coding RNA such as a 3′ or 5′ untranslated region (UTR). The method may also be used to screen such non-coding RNA sequences for eRNA elements. Short regions of homology between 19 and 200 nucleotides are considered significant to detect eRNA as it is known that short homologous regions of approximately 21 nucleotides act to modulate gene expression. The subject method identifies homologous sequences or complementary sequences which may be eRNA or target sequences.  
         [0195]    A predicted intron sequence derived from chr19:38234-167860 is used in a BlastN search of the non-redundant human genome database to identify potential eRNA elements. The search reveals that this intron sequence comprise a number of candidate eRNA elements which may be directed to the regulation of multiple genes. eRNA elements are identified within introns by searching other parts of the genome, including protein- and non-protein-encoding regions, for homology with a candidate eRNA sequence. eRNA elements from this intron are proposed to be involved in regulation of activity of the ets-domain transcription factor, the human chloride channel transporter gene and the developmentally regulated HOX gene. This intron potentially contains an eRNA element directed to the regulation of immunoglobulin gene expression and an eRNA element directed to the regulation of expression of the gene encoding the nuclear factor of κ light polypeptide enhancer (NFκB1).  
         [0196]    Predicted intron derived from chr19 between nucleotide sequences 38234-167860:  
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   gtaggtggggaaggggtgtcaggtgggtactgcagatgggctctaggacctcggccttcaag                           ttgtgtctgcccgcctcttgctactgtcttggatattttaaagtccttttgacgttgttctg                       atttctgggcaggggacagagtaagtgtgtatttgctctgagactgttaatttggtatttcc                       atcccaagttacagggaagacctcaggctgcaggttcctagctccgggctgaggtggcttgt                       ggaggcagacagctgttgtctggaagtgcagagggctgggggctggccaggctgttactgag                       ttcagaataggaggaaagagtgtgtagcaaagtcggcgctccttggccactgccagcattca                       gagttgtcttgtttgccttgccttaaacgttgccttcctggacgcctacaaagtcaggttgt                       aaccgctggccactgctgtgctcactggcagcccctgatttacgtgaggacctcaagtgtgt                       gttgggcagaattccccagcgcttcccgtacaccccnccacccccagtgcagcatcgctcgg                       tgcgtggctggtggactggaggagtgtgcgtgccggcagcactgccaggcacgtgcctaatg                       ctctggccctgtgtgtttgtgttttcttcccgatttctgag [SEQ ID NO: 1]                        Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|10280826|gb|AC012531.11|AC012531  Homo sapiens, clone RP11-83K1,               complete sequence                        Length = 171949                             Score = 40.1 bits (20), Expect = 1.9                    Identities = 20/20 (100%)                Strand = Plus/Minus                    Query: 273   agtgcagagggctgggggct 292 [SEQ ID NO: 2]                       ||||||||||||||||||||               Sbjct: 168539   agtgcagagggctgggggct 168520 [SEQ ID NO: 3]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|2992476|gb|AC003666.1|AC003666  Homo sapiens Xp22 BAC GS-551019 (Genome               Systems Human BAC library) and                        cosmids U199A7 and U209F2 (Lawrence Livermore X chromosome                           cosmid library) containing part of human chloride channel 4                       gene, complete sequence                            Length = 151750                             Score = 40.1 bits (20), Expect = 1.9                    Identities = 20/20 (100%)                Strand = Plus/Plus                    Query: 264   ttgtctggaagtgcagaggg 283 [SEQ ID NO: 4]                       ||||||||||||||||||||               Sbjct: 102216   ttgtctggaagtgcagaggg 102235 [SEQ ID NO: 5]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|4689496|gb|AC006948.4|AC006948  Homo sapiens chromosome 17, clone                   hRPK.334_M_10, complete sequence                        Length = 168558                             Score = 40.1 bits (20), Expect = 1.9                    Identities = 20/20 (100%)                Strand = Plus/Minus                    Query: 563   tggctggtggactggaggag 582 [SEQ ID NO: 6]                       ||||||||||||||||||||               Sbjct: 20775   tggctggtggactggaggag 20756 [SEQ ID NO: 7]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|8894241|emb|AL157952.8|AL157952  Human DNA sequence from clone RP5-               875K15 on chromosome 11p12-14.1                        Contains the gene for the eta-domain transcription factor                           EHF, ESTs, STSs and GSSs, complete sequence [Homo sapiens]                            Length = 114022                             Score = 40.1 bits (20), Expect = 1.9                    Identities = 20/20 (100%)                Strand = Plus/Plus                    Query: 243   gcttgtggaggcagacagct 262 [SEQ ID NO: 8]                       ||||||||||||||||||||               Sbjct: 64983   gcttgtggaggcagacagct 65002 [SEQ ID NO: 9]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|32387|emb|X61755.1|HSHOX3D  Human HOX3D gene for homeoprotein HOX3D                        Length = 4968                             Score = 40.1 bits (20), Expect = 1.9                    Identities = 20/20 (100%)                Strand = Plus/Minus                    Query: 273   agtgcagagggctgggggct 292 [SEQ ID NO: 10]                       ||||||||||||||||||||               Sbjct: 166   agtgcagagggctgggggct 147 [SEQ ID NO: 11]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 &gt;gi|14718391|gb|AC021120.6|AC021120  Homo sapiens clone RP11-34708,               complete sequence                        Length = 193980                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Minus                    Query: 156   tttgctctgagactgttaa 174 [SEQ ID NO: 12]                       |||||||||||||||||||               Sbjct: 131889   tttgctctgagactgttaa 131871 [SEQ ID NO: 13]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|2894631|gb|AC004152.1|AC004152  Homo sapiens chromosome 19, fosmid               37308, complete sequence                        Length = 37635                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Minus                    Query: 280   agggctgggggctggccag 298 [SEQ ID NO: 14]                       |||||||||||||||||||               Sbjct: 20673   agggctgggggctggccag 20655 [SEQ ID NO: 15]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|14091927|gb|AC025212.5|AC025212  Homo sapiens chromosome 18, clone               RP11-289A1, complete sequence                        Length = 182258                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Minus                    Query: 116   gttgttctgatttctgggc 134 [SEQ ID NO: 16]                       |||||||||||||||||||               Sbjct: 51238   gttgttctgatttctgggc 51220 [SEQ ID NO: 17]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|13489123|gb|AC078776.12|AC078776  Homo sapiens 12 BAC RP11-15519               (Roswell Park Cancer Institute Human BAC                        Library) complete sequence                                Length = 95801                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus                    Query: 630   tgtgtgtttgtgttttctt 648 [SEQ ID NO: 18]                       |||||||||||||||||||               Sbjct: 58720   tgtgtgtttgtgttttctt 58738 [SEQ ID NO: 19]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|1302657|gb|U52112.1|HSU52112  Homo sapiens Xq28 genomic DNA in the               region of the L1CAM locus                        containing the genes for neural cell adhesion molecule L1                           (L1CAM), arginine-vasopressin receptor (AVPR2), C1 p115                       (C1), ARD1 N-acetyltransferase related protein (TE2),                       renin-binding protein&gt;                            Length = 174424                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Minus                    Query: 278   agagggctgggggctggcc 296 [SEQ ID NO: 20]                       |||||||||||||||||||               Sbjct: 73811   agagggctgggggctggcc 73793 [SEQ ID NO: 21]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|10567853|gb|AC035147.3|AC035147  Homo sapiens chromosome 5 clone CTD-               2309M13, complete sequence                        Length = 104939                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 22/23 (95%)                Strand = Plus/Plus                    Query: 626   gccctgtgtgtttgtgttttctt 648 [SEQ ID NO: 22]                       ||||||||||||||| |||||||               Sbjct: 100838   gccctgtgtgtttgtcttttctt 100860 [SEQ ID NO: 23]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|9755473|gb|AC006452.4|AC006452  Homo sapiens PAC clone RP4-592P3 from               7q31-q35, complete sequence                        Length = 121703                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus                    Query: 278   agagggctgggggctggcc 296 [SEQ ID NO: 24]                       |||||||||||||||||||               Sbjct: 117068   agagggctgggggctggcc 117086 [SEQ ID NO: 25]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|9954648|gb|AC018758.2|AC018758  Homo sapiens chromosome 19, BAC CTB-               6117 (BC52850), complete sequence                        Length = 185409                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Minus                    Query: 630   tgtgtgtttgtgttttctt 648 [SEQ ID NO: 26]                       |||||||||||||||||||               Sbjct: 150073   tgtgtgtttgtgttttctt 150055 [SEQ ID NO: 27]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|9937750|gb|AC008750.7|AC008750  Homo sapiens chromosome 19 clone CTD-               2616J11, complete sequence                        Length = 143044                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Minus                    Query: 464   agcccctgatttacgtgag 482 [SEQ ID NO: 28]                       |||||||||||||||||||               Sbjct: 118714   agcccctgatttacgtgag 118696 [SEQ ID NO: 29]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|9506357|gb|M16230.2|SUSSMP1  Strongylocentrotus purpuratus spicule               matrix protein SM37, partial cds;                        and spicule matrix protein SM50 precursor, gene, exon 1                                Length = 14091                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus                    Query: 631   gtgtgtttgtgttttcttc 649 [SEQ ID NO: 30]                       |||||||||||||||||||               Sbjct: 14057   gtgtgtttgtgttttcttc 14075 [SEQ ID NO: 31]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|14596303|emb|AL356l57.14|AL356157  Human DNA sequence from clone RP11-               733D4 on chromosome 10, complete                        sequence [Homo sapiens]                                Length = 198917                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus                    Query: 276   gcagagggctgggggctgg 294 [SEQ ID NO: 32]                       |||||||||||||||||||               Sbjct: 86783   gcagagggctgggggctgg 86801 [SEQ ID NO: 33]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|14594822|emb|AJ314754.1|APL314754   Anas platyrhynchos  IgM gene               (partial), mIgM gene (partial), IgA gene                        (partial), mIgA gene (partial) and IgY gene (partial),                           clones 5.1, 13.1, 2.1 and PCR 00-106                            Length = 48796                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus                    Query: 404   gccttcctggacgcctaca 422 [SEQ ID NO: 34]                       |||||||||||||||||||               Sbjct: 19162   gccttcctggacgcctaca 19180 [SEQ ID NO: 35]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|7012904|gb|AF213884.1|AF213884S1  Homo sapiens nuclear factor of kappa               light polypeptide gene enhancer in                        B-cells 1 (NFKB1) gene, complete cds                                Length = 190000                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus                    Query: 156   tttgctctgagactgttaa 174 [SEQ ID NO: 36]                       |||||||||||||||||||               Sbjct: 92988   tttgctctgagactgttaa 93006 [SEQ ID NO: 37]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|2588626|gb|AC003081.1|AC003081  Human BAC clone CTB-9H2 from 7q31,               complete sequence [Homo sapiens]                        Length = 149566                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus                    Query: 395   ttaaacgttgccttcctgg 413 [SEQ ID NO: 38]                       |||||||||||||||||||               Sbjct: 114135   ttaaacgttgccttcctgg 114153 [SEQ ID NO: 39]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|9187146|emb|AL133553.9|AL133553  Human DNA sequence from clone GS1-               174L6 on chromosome 1 Contains part of                        the gene for TPR (translocated promoter region (to                           activated MET oncogene)), a gene for a novel protein (MSF:                       megakaryocyte stimulating factor), ESTs, STSs and GSSs,                       complete sequ&gt;                            Length = 190655                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 25/27 (92%)                Strand = Plus/Plus                    Query: 126   tttctgggcaggggacagagtaagtgt 152 [SEQ ID NO: 40]                       |||||||| ||||||||||||| ||||               Sbjct: 182695   tttctgggtaggggacagagtatgtgt 182721 [SEQ ID NO: 41]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted                 gi|6735496|emb|AL121925.10|HSJ966J20  Human DNA sequence from clone RP5-               966J20 on chromosome 20 Contains                        STSs and GSSs, complete sequence [Homo sapiens]                                Length = 39260                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus                    Query: 505   gaattccccagcgcttccc 523 [SEQ ID NO: 42]                       |||||||||||||||||||               Sbjct: 1220   gaattccccagcgcttccc 1238 [SEQ ID NO: 43]                    Predicted intron sequence from chr19 between nucleotide 38234-167860                   comprises potential eRNA elements targeted to                 gi|5123778|emb|AL035461.11|HS967N21  Human DNA sequence from clone RP5-               967N21 on chromosome 20p12.3-13.                        Contains the CHGB gene for chromogranin B (secretogranin                           1, SCG1), a pseudogene similar to part of KIAA0172, the                       gene for a novel protein and KIAA1153, the gene for a                       novel MCM2/3/5 fam&gt;                            Length = 139352                             Score = 38.2 bits (19), Expect = 7.6                    Identities = 19/19 (100%)                Strand = Plus/Plus          
 
       EXAMPLE 11  
     eRNA Elements are Involved in the Regulation of Genes Expressed in Cancer  
       [0197]    Jun Dimerization and TNFRSF6B Gene eRNA Element  
         [0198]    A predicted intron sequence from chromosome 12 between nucleotide 156966-180225 is used in a BlastN search of the human genome database. The search identified eRNA elements residing in the intron with potential activities in the regulation of genes known to expressed in cancer.  
         [0199]    A predicted intron residing on a fragment of DNA derived from chr12 between nucleotide sequences 156966-180225:— 
                                                                                                                                                                                                                                        gtaagtgcccttccgggagctcacacccgctctctgtctcccctgtccttcctctgcttcat                      tttttcctggactctgaccgatgtttgcgttagagtatgtttgaacgtggggtcgattggga                  aggattaagccttggtgctgaggctggatattgcaggaggatacagggtgaatggagccggc                  ggggcggggcgggccgggctgctgtgccgtggctgctgttgtgctgacaccctctttcctag                  agaaacagcctcttattcacaaccagctgatttgaaatttcctgcag [SEQ ID NO: 44]               Predicted intron sequence from chr12 between nucleotide 156966-180225               comprises potential eRNA elements targeted to                 gi|14749255|ref|XM_034220.1|  Homo sapiens Jun dimerization protein               p21SNFT (SNFT), mRNA                        Length = 980                             Score = 44.1 bits (22), Expect = 0.053                    Identities = 22/22 (100%)                Strand = Plus/Plus                    Query: 184   ggcggggcggggcgggccgggc 205 [SEQ ID NO: 45]                       ||||||||||||||||||||||               Sbjct: 186   ggcggggcggggcgggccgggc 207 [SEQ ID NO: 46]                    Predicted intron sequence from chr12 between nucleotide 156966-180225                   comprises potential eRNA elements targeted to                 gi|8246778|emb|AL121845.20|HSJ583P15  Human DNA sequence from clone RP4-               583P15 on chromosome 20 Contains                        ESTs, STSs, GSSs and ten CpG islands. Contains the                           TNFRSF6B gene for tumor necrosis factor receptor 6b                       (decoy), the 3′ part of the KIAA1088 gene, the ARFRP1 gene                       for ADP-ribosylation fa&gt;                            Length = 120917                             Score = 44.1 bits (22), Expect = 0.053                    Identities = 22/22 (100%)                Strand = Plus/Plus                    Query: 184   ggcggggcggggcgggccgggc 205 [SEQ ID NO: 47]                       ||||||||||||||||||||||               Sbjct: 43351   ggcggggcggggcgggccgggc 43372 [SEQ ID NO: 48]                    Predicted intron sequence from chr12 between nucleotide 156966-180225                   comprises potential eRNA elements targeted to                 gi|14523048|ref|NG_000006.1|  Homo sapiens genomic alpha globin region               (HBA@) on chromosome 16                        Length = 43058                             Score = 42.1 bits (21), Expect = 0.21                    Identities = 21/21 (100%)                Strand = Plus/Plus                    Query: 185   gcggggcggggcgggccgggc 205 [SEQ ID NO: 49]                       |||||||||||||||||||||               Sbjct: 25749   gcggggcggggcgggccgggc 25769 [SEQ ID NO: 50]                     Score = 38.2 bits (19), Expect = 3.3                    Identities = 22/23 (95%)                Strand = Plus/Plus               Predicted intron sequence from chr12 between nucleotide 156966-180225               comprises potential eRNA elements targeted to                 gi|14336674|gb|AE006462.1|AE006462  Homo sapiens 16p13.3 sequence section               1 of 8                        Length = 258002                             Score = 42.1 bits (21), Expect = 0.21                Identities = 21/21 (100%)                Strand = Plus/Plus                    Query: 185   gcggggcggggcgggccgggc 205 [SEQ ID NO: 51]                   |||||||||||||||||||||               Sbjct: 154885   gcggggcggggcgggccgggc 154905 [SEQ ID NO: 52]                     Score = 38.2 bits (19), Expect = 3.3                    Identities = 22/23 (95%)                Strand = Plus/Plus          
 
       EXAMPLE 12  
     eRNA Elements Which Overlap and Which are Directed to the Regulation of Multiple Genes  
       [0200]    A predicted intron sequence derived from chr12 between nucleotides: 156966-18022 is used in a BlastN search of the non-redundant human genome database to identify potential eRNA elements. The search reveals that a plurality of putative eRNA elements are embedded within a single intron and that a single eRNA element may perform regulatory functions directed at multiple genes. eRNA elements are identified within introns by searching other parts of the genome, including protein- and non-protein-encoding regions, for homology with a candidate eRNA sequence. eRNA elements from this intron are potentially involved in regulation of X-chromosome activity as well as several unannotated genes derived from human DNA.  
         [0201]    Predicted intron sequence from chr12 between nucleotide 156966-180225:— 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          gtatgtaccgtgctgggaccacttccccaggtgccttccccacccagccaggtctgtagttt                      tgaaagtcttgtatagctttttccttggtttaaaagcaataaatgcccactggagataaatt                  agaaaatatggaagaaagctataaaaaagaaactaaaaaaatctcttgtaattccaccactc                  aaatataactttttttcttaaaaaattttttttctcttacttagagacaggcagggtctggc                  tctgtcccccaggctggagtgcagtggtgccatcatagctcactgcagcctcaacctcttgg                  gctcaaggcattctctcgcctcagcctcctgagcagctgggactgcaggcatgagccatggt                  tcctgggcattttctcttgatattttgatgaagcagcctctttgtccccaggtcatagctgc                  ttaagacactatgtacagagatcttagttgaatgagacaagtgacttctggctgtgccctgc                  agataggccttgggtgcagccatggtttgtagattcccctggagaaatccaagcaacacaca                  tgtatttggtactcactaagtgcctacagaaccaaaccgaaactgggccgcactggggagga                  gatcaccgtggagaccggagggcgcactcacggagagt [SEQ ID NO: 53]               Predicted intron sequence from chr12 between nucleotide 156966-180225               comprises potential eRNA elements targeted to:                 gi|13162510|gb|AC011443.6|AC011443  Homo sapiens chromosome 19 clone CTC-               218B8, complete sequence                        Length = 156776                             Score = 151 bits (76) , Expect = 7e-34                    Identities = 112/124 (90%)                Strand = Plus/Minus                    Query: 238   cagggtctggctctgtcccccaggctggagtgcagtggtgccatcatagctcactgcagc                        297 [SEQ ID NO: 54]                            |||||||| |||||||  |||||||||| ||||||||| || ||||| ||||||||||||                   Sbjct: 49308   cagggtcttgctctgttgcccaggctggggtgcagtggcgcaatcatggctcactgcagc                    49249 [SEQ ID NO: 55]                        Query: 298   ctcaacctcttgggctcaaggcattctctcgcctcagcctcctgagcagctgggactgca                        357 [SEQ ID NO: 56]                        ||||||||| |||||||||| ||| ||| |||||||||||||||||||||||||||| ||                   Sbjct: 49248   ctcaacctcctgggctcaagccatcctcccgcctcagcctcctgagcagctgggactaca                    49189 [SEQ ID NO: 57]                        Query: 358   ggca 361                       ||||                    Sbjct: 49188 ggca 49185                    Score = 101 bits (51), Expect = 6e-19                Identities = 93/107 (86%)                Strand = Plus/Minus                    Query: 247   gctctgtcccccaggctggagtgcagtggtgccatcatagctcactgcagcctcaacctc                        306 [SEQ ID NO: 58]                            |||||||| |||||||||||||| |||||||| |||| |||||||||||||||| | |||                   Sbjct: 81907   gctctgtcacccaggctggagtgtagtggtgcaatcagagctcactgcagcctccaactc                    81848 [SEQ ID NO: 59]                        Query: 307   ttgggctcaaggcattctctcgcctcagcctcctgagcagctgggac 353                        [SEQ ID NO: 60]                            ||||||||||  || ||| | ||||||||||||||| |||| ||||                   Sbjct: 81847   ctgggctcaagcaatcctcccacctcagcctcctgagtagctaggac 81801                    [SEQ ID NO: 61]                    Score = 101 bits (51), Expect = 6e-19                    Identities = 105/123 (85%)                Strand = Plus/Plus                    Query: 248   ctctgtcccccaggctggagtgcagtggtgccatcatagctcactgcagcctcaacctct                        307 [SEQ ID NO: 62]                            ||||||| ||||||||||||||||||||||| ||| | |||||||||| ||||  ||||                   Sbjct: 79220   ctctgtcacccaggctggagtgcagtggtgcgatcttggctcactgcaacctccgcctcc                    79279 [SEQ ID NO: 63]                        Query: 308   tgggctcaaggcattctctcgcctcagcctcctgagcagctgggactgcaggcatgagcc                        367 [SEQ ID NO: 64]                            |||| |||||  ||||||  |||||||||||| ||| |||||||||| ||||| || |||                   Sbjct: 79280   tgggttcaagtgattctcctgcctcagcctcccgagtagctgggactacaggcgtgtgcc                    79339 [SEQ ID NO: 65]                        Query: 368   atg 370                       |||               Sbjct: 79340   atg 79342                    Predicted intron sequence from chr12 between nucleotide 156966-180225                   comprises potential eRNA elements targeted to:                 gi|6649930|gb|AF031075.1|AF031075  Homo sapiens chromosome X, cosmid               Qc8D3, complete sequence                        Length = 44163                             Score = 1453 bits (733), Expect = 0.0                    Identities = 747/754 (99%)                Strand = Plus/Plus                    Query: 1   gtggggacaaacagaaagacacaaggaacaattagaggctctccatagcaatgtcagaga                        60 [SEQ ID NO: 66]                            ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||                   Sbjct: 22925   gtggggacaaacagaaagacacaaggaacaattagaggctctccatagcaatgtcagaga                    22984 [SEQ ID NO: 67]                        Query: 61   tagggcagagcggatggtggtgacaacgctctgacaaacgttactattgaacgagagtca                        120 [SEQ ID NO: 68]                            ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||                   Sbjct: 22985   tagggcagagcggatggtggtgacaacgctctgacaaacgttactattgaacgagagtca                    [SEQ ID NO: 69]                   Predicted intron sequence from chr12 between nucleotide 156966-180225                   comprises potential eRNA elements targeted to                 gi|4508111|gb|AC005072.2|AC005072  Homo sapiens BAC clone CTB-181H17 from               7q21.2-q31.1, complete sequence                        Length = 69367                             Score = 147 bits (74), Expect = 1e-32                    Identities = 110/122 (90%)                Strand = Plus/Plus                    Query: 238   cagggtctggctctgtcccccaggctggagtgcagtggtgccatcatagctcactgcagc                        297 [SEQ ID NO: 70]                            |||||||| |||||||| ||||||||||||| ||||||||| ||||||||||||||||||                   Sbjct: 46265   cagggtcttgctctgtcacccaggctggagttcagtggtgcaatcatagctcactgcagc                    46324 [SEQ ID NO: 71]                        Query: 298   ctcaacctcttgggctcaaggcattctctcgcctcagcctcctgagcagctgggactgca                        357 [SEQ ID NO: 72]                            ||||| ||| ||||||||||  || ||| | ||||||||||||||| |||||||||||||                   Sbjct: 46325   ctcaaactcctgggctcaagcaatcctcccacctcagcctcctgagtagctgggactgca                    46384 [SEQ ID NO: 73]                        Query: 358   gg 359                       ||                    Sbjct: 46385 gg 46386                    Score = 93.7 bits (47), Expect = 1e-16                Identities = 86/99 (86%)                Strand = Plus/Minus               Predicted intron sequence from chr12 between nucleotide 156966-180225                   comprises potential eRNA elements targeted to:                 gi|13624997|emb|AL356214.20|AL356214  Human DNA sequence from clone RP11-               30E16 on chromosome 10, complete                        sequence [Homo sapiens]                                Length = 163964                             Score = 133 bits (67) , Expect = 2e-28                    Identities = 106/119 (89%)                Strand = Plus/Minus               Query: 250                   ctgtcccccaggctggagtgcagtggtgccatcatagctcactgcagcctcaacctcttg 309               [SEQ ID NO: 74]                        ||||| |||||||||||||||||||| |||||||| |||||||||||||||||||||                            ||                   Sbjct: 115382               ctgtcacccaggctggagtgcagtggcgccatcatggctcactgcagcctcaacctcctg 115323               [SEQ ID NO: 75]                    Query: 310   ggctcaaggcattctctcgcctcagcctcctgagcagctgggactgcaggcatgagcca                        368 [SEQ ID NO: 76]                        +TL,1   |||||||| ||| ||  ||||||||||||||| |||||| ||| |||||||| ||||               Sbjct: 115322   ggctcaagccatcctaccacctcagcctcctgagtagctggaactacaggcatgggcca                    115264 [SEQ ID NO: 77]                    Score = 97.6 bits (49), Expect = 9e-18                Identities = 97/113 (85%)                Strand = Plus/Minus               Predicted intron sequence from chr12 between nucleotide 156966-180225                   comprises potential eRNA elements targeted to:                 gi|3165399|gb|AC003684.1|AC003684  Homo sapiens Xp22 BAC GSHB-519E5               (Genome Systems Human BAC library)                        complete sequence                                Length = 210954                             Score = 135 bits (68), Expect = 4e-29                    Identities = 95/104 (91%)                Strand = Plus/Plus                    Query: 241   ggtctggctctgtcccccaggctggagtgcagtggtgccatcatagctcactgcagcctc                        300 [SEQ ID NO: 78]                            ||||| |||||||| | |||||||||||||||||||||||||| ||||||||||||||||                   Sbjct: 46790   ggtctcgctctgtcactcaggctggagtgcagtggtgccatcacagctcactgcagcctc                    46849 [SEQ ID NO: 79]                        Query: 301   aacctcttgggctcaaggcattctctcgcctcagcctcctgagc 344                        [SEQ ID NO: 80]                            ||  ||||||||||||| ||| ||||| ||||||||||||||||                   Sbjct: 46850   aaattcttgggctcaagccatcctctcacctcagcctcctgagc 46893                    [SEQ ID NO: 81]                    Score = 113 bits (57), Expect = 2e-22                Identities = 99/113 (87%)                Strand = Plus/Minus          
 
       EXAMPLE 13  
     Generic Methods for Determining the Effect of Putative eRNA  
       [0202]    A protein-encoding gene (1), which comprises at least one intron suspected of encoding an eRNA, is modified to prevent translation of the encoded protein but to otherwise preserve transcription of the primary transcript.  
         [0203]    A gene so modified (2) is conveniently prepared by oligonucleotide-directed (or site-directed) mutagenesis to convert the start codon (ATG) of the gene to a non-start codon (e.g., AAG or TAG) and to introduce a stop codon (e.g., TAG, TAA, TGA) closely downstream (e.g., within 30 bases) of the normal start codon. The site-directed mutagenesis involves hybridizing an oligonucleotide encoding the desired mutation to a template DNA, wherein the template is the single-stranded form of a plasmid or bacteriophage containing the unaltered or parent gene sequence. After hybridization, a DNA polymerase is used to synthesize an entire second complementary strand of the template that will thus incorporate the oligonucleotide primer and will code for the selected alteration in the parent gene sequence. The resultant heteroduplex molecule is then transformed into a suitable host cell, usually a prokaryote such as  E. coli . After the cells are grown, they are plated onto agarose plates and screened using the oligonucleotide primer having a detectable label to identify the bacterial colonies having the mutated or modified gene.  
         [0204]    The intron(s) of the parent and modified genes are removed by site-directed mutagenesis or by other standard techniques to provide (3) a modified gene encoding an intronless primary transcript from which a wild-type protein can be translated and (4) a modified gene encoding an intronless primary transcript from which a wild-type protein cannot translated.  
         [0205]    Each of the above genes (1-4) is then inserted into a suitable expression vector and the construct so produced is transfected into cells. Expression of the inserted genes (1-4) in the transfected cells will result, respectively, in:— 
         [0206]    (a) a normal primary transcript, including introns, from which a functional wild-type protein can be produced;  
         [0207]    (b) a primary transcript, excluding introns, from which a functional wild-type protein can be produced;  
         [0208]    (c) a primary transcript, including introns, from which a functional wild-type protein cannot be produced; and  
         [0209]    (d) a primary transcript, excluding introns, from which a functional wild-type protein cannot be produced.  
         [0210]    The phenotypic effects of (a)-(d) are then compared (e.g., by pairwise comparisons) to discriminate which effects may be ascribed to protein and which may be ascribed to eRNA.  
         [0211]    Alternatively, genetic complementation to discriminate whether putative eRNA sequences are encoding genuine trans-acting RNAs or cis-acting transcription factor binding sites, can be assessed by allelic replacement with an intronless gene and determination of the phenotypic effect thereof, followed by complementation with the intron-containing gene which cannot produce a protein (e.g. because its translational start codon has ben rendered non-functional by site-directed mutation). If wild-type function is restored by the latter, the complementing genetic factor must be an eRNA derived from the intron. Appropriate secondary controls are employed to confirm whether a transcript is produced and spliced normally (e.g., using Northern blots) and whether a protein is or is not expressed (e.g., using Western blots) as appropriate to the particular construct.  
       EXAMPLE 14  
     Idenfication of eRNA Candidates in Meiotic Genes  
       [0212]    A subset of nucleotide repeats in the  S. cerevisiae  genome is obtained and then filtered by taking intronic sequences of all known meiotic genes and removing all repeated sequences not in the sequences of the introns. This leaves a putative signal of an eRNA gene regulation network. In Table 2, the gene carrying an intron which is repeated is identified in the left hand column. The nucleotide sequence of the repeat intronic sequence is then shown in the penultimate left hand column.  
         [0213]    These 16mer sequences are then screened for potential receiver sequences in 245,000 sequences in the genome. In Table 2, there are three types of putative receiver sequences which are located in two regions:  
         [0214]    i) within a gene (third most right column); or  
         [0215]    ii) in an intergenic region located:  
         [0216]    a) upstream (second most right hand column); or  
         [0217]    b) downstream (most right hand column).  
         [0218]    Many of these genes are known to be involved in meiotic processes, including cell division. The chance that any given sequence of 16 nucleotides would occur accidently at more than one locus in the yeast genome is less than 1 in 100. The odds against an accidental finding that sequences from introns of genes involved in meoisis occur in or near a set of other genes involved in meiosis is astronomically small, and thus this network must be real. Consequently, this confirms that the identifier of potential eRNA and receiver sequences is a significant event, supporting the concept of eRNA networking. The role of any particular candidate eRNAs in the network may be determined and confirmed by analyses such as set out in Example 13.  
                                                                               TABLE 2                           eRNA AND RECEIVE SEQUENCES IN  SACCHAROMYCES CEREVISIAE             MEIOTIC GENES            Intron                               Bearing Gene   SEQ ID No.   Repeat   Hit   Upstream   Downstream                    AMA1   82   CTTATTTTTTCATT         RPL15A     YLR030W (119)                           AT       (581)                   83   TTTTTCATTATGAA     PHA2                         AA                   84   AAAATATTTGTTAG     CWH43                         TA               DMC1   85   CTGCTGTAGAGGTT       RIM15     YFL032W (332)                         CT       (113)                   86   CTAATAATTTGGAA     YNL156C                         AGGA                   87   ATAACATTTTTAAA       ATP3 (167)   FIG1 (291)                       AC                             SEC8                     88   GGTTCTTTCCCCCT       MNN4 (136)   YKT9 (671)                       TT                   89   CTAATAATTTGGAA   YNL156C                       AGG   ARP8               HFM1   90   AAGTGGTTTTTCTG   YCR024C                       GA                   91   TAGATAATAAAAG       PPA1 (112)   RPN1 (133)                       AAA                   92   CTAGATAATAAAA       YPL141C   MKK2 (117)                       GAA       (1336)               HOP2   93   GTTAAGTATTTTTT         HXT12       YIL169C (273)                         TA         (2999)       YOL155C (102)                                 HXT11                               (1625)               MMS2   94   CCTTTCAAAACTTA         FIT1 (586)       YDR535C (1120)                         TA                   95   ATTTGTTAGTATAT       MAM33 (8)     RPS24B (473)                         GT               PCH2   96   TCTTTCTTTCCTTCT       SGT1 (201)     ASE1 (114)                         T                   97   TATGTTTTTTTCTTT   YLR379W                       T                   98   TCTTCATAAAAAA       YGL034C     HOP2 (165)                         GCA       (1881)                   99   TTCTTTTTCTTTCTT       NOG1 (144)   SSU1 (728)                       TC                   100   GTATGTTTTTTTCT       YKL063C   MSN4 (807)                       TT       (903)                   101   CTTTTTCTTTCTTTC   SPP41                       CTT                   102   TTTTTTTCTTTTATT   YGL131C                       CT                   103   TTTTATTCTACTTTT       TH(GUG)E1   CHO1 (64)                       A       (152)               RAD14   104   AATTTAACGATGA         NVJ1 (101)     UTP9 (118)                       GATG                   105   CAAACACAGAATC   YDL189W                       ATTT                   106   CGATGAGATGAGC         URA7 (144)     MRPL16 (315)                       TGTG               SRC1   107   TTTTTTTTGTTTTTG         VPS25 (888)     URA8 (101)                       A                   108   TTAATTTTTTTTGA   YMR192W                       AT                   109   TAATTTTTTTTGAA         SUL1 (333)     PCA1 (701)                       TTT                   110   TTTTTTTTGAATTTT         BUR6 (38)     TR(ACG)E (356)                       T         YAP3 (220)     TV(AAC)H (18)                               RPL34B   MMF1 (372)                               (409)                   111   TTTTTTTGAATTTTT       VPS45 (429)   PAN2 (82)                       T       YAP3 (219)   TV(AAC)H (19)                               YPR078C   MRL1 (332)                               (273)                   112   AGTTTTAATTTTTT       MSC6   GDS1 (354)                       TT       (1559)                   113   TTTTTTTTTGTTTTT     SAP4                         G                   114   TTTTTTTGTTTTTGA       YHR032W   YHR033W (60)                       TTT       (399)                   115   TTGAATTTTTTTTT     YOR154W                         GT                   116   TTTTAATTTTTTTTG     RAD59                         A                   117   AATAAATTGTACTC     STT4                         AC                   118   TTTTTGAATTTTTTT       YAP3 (216)   TV(AAC)H (22)                       TT       YPR078C   MRL1 (335)                               (270)   MCM1 (201)                               ARG80                               (534)                   119   AAAATTCAAAAAA       YAP3 (221)   TV(AAC)H (17)                       AAT                   120   AAAAAAATTCAAA       YAP3 (218)   TV(AAC)H (20)                       AAA       YPR078C   MRL1 (333)                               (272)               YLR211C   121   TTTTTTTTTGTTCAT       KGD1 (130)   AYR1 (341)                       G                  
 
       EXAMPLE 15  
     GRIA 3RNA Network  
       [0219]    [0219]FIG. 6 provides and example of an eRNA network centred around the GRIA2, GRIA3 and GRIA4 genes which all share parts of an intronic sequence shown in the Figure. It is proposed that this intronic sequence is an eRNA.  
         [0220]    Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications. The invention also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features.  
       BIBLIOGRAPHY  
       [0221]    Akam, M. E., A. Martinez-Arias, R. Weinzierl and C. D. Wilde. 1985. Function and expression of ultrabithorax in the  Drosophila  embryo. Cold Spring Harb. Symp.,  Quant. Biol.  50: 195-200.  
         [0222]    Albert, R., H. Jeong and A. L. Barabasi. 2000. Error and attack tolerance of complex networks.  Nature  406: 378-382.  
         [0223]    Allmang, C., J. Kufel, G. Chanfreau, P. Mitchell, E. Petfalski and D. Tollervey. 1999a. Functions of the exosome in rRNA, snoRNA and snRNA synthesis.  EMBO J.  18: 5399-5410.  
         [0224]    Allmang, C., E. Petfalski, A. Podtelejnikov, M. Mann, D. Tollervey and P. Mitchell. 1999b. The yeast exosome and human PM-Scl are related complexes of 3′→5′ exonucleases.  Genes Dev.  13: 2148-2158.  
         [0225]    Altschul et al., 1997,  Nucl. Acids Res.  25:3389.  
         [0226]    Ausubel et al., “Current Protocols in Molecular Biology” John Wiley &amp; Sons Inc, 1994-1998, Chapter 15.  
         [0227]    Almeida, A. C., V. M. Fernandes de Lima and A. F. Infantosi. 1998. Mathematical model of the CA1 region of the rat hippocampus.  Phys. Med. Biol.  43: 2631-2646.  
         [0228]    Andersen, R. A., L. H. Snyder, D. C. Bradley and J. Xing. 1997. Multimodal representation of space in the posterior parietal cortex and its use in planning movements.  Annu. Rev. Neurosci.  20: 303-330.  
         [0229]    Ashe, H. L., J. Monks, M. Wijgerde, P. Fraser and N. J. Proudfoot. 1997. Intergenic transcription and transinduction of the human beta-globin locus.  Genes Dev.  11: 2494-2509.  
         [0230]    Bachellerie, J. P., M. Nicoloso, L. H. Qu, B. Michot, M. Caizergues-Ferrer, J. Cavaille and M. H. Renalier. 1995. Novel intron-encoded small nucleolar RNAs with long sequence complementarities to mature rRNAs involved in ribosome biogenesis.  Biochem. Cell. Biol.  73: 835-843.  
         [0231]    Bass, B. L. 2000. Double-stranded RNA as a template for gene silencing.  Cell  101: 235-238.  
         [0232]    Becskei, A. and L. Serrano. 2000. Engineering stability in gene networks by autoregulation.  Nature  405: 590-593.  
         [0233]    Bhalla, U. S. and R. Iyengar. 1999. Emergent properties of networks of biological signaling pathways.  Science  283:381-387.  
         [0234]    Bortolin, M. L. and T. Kiss. 1998. Human U19 intron-encoded snoRNA is processed from a long primary transcript that possesses little potential for protein coding.  RNA  4: 445-454.  
         [0235]    Boutros, M. and M. Mlodzik. 1999. Dishevelled: at the crossroads of divergent intracellular signaling pathways.  Mech. Dev.  83: 27-37.  
         [0236]    Bridgeman, B. 1995. A review of the role of efference copy in sensory and oculomotor control systems.  Ann. Biomed. Eng.  23: 409-422.  
         [0237]    Brockdorff, N. 1998. The role of Xist in X-inactivation.  Curr. Opin. Genet. Dev.  8: 328-333.  
         [0238]    Caffarelli, E., L. Maggi, A. Fatica, J. Jiricny and I. Bozzoni. 1997. A novel Mn ++ -dependent ribonuclease that functions in U16 SnoRNA processing in  X. laevis. Biochem. Biophys. Res. Commun.  233: 514-517.  
         [0239]    Castelli-Gair, J., J. Muller and M. Bienz. 1992a. Function of an Ultrabithorax minigene in imaginal cells.  Development  114: 877-886.  
         [0240]    Castelli-Gair, J. E., M. P. Capdevila, J. L. Micol and A. Garcia-Bellido. 1992b. Positive and negative cis-regulatory elements in the bithoraxoid region of the  Drosophila  Ultrabithorax gene.  Mol. Gen. Genet.  234: 177-184.  
         [0241]    Castelli-Gair, J. E. and A. Garcia-Bellido. 1990. Interactions of Polycomb and trithorax with cis regulatory regions of Ultrabithorax during the development of  Drosophila melanogaster. EMBO J.  9: 4267-4275.  
         [0242]    Castelli-Gair, J. E., J. L. Micol and A. Garcia-Bellido. 1990. Transvection in the  Drosophila  Ultrabithorax gene: a Cbx1 mutant allele induces ectopic expression of a normal allele in trans.  Genetics  126: 177-184.  
         [0243]    Cavaille, J., K. Buiting, M. Kiefmann, M. Lalande, C. I. Brannan, B. Horsthemke, J. P. Bachellerie, J. Brosius and A. Huttenhofer. 2000. Identification of brain-specific and imprinted small nucleolar RNA genes exhibiting an unusual genomic organization.  Proc. Natl. Acad. Sci. USA  97: 14311-14316.  
         [0244]    Cavalier-Smith, T. 1991. Intron phylogeny: a new hypothesis.  Trends Genet.  7: 145-148.  
         [0245]    Cecconi, F., P. Mariottini and F. Amaldi. 1995. The  Xenopus  intron-encoded U17 snoRNA is produced by exonucleolytic processing of its precursor in oocytes.  Nucleic Acids Res.  23: 4670-4676.  
         [0246]    Chanfreau, G., G. Rotondo, P. Legrain and A. Jacquier. 1998. Processing of a dicistronic small nucleolar RNA precursor by the RNA endonuclease Rnt1 . EMBO J.  17: 3726-3737.  
         [0247]    Chervitz, S. A., L. Aravind, G. Sherlock et al. (13 co-authors). 1998. Comparison of the complete protein sets of worm and yeast: orthology and divergence.  Science  282: 2022-2028.  
         [0248]    Chinnaiyan, A. M. 1999. The apoptosome: heart and soul of the cell death machine.  Neoplasia  1: 5-15.  
         [0249]    Cho, G. and R. F. Doolittle. 1997. Intron distribution in ancient paralogs supports random insertion and not random loss.  J. Mol. Evol.  44: 573-584.  
         [0250]    Coffey, E. T., V. Hongisto, M. Dickens, R. J. Davis and M. J. Courtney. 2000. Dual roles for c-Jun N-terminal kinase in developmental and stress responses in cerebellar granule neurons.  J. Neurosci.  20: 7602-7613.  
         [0251]    Consortium, I. H. G. S. 2001. Initial sequencing and analysis of the human genome.  Nature  409: 860-921.  
         [0252]    Cousineau, B., S. Lawrence, D. Smith and M. Belfort. 2000. Retrotransposition of a bacterial group II intron.  Nature  404: 1018-1021.  
         [0253]    Croft, L., S. Schandorff, F. Clark, K. Burrage, P. Arctander and J. S. Mattick. 2000. ISIS, the intron information system, reveals the high frequency of alternative splicing in the human genome.  Nature Genet.  24: 340-341.  
         [0254]    Dano, S., P. G. Sorensen and F. Hynne. 1999. Sustained oscillations in living cells.  Nature  402: 320-322.  
         [0255]    Daugas, E., D. Nochy, L. Ravagnan, M. Loeffler, S. A. Susin, N. Zamzami and G. Kroemer. 2000. Apoptosis-inducing factor (AIF): a ubiquitous mitochondrial oxidoreductase involved in apoptosis.  FEBS Lett.  476: 118-123.  
         [0256]    Davidson, E. H., W. H. Klein and R. J. Britten. 1977. Sequence organization in animal DNA and a speculation on hnRNA as a coordinate regulatory transcript.  Dev. Biol.  55: 69-84.  
         [0257]    Delihas, N. 1995. Regulation of gene expression by trans-encoded antisense RNAs.  Mol. Microbiol.  15: 411-414.  
         [0258]    Dernburg, A. F., J. Zalevsky, M. P. Colaiacovo and A. M. Villeneuve. 2000. Transgene-mediated cosuppression in the  C. elegans  germ line.  Genes Dev.  14: 1578-1583.  
         [0259]    Deutsch, M. and M. Long. 1999. Intron-exon structures of eukaryotic model organisms.  Nucleic Acids Res.  27: 3219-3228.  
         [0260]    Dover, G. A. and D. Tautz. 1986. Conservation and divergence in multigene families: alternatives to selection and drift. Philos. Trans. R. Soc. Lond. B.  Biol. Sci.  312: 275-289.  
         [0261]    Duboule, D. and A. S. Wilkins. 1998. The evolution of ‘bricolage’.  Trends Genet.  14: 54-59.  
         [0262]    Duncan, I. 1987. The bithorax complex.  Annu. Rev. Genet.  21: 285-319.  
         [0263]    Eddy, S. R. 1999. Noncoding RNA genes.  Curr. Opin. Genet. Dev.  9: 695-699.  
         [0264]    Eickbush, T. H. 2000. Molecular biology: Introns gain ground.  Nature  404: 940-941.  
         [0265]    Elgar, G. 1996. Quality not quantity: the pufferfish genome.  Hum. Mol. Genet.  5: 1437-1442.  
         [0266]    Elman, J. L. 1998. Connectionism, artificial life, and dynamical systems: new approaches to old questions. In W. Bechtel and G. Graham, eds. A Companion to Cognitive Science. Basil Blackwood.  
         [0267]    Elowitz, M. B. and S. Leibler. 2000. A synthetic oscillatory network of transcriptional regulators.  Nature  403: 335-338.  
         [0268]    Erdmann, V. A., M. Szymanski, A. Hochberg, N. de Groot and J. Barciszewski. 1999. Collection of mRNA-like non-coding RNAs.  Nucleic Acids Res.  27: 192-195.  
         [0269]    Feinbaum, R. and V. Ambros. 1999. The timing of lin-4 RNA accumulation controls the timing of postembryonic developmental events in  Caenorhabditis elegans. Dev. Biol.  210: 87-95.  
         [0270]    Ferat, J. L. and F. Michel. 1993. Group II self-splicing introns in bacteria.  Nature  364: 358-361.  
         [0271]    Filipowicz, W. 2000. Imprinted expression of small nucleolar RNAs in brain: Time for RNomics.  Proc. Natl. Acad. Sci. USA  97: 14035-14037.  
         [0272]    Filipowicz, W., P. Pelczar, V. Pogacic and F. Dragon. 1999. Structure and biogenesis of small nucleolar RNAs acting as guides for ribosomal RNA modification.  Acta. Biochim. Pol.  46: 377-389.  
         [0273]    Gardner, T. S., C. R. Cantor and J. J. Collins. 2000. Construction of a genetic toggle switch in  Escherichia coli. Nature  403: 339-342.  
         [0274]    Gemkow, M. J., P. J. Verveer and D. J. Arndt-Jovin. 1998. Homologous association of the Bithorax-Complex during embryogenesis: consequences for transvection in  Drosophila melanogaster. Development  125: 4541-4552.  
         [0275]    Geyer, P. K., M. M. Green and V. G. Corces. 1990. Tissue-specific transcriptional enhancers may act in trans on the gene located in the homologous chromosome: the molecular basis of transvection in  Drosophila. EMBO J.  9: 2247-2256.  
         [0276]    Goldsborough, A. S. and T. B. Kornberg. 1996. Reduction of transcription by homologue asynapsis in  Drosophila  imaginal discs.  Nature  381: 807-810.  
         [0277]    Haase, S. B. and S. I. Reed. 1999. Evidence that a free-running oscillator drives G1 events in the budding yeast cell cycle.  Nature  401: 394-397.  
         [0278]    Hastings, M. L., H. A. Ingle, M. A. Lazar and S. H. Munroe. 2000. Post-transcriptional regulation of thyroid hormone receptor expression by cis-acting sequences and a naturally occurring antisense RNA.  J. Biol. Chem.  275: 11507-11513.  
         [0279]    Hartwell, L. H., J. J. Hopfield, S. Leibler and A. W. Murray. 1999. From molecular to modular cell biology.  Nature  402: C47-52.  
         [0280]    Hasty, J., J. Pradines, M. Dolnik and J. J. Collins. 2000. Noise-based switches and amplifiers for gene expression.  Proc. Natl. Acad. Sci. USA  97: 2075-2080.  
         [0281]    Hendrickson, J. E. and S. Sakonju. 1995. Cis and trans interactions between the iab regulatory regions and abdominal-A and abdominal-B in  Drosophila melanogaster. Genetics  139: 835-848.  
         [0282]    Herbert, A. and A. Rich. 1999a. RNA processing and the evolution of eukaryotes.  Nature Genet.  21: 265-269.  
         [0283]    Herbert, A. and A. Rich. 1999b. RNA processing in evolution: The logic of soft-wired genomes. Ann. N.Y.  Acad. Sci.  870:119-132.  
         [0284]    Hermann, T. and Westhof, E. 1999. Non-Watson-Crick base pairs in RNA-protein recognition.  Chem. Biol.  6: R335-43.  
         [0285]    Hoeflich, K. P., J. Luo, E. A. Rubie, M. S. Tsao, O. Jin and J. R. Woodgett. 2000. Requirement for glycogen synthase kinase-3β in cell survival and NF-kappaB activation.  Nature  406: 86-90.  
         [0286]    Hogness, D. S., H. D. Lipshitz, P. A. Beachy, D. A. Peattie, R. B. Saint, M. Goldschmidt-Clermont, P. J. Harte, E. R. Gavis and S. L. Helfand. 1985. Regulation and products of the Ubx domain of the bithorax complex. Cold Spring Harb. Symp.  Quant. Biol.  50: 181-194.  
         [0287]    Holland, P. W. 1999. The future of evolutionary developmental biology.  Nature  402: C41-44.  
         [0288]    Hong, Y. K., S. D. Ontiveros and W. M. Strauss. 2000. A revision of the human XIST gene organization and structural comparison with mouse Xist.  Mamm. Genome  11: 220-224.  
         [0289]    Hopmann, R., D. Duncan and I. Duncan. 1995. Transvection in the iab-5,6,7 region of the bithorax complex of  Drosophila : homology independent interactions in trans.  Genetics  139: 815-833.  
         [0290]    Huang, F. 1998. Syntagms in development and evolution.  Int. J. Dev. Biol.  42: 487-494.  
         [0291]    Hunter, T. 2000a. Signaling—2000 and beyond.  Cell  100: 113-127.  
         [0292]    Hurst, L. D. and N. G. Smith. 1999. Molecular evolutionary evidence that H19 mRNA is functional.  Trends Genet.  15: 134-135.  
         [0293]    Irish, V. F., A. Martinez-Arias and M. Akam. 1989. Spatial regulation of the Antennapedia and Ultrabithorax homeotic genes during  Drosophila  early development.  EMBO J.  8: 1527-1537.  
         [0294]    Jan, Y. N. and L. Y. January 1993. Functional gene cassettes in development.  Proc. Natl. Acad. Sci. USA  90: 8305-8307.  
         [0295]    Jiang, Z. H. and J. Y. Wu. 1999. Alternative splicing and programmed cell death.  Proc. Soc. Exp. Biol. Med.  220: 64-72.  
         [0296]    Judd, B. H. 1988. Transvection: allelic cross talk.  Cell  53: 841-843.  
         [0297]    Kreivi, J. P. and A. I. Lamond. 1996. RNA splicing: unexpected spliceosome diversity.  Curr. Biol.  6: 802-805.  
         [0298]    Lambowitz, A. M. and M. Belfort. 1993. Introns as mobile genetic elements.  Annu. Rev. Biochem.  62: 587-622.  
         [0299]    Laney, J. D. and M. D. Biggin. 1992. zeste, a nonessential gene, potently activates Ultrabithorax transcription in the  Drosophila  embryo.  Genes Dev.  6: 1531-1541.  
         [0300]    Lee, J. T., L. S. Davidow and D. Warshawsky. 1999. Tsix, a gene antisense to Xist at the X-inactivation centre.  Nature Genet.  21: 400-404.  
         [0301]    Lee, R. C., R. L. Feinbaum and V. Ambros. 1993. The  C. elegans  heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 . Cell  75: 843-854.  
         [0302]    Lehmann, A. R. 2001. The xeroderma pigmentosum group D (XPD) gene: one gene, two functions, three diseases.  Genes Dev.  15: 15-23.  
         [0303]    Lipman, D. J. 1997. Making (anti)sense of non-coding sequence conservation.  Nucleic Acids Res.  25: 3580-3583.  
         [0304]    Lipshitz, H. D., D. A. Peattie and D. S. Hogness. 1987. Novel transcripts from the Ultrabithorax domain of the bithorax complex.  Genes Dev.  1: 307-322.  
         [0305]    Loeffler, M. and G. Kroemer. 2000. The mitochondrion in cell death control: certainties and incognita.  Exp. Cell Res.  256: 19-26.  
         [0306]    Lopez, A. J. 1998. Alternative splicing of pre-mRNA: developmental consequences and mechanisms of regulation.  Annu. Rev. Genet.  32: 279-305.  
         [0307]    Martinez-Abarca, F. and N. Toro. 2000. Group II introns in the bacterial world.  Mol. Microbiol  38: 917-926.  
         [0308]    Masquida, B. and Westhof, E. 2000. On the wobble GoU and related pairs.  Rna  6: 9-15  
         [0309]    Mattick, J. S. 1994. Introns: evolution and function.  Curr. Opin. Genet. Dev.  4: 823-831.  
         [0310]    Maxwell, E. S. and M. J. Fournier. 1995. The small nucleolar RNAs.  Annu. Rev. Biochem.  64: 897-934.  
         [0311]    McAdams, H. H. and A. Arkin. 1997. Stochastic mechanisms in gene expression.  Proc. Natl. Acad. Sci. USA  94: 814-819.  
         [0312]    McAdams, H. H. and L. Shapiro. 1995. Circuit simulation of genetic networks.  Science  269: 650-656.  
         [0313]    McClelland, J. L. and D. C. Plaut. 1993. Computational approaches to cognition: top-down approaches.  Curr. Opin. Neurobiol.  3: 209-216.  
         [0314]    McClelland, J. L. and D. E. Rumelhart. 1985. Distributed memory and the representation of general and specific information.  J. Exp. Psychol. Gen.  114:159-197.  
         [0315]    Mendoza, L. and E. R. Alvarez-Buylla. 1998. Dynamics of the genetic regulatory network for  Arabidopsis thaliana  flower morphogenesis.  J. Theor. Biol.  193: 307-319.  
         [0316]    Mestl, T., E. Plahte and S. W. Omholt. 1995. A mathematical framework for describing and analysing gene regulatory networks.  J. Theor. Biol.  176: 291-300.  
         [0317]    Mette, M. F., W. Aufsatz, J. van Der Winden, M. A. Matzke and A. J. Matzke. 2000. Transcriptional silencing and promoter methylation triggered by double-stranded RNA.  EMBO J.  19: 5194-5201.  
         [0318]    Micol, J. L., J. E. Castelli-Gair and A. Garcia-Bellido. 1990. Genetic analysis of transvection effects involving cis-regulatory elements of the  Drosophila  Ultrabithorax gene.  Genetics  126: 365-373.  
         [0319]    Mitchell, P., E. Petfalski, A. Shevchenko, M. Mann and D. Tollervey. 1997. The exosome: a conserved eukaryotic RNA processing complex containing multiple 3′→5′ exoribonucleases.  Cell  91: 457-466.  
         [0320]    Mitchell, P. and D. Tollervey. 2000. Musing on the structural organization of the exosome complex.  Nature Struct. Biol.  7: 843-846.  
         [0321]    Nashimoto, M. 2000. Anomalous RNA substrates for mammalian tRNA 3′ processing endoribonuclease.  FEBS Lett.  472: 179-186.  
         [0322]    Nemes, J. P., K. A. Benzow and M. D. Koob. 2000. The SCA8 transcript is an antisense RNA to a brain-specific transcript encoding a novel actin-binding protein (KLHL1).  Hum. Mol. Genet.  9: 1543-1551.  
         [0323]    Newman, A. J. 1994. Pre-mRNA splicing.  Curr. Opin. Genet. Dev.  4: 298-304.  
         [0324]    Nicoloso, M., L. H. Qu, B. Michot and J. P. Bachellerie. 1996. Intron-encoded, antisense small nucleolar RNAs: the characterization of nine novel species points to their direct role as guides for the 2′-O-ribose methylation of rRNAs.  J. Mol. Biol.  260: 178-195.  
         [0325]    Niehrs, C. and N. Pollet. 1999. Synexpression groups in eukaryotes.  Nature  402: 483-487.  
         [0326]    O&#39;Brien, S. P., K. Seipel, Q. G. Medley, R. Bronson, R. Segal and M. Streuli. 2000. Skeletal muscle deformity and neuronal disorder in trio exchange factor-deficient mouse embryos.  Proc. Natl. Acad. Sci. USA  97: 12074-12078.  
         [0327]    Palmer, J. D. and J. M. Logsdon, Jr. 1991. The recent origins of introns.  Curr. Opin. Genet. Dev.  1: 470-477.  
         [0328]    Parrish, S., J. Fleenor, S. Xu, C. Mello and A. Fire. 2000. Functional anatomy of a dsRNA trigger. Differential requirement for the two trigger strands in RNA interference.  Mol. Cell  6: 1077-1087.  
         [0329]    Pasquinelli, A. E., B. J. Reinhart, F. Slack et al. (11 co-authors). 2000. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA.  Nature  408: 86-89.  
         [0330]    Pawson, T. 1995. Protein modules and signalling networks.  Nature  373: 573-580.  
         [0331]    Pelczar, P. and W. Filipowicz. 1998. The host gene for intronic U17 small nucleolar RNAs in mammals has no protein-coding potential and is a member of the 5′-terminal oligopyrimidine gene family.  Mol. Cell Biol.  18: 4509-4518.  
         [0332]    Pirrotta, V. 1990. Transvection and long-distance gene regulation.  Bioessays  12: 409-414.  
         [0333]    Pirrotta, V. 1999. Transvection and chromosomal trans-interaction effects.  Biochim. Biophys. Acta  1424: M1-8.  
         [0334]    Plunkett, K., A. Karmiloff-Smith, E. Bates, J. L. Elman and M. H. Johnson. 1997. Connectionism and developmental psychology.  J. Child Psychol. Psychiatry  38: 53-80.  
         [0335]    Potter, S. S. and W. W. Branford. 1998. Evolutionary conservation and tissue-specific processing of Hoxa 11 antisense transcripts.  Mamm. Genome  9: 799-806.  
         [0336]    Praseuth, D., Guieysse, A. L. and Helene, C. 1999. Triple helix formation and the antigene strategy for sequence-specific control of gene expression.  Biochim Biophys Acta,  1489: 181-206  
         [0337]    Prislei, S., A. Fatica, E. De Gregorio, M. Arese, P. Fragapane, E. Caffarelli, C. Presutti and I. Bozzoni. 1995. Self-cleaving motifs are found in close proximity to the sites utilized for U16 snoRNA processing.  Gene  163: 221-226.  
         [0338]    Qian, L., M. N. Vu, M. Carter and M. F. Wilkinson. 1992. A spliced intron accumulates as a lariat in the nucleus of T cells.  Nucleic Acids Res.  20: 5345-5350.  
         [0339]    Qu, L. H., A. Henras, Y. J. Lu, H. Zhou, W. X. Zhou, Y. Q. Zhu, J. Zhao, Y. Henry, M. Caizergues-Ferrer and J. P. Bachellerie. 1999. Seven novel methylation guide small nucleolar RNAs are processed from a common polycistronic transcript by Rat1p and RNase III in yeast.  Mol. Cell Biol.  19: 1144-1158.  
         [0340]    Rebane, A., R. Tamme, M. Laan, I. Pata and A. Metspalu. 1998. A novel snoRNA (U73) is encoded within the introns of the human and mouse ribosomal protein S3a genes.  Gene  210: 255-263.  
         [0341]    Reinhart, B. J., F. J. Slack, M. Basson, A. E. Pasquinelli, J. C. Bettinger, A. E. Rougvie, H. R. Horvitz and G. Ruvkun. 2000. The 21-nucleotide let-7 RNA regulates developmental timing in  Caenorhabditis elegans. Nature  403: 901-906.  
         [0342]    Roest Crollius, H., O. Jaillon, A. Bernot et al. (12 co-authors). 2000. Estimate of human gene number provided by genome-wide analysis using  Tetraodon nigroviridis  DNA sequence.  Nature Genet.  25: 235-238.  
         [0343]    Rubin, G. M., M. D. Yandell, J. R. Wortman et al. (55 co-authors). 2000. Comparative genomics of the eukaryotes.  Science  287: 2204-2215.  
         [0344]    Ruskin, B. and M. R. Green. 1985. An RNA processing activity that debranches RNA lariats.  Science  229: 135-140.  
         [0345]    Sanchez-Herrero, E. and M. Akam. 1989. Spatially ordered transcription of regulatory DNA in the bithorax complex of  Drosophila. Development  107: 321-329.  
         [0346]    Santoro, B., E. De Gregorio, E. Caffarelli and I. Bozzoni. 1994. RNA-protein interactions in the nuclei of  Xenopus  oocytes: complex formation and processing activity on the regulatory intron of ribosomal protein gene L1.  Mol. Cell Biol.  14: 6975-6982.  
         [0347]    Sharp, P. A. 2001. RNA interference-2001 . Genes Dev  15: 485-490.  
         [0348]    Shearman, L. P., S. Sriram, D. R. Weaver et al. (11 co-authors). 2000. Interacting molecular loops in the mammalian circadian clock.  Science  288: 1013-1019.  
         [0349]    Sipos, L., J. Mihaly, F. Karch, P. Schedl, J. Gausz and H. Gyurkovics. 1998. Transvection in the  Drosophila  Abd-B domain: extensive upstream sequences are involved in anchoring distant cis-regulatory regions to the promoter.  Genetics  149: 1031-1050.  
         [0350]    Sit, T. L., A. A. Vaewhongs and S. A. Lommel. 1998. RNA-mediated trans-activation of transcription from a viral RNA.  Science  281: 829-832.  
         [0351]    Smith, C. M. and J. A. Steitz. 1998. Classification of gas5 as a multi-small-nucleolar-RNA (snoRNA) host gene and a member of the 5′-terminal oligopyrimidine gene family reveals common features of snoRNA host genes.  Mol. Cell Biol.  18: 6897-6909.  
         [0352]    Smolen, P., D. A. Baxter and J. H. Byrne. 1999. Effects of macromolecular transport and stochastic fluctuations on dynamics of genetic regulatory systems.  Am. J. Physiol.  277: C777-790.  
         [0353]    Smolen, P., D. A. Baxter and J. H. Byrne. 2000. Modeling transcriptional control in gene networks—methods, recent results, and future directions.  Bull. Math. Biol.  62: 247-292.  
         [0354]    Sollner-Webb, B. 1993. Novel intron-encoded small nucleolar RNAs.  Cell  75: 403-405.  
         [0355]    Stoltzfus, A. 1999. On the possibility of constructive neutral evolution.  J. Mol. Evol.  49: 169-181.  
         [0356]    Stoltzfus, A., D. F. Spencer, M. Zuker, J. M. Logsdon, Jr. and W. F. Doolittle. 1994. Testing the exon theory of genes: the evidence from protein structure.  Science  265: 202-207.  
         [0357]    Szebenyi, G. and J. F. Fallon. 1999. Fibroblast growth factors as multifunctional signaling factors.  Int. Rev. Cytol.  185: 45-106.  
         [0358]    Tanaka, R., H. Satoh, M. Moriyama, K. Satoh, Y. Morishita, S. Yoshida, T. Watanabe, Y. Nakamura and S. Mori. 2000. Intronic U50 small-nucleolar-RNA (snoRNA) host gene of no protein-coding potential is mapped at the chromosome breakpoint t(3;6)(q27;q15) of human B-cell lymphoma.  Genes Cells  5: 277-287.  
         [0359]    Tarrio, R., F. Rodriguez-Trelles and F. J. Ayala. 1998. New  Drosophila  introns originate by duplication.  Proc. Natl. Acad. Sci. USA  95: 1658-1662.  
         [0360]    Tautz, D., M. Trick and G. A. Dover. 1986. Cryptic simplicity in DNA is a major source of genetic variation.  Nature  322: 652-656.  
         [0361]    Thieffry, D., A. M. Huerta, E. Perez-Rueda and J. Collado-Vides. 1998. From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in  Escherichia coli. Bioessays  20: 433-440.  
         [0362]    Tycowski, K. T., M. D. Shu and J. A. Steitz. 1996. A mammalian gene with introns instead of exons generating stable RNA products.  Nature  379: 464-466.  
         [0363]    van der Gugten, A. A. and H. V. Westerhoff. 1997. Internal regulation of a modular system: the different faces of internal control.  Biosystems  44: 79-106.  
         [0364]    van Hoof, A., P. Lennertz and R. Parker. 2000. Three conserved members of the RNase D family have unique and overlapping functions in the processing of 5S, 5.8S, U4, U5, RNase MRP and RNase P RNAs in yeast.  EMBO J.  19: 1357-1365.  
         [0365]    van Hoof, A. and R. Parker. 1999. The exosome: a proteasome for RNA?  Cell  99: 347-350.  
         [0366]    Varani, G. and McClain, W. H. 2000. The G x U wobble base pair. A fundamental building block of RNA structure crucial to RNA function in diverse biological systems.  EMBO Rep,  1: 18-23  
         [0367]    Venter, J. C., M. D. Adams, E. W. Myers, P. W. Li, R. J. Mural, G. G. Sutton, H. O. Smith, M. Yandell et al. (274 co-authors). 2001. The sequence of the human genome.  Science  291: 1304-1351.  
         [0368]    von Neumann, J. 1982. First draft of a report on the EDVAC. In B. Randall, ed. The origins of digital computers: selected papers. Springer, Berlin.  
         [0369]    Weng, G., U.S. Bhalla and R. Iyengar. 1999. Complexity in biological signaling systems.  Science  284: 92-96.  
         [0370]    Wightman, B., I. Ha and G. Ruvkun. 1993. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in  C. elegans. Cell  75: 855-862.  
         [0371]    Wolf, D. M. and F. H. Eeckman. 1998. On the relationship between genomic regulatory element organization and gene regulatory dynamics.  J. Theor. Biol.  195: 167-186.  
         [0372]    Wrana, J. L. 1994. H19, a tumour suppressing RNA?  Bioessays  16: 89-90.  
         [0373]    Wu, C. T. and M. L. Goldberg. 1989. The  Drosophila  zeste gene and transvection.  Trends Genet.  5: 189-194.  
         [0374]    Wu, C. T. and J. R. Morris. 1999. Transvection and other homology effects.  Curr. Opin. Genet. Dev.  9: 237-246.  
         [0375]    Yang, D., H. Lu and J. W. Erickson. 2000. Evidence that processed small dsRNAs may mediate sequence-specific mRNA degradation during RNAi in  drosophila  embryos.  Curr. Biol.  10: 1191-1200.  
         [0376]    Yean, S. L., G. Wuenschell, J. Termini and R. J. Lin. 2000. Metal-ion coordination by U6 small nuclear RNA contributes to catalysis in the spliceosome.  Nature  408: 881-884.  
         [0377]    Yuh, C. H., H. Bolouri and E. H. Davidson. 1998. Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene.  Science  279: 1896-1902.  
         [0378]    Zamore, P. D., T. Tuschl, P. A. Sharp and D. P. Bartel. 2000. RNAi: double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals.  Cell  101: 25-33.  
     
       
       
         1 
         
           
             121  
           
           
             1  
             661  
             DNA  
             human  
             
               misc_feature  
               (533)..(533)  
               n = any nucleotide  
             
           
            1 

gtaggtgggg aaggggtgtc aggtgggtac tgcagatggg ctctaggacc tcggccttca     60 

agttgtgtct gcccgcctct tgctactgtc ttggatattt taaagtcctt ttgacgttgt    120 

tctgatttct gggcagggga cagagtaagt gtgtatttgc tctgagactg ttaatttggt    180 

atttccatcc caagttacag ggaagacctc aggctgcagg ttcctagctc cgggctgagg    240 

tggcttgtgg aggcagacag ctgttgtctg gaagtgcaga gggctggggg ctggccaggc    300 

tgttactgag ttcagaatag gaggaaagag tgtgtagcaa agtcggcgct ccttggccac    360 

tgccagcatt cagagttgtc ttgtttgcct tgccttaaac gttgccttcc tggacgccta    420 

caaagtcagg ttgtaaccgc tggccactgc tgtgctcact ggcagcccct gatttacgtg    480 

aggacctcaa gtgtgtgttg ggcagaattc cccagcgctt cccgtacacc ccnccacccc    540 

cagtgcagca tcgctcggtg cgtggctggt ggactggagg agtgtgcgtg ccggcagcac    600 

tgccaggcac gtgcctaatg ctctggccct gtgtgtttgt gttttcttcc cgatttctga    660 

g                                                                    661 

 
           
             2  
             20  
             DNA  
             human  
           
            2 

agtgcagagg gctgggggct                                                 20 

 
           
             3  
             20  
             DNA  
             human  
           
            3 

agtgcagagg gctgggggct                                                 20 

 
           
             4  
             20  
             DNA  
             human  
           
            4 

ttgtctggaa gtgcagaggg                                                 20 

 
           
             5  
             20  
             DNA  
             human  
           
            5 

ttgtctggaa gtgcagaggg                                                 20 

 
           
             6  
             20  
             DNA  
             human  
           
            6 

tggctggtgg actggaggag                                                 20 

 
           
             7  
             20  
             DNA  
             human  
           
            7 

tggctggtgg actggaggag                                                 20 

 
           
             8  
             20  
             DNA  
             human  
           
            8 

gcttgtggag gcagacagct                                                 20 

 
           
             9  
             20  
             DNA  
             human  
           
            9 

gcttgtggag gcagacagct                                                 20 

 
           
             10  
             20  
             DNA  
             human  
           
            10 

agtgcagagg gctgggggct                                                 20 

 
           
             11  
             20  
             DNA  
             human  
           
            11 

agtgcagagg gctgggggct                                                 20 

 
           
             12  
             19  
             DNA  
             human  
           
            12 

tttgctctga gactgttaa                                                  19 

 
           
             13  
             19  
             DNA  
             human  
           
            13 

tttgctctga gactgttaa                                                  19 

 
           
             14  
             19  
             DNA  
             human  
           
            14 

agggctgggg gctggccag                                                  19 

 
           
             15  
             19  
             DNA  
             human  
           
            15 

agggctgggg gctggccag                                                  19 

 
           
             16  
             19  
             DNA  
             human  
           
            16 

gttgttctga tttctgggc                                                  19 

 
           
             17  
             19  
             DNA  
             human  
           
            17 

gttgttctga tttctgggc                                                  19 

 
           
             18  
             19  
             DNA  
             human  
           
            18 

tgtgtgtttg tgttttctt                                                  19 

 
           
             19  
             19  
             DNA  
             human  
           
            19 

tgtgtgtttg tgttttctt                                                  19 

 
           
             20  
             19  
             DNA  
             human  
           
            20 

agagggctgg gggctggcc                                                  19 

 
           
             21  
             19  
             DNA  
             human  
           
            21 

agagggctgg gggctggcc                                                  19 

 
           
             22  
             23  
             DNA  
             human  
           
            22 

gccctgtgtg tttgtgtttt ctt                                             23 

 
           
             23  
             23  
             DNA  
             human  
           
            23 

gccctgtgtg tttgtctttt ctt                                             23 

 
           
             24  
             19  
             DNA  
             human  
           
            24 

agagggctgg gggctggcc                                                  19 

 
           
             25  
             19  
             DNA  
             human  
           
            25 

agagggctgg gggctggcc                                                  19 

 
           
             26  
             19  
             DNA  
             human  
           
            26 

tgtgtgtttg tgttttctt                                                  19 

 
           
             27  
             19  
             DNA  
             human  
           
            27 

tgtgtgtttg tgttttctt                                                  19 

 
           
             28  
             19  
             DNA  
             human  
           
            28 

agcccctgat ttacgtgag                                                  19 

 
           
             29  
             19  
             DNA  
             human  
           
            29 

agcccctgat ttacgtgag                                                  19 

 
           
             30  
             19  
             DNA  
             human  
           
            30 

gtgtgtttgt gttttcttc                                                  19 

 
           
             31  
             19  
             DNA  
             human  
           
            31 

gtgtgtttgt gttttcttc                                                  19 

 
           
             32  
             19  
             DNA  
             human  
           
            32 

gcagagggct gggggctgg                                                  19 

 
           
             33  
             19  
             DNA  
             human  
           
            33 

gcagagggct gggggctgg                                                  19 

 
           
             34  
             19  
             DNA  
             human  
           
            34 

gccttcctgg acgcctaca                                                  19 

 
           
             35  
             19  
             DNA  
             human  
           
            35 

gccttcctgg acgcctaca                                                  19 

 
           
             36  
             19  
             DNA  
             human  
           
            36 

tttgctctga gactgttaa                                                  19 

 
           
             37  
             19  
             DNA  
             human  
           
            37 

tttgctctga gactgttaa                                                  19 

 
           
             38  
             19  
             DNA  
             human  
           
            38 

ttaaacgttg ccttcctgg                                                  19 

 
           
             39  
             19  
             DNA  
             human  
           
            39 

ttaaacgttg ccttcctgg                                                  19 

 
           
             40  
             27  
             DNA  
             human  
           
            40 

tttctgggca ggggacagag taagtgt                                         27 

 
           
             41  
             27  
             DNA  
             human  
           
            41 

tttctgggta ggggacagag tatgtgt                                         27 

 
           
             42  
             19  
             DNA  
             human  
           
            42 

gaattcccca gcgcttccc                                                  19 

 
           
             43  
             19  
             DNA  
             human  
           
            43 

gaattcccca gcgcttccc                                                  19 

 
           
             44  
             295  
             DNA  
             human  
           
            44 

gtaagtgccc ttccgggagc tcacacccgc tctctgtctc ccctgtcctt cctctgcttc     60 

attttttcct ggactctgac cgatgtttgc gttagagtat gtttgaacgt ggggtcgatt    120 

gggaaggatt aagccttggt gctgaggctg gatattgcag gaggatacag ggtgaatgga    180 

gccggcgggg cggggcgggc cgggctgctg tgccgtggct gctgttgtgc tgacaccctc    240 

tttcctagag aaacagcctc ttattcacaa ccagctgatt tgaaatttcc tgcag         295 

 
           
             45  
             22  
             DNA  
             human  
           
            45 

ggcggggcgg ggcgggccgg gc                                              22 

 
           
             46  
             22  
             DNA  
             human  
           
            46 

ggcggggcgg ggcgggccgg gc                                              22 

 
           
             47  
             22  
             DNA  
             human  
           
            47 

ggcggggcgg ggcgggccgg gc                                              22 

 
           
             48  
             22  
             DNA  
             human  
           
            48 

ggcggggcgg ggcgggccgg gc                                              22 

 
           
             49  
             21  
             DNA  
             human  
           
            49 

gcggggcggg gcgggccggg c                                               21 

 
           
             50  
             21  
             DNA  
             human  
           
            50 

gcggggcggg gcgggccggg c                                               21 

 
           
             51  
             21  
             DNA  
             human  
           
            51 

gcggggcggg gcgggccggg c                                               21 

 
           
             52  
             21  
             DNA  
             human  
           
            52 

gcggggcggg gcgggccggg c                                               21 

 
           
             53  
             658  
             DNA  
             human  
           
            53 

gtatgtaccg tgctgggacc acttccccag gtgccttccc cacccagcca ggtctgtagt     60 

tttgaaagtc ttgtatagct ttttccttgg tttaaaagca ataaatgccc actggagata    120 

aattagaaaa tatggaagaa agctataaaa aagaaactaa aaaaatctct tgtaattcca    180 

ccactcaaat ataacttttt ttcttaaaaa attttttttc tcttacttag agacaggcag    240 

ggtctggctc tgtcccccag gctggagtgc agtggtgcca tcatagctca ctgcagcctc    300 

aacctcttgg gctcaaggca ttctctcgcc tcagcctcct gagcagctgg gactgcaggc    360 

atgagccatg gttcctgggc attttctctt gatattttga tgaagcagcc tctttgtccc    420 

caggtcatag ctgcttaaga cactatgtac agagatctta gttgaatgag acaagtgact    480 

tctggctgtg ccctgcagat aggccttggg tgcagccatg gtttgtagat tcccctggag    540 

aaatccaagc aacacacatg tatttggtac tcactaagtg cctacagaac caaaccgaaa    600 

ctgggccgca ctggggagga gatcaccgtg gagaccggag ggcgcactca cggagagt      658 

 
           
             54  
             60  
             DNA  
             human  
           
            54 

cagggtctgg ctctgtcccc caggctggag tgcagtggtg ccatcatagc tcactgcagc     60 

 
           
             55  
             60  
             DNA  
             human  
           
            55 

cagggtcttg ctctgttgcc caggctgggg tgcagtggcg caatcatggc tcactgcagc     60 

 
           
             56  
             60  
             DNA  
             human  
           
            56 

ctcaacctct tgggctcaag gcattctctc gcctcagcct cctgagcagc tgggactgca     60 

 
           
             57  
             60  
             DNA  
             human  
           
            57 

ctcaacctcc tgggctcaag ccatcctccc gcctcagcct cctgagcagc tgggactaca     60 

 
           
             58  
             60  
             DNA  
             human  
           
            58 

gctctgtccc ccaggctgga gtgcagtggt gccatcatag ctcactgcag cctcaacctc     60 

 
           
             59  
             60  
             DNA  
             human  
           
            59 

gctctgtcac ccaggctgga gtgtagtggt gcaatcagag ctcactgcag cctccaactc     60 

 
           
             60  
             47  
             DNA  
             human  
           
            60 

ttgggctcaa ggcattctct cgcctcagcc tcctgagcag ctgggac                   47 

 
           
             61  
             47  
             DNA  
             human  
           
            61 

ctgggctcaa gcaatcctcc cacctcagcc tcctgagtag ctaggac                   47 

 
           
             62  
             60  
             DNA  
             human  
           
            62 

ctctgtcccc caggctggag tgcagtggtg ccatcatagc tcactgcagc ctcaacctct     60 

 
           
             63  
             60  
             DNA  
             human  
           
            63 

ctctgtcacc caggctggag tgcagtggtg cgatcttggc tcactgcaac ctccgcctcc     60 

 
           
             64  
             60  
             DNA  
             human  
           
            64 

tgggctcaag gcattctctc gcctcagcct cctgagcagc tgggactgca ggcatgagcc     60 

 
           
             65  
             60  
             DNA  
             human  
           
            65 

tgggttcaag tgattctcct gcctcagcct cccgagtagc tgggactaca ggcgtgtgcc     60 

 
           
             66  
             60  
             DNA  
             human  
           
            66 

gtggggacaa acagaaagac acaaggaaca attagaggct ctccatagca atgtcagaga     60 

 
           
             67  
             60  
             DNA  
             human  
           
            67 

gtggggacaa acagaaagac acaaggaaca attagaggct ctccatagca atgtcagaga     60 

 
           
             68  
             60  
             DNA  
             human  
           
            68 

tagggcagag cggatggtgg tgacaacgct ctgacaaacg ttactattga acgagagtca     60 

 
           
             69  
             60  
             DNA  
             human  
           
            69 

tagggcagag cggatggtgg tgacaacgct ctgacaaacg ttactattga acgagagtca     60 

 
           
             70  
             60  
             DNA  
             human  
           
            70 

cagggtctgg ctctgtcccc caggctggag tgcagtggtg ccatcatagc tcactgcagc     60 

 
           
             71  
             60  
             DNA  
             human  
           
            71 

cagggtcttg ctctgtcacc caggctggag ttcagtggtg caatcatagc tcactgcagc     60 

 
           
             72  
             60  
             DNA  
             human  
           
            72 

ctcaacctct tgggctcaag gcattctctc gcctcagcct cctgagcagc tgggactgca     60 

 
           
             73  
             60  
             DNA  
             human  
           
            73 

ctcaaactcc tgggctcaag caatcctccc acctcagcct cctgagtagc tgggactgca     60 

 
           
             74  
             60  
             DNA  
             human  
           
            74 

ctgtccccca ggctggagtg cagtggtgcc atcatagctc actgcagcct caacctcttg     60 

 
           
             75  
             60  
             DNA  
             human  
           
            75 

ctgtcaccca ggctggagtg cagtggcgcc atcatggctc actgcagcct caacctcctg     60 

 
           
             76  
             59  
             DNA  
             human  
           
            76 

ggctcaaggc attctctcgc ctcagcctcc tgagcagctg ggactgcagg catgagcca      59 

 
           
             77  
             59  
             DNA  
             human  
           
            77 

ggctcaagcc atcctaccac ctcagcctcc tgagtagctg gaactacagg catgggcca      59 

 
           
             78  
             60  
             DNA  
             human  
           
            78 

ggtctggctc tgtcccccag gctggagtgc agtggtgcca tcatagctca ctgcagcctc     60 

 
           
             79  
             60  
             DNA  
             human  
           
            79 

ggtctcgctc tgtcactcag gctggagtgc agtggtgcca tcacagctca ctgcagcctc     60 

 
           
             80  
             44  
             DNA  
             human  
           
            80 

aacctcttgg gctcaaggca ttctctcgcc tcagcctcct gagc                      44 

 
           
             81  
             44  
             DNA  
             human  
           
            81 

aaattcttgg gctcaagcca tcctctcacc tcagcctcct gagc                      44 

 
           
             82  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            82 

cttatttttt cattat                                                     16 

 
           
             83  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            83 

tttttcatta tgaaaa                                                     16 

 
           
             84  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            84 

aaaatatttg ttagta                                                     16 

 
           
             85  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            85 

ctgctgtaga ggttct                                                     16 

 
           
             86  
             18  
             DNA  
             Saccharomyces cerevisiae  
           
            86 

ctaataattt ggaaagga                                                   18 

 
           
             87  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            87 

ataacatttt taaaac                                                     16 

 
           
             88  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            88 

ggttctttcc cccttt                                                     16 

 
           
             89  
             17  
             DNA  
             Saccharomyces cerevisiae  
           
            89 

ctaataattt ggaaagg                                                    17 

 
           
             90  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            90 

aagtggtttt tctgga                                                     16 

 
           
             91  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            91 

tagataataa aagaaa                                                     16 

 
           
             92  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            92 

ctagataata aaagaa                                                     16 

 
           
             93  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            93 

gttaagtatt ttttta                                                     16 

 
           
             94  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            94 

cctttcaaaa cttata                                                     16 

 
           
             95  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            95 

atttgttagt atatgt                                                     16 

 
           
             96  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            96 

tctttctttc cttctt                                                     16 

 
           
             97  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            97 

tatgtttttt tctttt                                                     16 

 
           
             98  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            98 

tcttcataaa aaagca                                                     16 

 
           
             99  
             17  
             DNA  
             Saccharomyces cerevisiae  
           
            99 

ttctttttct ttctttc                                                    17 

 
           
             100  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            100 

gtatgttttt ttcttt                                                     16 

 
           
             101  
             18  
             DNA  
             Saccharomyces cerevisiae  
           
            101 

ctttttcttt ctttcctt                                                   18 

 
           
             102  
             17  
             DNA  
             Saccharomyces cerevisiae  
           
            102 

tttttttctt ttattct                                                    17 

 
           
             103  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            103 

ttttattcta ctttta                                                     16 

 
           
             104  
             17  
             DNA  
             Saccharomyces cerevisiae  
           
            104 

aatttaacga tgagatg                                                    17 

 
           
             105  
             17  
             DNA  
             Saccharomyces cerevisiae  
           
            105 

caaacacaga atcattt                                                    17 

 
           
             106  
             17  
             DNA  
             Saccharomyces cerevisiae  
           
            106 

cgatgagatg agctgtg                                                    17 

 
           
             107  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            107 

ttttttttgt ttttga                                                     16 

 
           
             108  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            108 

ttaatttttt ttgaat                                                     16 

 
           
             109  
             17  
             DNA  
             Saccharomyces cerevisiae  
           
            109 

taattttttt tgaattt                                                    17 

 
           
             110  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            110 

ttttttttga attttt                                                     16 

 
           
             111  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            111 

tttttttgaa tttttt                                                     16 

 
           
             112  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            112 

agttttaatt tttttt                                                     16 

 
           
             113  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            113 

tttttttttg tttttg                                                     16 

 
           
             114  
             18  
             DNA  
             Saccharomyces cerevisiae  
           
            114 

tttttttgtt tttgattt                                                   18 

 
           
             115  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            115 

ttgaattttt ttttgt                                                     16 
 
           
             116  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            116 

ttttaatttt ttttga                                                     16 
 
           
             117  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            117 

aataaattgt actcac                                                     16 
 
           
             118  
             17  
             DNA  
             Saccharomyces cerevisiae  
           
            118 

tttttgaatt ttttttt                                                    17 
 
           
             119  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            119 

aaaattcaaa aaaaat                                                     16 
 
           
             120  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            120 

aaaaaaattc aaaaaa                                                     16 
 
           
             121  
             16  
             DNA  
             Saccharomyces cerevisiae  
           
            121 

tttttttttg ttcatg                                                     16