PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of ploscompComputational BiologyView this ArticleSubmit to PLoSGet E-mail AlertsContact UsPublic Library of Science (PLoS)
 
PLoS Comput Biol. Mar 2007; 3(3): e33.
Published online Mar 2, 2007. doi:  10.1371/journal.pcbi.0030033
PMCID: PMC1808492
The (In)dependence of Alternative Splicing and Gene Duplication
David Talavera,#1,2 Christine Vogel,#3,4* Modesto Orozco,1,2,5,6 Sarah A Teichmann,3 and Xavier de la Cruz1,2,7*
Philip E Bourne, Editor
1 Molecular Modeling and Bioinformatics Unit, Parc Científic de Barcelona, Barcelona, Spain
2 Protein Structure and Modelling Node, Instituto Nacional de Bioinfomática, Genoma España, Parc Científic de Barcelona, Barcelona, Spain
3 Medical Research Council Laboratory of Molecular Biology, Cambridge, United Kingdom
4 Institute for Cellular and Molecular Biology, University of Texas Austin, Austin, Texas, United States of America
5 Departament de Bioquímica i Biologia Molecular, Universitat de Barcelona, Barcelona, Spain
6 Computational Biology Program, Barcelona Supercomputing Center, Barcelona, Spain
7 Institut per a la Recerca i Estudis Avançats (IRCA), Barcelona, Spain
University of California San Diego, United States of America
#Contributed equally.
* To whom correspondence should be addressed. E-mail: xavier/at/mmb.pcb.ub.es (XdlC); cvogel/at/mail.utexas.edu (CV)
Received June 30, 2006; Accepted January 4, 2007.
Alternative splicing (AS) and gene duplication (GD) both are processes that diversify the protein repertoire. Recent examples have shown that sequence changes introduced by AS may be comparable to those introduced by GD. In addition, the two processes are inversely correlated at the genomic scale: large gene families are depleted in splice variants and vice versa. All together, these data strongly suggest that both phenomena result in interchangeability between their effects. Here, we tested the extent to which this applies with respect to various protein characteristics. The amounts of AS and GD per gene are anticorrelated even when accounting for different gene functions or degrees of sequence divergence. In contrast, the two processes appear to be independent in their influence on variation in mRNA expression. Further, we conducted a detailed comparison of the effect of sequence changes in both alternative splice variants and gene duplicates on protein structure, in particular the size, location, and types of sequence substitutions and insertions/deletions. We find that, in general, alternative splicing affects protein sequence and structure in a more drastic way than gene duplication and subsequent divergence. Our results reveal an interesting paradox between the anticorrelation of AS and GD at the genomic level, and their impact at the protein level, which shows little or no equivalence in terms of effects on protein sequence, structure, and function. We discuss possible explanations that relate to the order of appearance of AS and GD in a gene family, and to the selection pressure imposed by the environment.
Alternative splicing (AS) and gene duplication (GD) followed by sequence divergence constitute two fundamental biological processes contributing to proteome variability. The former reflects the ability of many genes to express different products, while the latter results in several copies of the same gene that are similar but not identical. In spite of these obvious differences, recent computational studies as well as anecdotal experimental evidence suggested that AS and GD produce functionally interchangeable protein variants. We provide a detailed study of the differences between alternative splicing and gene duplication and discuss potential interchangeability with respect to variation in expression, protein structure, and function. In general, the contribution of these two processes to the proteome variability is substantially different, and we advance some explanations that may explain this apparent contradiction and contribute to our understanding of the evolution of complex, eukaryotic proteomes.
Alternative splicing (AS) and gene duplication (GD) are two main contributors to the diversity of the protein repertoire with enormous impact on protein sequence, structure, and function [15]. Interestingly, several recent studies point to a direct equivalence between AS and GD. There are some cases where alternative splice variants in one organism are similar to gene duplicates in another organism [69]. For example, the eukaryotic splicing factor U2AF35 has at least two functional splice variants in human, U2AF35a and U2AF35b, which differ by seven amino acids in the RNA recognition motif (Figure 1A). The fugu orthologue U2AF35-a has no splice variant; instead there is a duplicate gene U2AF35-b with changes identical to those found in the human splice variant U2AF35b [9].
Figure 1
Figure 1
Equivalence between Alternative Splicing and Gene Duplication
Further, the changes introduced to a sequence are constrained by the need to preserve a stable and functional three-dimensional (3-D) fold [10]. Indeed, structural studies have shown that insertions and deletions between gene duplicates tend to happen at sequence locations where they are less damaging [11], such as loops at solvent-accessible locations. These restrictions will apply irrespective of the source of the changes and thus may introduce a certain degree of similarity between the sequence changes associated with GD and AS. Finally, recent studies have shown that AS and GD are inversely correlated on a genome-wide scale [12,13], i.e., small gene families tend to have more genes with alternative splice variants than do large families. These findings together—i.e., anecdotal examples, structural constraints, and anticorrelation at the genomic level—suggest that AS and GD are interchangeable sources of functional diversification [12]. Genes with AS would not need to produce additional variants in the form of duplicates, and vice versa.
Here, we first tested the anticorrelation between AS and GD with respect to sequence divergence, function, and gene expression. Second, we studied the interchangeability hypothesis at the protein structure level and asked to what extent AS and GD introduce changes to the sequence that are equivalent in their nature and effect on structure and function. To this end, we conducted a large-scale comparison of the effects of AS and GD on human and mouse proteins (Figure 1B and and1C;1C; the results for the analysis of the mouse data can be found in Figure S4). For the vast majority of cases, the two processes result in different protein modifications with different functional implications. This finding, while consistent with the different molecular mechanisms underlying both phenomena, contradicts the anticorrelation observed at the genomic level. We discuss some possible explanations for this paradox.
Genomic Analysis
In accordance with recent findings [12,13], AS and GD are anticorrelated at the genomic level (Figure 2A). There are fewer alternatively spliced genes in large families of gene duplicates than expected in an unbiased distribution. This depletion is strongest in gene families of high sequence identity (seq.id) (>80% seq.id., GD80, Figure 2A), and weaker but still present for more diverged families (>40% seq.id, GD40). The same trend holds true when examining orthologues: the bias is stronger amongst human–mouse or human–fly orthologues (GD80, χ2-value = 150 and χ2-value = 105, respectively; p-value [double less-than sign] 0.001 in both cases) than amongst human–yeast orthologues (GD80, χ2-value = 84; p-value [double less-than sign] 0.001).
Figure 2
Figure 2
The Relationship between AS and GD at the Genomic Level
Gene function.
The anticorrelation between AS and GD could arise from the preferential duplication or introduction of AS in genes of particular function. In general, genes with AS have similar distributions across functional categories as genes with GD (see Table S3, Figure S2), with two exceptions. Ribosomal and receptor proteins (e.g., G protein–coupled receptors) belong to the largest protein families in human [14], and thus their enrichment in families of GD is unsurprising (E-value < 10−10). At the same time, these functions are depleted amongst genes with alternative splice variants [15], which is also reflected in our data (E-value < 10−10).
When removing from our dataset the 855 and 293 proteins predicted to be G protein–coupled receptor-like or ribosomal proteins [14], respectively, the bias against genes with both AS and GD (AS+/GD+) is still strongly significant (p-value [double less-than sign] 0.001). See Table S3 for details. Thus, the anticorrelation between AS and GD is not due to biases amongst genes of different functions.
Variation in expression patterns.
We further characterized the relationship between AS and GD by comparing their patterns of expression among different tissues, which reflect corresponding regulatory processes. A previous study reported that AS and general transcription regulation act independently on different groups of genes with tissue-specific expression [16]. Here we do not compare tissue specificity, but the overall extent of variation in expression introduced by AS and GD. More precisely, we studied whether the extent of coexpression (i.e., the lack of expression variation) between alternative splice isoforms is comparable to that found between duplicates (Figure 2B). If AS and GD are functionally interchangeable, then we would expect a similar amount of coexpression amongst their products.
The level of coexpression was measured using the Pearson correlation coefficient (PC) between the expression patterns of two isoforms among a set of tissues, in the case of AS, or of two duplicates, in the case of GD. When more than two isoforms (or duplicates) were available, we averaged the PCs resulting from all the possible comparisons between them (see Material and Methods and Figure S10). PC values near 1 or 0 correspond to high or low coexpression, respectively.
We directly compared the coexpression in alternative splice variants (AS coexpression) and in gene duplicates (GD coexpression) using data from the same microarray experiment [17] (white diamonds in Figure 2C–2D). We observe that the overall AS coexpression (PC = 0.27 ± 0.23) is comparable to the overall GD coexpression of GD families with >40% sequence similarity (PC = 0.24 ± 0.33) (Figure 2C–2D, red line and diamond). This suggests that AS and GD40 may have comparable levels of coexpression—that is, they both span similar ranges of tissue expression, though the underlying molecular regulation generating coexpression of AS isoforms and GD40 transcripts is different. However, due to the relatively small size of the dataset and the use of exon junctions as a proxy for alternative splice variants, this result should be treated with care.
We also explored whether we can observe, at the expression level, an anticorrelation in analogy to that found at the sequence level [12,13] (Figure 2A). A previous study has shown that duplicates diverge quickly in their expression patterns [18], so we would expect a smaller amount of GD coexpression for genes without AS (AS–/GD+) than for those with AS (AS+/GD+). However, we find the opposite (Figure 2C), although the trend is very weak and in most cases the statistical power is low. This reverse trend suggests that in AS+/GD+ families, expression variation amongst alternative splice isoforms may contribute independently of the GD expression variation to the overall expression variation amongst all AS and GD variants of the gene; we observe some additivity rather than complementarity. Figure 2D confirms this finding for AS coexpression: there is no obvious anticorrelation between AS and GD in terms of their effect on expression variation amongst their variants.
We note that the coexpression analysis is at present still limited by the amount of data available. Future availability of large-scale datasets suited for expression comparison of AS and GD will help refine our results.
Protein Sequence and Structure Analysis
To test whether AS and GD are interchangeable at the structural and thus functional level, we compare sequence changes between duplicate proteins to those between alternative splice isoforms (Figure 1B–1C). There are two basic types of sequence changes in both AS and GD: substitutions, and insertions or deletions (indels) (Figure 1B). Both can be described in terms of their length, the nature of the affected amino acid residues, and their location in the structure or relative to each other in the sequence. We use these properties as changes in their values can be directly related to changes in protein structure and function [19]. We analyzed the properties strictly from a protein structure and function point of view; we do not examine the evolutionary processes (such as selection) that led to them.
We focus on gene families defined using two seq.id. thresholds: 80% and 40%. The former was chosen because the anticorrelation at the genomic level is stronger (Figure 2A), and global seq.id. between alternative splice variants is >80% (see below). However, GD data at the 80% seq.id. level may display only a few sequence changes, resulting in larger than expected differences between AS and GD. To avoid this bias we have also considered more diverged families, defined by a 40% seq.id. threshold. Figure S5 contains additional analyses on GD, using families defined by common protein domains. In our analyses described below, we focus on gene families that have both alternative splice variants (AS+) and gene duplicates (GD+), i.e., AS+/GD+, except in the case of local sequence identities, for which we also extend our study to families AS–/GD+ and AS+/GD–.
Substitutions
First we examine substitutions, i.e., the extent and nature of amino acid changes and the length of the substituted region. In general, substitutions amongst GD range from a small number of amino acid replacements in recent homologues to a large number of replacements in proteins as divergent as haemoglobin, for instance [10]. In AS, substitutions in one isoform as compared with another one arise by the use of mutually exclusive exons [20], although they can also be due to intron retentions accompanied by stop codons [21].
Global versus local sequence identity.
Global seq.id. is the seq.id. along the whole alignment of two sequences, and it can be used to assess the overall degree of function conservation between proteins [22]. However, same global seq.id. values may correspond to very different distributions of amino acid replacements along the sequence. For this reason, we also examined local seq.id., i.e., the seq.id. between only parts of two sequences. For alternative splice variants, the local seq.id. corresponds to substituted regions, i.e., mostly mutually exclusive exons. For GD, we estimated the corresponding sequence stretches by different methods as described below.
In Figure 3 we show the distributions of global and local seq.id. for AS and GD. In the case of AS we see that global seq.id. (Figure 3A) between splice isoforms is very high (>90%), but local seq.id. (Figure 3B) between the substituted segments in splice isoforms is low (<30%). This is in accordance with the underlying molecular mechanisms of AS [20,21], which point to very localized sequence changes. The global seq.id. for GD families are broadly distributed above the respective seq.id. cutoff (Figure 3A).
Figure 3
Figure 3
Global and Local Sequence Identity in AS and GD Substitutions
In contrast, while the local seq.id. in alternative splice variants is usually low, it is clearly higher for GD, in particular for GD80 families (Figure 3B). This result implies that at comparable global seq.id. (i.e., >80%; see Figure 3A), alternative splice variants have more local changes than gene duplicates. The differences are smaller, although still clear, if we consider local seq.id. GD40 families.
Global and local seq.id. provide a first view on interchangeability between AS and GD. However, to understand the effects of substitutions, it is also important to know the location of the changes [23,24]. To retrieve such information, we directly compared seq.id. between substituted regions in AS with the equivalent regions of their gene duplicates (AS+/GD+, Figure 3C). In the majority of cases, local seq.id. for GD is higher than the local seq.id. of AS (GD80: 78%, χ2-test p-value < 1.9 × 10−11; GD40: 64%, χ2-test p-value < 6.5 × 10−15).
In general, we find that the distribution of amino acid replacements along the protein sequence is different between AS and GD substitutions. In gene duplicates, changes are spread all over the sequence, while in alternative splice variants the changes are concentrated at very precise locations, in accordance with the underlying molecular mechanisms [20,21]. In general, these data point to a noninterchangeability between AS and GD changes. However, when the sequence divergence between duplicates is large, as is the case for GD40 families, there may be some cases for which AS and GD changes have comparable impact on protein function.
The comparisons described above have been derived from gene families with alternative splice variants and gene duplicates (AS+/GD+, Figure 3A–3C). Next, we extend our analysis to genes for which only either AS or GD, but not both, are known to exist (AS+/GD– and AS–/GD+, Figure 3D). Because AS information is not available for the AS–/GD+ families, local sequence changes in gene duplicates were estimated with a sliding window of 100 aa (see Materials and Methods). Similar to what we found above for AS+/GD+ sequence families (Figure 3B), for GD80 families the local seq.id. between gene duplicates without AS (AS–/GD+) is substantially higher than the local seq.id. between alternative splice variants without duplicates (AS+/GD–) (Figure 3D). For GD40 families, the overlap with AS data is larger; nonetheless, the overall trend is consistent with that shown in Figure 3C.
The nature of the sequence changes.
To complete our analysis of substitutions, we compare the nature of the amino acid replacements in AS and GD, focusing on nonconservative replacements. These involve amino acids of very different physico–chemical properties, and are thus more likely to alter protein structure, stability, and function [25,26] We find that, in general, the percentage of nonconservative amino acid replacements is higher for AS than for GD substitutions, a situation that holds whether we consider GD families at the 80% (58% and 44% nonconservative replacements for AS and GD, respectively) or 40% seq.id. level (60% and 43% nonconservative replacements for AS and GD, respectively). Thus, AS substitutions are physico–chemically more aggressive than GD substitutions.
Further, we use the maximal distance between replacements as a measure of the distribution of nonconservative changes along the sequence. We find clear differences between AS and GD (Figure 4), in accordance with the previous results. In particular, we observe that, for GD families at both the 40% and 80% levels, replacements are significantly more spread along the sequence than those for AS substitutions. This concentration of AS changes in the sequence in turn may result in a highly localized change in the 3-D distribution of physico–chemical properties, contrary to what happens with GD. Figure 5 illustrates this point using the example of MAPK9.
Figure 4
Figure 4
The Distribution of Nonconservative Changes along Sequences
Figure 5
Figure 5
The 3-D Distribution of Physico–Chemical Changes in the Affected Residues of AS and GD
In summary, AS replacements are generally more concentrated and physico–chemically drastic than those observed for GD. The nature of these differences argue against interchangeability between AS and GD, on the basis of existing studies [2326].
Insertions and Deletions
Size.
Second, we studied indels, which modify protein structure in a different way than substitutions. A first and intuitive measure of their impact is provided by indel size: small indels are more likely to have a small effect on structure than larger ones. We find that indel sizes are substantially different for AS and GD (Figure 6A) for both GD40 and GD80. AS indels are of domain size (≥30aa, many even >100aa) in agreement with previous results [27,28]. In contrast, about three-quarters of GD indels are fewer than five residues long, which means that they are shorter than a domain (Figure 6A). Thus, AS has a strong prevalence over GD for indels of whole domains. Changes in the domain composition, in turn, can modulate protein function very abruptly—for example, by on/off switch mechanisms that result in dominant-negative regulators [29,30].
Figure 6
Figure 6
The Size Distribution of Insertions/Deletions in AS and GD
Location of indels.
Given that, in general, sequence changes are severely constrained by structure and stability requirements [10], it seems difficult to rationalize how AS and GD indels can be so different. A detailed explanation can only come from the structure comparison of many pairs of alternative splice isoforms for which we still lack data [3133]. Nonetheless, a reasonable approximation can be obtained following simple considerations. The N- and C-termini usually occur at the protein surface [34]. At the terminus, inserting or deleting a sequence stretch is likely to have less of an effect on the protein's structure than internal indels, and thus external indels may be less restrained in their size.
We separated the AS and GD indels according to their location in sequence (N-/C-terminal ends or internal) and plotted the corresponding size distributions (Figure 6B–6C). The very large AS indels (>100 aa) are usually located at the N- or C-terminal end where they are less likely to disrupt protein structure/function. Similarly, GD indels are generally larger at external than at internal positions, but the trend is much weaker than for AS. More importantly, internal AS indels tend to be larger than internal GD indels, indicating that they have a stronger potential to interfere with the structure of the protein core.
Overlap between indels in AS and GD.
We also examined the overlap between the location of indels in splice variants and in duplicates of the same gene (Figure 7). Most AS indels (~80%) show no or negligible overlap with GD indels. The same holds true when focusing on very short indels of <30 aa length. Thus, AS indels affect different regions of protein structure, diversifying protein function in different ways from GD indels. As shown in Figure 7, short AS indels occur in sequence regions different from those affected by GD and thus are more likely to involve core residues, or relevant secondary structure elements. An example of such drastic effect of AS is the rat Piccolo C2A domain, in which splicing removes a nine-residue sequence stretch, which changes secondary structure and abrogates dimerization [35].
Figure 7
Figure 7
The Overlap between AS and GD Insertions/Deletions
There is also a small fraction of instances (~15%) with an obvious overlap between AS and GD indels (Figure 7). Examined closely, we find that some of them could be instances of equivalent changes in AS and GD that support molecular and direct interchangeability. However, in contrast to examples such as that of U2AF35, which represents substitutions [9] (Figure 1A), the few equivalent changes in our dataset consisted of a simpler event, i.e., a deletion, producing a long and a short alternative splice isoform and a gene duplicate homologous to the short alternative splice isoform.
In summary, the results obtained in the study of indels lead to the same conclusions as for substitutions: in general, the impact of AS and GD on protein function is not interchangeable, irrespective of whether we consider GD at the 80% or 40% seq.id. levels.
Conclusions and Possible Explanations of the Inverse Correlation between AS and GD
AS and GD are anticorrelated at the genomic level (Figure 2A) [12,13]. This relationship holds true for genes of different degrees of sequence divergence and of different functions, and suggests functional interchangeability between both phenomena [12]. This hypothesis is supported by known examples of equivalence [69] and by general, structural constraints on sequence changes [10]. In contrast, our sequence analysis shows that AS and GD followed by divergence are not directly interchangeable in their effect on protein structure and function. Indeed, they introduce very different changes with respect to indels and substitutions. The differences are summarised in Table 1. Indels caused by AS are more drastic than those observed in GD, both in type and location of affected residues. In the case of substitutions, we find that for GD they are more conservative and more broadly distributed along the whole sequence than those of AS. However, we also observe a small number of cases in which the interchangeability hypothesis may be true, and the sequence changes introduced by AS and GD are equivalent (Figure 3C, Figure 7).
Table 1
Table 1
Summary of the Effects of Alternative Splicing and Gene Duplication on Sequence and Structure
To explain the apparent paradox between the relationship of AS and GD at the genome and at the protein level, we speculate on alternative explanations for the depletion of AS observed in large GD families (Figure 2A) [12,13], discussing several effects that may contribute to it. Duplicated genes tend to have fewer exons than genes that have no duplicates but alternative splice variants (see Figure S8). This trend may in part be due to retrotransposition events which create duplicates which are single-exon genes that cannot have splice variants. However, when accounting for single-exon genes, the anticorrelation between AS and GD is still observed (see Kopelman et al. [12] and Figure S11). In addition, the anticorrelation may arise from a combination of the negative selection pressures introduced by dosage balance effects. In singleton genes that have evolved a splice variant, duplication may be disfavoured due to a multiple simultaneous dosage balance effect [36]: a single duplication event would produce a multitude of additional gene variants whose functions interfere with the existing, tightly regulated system of biological processes. This effect would be weaker for genes without AS, thus introducing a bias towards their duplication. This bias may be reinforced, under conditions of environmental stress [37,38], by the need to increase expression levels of a desirable function [39,40]. Further, the retention of duplicates as a potential backup system may be fostered if a gene is essential for the cell's survival upon gene knockout [41]. In contrast, the introduction of AS in essential genes does not directly support robustness against null-mutations, and may even be slightly deleterious. Thus, essentiality of genes may contribute to the inverse relationship between AS and GD.
Finally, if a gene with AS has duplicated, subsequent loss of an isoform in one of the copies may be tolerated due to the existence of an identical version of this isoform in the other copy of the gene. This explanation is supported by recent findings on the evolution of AS upon GD [13], and the fact that the depletion in AS is stronger for closely related duplicates [12,13] (Figure 2A, 80% seq.id.). Such a scenario would also be an extension of the backup role shown for paralogues [42].
A combination of these effects would, in general, result in a smaller proportion of genes with AS in gene families with more than one duplicate, in particular for recent duplicates, suggesting that the chronological order of events plays a role. Subsequent divergence of the gene duplicates may alleviate the negative impact of the dosage balance effect, allowing the evolution of AS and reducing the anticorrelation between AS and GD.
Genomic Analysis
Datasets.
To test the relationship between GD and AS at the genomic level, we used: (i) clusters of homologous sequences inferred from the seq.id. (equivalent to gene families); (ii) sets of known or predicted alternative splice variants, or isoforms, for a particular gene. An overview of the data is provided in Table S1. For AS genes, we primarily used the datasets downloadable from EBI AltSplice server (releases 2.0 for both mouse and human) [43].
In addition, we compared the findings using the AltSplice dataset with findings using data from the SwissProt database [44] and the Ensembl predictions of splice variants [45] (see Table S1). Genes that have more than one transcript variant in either one of the three sets are denoted AS+. A small number of false positives can be expected in each of the three AS+ sets. Within the set of sequences without AS (AS−), we expect a fraction of false negatives, that is, genes that splice alternatively but for which we have no data yet. However, it is unlikely that these false positives or false negatives coincide across each dataset, as each database used its own approach to derive the data, and we expect no systematic error. All three datasets show the same trends (see Table S1), consistent with previous findings [12,13].
To estimate GD, we used the Ensembl protein predictions for human (version 37.35j) and mouse (version 37.34e) (Table S1). Both genomes were made nonredundant in that we only included the longest, predicted transcript for each gene, and the sequences were filtered for low-complexity regions using the seg program [46]. To obtain families of paralogous genes, the sequences were clustered to 40%, 60%, 80%, and 90% seq.id. using CD-HIT [47]. The higher the seq.id. cutoff for a family, the more conserved are the family members: clusters of 40% seq.id. are expected to be larger and contain more distantly related sequences than clusters of 80% seqid. Clusters with more than one sequence are termed GD+, denoting the existence of homologues. Figure S1 shows the distribution of human sequences across GD families of different sizes, using a variety of seq.id. cutoffs. The human–yeast, human–fly, and human–mouse orthologues were derived from the InParanoid database [48].
Retrotransposition creates gene duplicates that have only a single exon, and hence are unlikely to show any AS. To test for a possible bias in GD families (GD+) stemming from retrotransposition, we examined the distribution of single-exon genes across AS and GD sets using the SEGE database [49], similar to an approach described by Kopelman and colleagues [12]. The procedure followed was: (i) all human genes were clustered according to 80% seq.id.; (ii) all genes were labelled according to their known AS; and (iii) the number of single-exon genes [49] amongst singletons, gene families, and genes with/without AS was calculated. This distribution across genes with AS and/or GD is shown in Table S2. If retrotransposition was a major source of GD without AS (AS−/GD+), then we would expect to see a bias towards single-exon genes in this category. This is not the case; also see Kopelman et al. [12].
Function analysis.
To determine whether the inverse relationship between AS and GD is specific to genes of particular functions, we analyzed functions for human proteins of the four different sets of genes with or without AS or GD (AS+/GD+, AS+/GD−, AS−/GD+, AS−/GD−). We analyzed gene functions from both the SwissProt and the AltSplice database using the DAVID Web server (http://david.abcc.ncifcrf.gov/home.jsp) [50]. DAVID analyzes human proteins for biases in terms of Gene Ontology [51] terms, protein domains, pathways, functional categories, protein interactions, disease, literature, and general annotations (sequence features). As a background list for comparisons, we used the union of the four sets described above. The detailed results are listed in Table S3.
Expression analysis.
In this part of our work, we compared the extent of coexpression between alternative splice isoforms (AS coexpression) with that of coexpression between gene duplicates (GD coexpression). To estimate AS coexpression across human genes, we analyzed data on absolute expression levels of exon junctions of 3,840 human genes, measured across 44 different tissues [17] (Geo [52] GDS829–GDS834). AS coexpression of a gene was measured as the average pairwise PC between the expression vectors of all its exon junctions. A high PC indicates low coexpression (low variation) amongst the exon junctions and hence splice variants, and vice versa.
The expression of all exon junctions of a particular gene was then summarised (averaged) to form one vector representing the overall expression pattern of a gene. We measured GD coexpression of a GD family, i.e., the amount of coexpression amongst gene duplicates, as the average pairwise PC between the gene expression vectors of all family members. For gene families of >80% seq.id., the 3,840 genes in the dataset [17] did not provide enough families with more than two members to be suitable for further analysis. Thus, we examined another dataset with absolute expression values of human genes across 79 different tissues published by Su et al. [53] (Geo [52], GDS596). In contrast to the first dataset, GDS596 reports expression per gene and not per exon junction, and thus is only suited for analysis of GD coexpression, not AS coexpression. AS+ genes were defined as given by the AltSplice database [43]. Please refer to Table S4 for analysis of a third dataset, use of other measures of coexpression, and different post-processing procedures of the expression data.
Protein Sequence Analysis
Datasets.
We assessed GD at the whole-gene level in which two proteins are assigned to the same gene family when their seq.id. is above 40%, or above 80% (GD40 or GD80, respectively). These families were obtained by clustering the SwissProt [44] human proteins using the program CD-HIT [47] (http://bioinformatics.burnham-inst.org/cd-hit). We also used the Pfam [54] domain families as a model of highly diverged gene families. More precisely, we used all the Pfam [54] families that mapped to one or several of the SwissProt [44] proteins in the AS dataset. The results obtained with this model are shown in Figure S5. To allow proper testing of the interchangeability, we focused on AS+/GD+ families, i.e., those families for which at least one gene duplicate and one splice variant are known.
Our set of genes with AS was obtained after querying the SwissProt database [44] version 40, with the keywords VARSPLIC and HUMAN, or MOUSE, respectively. A summary of the number of genes, isoforms, and duplicates in the datasets used in this work is given in Table S5.
SwissProt [44] is a high-quality, manually curated database that has been recently used by different research groups in the study of AS at the protein level [20,27,28,31,5557]. While the data may be biased by the curation process, several facts suggest that this potential bias would not affect our results. First, the proportions of isoforms showing indels and substitutions in our sample, 73% and 27%, respectively, are comparable with those inferred from other studies: 76% and 24% [58], and 67% and 33% [59]. Second, the anticorrelation observed between AS and GD [12,13] (Figure 2A) can be reproduced with SwissProt data [44] (Figure S11). Third, our data could be biased by the existence of nonfunctional isoforms targeted for nonsense-mediated mRNA decay (NMD) [60]. When repeating our analysis after eliminating those isoforms that can be targeted to NMD (about 8% of the data [61]), there was no difference in the results compared with those from the original data (see Figure S6A). Finally, for AS indels, results obtained from SwissProt [44] are comparable with those from another AS database, ASAP [62] (Figure S7). Therefore, we do not expect a significant bias in our results when using SwissProt AS data.
For all of the distributions shown in the different figures, we computed the confidence interval corresponding to each proportion in the distribution, following Goodman [63].
Characterization of substitutions.
Our work involves detailed comparison between AS and GD sequence changes which occur between alternative splice isoforms or between gene duplicates (Figure 1B and and11C).
Global and local seq.id. Global seq.id. corresponds to the commonly used percentage of seq.id. between aligned sequences. It was computed from a whole-sequence alignment between the sequences of either two duplicates or two alternative splice isoforms, using standard dynamic programming [64]. When comparing alternative splice isoforms, one of the sequences was always that of the SwissProt [44] reference isoform.
Local seq.id. refers to the seq.id. between parts of the sequences. Local seq.id. between alternative splice isoforms was always computed in the same way, comparing the sequence stretches substituted between them. To this end, we first obtained the location of these stretches from SwissProt [44], and then we aligned them using a standard dynamic programming method [64]; the local seq.id. was computed from this alignment. To avoid meaningless comparisons, we introduced some restrictions [56]: (i) both sequence stretches must be >10 aa, and (ii) the size of the shorter stretch must be at least 60% that of the larger stretch. These filters were only applied when computing the local seq.id. but for no other variable.
To obtain local seq.id. between gene duplicates, we distinguished two cases (Figure S9): either we observe both AS and GD for a given gene (AS+/GD+), or we only observe GD but not AS (AS–/GD+).
In AS+/GD+ cases, we followed two different procedures. The first one uses a sliding window of the size of the AS substitution, N (Figure S9A). For each gene, we then (i) aligned the sequence of the SwissProt reference sequence for the gene (usually that of the longer isoform) with that of one of the gene duplicates; (ii) computed the identity percentage between positions i and i + N − 1, at all possible i locations of the window along the alignment; and (iii) repeated steps (i) and (ii) for each comparison between the first gene and any of its duplicates. N, the size of the window, and the AS substitution were obtained from SwissProt annotations [44] (Figure 3B).
In the second procedure analysing the AS+/GD+ case (Figure S9B), we studied interchangeability of sequence changes at the AS location. To this end, we first aligned the sequence of the protein with known splicing to one of its duplicates. The former was always the sequence of the SwissProt [44] reference isoform. Then, we mapped location and length of the AS substituted stretch to the sequence of the gene duplicate and computed seq.id. between both sequence stretches. The information on the location and length of the AS substituted stretch was obtained from the SwissProt [44] annotations (Figure 3C).
In AS−/GD+ cases, a direct comparison between AS and GD local seq.id. is no longer possible. Here, local seq.id. was estimated using a moving window of size N: (i) we aligned the sequences of the two duplicates, and (ii) we computed the identity percentage between the positions i and I + N – 1, at all possible locations i of the moving window along the alignment. We calculated these local seq.id. for GD using N = 100 aa; the resulting distribution is shown in Figure 3D.
Nonconservative changes. We define nonconservative changes as those for which the corresponding value of the Blosum62 [65] substitution matrix is negative. This criterion has been used in the annotation of SNPs [66]. The fraction of nonconservative mismatches between two sequences was obtained by dividing the number of mismatches with a negative Blosum62 value by the total number of mismatches in the alignment. The percentages of nonconservative mismatches for AS and GD substitutions were compared using the T-test (http://home.clara.net/sisa).
Distribution of the maximal distance between nonconservative mismatches. The maximal distance between mismatches corresponds to the sequence separation between the two most distant mismatches in a sequence alignment. In the case of GD, we (i) aligned all the sequences of a given gene family with the reference sequence of the member (or members) that has AS; (ii) for each alignment, mapped the mismatches to the sequence which is the reference isoform in SwissProt; (iii) for each alignment, computed the distance between the two most separated mismatches as follows: (jr – ir)/Nr, where jr and ir are the sequence locations of the closest mismatches to the C- and N- termini, respectively; Nr is the size of the latter; and (iv) repeated steps (ii)–(iii) for all the alignments obtained in (i) and binned the resulting values.
In the case of AS, the maximal distance between mismatches was computed using the same equation as before: (jr – ir)/Nr, where in this case jr and ir correspond to the locations of the end and beginning of the substituted fragment, as provided by SwissProt.
Note that the maximal mismatch distance was normalized by the sequence length to allow comparison of all results independent of the protein size.
Comparative modeling. The structure of mitogen-activated protein kinase 9 was modelled using that of mitogen-activated protein kinase 10 [67]. The seq.id. between both proteins is 84%, which guarantees a good modelling result. The alignment between the two protein sequences was generated employing standard dynamic programming [64]. The resulting alignment was used as input to run the comparative modelling program MODELLER [68].
Characterization of indels.
For all the variables, we conducted a comparison between AS and GD as described before (Figure 1B–1C).
Indel sizes. The indel size distribution for the AS events was obtained from the SwissProt [44] records of the proteins in our dataset. The procedure was: (i) records with VARSPLIC annotations were parsed for the presence of MISSING annotations; (ii) the MISSING annotation provided the initial (M) and final (N) positions of the inserted/deleted fragment; and (iii) the length of the indel was computed as N − M + 1. The resulting lengths were binned to give the size distribution shown in Figure 6A.
In the case of the indel size distribution for GD the procedure was: (i) for each gene family in our dataset (see above) we obtained the length of all indels (gaps) for all the possible alignments between the proteins in the family, and (ii) the resulting lengths were binned after a simple redundancy correction. The redundancy correction consisted of dividing the contribution of each indel in the frequency histogram by the number of sequences in the family cluster. The resulting distribution follows a power law very similar to that previously found by Benner and colleagues in a massive alignment experiment [69], supporting the reliability of our data.
Both the AS and GD indels datasets were subsequently broken down in two subsets, according to whether indels were external (positioned at the N- or C-terminal ends of the protein sequence) or internal (positioned within the protein sequence). The resulting length distributions are shown in Figure 6B–6C.
Overlap between indels. The procedure to estimate overlaps between AS and GD indels was: (i) map the AS indel to the sequence of the longest isoform; (ii) align the sequence of that isoform to that of the other genes in the family; (iii) map the indels from the previous alignments to the longest isoform; (iv) for each possible comparison between the AS indel and one GD indel compute the amount of common amino acids and divide it by the size of the AS indel; and (v) bin the results after redundancy correction. The redundancy correction consisted of only adding one count to the frequency histogram when the overlaps between a given AS indel and a series of GD indels were always the same.
As mentioned before, the SwissProt database [44] is of high quality but small; thus, we expect to have missed a certain number of alternative splice isoforms, which are likely to increase the number of cases with high overlap between AS and GD indels.
Figure S1: Distribution of GD Family Sizes
Distribution of human sequences across GD families as determined by different seq.id. cutoffs (40%, 60%, 80%, 90%). GD families of size 1 denote singletons, i.e., genes without paralogues (GD−).
(54 KB PPT)
Figure S2: The Distribution of Molecular Function and Biological Process
We tested for functional biases across proteins with AS and/or GD using the GO annotation available for humans from the GO database [70]. For a more detailed analysis of function characteristics, see Table S3.
Human genes were annotated with respect to biological process (A) and molecular function (B) using GO annotation [51,70]. GD families were determined according to an 80% seq.id. cutoff; AS family information was taken from the AltSplice database [43]. All sequences were assigned to one of the four sets, and the distribution of biological processes (A) and molecular functions (B) is shown for the four sets separately: AS−/GD− no duplication or AS known; AS−/GD+ duplicates, but no AS known; AS+/GD− no duplication, but AS known; and AS+/GD+ both duplicates and alternative splice variants known. There are no obvious biases in the function composition for any of the four constellations of AS/GD.
(749 KB PPT)
Figure S3: Chromosomal Location of the Duplicated Genes
We show the fraction of duplicated genes per gene family that have different chromosomal location, using a 40% seq.id. cutoff (dark red). (Data for GD80 families are not shown because of the small amount of data.) In all except one group of families, on average >55% genes within a family have different chromosomal locations. This indicates different regulation between duplicates [71] and therefore no interchangeability between AS and GD, given that transcription and mRNA splicing are tightly coupled [72,73].
(55 KB PPT)
Figure S4: Analysis for Mouse (40% seq.id. Cutoff)
The four figures reproduce, for mouse, the analysis shown in Figures 3A, A,4,4, ,6A,6A, and 7, respectively The results are qualitatively identical to those discussed for human in the main text, and support the idea that in general AS and GD are not interchangeable at the molecular level. Due to the smaller amount of data, the analysis at 80% cutoff did not produce statistically meaningful results.
(A) Substitutions in AS have different effects on global versus local seq.id. Light and dark green correspond to global and local seq.id. for AS substitutions, respectively. Global seq.id. is obtained after aligning two isoforms for the same gene, for which the AS event involved a substitution. Local identity applies only to the substituted stretches. Dark red corresponds to the seq.id. distribution for GD families at 40%, after sequence alignment between paralogues. The global seq.id. between splice isoforms is very high while the local seq.id. in alternative splicing variants is very low. Both seq.id. distributions for AS contrast with those of GD families.
(B) Maximal mismatch distance between nonconservative substitutions is much smaller in AS than in GD. The maximal mismatch distance is the number of residues between the two most distant, nonconservative substitutions, normalized by whole sequence length. Nonconservative mismatches have a negative value in the Blosum62 matrix and were chosen for their stronger impact in protein structure and function. The plot depicts AS data in green, and GD data for families at 40% seq.id. in dark red. Substitutions in alternative splice variants are much more localized than those in gene duplicates.
(C) Size distribution for indels. The AS distribution is shown in green. Indels for GD are shown for the whole-gene model (dark red). Clear differences are found between both distributions.
(D) Frequency distribution of the amount of overlap between AS and GD indels, taking as reference the sequence of the AS indel (see Materials and Methods). Dark blue bars correspond to the case when indels of any size are considered. Light blue bars correspond to the case when only subdomain indels (≤30 aa) are considered.
(1.1 MB PPT)
Figure S5: Comparison between AS, Whole-Gene, and Domain-Based GD Families
To provide another definition of gene families, we estimated GD families based on domain families. We used domain annotations from the Pfam database [54] that mapped to one or several of the SwissProt [44] proteins in the AS dataset. Nonhuman sequences were removed from the alignment.
(A) Global seq.id. distribution. The distribution of human AS sequences is shown in green; for GD whole-gene families (40% level) are shown in dark red; indel sizes for GD families defined by Pfam domains are shown in light red. We observe that the range of seq.id. for the latter is much lower than for AS and GD whole-gene families. At the local level (results not shown) the range of seq.id. for the Pfam model of GD is lower than that observed for AS. However, for the former the amino acid replacements spread over the whole sequence, contrary to what we observe for AS.
(B) The indel size distribution of human AS sequences is shown in green. Indel sizes for GD whole-gene families (seq.id. cutoff of 40%) are shown in dark red; indel sizes for GD families defined by Pfam domains are shown in light red. In the former, whole sequences were compared within each family to obtain the indel size distribution. In the domain-based GD families, indels were obtained from the multiple sequence alignments of the Pfam databank [54] Indels for both GD models show behaviour similar to that described by Benner and colleagues [69].
(C,D) Size distributions for external and internal indels, respectively, with the same colour code as in (B). These distributions indicate that indels from Pfam domains and GD families show similar trends when compared with AS indels. Overall, our results indicate that GD and AS are in general different in their sequence/structure changes, independently of the model representing GD.
(1.1 MB PPT)
Figure S6: Effect of Filtering Out Putative NMD Targets from the AS Data
No significant differences are found between the original results and those obtained after eliminating from the AS dataset all the isoforms that may be targets of NMD machinery [61].
(A) Overall versus local seq.id. Original AS global and local seq.id. are shown in light and dark green, respectively. Overall and local seq.id. for NMD-filtered AS are shown in orange and yellow, respectively.
(B) Maximal mismatch distance between nonconservative substitutions. Original AS data are shown in dark green, NMD-filtered data are shown in orange.
(C) Indel size. Original AS data are shown in dark green, NMD-filtered data are shown in orange.
(D) Overlap between AS and GD indels. Original data are shown in violet, dark blue, and light blue, while the corresponding NMD-filtered data are shown in yellow, orange, and light green.
(1.3 MB PPT)
Figure S7: Excluding Potential Database Biases
To exclude biases in our results introduced by the use of the SwissProt database [44] (dark green), we compared some of the findings with those obtained from using the ASAP database [62] (dark violet). The data are for human. Here we show the indel size distribution obtained using data from both databases. No obvious differences are found between the SwissProt and ASAP distributions that may affect the validity of our results.
(35 KB PPT)
Figure S8: Number of Exons per Gene
To obtain the number of exons per gene, we followed the procedure employed by Saxonov and colleagues to build the EID database [74]. For each sequence, we obtain the exon information from the corresponding NCBI's GenBank [75], looking at the CDS join feature.
Three distributions show the number of exons per gene, corresponding to the following cases: singleton genes with AS (AS+/GD−, dark green); genes that are both duplicated and have AS (AS+/GD+, light green), and duplicated genes with no AS (AS−/GD+, dark blue). The results are obtained for gene families at both the 80% level (A) and the 40% level (B). In both cases we see that there is a trend for AS−/GD+ to have a smaller number of exons than AS+/GD+ and AS+/GD− genes.
(525 KB PPT)
Figure S9: Computation of the Local Sequence Identity Between Gene Duplicates
We describe the two procedures followed to compute the local seq.id. between duplicates (see Materials and Methods).
(A) The first procedure is based on the use of a moving window the size, N, of the AS event. The window is moved along the aligned sequences of both duplicates, and at each position the seq.id. between them is computed (within the limits of the window).
(B) In the second procedure, we first aligned the sequence of the protein with known splicing to one of its duplicates. The former was always the sequence of the SwissProt [44] reference isoform. Then, we mapped location and length of the AS substituted stretch to the sequence of the duplicate and computed seq.id. between both sequence stretches.
(462 KB PPT)
Figure S10: Overview of the Expression Data Analysis
(A) Illustrates the basic comparisons of coexpression, whose results are shown in Figure 2C and and2D.2D. In Figure 2C, we compare coexpression amongst gene duplicates (GD coexpression) of GD families with and without alternative splice variants. Data on expression of gene duplicates come from two “conventional” datasets [53,76] (GDS596 and GDS1096) and the data from Johnson et al. (GDS829–834; (B)) [17]. Data on the existence of alternative splice variants is from the AltSplice database [43]. In Figure 2D, we compare coexpression amongst exon junctions [17], approximating the extent of AS (AS coexpression) of GD− families and singleton genes.
(B) In the datasets published by Johnson et al. [17], each of the 3,840 human genes is represented by a matrix of absolute expression values of all exon junctions across 44 different tissues. We estimate AS coexpression by analyzing the variation of expression values in each gene's matrix. The average expression value of all exon junctions across the different tissues forms a vector representing the gene's overall expression pattern.
For each gene family, we can produce a second matrix of gene expression patterns of the duplicates across different tissues. We estimate GD coexpression by analyzing the variation of expression values in each gene family's matrix. GD coexpression was analyzed for the dataset by Johnson et al. [17] and two conventional [53] gene expression datasets (see Table S4).
We tested the following measures for analysis of coexpression. (i) The average pairwise PC. We calculated average PC between each pair of row vectors in the AS or GD matrix. PC close to 0 indicates no correlation in expression between exon junctions (representing AS) or gene duplicates, respectively. PC close to 1 indicates strong correlation between the row vectors and is indicative of little AS or differential expression amongst gene duplicates. (ii) The number of unique binarized row vectors per matrix. To normalize for the number of exon junctions per gene or number of gene duplicates in a family, we divided the number of unique row vectors by the total number of row vectors per matrix. We also tested relative entropy RE as a measure of coexpression. We calculated the relative entropy RE (mutual information) for each AS or GD matrix as the sum of pobs*log2(pobs/pexp) calculated for each column, where pobs is the observed frequency of the exon junctions or gene duplicates in one column and pexp is the expected frequency of all exon junctions or gene duplicates across all experiments. However, relative entropy did not prove to be a useful measure of matrix variation in our case, as it did not capture differential expression patterns (row vectors) but only general entropy in the matrix.
While matrices in the figure show binary expression data, calculations were done on both raw and binary data. All results are similar irrespective of the cutoff for binarization (600 or 150). They are also similar irrespective of the cutoff for gene family definitions (40%, 60%, or 80% seq.id.) or of the underlying AS+ datasets employed (SwissProt or AltSplice).
(746 KB PPT)
Figure S11: Anticorrelation between Family Size and Percentage of Genes with AS
An anticorrelation between AS and GD [12,13] can also be produced using SwissProt [44] data.
(66 KB PPT)
Table S1: Genomic Data Overview
Provides an overview of the genomic data from the Ensembl database (human release 37.35j, mouse release 37.34e) [1] and the AS data from the AltSplice database (release 2.0 for human and mouse AS) [2], SwissProt [3], and from the Ensembl annotations.
(54 KB DOC)
Table S2: The Distribution of Single-Exon Genes across Human Sequences
Retrotransposition produces duplicates that consist of only one exon. To test for possible bias in families of gene duplicates (GD+) stemming from retrotransposition, we examined the distribution of single-exon genes across AS and GD sets using the SEGE database [1], similar to an approach described by Kopelman and colleagues [2]. The procedure followed was: (i) all human genes were clustered according to 80% seq.id.; (ii) all genes were labelled according to their known AS; and (iii) the number of single-exon genes [1] amongst singletons, gene families, and genes with/without AS was calculated.
(53 KB DOC)
Table S3: Function Analysis
The table lists a selection of functions as obtained from the DAVID Web server [1], for the four protein sets (AS+/GD+, AS+/GD−, AS−/GD+, AS−/GD−) derived from SwissProt (A) and AltSplice (B), using an 80% seq.id. threshold to estimate GD. A more general overview of GO functions and biological processes across the datasets is shown in Figure S2.
All function annotations are significantly different from the background (E-value < 10−10). We removed redundant annotations and annotations that were too broad to be meaningful (e.g., “binding”). Duplication of particular gene families that are depleted in AS, such as ribosomal proteins or some receptors, has contributed to the inverse relationship between AS and GD, but cannot explain it completely.
(96 KB DOC)
Table S4: Analysis of Expression Data
(375 KB DOC)
Table S5: Overview of the Dataset Employed in the Protein Sequence/Structure Analysis
The table shows the number of genes with AS, and the number of multiple gene families, together with the respective number of sequences. Information on AS was taken from SwissProt [1]. The data are provided for human and mouse, for 40% and 80% seq.id. clusters.
(50 KB DOC)
Accession Numbers
The accession numbers used in this paper are from Swiss-Prot (http://www.ebi.ac.uk/swissprot): rat Piccolo C2A Q9JKS6), human MAPK9 (P45984), MAPK10 (P53779), and MAPK13 (O15264); and from the Protein Databank (http://www.rcsb.org/pdb): MAPK10 (1jnk).
Acknowledgments
The authors are grateful to M. Brandl, J. Castresana, C. Chothia, K. Hannay, D. Kramer, M. A. Martínez-Balbás, A. Ortiz, J. Pereira-Leal, J. Valcárcel, and C. Voelckel for helpful comments on the manuscript, and H. Dopazo, J. Dopazo, and N. L. Barbosa-Morais for useful discussions. We are grateful to the SwissProt team for their support. We thank M. Carmo-Fonseca and T. R. Pacheco for kindly providing the U2AF35 sequences from their analysis. We are grateful to the anonymous reviewers whose suggestions led to valuable additions to our work. CV acknowledges funding by the Boehringer Ingelheim Foundation, the Medical Research Council, and the International Human Frontier of Science Program. XdlC and DT acknowledge funding from the Spanish government (grant BIO2003–09327).
Abbreviations
aaamino acid(s)
ASalternative splicing
AS−gene without known splice variants
AS+gene with splice variants
GDgene duplication
GD−gene without duplicates (as inferred from lacking sequence similarity to other sequences)
GD+gene with duplicates (inferred from sequence similarity to other sequences)
GD40gene families clustered at >40% sequence identitity
GD80gene families clustered at >80% sequence identity
indelinsertion/deletion
NMDnonsense-mediated decay
PCPearson correlation coefficient
seq.id.sequence identity

Footnotes
Author contributions. CV, SAT, and XdlC conceived and designed the experiments and wrote the paper. DT, CV, and MO performed the experiments. DT, CV, MO, SAT, and XdlC analyzed the data.
Funding. The authors received no specific funding for this study.
Competing interests. The authors have declared that no competing interests exist.
  • Lynch M, Conery JS. The evolutionary fate and consequences of duplicate genes. Science. 2000;290:1151–1155. [PubMed]
  • Chothia C, Gough J, Vogel C, Teichmann SA. Evolution of the protein repertoire. Science. 2003;300:1701–1703. [PubMed]
  • Koonin EV, Aravind L, Kondrashov AS. The impact of comparative genomics on our understanding of evolution. Cell. 2000;101:573–576. [PubMed]
  • Graveley BR. Alternative splicing: Increasing diversity in the proteomic world. Trends Genet. 2001;17:100–107. [PubMed]
  • Brett D, Pospisil H, Valcarcel J, Reich J, Bork P. Alternative splicing and genome complexity. Nat Genet. 2002;30:29–30. [PubMed]
  • Altschmied J, Delfgaauw J, Wilde B, Duschl J, Bouneau L, et al. Subfunctionalization of duplicate mitf genes associated with differential degeneration of alternative exons in fish. Genetics. 2002;161:259–267. [PubMed]
  • Dominguez M, Ferres-Marco D, Gutierrez-Avino FJ, Speicher SA, Beneyto M. Growth and specification of the eye are controlled independently by Eyegone and Eyeless in Drosophila melanogaster. Nat Genet. 2004;36:31–39. [PubMed]
  • Lister JA, Close J, Raible DW. Duplicate mitf genes in zebrafish: Complementary expression and conservation of melanogenic potential. Dev Biol. 2001;237:333–344. [PubMed]
  • Pacheco TR, Gomes AQ, Barbosa-Morais NL, Benes V, Ansorge W, et al. Diversity of vertebrate splicing factor U2AF35: Identification of alternatively spliced U2AF1 mRNAS. J Biol Chem. 2004;279:27039–27049. [PubMed]
  • Chothia C, Lesk AM. The relation between the divergence of sequence and structure in proteins. EMBO J. 1986;5:823–826. [PubMed]
  • Pascarella S, Argos P. Analysis of insertions/deletions in protein structures. J Mol Biol. 1992;224:461–471. [PubMed]
  • Kopelman NM, Lancet D, Yanai I. Alternative splicing and gene duplication are inversely correlated evolutionary mechanisms. Nat Genet. 2005;37:588–589. [PubMed]
  • Su Z, Wang J, Yu J, Huang X, Gu X. Evolution of alternative splicing after gene duplication. Genome Res. 2006;16:182–189. [PubMed]
  • Madera M, Vogel C, Kummerfeld SK, Chothia C, Gough J. The SUPERFAMILY database in 2004: Additions and improvements. Nucleic Acids Res. 2004;32:D235–D239. [PMC free article] [PubMed]
  • Neverov AD, Artamonova II, Nurtdinov RN, Frishman D, Gelfand MS, et al. Alternative splicing and protein function. BMC Bioinformatics. 2005;6:266. [PMC free article] [PubMed]
  • Pan Q, Shai O, Misquitta C, Zhang W, Saltzman AL, et al. Revealing global regulatory features of mammalian alternative splicing using a quantitative microarray platform. Mol Cell. 2004;16:929–941. [PubMed]
  • Johnson JM, Castle J, Garrett-Engele P, Kan Z, Loerch PM, et al. Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science. 2003;302:2141–2144. [PubMed]
  • Makova KD, Li WH. Divergence in the spatial pattern of gene expression between human duplicate genes. Genome Res. 2003;13:1638–1645. [PubMed]
  • Shortle D, Sondek J. The emerging role of insertions and deletions in protein engineering. Curr Opin Biotechnol. 1995;6:387–393. [PubMed]
  • Kondrashov FA, Koonin EV. Origin of alternative splicing by tandem exon duplication. Hum Mol Genet. 2001;10:2661–2669. [PubMed]
  • Bracco L, Kearsey J. The relevance of alternative RNA splicing to pharmacogenomics. Trends Biotechnol. 2003;21:346–353. [PubMed]
  • Tian W, Skolnick J. How well is enzyme function conserved as a function of pairwise sequence identity? J Mol Biol. 2003;333:863–882. [PubMed]
  • Wells JA. Additivity of mutational effects in proteins. Biochemistry. 1990;29:8509–8517. [PubMed]
  • Shoichet BK, Baase WA, Kuroki R, Matthews BW. A relationship between protein stability and protein function. Proc Natl Acad Sci U S A. 1995;92:452–456. [PubMed]
  • Topham CM, Srinivasan N, Blundell TL. Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitution and propensity tables. Protein Eng. 1997;10:7–21. [PubMed]
  • Ferrer-Costa C, Orozco M, de la Cruz X. Characterization of disease-associated single amino acid polymorphisms in terms of sequence and structure properties. J Mol Biol. 2002;315:771–786. [PubMed]
  • Kriventseva EV, Koch I, Apweiler R, Vingron M, Bork P, et al. Increase of functional diversity by alternative splicing. Trends Genet. 2003;19:124–128. [PubMed]
  • Wang P, Yan B, Guo JT, Hicks C, Xu Y. Structural genomics analysis of alternative splicing and application to isoform structure modeling. Proc Natl Acad Sci U S A. 2005;102:18920–18925. [PubMed]
  • Lopez AJ. Developmental role of transcription factor isoforms generated by alternative splicing. Dev Biol. 1995;172:396–411. [PubMed]
  • Goeke S, Greene EA, Grant PK, Gates MA, Crowner D, et al. Alternative splicing of lola generates 19 transcription factors controlling axon guidance in Drosophila. Nat Neurosci. 2003;6:917–924. [PubMed]
  • Boue S, Vingron M, Kriventseva E, Koch I. Theoretical analysis of alternative splice forms using computational methods. Bioinformatics. 2002;18(Supplement 2):S65–S73. [PubMed]
  • Lee C, Wang Q. Bioinformatics analysis of alternative splicing. Brief Bioinform. 2005;6:23–33. [PubMed]
  • Stetefeld J, Ruegg MA. Structural and functional diversity generated by alternative mRNA splicing. Trends Biochem Sci. 2005;30:515–521. [PubMed]
  • Hovmoller S, Zhou T. Why are both ends of the polypeptide chain on the outside of proteins? Proteins. 2004;55:219–222. [PubMed]
  • Garcia J, Gerber SH, Sugita S, Sudhof TC, Rizo J. A conformational switch in the Piccolo C2A domain regulated by alternative splicing. Nat Struct Mol Biol. 2004;11:45–53. [PubMed]
  • Papp B, Pal C, Hurst LD. Dosage sensitivity and the evolution of gene families in yeast. Nature. 2003;424:194–197. [PubMed]
  • Kondrashov FA, Rogozin IB, Wolf YI, Koonin EV. Selection in the evolution of gene duplications. Genome Biol. 2002;3:RESEARCH0008. [PMC free article] [PubMed]
  • Otto SP, Yong P. The evolution of gene duplicates. Adv Genet. 2002;46:451–483. [PubMed]
  • Seoighe C, Wolfe KH. Yeast genome evolution in the post-genome era. Curr Opin Microbiol. 1999;2:548–554. [PubMed]
  • Guillemaud T, Raymond M, Tsagkarakou A, Bernard C, Rochard P, et al. Quantitative variation and selection of esterase gene amplification in Culex pipiens. Heredity. 1999;83(Part 1):87–99. [PubMed]
  • Shakhnovich BE, Koonin EV. Origins and impact of constraints in evolution of gene families. Genome Res. 2006;16:1529–1536. [PubMed]
  • Kafri R, Bar-Even A, Pilpel Y. Transcription control reprogramming in genetic backup circuits. Nat Genet. 2005;37:295–299. [PubMed]
  • Thanaraj TA, Stamm S, Clark F, Riethoven JJ, Le Texier V, et al. ASD: The Alternative Splicing Database. Nucleic Acids Res. 2004;32:D64–D69. [PMC free article] [PubMed]
  • Bairoch A, Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000;28:45–48. [PMC free article] [PubMed]
  • Birney E, Andrews D, Caccamo M, Chen Y, Clarke L, et al. Ensembl 2006. Nucleic Acids Res. 2006;34:D556–D561. [PMC free article] [PubMed]
  • Wootton JC, Federhen S. Analysis of compositionally biased regions in sequence databases. Methods Enzymol. 1996;266:554–571. [PubMed]
  • Li W, Jaroszewski L, Godzik A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics. 2001;17:282–283. [PubMed]
  • O'Brien KP, Remm M, Sonnhammer EL. Inparanoid: A comprehensive database of eukaryotic orthologs. Nucleic Acids Res. 2005;33:D476–D480. [PMC free article] [PubMed]
  • Sakharkar MK, Kangueane P. Genome SEGE: A database for “intronless” genes in eukaryotic genomes. BMC Bioinformatics. 2004;5:67. [PMC free article] [PubMed]
  • Dennis G, Jr, Sherman BT, Hosack DA, Yang J, Gao W, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4:P3. [PubMed]
  • Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29. [PMC free article] [PubMed]
  • Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, et al. NCBI GEO: Mining millions of expression profiles—Database and tools. Nucleic Acids Res. 2005;33:D562–D566. [PMC free article] [PubMed]
  • Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 2004;101:6062–6067. [PubMed]
  • Bateman A, Birney E, Cerruti L, Durbin R, Etwiller L, et al. The Pfam protein families database. Nucleic Acids Res. 2002;30:276–280. [PMC free article] [PubMed]
  • Furnham N, Ruffle S, Southan C. Splice variants: A homology modeling approach. Proteins. 2004;54:596–608. [PubMed]
  • Valenzuela A, Talavera D, Orozco M, de la Cruz X. Alternative splicing mechanisms for the modulation of protein function: Conservation between human and other species. J Mol Biol. 2004;335:495–502. [PubMed]
  • Romero PR, Zaidi S, Fang YY, Uversky VN, Radivojac P, et al. Alternative splicing in concert with protein intrinsic disorder enables increased functional diversity in multicellular organisms. Proc Natl Acad Sci U S A. 2006;103:8390–8395. [PubMed]
  • Stamm S, Zhu J, Nakai K, Stoilov P, Stoss O, et al. An alternative-exon database and its statistical analysis. DNA Cell Biol. 2000;19:739–756. [PubMed]
  • Sugnet CW, Kent WJ, Ares M, Jr, Haussler D. Transcriptome and genome conservation of alternative splicing events in humans and mice. Pac Symp Biocomput. 2004. pp. 66–77. [PubMed]
  • Lewis BP, Green RE, Brenner SE. Evidence for the widespread coupling of alternative splicing and nonsense-mediated mRNA decay in humans. Proc Natl Acad Sci U S A. 2003;100:189–192. [PubMed]
  • Hillman RT, Green RE, Brenner SE. An unappreciated role for RNA surveillance. Genome Biol. 2004;5:R8. [PMC free article] [PubMed]
  • Lee C, Atanelov L, Modrek B, Xing Y. ASAP: The Alternative Splicing Annotation Project. Nucleic Acids Res. 2003;31:101–105. [PMC free article] [PubMed]
  • Goodman LA. On simultaneous confidence intervals for multinomial proportions. Technometrics. 1965;7:247–254.
  • Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol. 1970;48:443–453. [PubMed]
  • Henikoff S, Henikoff JG. Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A. 1992;89:10915–10919. [PubMed]
  • Cargill M, Altshuler D, Ireland J, Sklar P, Ardlie K, et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet. 1999;22:231–238. [PubMed]
  • Xie X, Gu Y, Fox T, Coll JT, Fleming MA, et al. Crystal structure of JNK3: A kinase implicated in neuronal apoptosis. Structure. 1998;6:983–991. [PubMed]
  • Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 1993;234:779–815. [PubMed]
  • Benner SA, Cohen MA, Gonnet GH. Empirical and structural models for insertions and deletions in the divergent evolution of proteins. J Mol Biol. 1993;229:1065–1082. [PubMed]
  • Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004;32:D258–D261. [PMC free article] [PubMed]
  • Semon M, Duret L. Evolutionary origin and maintenance of coexpressed gene clusters in mammals. Mol Biol Evol. 2006;23:1715–1723. [PubMed]
  • Maniatis T, Reed R. An extensive network of coupling among gene expression machines. Nature. 2002;416:499–506. [PubMed]
  • Maniatis T, Tasic B. Alternative pre-mRNA splicing and proteome expansion in metazoans. Nature. 2002;418:236–243. [PubMed]
  • Saxonov S, Daizadeh I, Fedorov A, Gilbert W. EID: the Exon–Intron Database—An exhaustive database of protein-coding intron-containing genes. Nucleic Acids Res. 2000;28:185–190. [PMC free article] [PubMed]
  • Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res. 2006;34:D16–D20. [PMC free article] [PubMed]
  • Ge X, Yamamoto S, Tsutsumi S, Midorikawa Y, Ihara S, et al. Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics. 2005;86:127–141. [PubMed]
  • Fauchere JL, Charton M, Kier LB, Verloop A, Pliska V. Amino acid side chain parameters for correlation studies in biology and pharmacology. Int J Pept Protein Res. 1988;32:269–278. [PubMed]
Articles from PLoS Computational Biology are provided here courtesy of
Public Library of Science