Tu quoque. (My doctoral thesis work is in molecular/cell biology.) Here are two sources that relate to differential splicing and the multiplier effect it has on the apparent number of 30-35,000 human genes. Their citations should also provide lots of background. A quick precis of all this is as follows:
Based on protein diversity and the idea of one "gene" per protein, there was an expected 100,000 or so human genes. Genes became defined as DNA stretches flanked by untranslated regions. Defined this way, there are, as of now, an estimated 30-35,000 genes. However, if "gene" is defined as a stretch of DNA which will give rise to a functional protein of biological significance (some virtually identical proteins do different things in different tissues or at different times of development), then the original estimates of gene number in the human genome are probably on the low side. It is now becoming apparent that a single stretch of DNA formerly called a single gene is, in actuality, capable of coding for two, three, or more functionally-different proteins. Alternate or differential splicing, once thought to be rare, is now seen to be commonplace.
1.
Commentary2. I had to reformat this since it didn't come across via source in double columns. The figures and tables didn't appear, either. For figures, tables, and legends, see the original article.
March 2001 Volume 19 Number 3 p 196
Piecing together the significance of splicing
Rotem Sorek & Mor Amitai
Rotem Sorek is a scientist (e-mail: rotem.sorek@cgen.com) and Mor Amitai (e-mail: mor.amitai@cgen.com) is the president and chief executive officer at Compugen, 72 Pinchas Rosen St., Tel-Aviv, Israel, 69512.
Alternative splicing increases protein diversity by allowing multiple, sometimes functionally distinct, proteins to be encoded by the same gene. It can be specific to tissues, stress conditions, and developmental and pathological states. In many cases, it serves as an on/off regulation mechanism by introducing a premature stop codon1. We still do not understand how alternative splicing is regulated, but the following fact is now quite clear: in metazoans, it happens very often.
Computational analysis of expressed sequences can teach us a great deal about alternative splicing, because aligning expressed sequences of different splice variants of the same gene usually results in a typical gapped pattern of alignment. The recent information explosion in nucleotide databases gives us the possibility of analysis at the transcriptome (the set of messenger RNAs) level. We can take the entire human expressed sequence tags (ESTs) database (http://www.ncbi.nlm.nih.gov/dbEST/) (currently containing more than 3.1 million ESTs), together with the known complementary DNAs and genomic sequence data, and cluster them by alignment overlaps.
Such large-scale analyses were conducted in the past few years by four independent groups using different methods2-5. They estimated that 33–59% of human genes have at least two splice variants, with the highest estimation being the most recent one2. All four groups also pointed out that these figures are probably an underestimation, because the EST database does not cover the entire repertoire of tissues or developmental states, and precautions taken to avoid false positives were extremely stringent. Because the latest estimation of the total number of human genes is 30,000–40,000 (ref. 2), one must bear in mind that at least 10,000 of our genes, and probably many more, undergo alternative splicing.
Understanding alternative splicing and gaining knowledge of the transcriptome are crucial for the design and interpretation of expression profiling experiments, in particular DNA chip experiments. Such experiments enable comparisons between transcriptomes of different cell types or under different conditions. Designing DNA chips that will effectively report on the transcriptional levels of genes must take into account their alternative splicing patterns, even if alternative splicing is not the subject being studied.
To demonstrate this, let us assume a putative gene X that has three exons (A, B, and C) and two splice variants, ABC and AC. To design a chip that will measure the transcriptional levels of gene X in different tissues, we must use a probe from exons A or C to which both variants will hybridize in the assay. Taking the probe from exon B will cause the hybridization of variant ABC only, and will not correctly measure the transcriptional level of the gene. The accuracy of the experiment can be increased by measuring the level of each variant separately; hence, two probes will have to be taken, one from exon B to measure variant ABC, and one from exon A or C to measure both variants.
Another illustration of the importance of awareness of alternative splicing comes from the field of gene prediction. One thing gene prediction programs do not predict is alternative splicing, because sequences that regulate alternative splicing are generally unknown. Because alternative splice sites often correspond weakly to the splice consensus sites, gene prediction programs will probably frequently fail to identify alternative exons or introns.
Alternatively spliced genes are likely to take center stage as drug targets, therapeutic agents, and diagnostics markers in the next decade. First, there are many splice variants of pharmaceutically important genes that have been detected but not yet studied in depth. The function of the known variant gives us a clue to the function of the new variant, especially if we know which domain was added or removed. For example, we have identified some 60 kinase enzymes that undergo alternative splicing that eliminates their catalytic domains (E. Levanon et al., unpublished data). Although many of them have not been biochemically studied, our educated guess is that they function as competitive inhibitors of the known kinases.
Second, it has been estimated that 15% of the point mutations that cause genetic diseases in humans alter the normal splicing pattern6. Splice variants that are disease specific can be excellent diagnostic markers for these and other human diseases, being easily identifiable by PCR reactions.
Alternative splicing is no longer considered an esoteric twist of nature. Articles with the phrase "alternative splicing" or "splice variants" in their title or abstract are published at the rate of two a day (according to Medline query). In many ways, the concept is breaking our iron-clad rules: exons are not always exons, and introns are sometimes expressed.
Indeed, the very definition of "gene" should be reconsidered in light of discoveries of unusual alternative splicing events, such as the one yielding a novel splice variant of PSA (prostate specific antigen)—the standard prostate cancer marker7. This variant shares with PSA only the first exon, which encodes only a signal peptide, leaving the two mature proteins with no common protein sequence. The only connection between them is that they are coded by the same genomic region and probably share the same transcriptional regulation (A. David et al., unpublished data).
Even more extreme is the example of the p19 and p16 protein products of the INK4a/ARF locus8. The two transcripts are synthesized from different promoters and have different first exons, but share exons 2 and 3, and are encoded in two distinct reading frames, in a process that yields two entirely different protein products. Although the p19 and p16 proteins are clearly the products of the same genomic locus, can we say that these two unusual and entirely distinct splice variants are coded by the same gene?
Clearly, increasing protein diversity does not simply correlate with increasing gene number. It is dependent both on the number of genes in the genome and on the rate of alternative splicing of those genes. Work is now needed to characterize in greater detail the molecular basis for this process and its regulation. This will likely uncover a host of new targets for drug discovery, yield new diagnostic markers for disease, and perhaps even help us unravel the mechanisms underlying biological complexity.
REFERENCES
- Smith, C.W. & Valcarcel, J. Trends Biochem. Sci. 25, 381-388 (2000). | PubMed | ISI |
- International Human Genome Sequencing Consortium. Nature 409, 860-921 (2001). | Article | PubMed |
- Brett, D. et al. FEBS Lett. 474, 83-86 (2000). | Article | PubMed | ISI |
- Mironov, A.A., Fickett, J.W. & Gelfand, M.S. Genome Res. 9, 1288-1293 (1999). | Article | PubMed | ISI |
- Croft, L. et al. Nat. Genet. 24, 340-341 (2000). | Article | PubMed | ISI |
- Cooper, T.A. & Mattox, W. Am. J. Hum. Genet. 61, 259-266 (1997). | PubMed | ISI |
- Diamandis, E.P. Trends Endocrinol. Metab. 9, 310-316 (1998). | Article | ISI |
- Sharpless, N.E. & DePinho, R.A. Curr. Opin. Genet. Dev. 9, 22-30 (1999). | Article | PubMed | ISI |
Nature Genetics 30 * January 13 2002
A genomic view of alternative splicing
Barmak Modrek & Christopher Lee
Departments of Chemistry and Biochemistry,
University of California Los Angeles,
Los Angeles, California 90095-1570, USA.
Correspondence should be addressed to C.L.
(e-mail: leec@mbi.ucla.edu).
Recent genome-wide analyses of alternative splicing indicate that 40–60% of human genes have alter- native splice forms, suggesting that alternative splicing is one of the most significant components of the functional complexity of the human genome. Here we review these recent results from bioinformatics studies, assess their reliability and consider the impact of alternative splicing on biological functions. Although the `big picture' of alternative splicing that is emerging from genomics is exciting, there are many challenges. High-throughput experimental verification of alternative splice forms, functional characterization, and regulation of alternative splicing are key directions for research. We recommend a community-based effort to discover and characterize alternative splice forms comprehensively throughout the human genome.
Introduction
The sequencing of the human genome has raised important questions about the nature of genomic complexity. It was widely anticipated that the human genome would contain a much larger number of genes (estimates based on expressed-sequence clustering ran as high as 150,000 genes) than Drosophila (14,000 genes) or Caenorhabditis elegans (19,000 genes) 1–3 . The report of only 32,000 human genes thus came as a surprise 4,5 . This basic disparity indicated that the number of human expressed- sequence (mRNA) forms was much higher than the number of genes, suggesting a major role for alternative splicing in the production of complexity. Many groups have recently presented genomic analyses of alternative splicing that strongly support this hypothesis, raising intriguing questions about the identification, functional roles and regulation of alternative splice forms across the whole genome. The study of alternative splicing has long been a valuable subfield of molecular biology, but has received comparatively little attention compared with major fields such as the discovery of new genes or transcriptional regulation. Only several hundred alternatively spliced genes have been identified so far by molecular biologists (see Table 1 for database resources). After the discovery of exons and introns in the Adenovirus hexon gene in 1977 (ref. 6), Walter Gilbert proposed that different combinations of exons could be spliced together (`alternative splicing') to produce different mRNA isoforms of a gene 7 . By the early 1980s, alternative splicing was well documented in several genes 8,9 , and researchers estimated that 5% of genes in higher eukaryotes might have alternative splicing 10 . A range of processes from sex determination to apoptosis use alternative splicing 11,12 . Its regulatory mechanisms have recently been discovered in several genes 11,13 .
Genome-scale analyses of alternative splicing
High-throughput sequencing of the human genome and especially of expressed sequence tag (EST) sequences has enabled a completely different approach based on bioinformatics. Because ESTs are derived from fully processed mRNA (after 5 capping, splicing and polyadenylation), they provide a broad sample of mRNA diversity. This diversity can be analyzed computationally. In the last two years, bioinformatics studies have identified an order of magnitude more alternatively spliced genes than were found in the past 20 years and are beginning to provide a global view of alternative splicing in humans. We will first describe these studies and then assess the evidence. Bioinformatics approaches. Most bioinformatics studies 4,14–18 (Table 2) rely on identifying ESTs that come from the same gene and looking for differences between them that are consistent with alternative splicing, such as a large insertion or deletion in one EST (Fig. 1 a ). Each candidate splice can be fur- ther assessed by aligning the ESTs exactly to their gene sequence in the draft genome (Fig. 1 b ). This reveals candidate exons (matches to the genomic sequence) separated by candidate splices (large gaps in the EST-genomic alignment; Fig. 1 b ). As intronic sequences at splice junctions are highly conserved (99.24% of introns have a GT-AG at their 5 and 3 ends, respectively), they can be used to verify candidate splices 19 . In the earliest large-scale discovery of new alternative splicing, Mironov et al. 14 aligned ESTs to genomic sequence for 392 known genes and found alternative splicing in 133 of these genes 14 . Croft et al . 15 took a different approach that did not rely on aligning ESTs to the complete genomic sequence: they created a database of individual intron sequences annotated in GenBank and searched for EST sequences that matched intronic sequence. They found matches to introns from 582 genes, suggesting an alternative splice. Brett et al . 16 looked for insertions or deletions in ESTs relative to a set of known mRNAs, indicative of alternative splices, but without EST alignment to the genomic sequence. This work identified 3,011 alternatively spliced genes 16 . The International Human Genome Sequencing Consortium reported 145 alternatively spliced genes from a comprehensive analysis of chromosome 22 based on aligning ESTs to the genomic sequence 4 . Modrek et al . 18 aligned available human EST and mRNA sequences (2.1 million) to the whole draft genome, applying strict matching, splice site and alternative splice detection criteria, to identify 6,201 alternative splices in 2,272 genes.
Alternative splicing frequency.
These studies have consistently reported a high rate of alternative splicing in the human genome, with 35–59% of human genes showing evidence of at least one alternative splice form 4,14,16–18 . Moreover, given that only a few ESTs have been sequenced for most genes, it seems possible that even more alternative splicing exists that is not yet detectable in the available ESTs. These studies indicate that alter- native splicing is far more abundant, ubiquitous and functionally important than previously thought. And there are more types of mRNA isoforms. For example, bioinformatics studies have reported that about 25% of genes have alternative polyadenylation forms, that is, mRNAs that are cleaved and polyadenylated at different sites 4,20 .
Functional impact.
How do these newly discovered alternative mRNA forms affect protein function? Despite an early report that most alternative splices occur within the 5 untranslated region 14, recent studies indicate that 70–88% of alternative splices change the protein product 4,17,18 . The majority of these changes appear to be functionally interesting, such as replacement of the amino or carboxy terminus, or in-frame addition and removal of a functional unit (Fig. 2 b ) 18 . Only 19% of the alternative protein forms were shortened due to frameshift 18 . Fig. 2 c shows an alternative isoform of a new FC receptor -like protein, whose C-terminal transmembrane domain (TM) and cytoplasmic tail (important for signal transduction in this class of receptors) is neatly replaced with a new TM domain and tail by alternative polyadenylation 18 . What is the functional pattern of alternative splicing across the genome? A random sample of 50 alternatively spliced genes showed that over three-quarters were involved in signaling and regulation (such as receptors, signal transduction, transcription factors, and so on). Moreover, the systemic categories most highly represented in this sample were genes specific to the immune and nervous systems 18 . This should be interpreted cautiously, as the overall breakdown of gene functions in the whole genome is still unclear. However, alternative splicing may be most important in complex systems where information must be processed differently at different times (such as immune tolerance, or development) or a very high level of diversity is required (such as axonal guidance). Notable examples of combinatorial alternative splicing of multiple cassettes of exons, generating up to 40,000 isoforms of a single gene, have recently been discovered in the nervous system, including Dscam (axonal guidance receptor in Drosophila ) and neurexin (neuropeptide receptor) 21 .
Fig. 2 Types of alternative splicing and possible effects on protein. a , Alternative splicing can lead to either the inclusion or exclusion of an exon, use of a different 5 site, or use of a different 3 site. b , Alternative splicing can lead to use of a different site for translation initiation (alternative initiation), a different translation termination site due to a frameshift (truncation or extension), or the addition or removal of a stop codon in the alternative coding sequence (alternative termination). Alternative splicing can also change the internal region because of an in-frame insertion or deletion. c , Alternative splicing of Hs.11090, a putative FC receptor chain homolog: genomic structure and two alternative spliced (and polyadenylated) mRNA forms. The differential RNA processing results in substitution of one transmembrane domain instead of another. However, one form has a different cytoplasmic tail (involved in signaling in this family), whereas the other does not.
Bioinformatics evidence for alternative splicing.
It is essential that biologists understand the forms of evidence and problems that underlie this new `big picture' view of alternative splicing. Bioinformatics is an automated analysis of high-through-put experimental data and follows a very different process than traditional molecular biology. It can be simultaneously `more rigorous' (much more detailed, mathematical measures of evidence are required for a computer to do this analysis at all) and much less rigorous (bioinformaticists typically cannot order a new set of experimental tests for all the isoforms they detect, as is common in molecular biology labs studying a specific isoform). Two kinds of problems must be distinguished: (i) a false negative, the failure to detect a real splice form, and (ii) a false positive, a reported result that is not a true, functional splice form. Analyzing the causes of these problems during cDNA library construction, EST sequencing and sequence comparison suggests many interesting questions for the next stage of this research (Table 3). Detection of alternative splicing through bioinformatics depends on finding deviant EST forms within the mass of data produced by undirected EST sequencing, raising a fundamental question: when an analysis is used to look for some form of deviation in a very large data set, other causes of deviation, even if infrequent, could add up to a substantial fraction of the result. How can we be sure this is real alternative splicing? The bioinformatics studies have tried carefully to screen out many possible sources of false positives. Simple forms of EST deviation, such as random variation in where a given EST sequence begins or ends within a gene, and potential vector contamination at the ends of ESTs, are excluded. The most important screen is provided by mapping (aligning) ESTs to the draft human genome sequence. Chimeric ESTs can be easily excluded by requiring that each EST align completely to a single genomic locus. The genomic location found by homology search and alignment can often be checked against radiation hybrid mapping data. As the genomic regions that match the ESTs should be exons and the alignment gaps between them should be introns, the putative splice sites at their boundaries can be carefully checked. Because the splice-site motifs (GT-AG, polypyrimidine tract, and so on) are primarily in the intron, this provides a validation that is independent of the EST evi- dence. Reverse transcriptase artifacts or other problems causing imperfect cDNA construction may be screened out in this way. Improper inclusion of genomic sequence in ESTs (due to either mRNA purification problems or incomplete splicing) can also be excluded by requiring pairs of mutually exclusive splices in different ESTs. Observing a given splice in one EST but not in a second EST may be insufficient, because the latter could be an un-spliced EST rather than a biologically significant intron inclusion. This problem can be eliminated by focusing on mutually exclusive splices, two different splices seen in different ESTs, that overlap in the genomic sequence. One can make this even stricter by requiring that the two splices share one splice site but differ at the other. This approach detects the classic forms of alternative splicing, such as alternative exon usage and alternative 5 or 3 splicing (Fig. 2 a ). Detection of valid intron inclusions will probably require further statistical analysis. The presence in the human genome of many pseudogenes and paralogous genes resembling other genes greatly complicates the problem. Correct alternative splice detection depends on clustering the EST data into separate groups representing individual genes. EST clustering (such as UniGene) is well known to have both exces- sive `splitting' of genes (there are 80,000 UniGene clusters, versus the estimate of 32,000 human genes) and excessive `lumping', in which paralogous gene sequences are mixed together 4,22 . This mixing can suggest spurious alternative splices that are actually just differences between similar but distinct genes 23 . Methods that map the ESTs onto genomic sequence with a high level of identity (95–98%) probably exclude much of this paralog mixing, but not all. Ultimately, mapping ESTs to their unique gene location in the genomic sequence is the only way to sort out paralogs. Requiring that the consensus sequence for an EST cluster match completely, over its full length, to its genomic contig can help exclude artifacts where the genomic sequence has been misassembled. Instead of getting false positives (incorrect alternative splices), this may cause false negatives due to refusing to map the EST cluster at all. A high rate of false negatives is the greatest disadvantage of methods that require mapping ESTs to the draft genome sequence. Despite these sources of uncertainty, the agreement among many studies on a high frequency of alternatively spliced genes (35–60%) suggests that this result is valid. These studies support each other persuasively, because they differ not only in the sets of genes sampled (ranging from well-characterized mRNAs, to specific chromosomes, to a whole-genome study), but also in their specific criteria for reporting an alternative splice. It is important, however, to emphasize that there has only been one study so far verifying alternative splices detected by bioinformatics. Twenty genes with putative alternative splices were amplified from a multiple tissue cDNA panel by RT–PCR, with primers flanking the alternative splice (Fig. 3 a ). Sixteen were confirmed to be alternatively spliced, although thirteen of them were already recognized in the literature 16 .
Future Challenges
High-throughput validation.
Large-scale experimental verification of alternative splicing will be needed to assess the accuracy of the bioinformatics-based analyses. One promising technology is inkjet printing of long probes (up to 60 nt) to make rapidly customizable microarrays. Shoemaker et al . 24 used this technology to monitor the coordinate expression of 8,183 exons annotated on chromosome 22q. This technology could easily be adapted to detect alternative splicing, by designing probes that span specific exon–exon junctions. As alternative splicing of a given gene creates different exon–exon junctions, it can be detected by measuring hybridization of mRNA samples from different tissues to these probes (Fig. 3 b ). Whereas the hybridization ratios of most exon–exon junction probes for a given gene will be constant, alternative splicing will cause some junctions to be up- or down- regulated in different tissues. Rapid printing of such `splicing chips' will enable cataloging of splice forms for all genes, in different tissues, developmental states and conditions. Com- bined with the human genome sequence, this data can in turn be used to identify cis elements that regulate these forms. Recently, the Affymetrix microarray design has also been used to identify potential alternative splices within the rat genome. The Affymetrix array uses 20 probe pairs (25 nt) representing different exons of a gene. Whereas the intensities of most probes for a gene varied together in different tissues, probes for certain exons were anomalously depressed in some tissues, indicating potential alternative splices 25 . Other methodologies that use microarray technology to assess alternative splicing have also been developed (X.-D. Fu and M. Ares Jr, personal communication).
Rigorous measures of evidence.
It should be emphasized that microarray approaches will not settle the question of identifying alternative splices independent of bioinformatics analysis. If any- thing, these data are likely to increase the need for bioinformatics, to measure rigorously the strength of the evidence for alternative splices in all the raw experimental data (ESTs, microarrays, and so on). For example, the original inkjet microarray paper treated differences in probe hybridization among exons in a gene as indicators that low-expressed probes were not real exons but simply gene prediction errors. By con- trast, the Affymetrix study treated such differences as evidence of alternative splicing. The assessment of both competing interpretations is a bioinformatics analysis problem. This will require moving beyond simple `rules' for filtering out potentially misleading data to probabilistic measurement of the relative strength of the evidence for the competing interpretations.
Cataloguing alternative splice forms.
Although the new bioinformatics results are based on data from the whole genome, it is important to understand they are highly incomplete. They detect many new splice forms but miss many known isoforms. This is a result of both the incompleteness and fragmentation of the EST and genomic sequence data, as well as many causes of false negatives in the bioinformatics methods (Table 3). In Modrek et al . 18 , at least 50% of the EST data (and their potential alter- native splices) were excluded by these problems. These studies are just the beginning of an accelerating process of mRNA isoform discovery. The EST sequence data are growing rapidly, the draft genome sequence is being completed and new streams of high-throughput data (such as splice-detection microarrays) are beginning. Thus, a worthwhile goal is simply to build a catalog of alternative splice forms, just as the human genome sequence is being used to build a catalog of the genes. The development of new high-throughput technologies for detecting the protein products of alternative splicing will be needed to streamline this process.
What is truly functional?
Although bioinformatics and high- throughput experiments can have a key role in building a catalog, in our view this can only succeed as a community annotation process involving all molecular biology researchers. For example, how can one prove that a particular splice form is actually carrying out an important biological function? Even with strong evidence that a form is real (that it was actually made by the spliceosome in a living cell), it does not seem safe to assume that it has a biological function. If the spliceosome had a 0.1% rate of mis-splicing, it could produce over 4,000 meaningless `alternatively spliced' ESTs among the approximately 4 million ESTs. Bioinformatics can partly address this by discerning that a large subset of alternative splice forms (47%) are observed in multiple ESTs (often from different libraries) and thus are unlikely to be low-frequency error products 18 . At the same time, it is also not safe to dismiss a given form as `functionless' simply because it has no obvious function. For example, even an alternative splice form that causes early translational termination (and an inactive protein product) can act as an important form of regulation of biological activity 13 . Only detailed functional studies can resolve these questions. Bioinformatics can infer likely functional impacts, however, by detecting the addition or removal of known domains, and can predict how experimenters could verify the presence of these forms and their likely disease or tissue specificity. Biologists interested in some of these putative forms could then use a variety of techniques (PCR, northern and western blots) to test these predictions. This process will be best served by a central repository for both the bioinformatics predictions and subsequent experimental verification and functional studies, which would act as a community annotation database (Fig. 4). We hope this process can evolve rapidly into an active partnership between prediction and experiment.
Alternative splicing regulation.
One intriguing new area is the study of alternative splicing regulation. Regulation of splicing could be involved in 15% of genetic diseases 26 and may contribute to cancer by missplicing of exon 18 in BRCA1 , which is caused by a polymorphism in an exonic enhancer 27 . If alternative splicing is as widespread as bioinformatics studies indicate, how different splice forms are turned on and off may become a major research area, like transcriptional regulation. So far, molecular biology has identified some cis regulatory elements (such as exonic splicing enhancers) and trans factors (SR proteins, PTB, and so on) 11,13 . Bioinformatics could make important contributions, for example, in the identification of cis regulatory elements 28–31 . Recently, Brudno et al . 31 analyzed intronic sequence upstream and downstream of 25 alternatively spliced brain specific exons. They detected the motif UGCAUG at a much higher frequency downstream of alternatively spliced exons (relative to constitutive exons), for both brain-specific and muscle-specific alternative splicing 31 . This motif had previously been implicated in the alternative splicing of several genes including c-src, fibronectin, calcitonin/CGRP, and nonmuscle myosin II heavy chain-B 32–35 , so this result is very suggestive. It bodes well for genome-wide studies that combine the flood of new alternative splicing data with complete genome sequences for multiple organisms.
Acknowledgments
We are grateful to D. Black, S. Galbraith and K. Ke for their critical comments and suggestions. C.L. was supported by a grant from the Department of Energy. B.M. was supported by National Science Foundation Integrative Graduate Education and Research Training award. Received 16 August; accepted 20 November 2001.
1.Pennisi, E. Human genome project: and the gene number is...? Science 288 , 1146–1147 (2000).
2.Adams, M.D. et al. The genome sequence of Drosophila melanogaster . Science 287 , 2185–2195 (2000).
3. The C. elegans Sequencing Consortium. Genome sequence of the nematode C. elegans : a platform for investigating biology. Science 282 , 2012–2018 (1998).
4. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409 , 860–921 (2001).
5. Venter, J.C. et al. The sequence of the human genome. Science 291 , 1304–1351 (2001).
6. Sambrook, J. Adenovirus amazes at Cold Spring Harbor. Nature 268 , 101–104 (1977).
7. Gilbert, W. Why genes in pieces? Nature 271 , 501 (1978).
8. Early, P. et al. Two mRNAs can be produced from a single immunoglobulin m gene by alternative RNA processing pathways. Cell 20 , 313–319 (1980).
9. Rosenfeld, M.G. et al. Calcitonin mRNA polymorphism: peptide switching associated with alternative RNA splicing events. Proc. Natl Acad. Sci. USA 79 , 1717–1721 (1982).
10. Sharp, P.A. Split genes and RNA splicing. Cell 77 , 805–815 (1994).
11. Lopez, A.J. Alternative splicing of pre-mRNA: developmental consequences and mechanisms of regulation. Annu. Rev. Genet. 32 , 279–305 (1998).
12. Boise, L.H. et al. bcl-x, a bcl-2-related gene that functions as a dominant regulator of apoptotic cell death. Cell 74 , 597–608 (1993).
13. Smith, C.W.J. & Valcarcel, J. Alternative pre-mRNA splicing: the logic of combinatorial control. Trends. Biochem. Sci. 25 , 381–388 (2000).
14. Mironov, A.A., Fickett, J.W. & Gelfand, M.S. Frequent alternative splicing of human genes. Genome Res. 9 , 1288–1293 (1999).
15. Croft, L. et al. ISIS, the intron information system, reveals the high frequency of alternative splicing in the human genome. Nature Genet. 24 , 340–341 (2000).
16. Brett, D. et al. EST comparison indicates 38% of human mRNAs contain possible alternative splice forms. FEBS Lett. 474 , 83–86 (2000).
17. Kan, Z., Rouchka, E.C., Gish, W.R. & States, D.J. Gene structure prediction and alternative splicing analysis using genomically aligned ESTs. Genome Res. 11 , 889–900 (2001).
18. Modrek, B., Resch, A., Grasso, C. & Lee, C. Genome-wide analysis of alternative splicing using human expressed sequence data. Nucleic Acids Res. 29 , 2850–2859 (2001).
19. Burset, M., Seledtsov, I.A. & Solovyev, V.V. Analysis of canonical and non-canonical splice sites in mammalian genomes. Nucleic Acids Res. 28 , 4364–4375 (2000).
20. Beaudoing, E., Freier, S., Wyatt, J.R., Claverie, J. & Gautheret, D. Patterns of variant polyadenylation signal usage in human genes. Genome Res. 10 , 1001–1010 (2000).
21. Graveley, B.R. Alternative splicing: increasing diversity in the proteomic world. Trends Genet. 17 , 100–107 (2001).
22. Wheeler, D.L. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 28 , 10–14 (2000).
23. Burke, J., Wang, H., Hide, W. & Davison, D.B. Alternative gene form discovery and candidate gene selection from gene indexing projects. Genome Res. 8 , 276–290 (1998).
24. Shoemaker, D.D. et al. Experimental annotation of the human genome using microarray technology. Nature 409 , 922–927 (2001).
25. Hu, G.K. et al. Predicting splice variant from DNA chip expression data. Genome Res. 11 , 1237–1245 (2001).
26. Krawzczak, M., Reiss, J. & Cooper, D.N. The mutational spectrum of single base- pair substitutions in mRNA splice junctions of human genes: causes and consequences. Hum. Genet. 90 , 41–54 (1992).
27. Liu, H.X., Cartegni, L., Zhang, M.Q. & Krainer, A.R. A mechanism for exon skipping caused by nonsense or missense mutations in BRCA1 and other genes. Nature Genet. 27 , 55–58 (2001).
28. Stamm, S., Zhang, M.Q., Marr, T.G. & Helfman, D.M. A sequence compilation and comparison of exons that are alternatively spliced in neurons. Nucleic Acids Res. 22 , 1515–1526 (1994).
29. Kent, W.J. & Zahler, A.M. Conservation, regulation, synteny, and introns in a large-scale C. briggsae – C. elegans genomic alignment. Genome Res. 10 , 1115–1125 (2000).
30. Stamm, S. et al. An alternative-exon database and its statistical analysis. DNA Cell Biol. 19 , 739–756 (2000).
31. Brudno, M. et al. Computational analysis of candidate intron regulatory elements for tissue-specific alternative pre-mRNA splicing. Nucleic Acids Res. 29 , 2338–2348 (2001).
32. Modafferi, E.F. & Black, D.L. A complex intronic splicing enhancer from the c-src pre-mRNA activates inclusion of a heterologous exon. Mol. Cell. Biol. 17 , 6537–6545 (1997).
33. Huh, G.S. & Hynes, R.O. Regulation of alternative pre-mRNA splicing by a novel repeated hexanucleotide element. Genes Dev. 8 , 1561–1574 (1994).
34. Hedjran, F., Yeakley, J.M., Huh, G.S., Hynes, R.O. & Rosenfeld, M.G. Control of alternative pre-mRNA splicing by distributed pentameric repeats. Proc. Natl Acad. Sci. USA 94 , 12343–12347 (1997).
35. Kawamoto, S. Neuron-specific alternative splicing of nonmuscle myosin II heavy chain-B pre-mRNA requires a cis -acting intron sequence. J. Biol. Chem. 271 , 17613–17616 (1996).
36. Dralyuk, I., Brudno, M., Gelfand, M.S., Zorn, M. & Dubchak, I. ASDB: database of alternatively spliced genes. Nucleic Acids Res. 28 , 296–297 (2000).
37. Ji, H. et al. AsMamDB: an alternative splice database of mammals. Nucleic Acids Res. 29 , 260–263 (2001).
38. Spingola, M., Grate, L., Haussler, D. & Ares, M.J. Genome-wide bioinformatic and molecular analysis of introns in Saccharomyces cervisiae . RNA 5 , 221–234 (1999).
39. Kent, W.J. & Zahler, A.M. The intronerator: exploring introns and alternative splicing in Caenorhabditis elegans . Nucleic Acids Res. 28 , 91–93 (2000). ©2002 Nature Pub lishing Gr oup http://g enetics.nature .com