Exon creation and establishment in human genes

Corvelo, André; Eyras, Eduardo

doi:10.1186/gb-2008-9-9-r141

Exon creation and establishment in human genes

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Published: 23 September 2008

Volume 9, article number R141, (2008)
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Exon creation and establishment in human genes

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André Corvelo^1,2 &
Eduardo Eyras^1,3

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Abstract

Background

A large proportion of species-specific exons are alternatively spliced. In primates, Alu elements play a crucial role in the process of exon creation but many new exons have appeared through other mechanisms. Despite many recent studies, it is still unclear which are the splicing regulatory requirements for de novo exonization and how splicing regulation changes throughout an exon's lifespan.

Results

Using comparative genomics, we have defined sets of exons with different evolutionary ages. Younger exons have weaker splice-sites and lower absolute values for the relative abundance of putative splicing regulators between exonic and adjacent intronic regions, indicating a less consolidated splicing regulation. This relative abundance is shown to increase with exon age, leading to higher exon inclusion. We show that this local difference in the density of regulators might be of biological significance, as it outperforms other measures in real exon versus pseudo-exon classification. We apply this new measure to the specific case of the exonization of anti-sense Alu elements and show that they are characterized by a general lack of exonic splicing silencers.

Conclusions

Our results suggest that specific sequence environments are required for exonization and that these can change with time. We propose a model of exon creation and establishment in human genes, in which splicing decisions depend on the relative local abundance of regulatory motifs. Using this model, we provide further explanation as to why Alu elements serve as a major substrate for exon creation in primates. Finally, we discuss the benefits of integrating such information in gene prediction.

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Background

It is well established that alternative splicing (AS) is a widespread mechanism responsible for increased protein diversity and complexity among eukaryotes. The importance of this mechanism in the regulation of gene function has raised the question of its role in the context of evolution. Recent studies separating exons by evolutionary ages have shown that species-specific exons are mostly alternatively spliced [1, 2] and previous analyses have shown that the converse seems to be the case, that is, many alternative exons are species-specific [3, 4]. Moreover, evolutionary rate measurements show differences between alternatively and constitutively spliced regions [5, 12]. These exons may occur as fortuitous additions to existing transcripts, but they confer an opportunity to explore new functions with negligible disruption of the usual protein function [3]. The study of the mechanisms by which these species-specific exons can appear and become established is therefore key for the understanding of splicing regulation.

Three main mechanisms have been identified as being responsible for the appearance of new exons: gene duplication events, tandem exon duplication events [13], and exaptation, whereby a genomic sequence that did not function as an exon becomes exonized. This last mechanism is mostly driven by transposable elements (TEs) in mammals [14–18]. In particular, Alu elements play a prominent role in exon creation in primates [19–21]. These elements have motifs that resemble splice sites as part of their consensus sequence, especially in the opposite orientation, which can become functional through specific mutations [22–24], allowing exonization of part of the element. RNA editing has also been identified as a mechanism triggering exon creation from Alu elements [25]. In this case, however, the splice site is not in the genomic sequence, but it is instead created during the RNA editing process.

The fact that species-specific exons are, in general, poorly included suggests that they mainly appear with weakly recognized splicing signals. In particular, this is the case for some examples of exonized Alu elements [20], for which the strength of the base pairing between the U1 snRNA and the functional 5' splice site of the Alu determines the level of inclusion [23]. Although alternative exons are generally associated with weaker splice sites compared to constitutive exons [26], the distributions of splice site scores for both types of exon greatly overlap, suggesting that the strength of the splice site alone cannot explain the observed differences in inclusion levels between species-specific and evolutionarily conserved exons. Indeed, splice sites are not the only signals governing the recognition of an exon. There are also splicing enhancers and silencers, which function as activators and repressors of the splicing mechanism, respectively. These can occur in exons as exonic splicing enhancers (ESEs) or silencers (ESSs), and in introns as intronic splicing enhancers or silencers. Many of these regulators have been identified using experimental [27] and computational [28, 29] methods, and recent analyses have recognized their changing role depending on their position along the exon or the intron [30, Full size image

Exon creation from repetitive sequences

Along with tandem duplication events [13], exonization of TEs is one of the most important mechanisms of exon creation [17, 35, 39, 40]. Therefore, we assessed the overlap between exons from the three age sets and TEs, considering as overlap the cases in which the TE covers at least one of the splice sites. We found that PS exons have a high density of TEs in their flanking intronic regions (Figure 2a) and about 43% of the cases overlap TEs (Table 1). On the other hand, MS and VO exons have a very low density of TEs in the proximal adjacent intronic regions (Figure 2b, c) and show negligible overlap of TEs with their splice sites. Additionally, excluding the eight cases in which the exon overlaps more than one TE, we found that for 116 (79.5%) of the PS exons overlap** TEs, the TE is in the opposite strand of the exon. Although Alu elements, unlike other TEs such as L1 and Long Terminal Repeat (LTR) retrotransposons [41], were not found to have a bias in the strand of insertion in human introns [40], we find that most of the Alu elements (88.3%) overlap** a PS exon occur in the strand opposite to the gene (anti-sense). In only 9 out of the 77 cases (11.7%) we found sense Alu elements, and in only 4 of these is the overlap complete. Moreover, the percentage of anti-sense cases for non-Alu TEs is 69.6%. This suggests that for TEs and, especially, for Alu elements, although insertion can potentially occur in either strand, exonization occurs mainly in the opposite strand. Interestingly, although we found no overlap in the MS set, we found 19 cases (less than 0.15%) in the VO set; many of these were simple-repeats (Table 1). More details about the type of TE overlap are given in Table A1 in Additional data file 1. Remarkably, more than 50% of PS exons do not overlap a TE and cannot be explained by tandem duplication, as those cases were discarded during the exon classification.

Table 1 Overlap with repetitive elements

Full size table

Analysis of the splicing regulatory content of exons

In order to understand the properties of the splicing regulatory content that determine the observed differences in inclusion between exon sets, we conducted an analysis of splicing cis-regulatory elements in exons and their flanking introns. For this analysis we used three sets of splicing regulatory elements (SREs): 666 ESE hexamers [4 shows that pseudo-exons have ERA values distributed around zero for all SREs tested (ESEcomb, -0.029; SRall, -0.006; and ESScomb, -0.055). On the other hand, all real exons show positive values for ESEs and negative for ESSs. In particular, PS exons show the closest ERA values to pseudo-INT, but they are still significantly different (Mann-Whitney, ESEcomb p = 1.45 × 10^-20, SRall p = 2.88 × 10^-8, and ESScomb p ≈ 0). Interestingly, we also observe differences between PS exons and MS/VO for two out the three SRE sets used. For ESEcomb and ESScomb, PS exons show lower absolute ERA values (0.164 and -0.302, respectively) than MS (0.284 and -0.499) and VO (0.258 and -0.387) (see Table A3 in Additional data file 1). Relative to SRall, no significant differences between age groups were observed. ERA was also calculated for the individual SRE sets (see Materials and methods for details). These results can be found in Figure A4 in Additional data file 1.

Focusing on MS and VO exons, we observe a surprising difference in the content of ESScomb motifs. VO exons present lower absolute ERA values than MS (Mann-Whitney, p = 3.06 × 10^-10). This result derives from the fact that VO exons show relatively higher exonic densities of ESSs (0.272) compared to MS (0.213), while for intronic content no significant differences were found (Table A3 in Additional data file 1). Also, VO exons show slightly lower exonic densities for ESEcomb with respect to MS (MS 0.665, and VO 0.633; Mann-Whitney, p = 4.56 × 10^-6). These results can be partially explained by the fact that VO exons have stronger splice sites. On the other hand, it also suggests that AS of VO exons may be more dependent on ESS content.

In order to understand if these regulatory elements were under different, possibly functional, constraints depending on the exon age, we investigated their conservation in the mouse orthologous exons (Figure 5). For this purpose, we have calculated the functional conservation score (FCS; see Materials and methods for detailed description) for all three SRE sets on both MS and VO exon sets. This measure reflects the fraction of nucleotides that are covered by motifs from the same SRE set in both human and mouse. This measure correlates with the percentage of sequence conservation but also takes into account cases where a substitution does not change the regulatory character of a region. In general, VO exons have higher FCS values compared to MS exons for ESEcomb (Mann-Whitney p = 8.42 × 10^-13), SRall (p = 4.64 × 10^-14) and ESScomb (p = 2.99 × 10^-16). Additionally, FCS is higher for ESEcomb than for ESScomb for both MS and VO exons (Mann-Whitney, p ≈ 0), which might reflect the importance of the conservation of the amount and position of ESEs in exons. In summary, although VO exons have lower density of ESEs, these are more conserved than in MS exons, indicating that ESE turnover is more frequent in MS compared to VO exons, in agreement with recent analyses [45]. Moreover, VO exons present a larger fraction of ESSs that are highly conserved, suggesting possible constraints due to AS regulation.

Interestingly, considering all exons from the three age groups, ERA values tend to increase for ESEs (ESEcomb and SRall) and decrease for ESSs (ESScomb). Figure 6 shows the mean ERA values plotted for bins of increasing EST inclusion levels. For ESEcomb (Figure 6a) and SRall (Figure 6b) we observe a consistent increase except at high EST inclusion levels, where SRall values slightly decrease. On the other hand, there is a consistent decrease for ESScomb at all EST inclusion levels (Figure 6c). Exonic and intronic densities do not show such gradients with EST inclusion levels (data not shown). Thus, inclusion levels seem to be determined by the local differences in the densities of motifs.

Study case: why Aluelements are a good substrate for exonization

It has been recently reported that all TEs have approximately the same exonization levels with the exception of Alu elements, which are almost three times higher than other TE families [40]. Additionally, the high number of Alu copies in the human genome and their propensity to accumulate in intronic regions[40] make this element the main source of new exons originating from TEs. It has been shown that in some cases, cryptic splice sites are enough to incorporate part of an Alu element in the mature transcript [22, 23] and that in other cases, specific splicing enhancers are needed for their inclusion [34]. We thus applied the ERA measure in order to understand which regulatory features, besides the presence of splice sites, may be responsible for the increased Alu exonization rate.

We compared the SRE densities between the subset of PS overlapped by Alu elements (PS-Alu) and a set of Alu pseudo-exons bigger than 80 bp (pseudo-Alu) (see Materials and methods for details). Figure 7a, b show the mean exonic and intronic densities of the two ESE sets considered (ESEcomb and SRall) for PS-Alu and pseudo-Alu. The mean exonic densities of ESEcomb and SRall for PS-Alu (0.597 and 0.649, respectively) were significantly higher (Mann-Whitney, p = 4.89 × 10^-12 and p = 9.78 × 10^-6) than the mean exonic densities for pseudo-Alu (0.514 and 0.593). Relative to ESScomb (Figure 7c), PS-Alu shows a mean value of exonic density of 0.150 while pseudo-Alu shows a mean value of 0.190 (Mann-Whitney, p = 1.09 × 10^-4).

Surprisingly, we observe the opposite behavior when considering adjacent intronic regions. The mean values of the intronic density of ESEs are significantly lower for PS-Alu when compared to pseudo-Alu (Mann-Whitney, ESEcomb p = 3.64 × 10^-4 and SRall p = 2.02 × 10^-5), while for ESScomb the mean density values are higher (Mann-Whitney, p = 1.12 × 10^-11). All these results suggest that ESEs and ESSs play a role in Alu exonization. In Figure 7d we can observe that for PS-Alu, the mean ERA values for ESEcomb and SRall distribute around positive values (0.276 and 0.177) while the ESScomb values tend to distribute around a negative mean (-0.625). The absolute values are significantly greater than those obtained for pseudo-Alu (Mann-Whitney, p = 8.26 × 10^-10, p = 1.31 × 10^-7 and p = 3.75 × 10^-10). Furthermore, the fact that ESScomb produces the greatest difference of means suggests that this sequence feature might be the main determinant in the exonization of Alu elements. Comparing PS exons overlapped and non-overlapped by Alus, we observe that the latter have higher exonic (0.247) and lower intronic (0.383) densities for ESScomb (Mann-Whitney, p = 6.29 × 10^-8 and p = 1.83 × 10^-4, respectively). Consequently, their absolute ERA mean values (-0.302) are lower than those observed for Alu overlapped exons and, surprisingly, lower than those observed for pseudo-Alu (-0.407) (Mann-Whitney, p = 3.94 × 10^-10 and p = 6.03 × 10^-5).

Finally, in order to test whether the found properties are Alu specific, we analyzed sets of pseudo-exons overlap** the other major families of mobile elements in the human genome: Long Interspersed Nuclear Elements (LINEs), LTRs, DNA transposons and non-Alu Short Interspersed Nuclear Elements (SINEs) (see Materials and methods for details). For each of these sets, we calculated the ERA distributions for the same SRE sets as before. As can be seen in Figure 7e, all the pseudo-exon sets show absolute ERA values close to zero. Moreover, they do not present the ERA pattern expected to favor exonization. Indeed, pseudo-exons overlap** DNA transposons and LINEs have negative ERA mean values for ESEcomb. The exception seems to be for LTR pseudo-exons, which have positive ERA values for ESEcomb and negative for ESScomb, but with very low absolute values. This suggests that the high rate of Alu exonization may simply be due to their lack of silencers.

Although Alu elements do not seem to have a strand bias inserting within introns in human genes, protein-coding exons are mostly created from anti-sense Alu elements [40]. In fact, we could only find 64 cases of sense Alu pseudo-exons. In comparison, we could find more than 30,000 Alu pseudo-exons with the Alu in anti-sense. This difference can be explained by the efficiency of the splice sites [22, 23], as sense Alu exons do not contain the strong poly-pyrimidine tract typical of anti-sense ones. Furthermore, most PS exons overlap** anti-sense Alu elements are normally 80 bp long or greater. These lengths correspond, in most cases, with the most commonly used splice sites created by the anti-sense Alu [46] (data not shown). In order to understand the differences in exonization levels, we compared the properties of these two under-represented cases, sense Alu exons and anti-sense Alu exons shorter than 80 bp, making use of pseudo-exons overlap** these elements: pseudo-exons overlap** and Alu in the same orientation (pseudoSS-Alu) and pseudo-exons smaller than 80 bp that overlap an Alu in the opposite strand (pseudoSH-Alu) (see Materials and methods for details). Interestingly, both sets have a different content of splicing regulatory motifs with respect to anti-sense Alu pseudo-exons (pseudo-Alu) bigger than 80 bp (Figure A5 in Additional data file 1). Even though pseudoSS-Alu shows for both sets of ESEs higher exonic densities with respect to the adjacent intronic regions (Figure A5A and A5B in Additional data file 1), no differences are observed for ESSs (Figure A5C in Additional data file 1). This leads to positive ERA values for ESEs (0.091 and 0.086) but close to zero values for ESSs (-0.023). On the other hand, pseudoSH-Alu shows negative ERA values for ESEs (-0.167 and -0.168) and close to zero mean ERA values (-0.040) for ESSs (Figure A5D in Additional data file 1). Thus, both pseudoSS-Alu and pseudoSH-Alu exons have ERA values for ESSs close to zero, as opposed to anti-sense Alu pseudo-exons and PS exons overlap** Alus, which have very large negative ERA values for ESSs. This suggests that the higher content ESSs make sense Alus and regions smaller than 80 bp within anti-sense Alus less prone to exonization.

Discussion

We have analyzed the regulatory requirements for exonization and how splicing regulation changes throughout the exon lifespan by comparing the splicing regulatory properties of human internal protein-coding exons classified into three age groups: primate specific (PS), mammalian specific (MS) and vertebrate and older exons (VO). Most of the PS exons are alternatively spliced and show low inclusion levels. We find only about 5% of PS exons to be constitutive, whereas previous analyses [1] report about 60% of exons to be constitutive in a PS set. This difference can be explained by the fact that our method is more stringent; hence it is less likely that older exons are misclassified as PS ones; and could also be due to the fact that we discarded exons that may have originated from tandem duplication events, which are copies of pre-existing exons and would be similar to older ones. Furthermore, we find that PS exons are more likely to maintain the reading frame, indicating an additional pressure to reduce their impact in protein-coding regions. This increased frequency of symmetric exons observed in the PS set, especially in highly included exons, is likely to be related to the fact that the isoform including the exon is a novel one. On the contrary, for MS and VO, lowly included exons are more frequently symmetric. This suggests that in these cases, or in a significant fraction of them, the ancestral form might have been constitutively spliced, having more recently become alternative. This provides extra evidence supporting the hypothesis that the appearance of novel isoforms is favored when their impact is reduced. In this scenario AS acts as a key player allowing the incorporation of novel regions in mature transcripts and resulting products, establishing a close relationship with the process of exon creation [3].

We have also investigated the splicing regulatory requirements for de novo exonization. We observed that real exons have significantly different content of regulatory elements compared with pseudo-exons. However, there are also significant differences in the flanking introns. Indeed, we observe significant differences in the adjacent intronic content of SREs that were originally classified as exonic. Intronic regions adjacent to real splice junctions present lower densities of ESEs and higher densities of ESSs when compared to regions adjacent to pseudo-exons. This does not necessarily imply that such motifs are active in these regions. However, these differences could be the result of a balance with other nearby regulatory elements.

As exonization is related to changes in the exonic and in the adjacent intronic regions, they should both be taken into account. Accordingly, we defined a single measure, ERA, which encapsulates the regulatory content of each exon and its flanking introns. We have shown that this measure can differentiate better real exons from pseudo-exons than the exonic or intronic densities alone. For the three motif sets used, ERA provides the best discriminatory power. We also found that ESEcomb and ESScomb, which are combined sets of ESEs and ESSs, respectively, performed better than the individual sets alone. Another result worth mentioning is the fact that these two computational defined sets, performed better than the experimentally determined SRall set. The fact that these two sets have been partly defined based on exon versus intron and exon versus pseudo-exon comparisons might favor their discriminative power when using exonic density as a factor. Interestingly, the same holds true for intronic density at a lower extent. Relative to a third set of SR protein binding sites (SRall), we observed that SF2/ASF binding motifs perform consistently better than SC35, SRp40 and SRp55 binding sites. We thus expect that ERA or any other measure that takes into account local differences in motif content will contribute to the improvement of current methods of splice site and exon prediction.

We observed that the difference in inclusion levels between the different exon age groups cannot be fully attributed to the splice site strength. Further studies on regulatory content have shown that PS exons have smaller differences in ESE motifs between exons and flanking introns than conserved exons, that is, they are more similar to pseudo-exons than to older exons. This indicates that a minimal amount of regulatory motifs is needed for exonization. Moreover, the greater difference in the local density of regulators for older exons means that they have acquired a consolidated set of regulators. In fact, our results indicate that the relative density of regulatory motifs increases with time, and at a higher rate in MS exons compared to VO exons. Additionally, we found that exons become more established, that is, exhibit higher inclusion, by acquiring more enhancers relative to the flanking introns and by increasing the density of silencers in introns relative to the exons they flank. This is ultimately reflected in the higher ERA absolute values obtained.

Our analyses suggest that the local sequence context in which the exon is located plays a role in how splicing is regulated. Although there is no direct experimental evidence of a mechanism in which the spliceosome senses the local densities of splicing motifs, there is plenty of evidence of how the relative abundance of motifs can determine the splicing phenotype. It has been shown previously that the density of motifs close to a splice site affects the splicing outcome [1]. In fact, it has been recently reported that exonization of TEs occurs more abundantly in UTRs [40]. Thus, new exons originating from TEs are accepted in protein-coding regions at a much lower rate than in UTRs. On the other hand, most of the new exons overlap** TEs have been found to introduce in-frame stop codons [40]. Many exonizations of TEs may occur as errors of the splicing mechanism, and are, therefore, less frequently included in the protein and, subsequently, are more often tolerated in UTRs. Since we started from a set of protein-coding exons, our PS exons are already part of an open reading frame, and can be considered as recently established, that is, have become accepted into the protein-coding region at low inclusion rates.

We observed that in most of the cases the Alu element overlaps the PS exon on the anti-sense strand, and that these are characterized by having a striking lack of silencers compared to the surrounding introns. As introns can be considered as regions with a basal density of splicing silencers [27, 29], the insertion of an anti-sense Alu therefore creates a local desert of splicing silencers in the intronic region into which they are inserted. Thus, the frequently observed Alu exonization might not only stem from the presence of optimal splice sites, but also from the creation of an environment favorable for exonization. Interestingly, Alu pseudo-exons with overlap on the sense strand and those in anti-sense shorter than 80 bp have over-representation of ESSs in the exonic region, providing a possible explanation as to why they are not so frequently exonized.

In the human genome there are around one million Alu copies, 66% of which accumulate in intronic regions[40]. We found approximately 256,000 Alu pseudo-exons with splice sites scoring above the first quartile of the distribution of scores for real splice sites, which fall within an intron flanked by protein-coding exons, and for which there is no evidence of exonization from ESTs, cDNAs or proteins. From these pseudo-exons, 15,048 (5.9%) are bigger than 80 bp, have a length multiple of three and have no stop-codons in frame. Moreover, 6795 (45,1%) of these are conserved in chimp and macaque with conserved flanking AG and GT dinucleotides. One possible reason why these conserved Alu pseudo-exons do not appear to be included in the mature transcript is because they have not been detected yet in EST/cDNA sequencing experiments. However, considering the extensive EST evidence that is available for human, one can assume that most of these pseudo-exons are, in fact, silenced or are not recognized by the spliceosome. After analyzing the regulatory content of these candidates, we observed that the ERA values differ strikingly from the Alu exons in all sets of SREs, suggesting that insufficient difference in density of SREs between the potential exon and corresponding flanking introns prevent their exonization (Table A4 in Additional data file 1). This provides further support to the idea that a minimum regulatory content is required for de novo exonization.

Conclusions

Our results suggest that specific sequence environments might be required for exonization. Namely, regions with lower ESS content contrasting with the surroundings may be more prone to exonization. Also, exon creation may require the acquisition of a sufficient number of ESEs. All this supports the notion that de novo exonization is more likely to occur when there is a sufficient difference in the density of splicing regulatory elements on either side of optimal splice sites. This, in fact, suggests a mechanism of exon creation and establishment in human. New exons appear with low inclusion level, as they do not have a sufficient amount of ESEs. In this context, Alu elements play a crucial role in de novo exon creation in primates. With time, the establishment of an exon is determined by the accumulation of ESEs. In parallel, the lack of ESSs plays an important role in distinguishing an exon from the adjacent introns. This acquisition of regulatory elements along with the differentiation with respect to the intronic context determine the establishment of an exon in the mature transcript.

In summary, exon establishment is determined by the acquisition of splicing regulation at a local level and, as shown, this can be measured using a specifically devised measure, the ERA. This measure can, in fact, distinguish better real exons from pseudo-exons than exonic or intronic densities of splicing motifs alone. We therefore conclude that local differences in motif densities affect splicing decisions and, subsequently, the recognition of exons. We expect that measures that take these differences into account will provide an improvement on standard exon and gene prediction methods.

Materials and methods

Datasets

Gene annotations for Homo sapiens (NCBI36, Apr 2006), Mus musculus (NCBI m36, Apr 2006), Bos taurus (Btau 2.0, Dec 2005), Gallus gallus (WASHUC 1, Dec 2005) and Tetraodon nigroviridis (TETRAODON 7, Sep 2004), and orthologous gene pairs between these species were downloaded from Ensembl [54]. From the set of orthologs, only unique best reciprocal hits were kept. Genes that had ambiguous orthologous assignations, that is, linked to more than one potential orthologous sequence in the other genome, were eliminated. EST, mRNA and RepeatMasker map**s were retrieved from UCSC Genome Browser Database [55].

Alignment of exon-intron structures

Transcripts and coding sequences for each gene were projected onto the genomic sequence producing an array-like structure of genomic regions. These structures were then aligned between pairs of orthologous genes using information about the splice sites and exon phases. Orthologous genes from closely related species generally have high conservation of their exonic structure. Taking this into account, we performed comparisons between all splice sites from one gene against all those from its orthologue. A score was defined using the sequence identity between 40 nucleotides around the splice junctions (20 nucleotides upstream and 20 nucleotides downstream of each splice site) and the exon phase. All these scores were placed in a matrix, where every entry represents the score from the comparison of two splice sites from the orthologous gene pair. Subsequently, using a dynamic programming algorithm with this matrix, we identified the putative orthologous splice sites. This was done pair-wise between all five species. From this calculation we could detect orthologous exons and exons with potentially no orthologue in another genome.

Classification of exons according to evolutionary ages

We considered those exons with the following properties: internal, protein-coding, longer than 30 nucleotides and without 3' or 5' AS. This last condition was required to guarantee that both regions upstream and downstream of the exon are fully intronic. The flanking introns were also required to be longer than 30 nucleotides each. Additionally, only exons with canonical splice sites (AG/GT) were considered. These requirements were necessary for the correct analysis of the densities of regulatory sequences (see below). In order to obtain the exons belonging to the three different age classes, comparisons using three species were performed. If a particular exon was present in one species (reference species) and absent in the most closely related one (target), this could mean that either that exon was created in the reference species or that it was lost in the target one. To resolve this question a third species (more distantly related to the reference) was used as out-group, to infer if the exon was present in the common ancestor of the first two species. Three different age classes were defined: primate specific (PS), mammalian specific (MS) and vertebrate and older (VO). PS exons were defined as human exons that were not present in mouse or cow (strictly speaking, PS exons are human exons that are possibly also present in other primates). MS exons were defined as human exons conserved in mouse and cow, but not present in chicken or Tetraodon. Finally, VO exons were defined as human exons that are conserved in all the other four species. Exons that were aligned to orthologous exons were considered as conserved. Exons that did not have an alignment and were located between, but not necessarily adjacent to, conserved exons were considered to be candidates for PS or MS exons. These candidates were then compared with TBLASTN against the region in the orthologous genes spanned between the nearest alignable splice sites. If any significant result was produced (e-value < 0.0005), that exon was discarded. In this way we do not consider as non-conserved exons that are evolving at a faster rate. In order to reduce the possibility that the remaining exons could have been originated by segmental duplication, exons that showed more than 80% similarity over 40% of coverage with respect to other exons from the same gene were discarded. As a final filter, we only kept exons that were supported by EST or mRNA evidence. As the search uses very stringent criteria of sequence conservation, we do not expect the sizes of the obtained age groups to necessarily reflect the real number of exons belonging to these age categories in the human genome.

Pseudo-exon sets

In addition to the three age groups, we built sets of pseudo-exons overlap** and not overlap** TEs. Pseudo-exons are defined as intronic sequences of length comparable to exons, flanked by canonical splice sites and not present in any ESTs or cDNAs. Moreover, these have have a length multiple of three and with no stop codons in frame. Using the RepeatMasker annotations retrieved from UCSC Genome Browser Database [55], repetitive and repetitive-free regions were determined from intronic regions located between protein-coding exons. As we needed to score splice sites and obtain pseudo-exons of size 30 nucleotides or longer, we considered regions bigger than 56 nucleotides (20 on the acceptor side + 30 exonic + 6 on the donor side). Then, all the candidate splice sites in the sense strand that score above the first quartile of all human protein-coding exons were taken and all the pairs of acceptor and donor producing an exon bigger than 30 nucleotides were determined. Finally, we also applied filters to extract exons with a length multiple of three and that did not produce a stop codon in frame. We obtained a set of pseudo-exons not overlap** any TE (pseudo-INT) and five sets of pseudo-exons overlap** the four main classes of repeats (SINEs: pseudo-MIR and pseudo-Alu; LINEs: pseudo-LINE; DNA repeats: pseudo-DNA; and LTRs: pseudo-LTR).

Alu elements contain several possible 5' and 3' splice sites [22, 23]. However, not all are commonly used. The splice sites most generally used in exonized anti-sense Alus make up for exons of a size of around 80 bp and bigger [46]. From our PS set, 95% of exons overlap** Alus are of length 80 bp or longer. Accordingly, all pseudo-exons analyzed were taken to be 80 bp or longer. We also created two additional sets of pseudo-exons following the above defined criteria: pseudo-exons overlap** sense Alu elements (pseudoSS-Alu) and a set of short (smaller than 80 nucleotides) pseudo-exons overlap** anti-sense Alu elements (pseudoSH-Alu).

EST inclusion level

EST alignments were retrieved from UCSC Genome Browser Database [55] and compared with the Ensembl [54] annotations. For each exon, the percentage of EST inclusion level is defined as:

% inclusion = 100 \frac{N_{i}}{N_{i} {+ N}_{s}}

where N_i is the number of ESTs including the exon and N_s the number of ESTs that cover the genomic region of the exon but skip it. Only exons with N_i + N_s ≥ 10 were considered. Some exons have zero EST inclusion, as all the corresponding ESTs show exon skip**, but their existence is supported by mRNA and/or protein evidence.

Density of repetitive elements

RepeatMasker map**s overlap** exons and both upstream and downstream introns were retrieved from UCSC Genome Browser Database [55]. For the main four categories of elements (SINEs, LINEs, LTRs, DNA) we calculated the intronic densities as the fraction of cases where a particular type of element overlaps each base. Also, we tested whether exons belonging to different age groups overlapped any of these elements.

Splice site strength

We scored all splice sites using position weight matrices for the human donors and acceptors. We considered positions (-20 nucleotides to +3 nucleotides) relative to the acceptor site and (-3 nucleotides to +6 nucleotides) to the donor site.

Relative abundance of regulatory motifs

We used three sets of regulatory motifs: 666 ESE hexamers [42], which we call ESEcomb, built from the combination of 238 RESCUE-ESE hexamers [28] and 2,069 PESE octamers [29]; all possible words obtained from the four position-specific weight matrices for SR-protein binding sites from ESE-finder (SF2/ASF, SC35, Srp40 and Srp55) using the proposed thresholds [43], which we called SRall; and 386 ESS hexamers [42], which we called ESScomb and which were built from a combination of 176 FAS-ESS hexamers [27] and 974 PESS octamers [29]. Some of these sets were partially defined based on exon/intron and on exon/pseudo-exon enrichment [28, 29]. Further, we introduced a new measure called the ERA. For each exon, and for a given set of motifs, we define the value r, calculated from the density of motifs in the exon (density_exon) and surrounding intronic sequences (density_intron) as follows:

r = \frac{{density}_{exon} - {density}_{intron}}{\max ({density}_{exon} {,density}_{intron})}

where density_exon and density_intron are calculated as the fraction of positions covered by the motifs in an exonic and intronic sequence, respectively. To calculate exonic densities we considered the whole exon length and for the intronic densities we took 200 bp from adjacent intronic regions (100 on each side). The results did not differ when considering only the regions from both exon ends (Figure A6 in Additional data file 1). We did not take into account positions that are part of the splice site signals - namely, 3 exonic and 6 intronic for the donor site, and 3 exonic and 20 intronic for the acceptor site - as these are biased in sequence content. We considered only exons of at least 46 bp and with flanking introns of at least 126 bp. The analyses performed on the SRE sets were also performed on the individual sets from which they were built (see Additional data file 1 for further details).

Classification of real versus pseudo-exons

We considered two initial groups consisting of 10,000 real exons and 10,000 pseudo-exons not overlap** any TE. These were merged into a single group for assessment of classification accuracy based on SRE content. Three sets of SREs were taken (ESEcomb, SRall and ESScomb) and three different measures (exonic density, intronic density and ERA) were tested as real/pseudo-exon classifiers. A 10-fold cross-validation was performed by randomly splitting the initial set into 10 parts of equal size. Each of these parts was scored using the remaining nine as training data for determining the cut-off leading to the highest accuracy. The performance was determined by calculating the accuracy value obtained in the test set. Additionally, in order to estimate the performance of each classifier, for all possible cut-off values, false positive rates and true positive rates were determined for each subset and ROC curves and AUCs were calculated.

SRE functional conservation score

For a given alignment of a human/mouse orthologous exon pair and a given SRE set, we calculate the FCS as defined in [11], that is, FCS = N/M, where N is the number of positions in the alignment that are covered by motifs in both species and M is the number of positions in the alignment that are covered in either human, mouse or both. FCS varies between 0 and 1, where 1 means that all bases covered by motifs in human are also covered by motifs in mouse; and 0 that none of the bases covered by motifs in one species is covered in the other.

Additional data files

The following additional data are available with the online version of this paper. Additional data file 1 contains all additional figures (Figures A1-6), additional tables (Tables A1-4) and corresponding captions. Additional data file 2 contains two tab separated files with table listings of the exons and Alu pseudo-exons used.

Abbreviations

AS:: alternative splicing
AUC:: area under the curve
ERA:: exonic relative abundance
ESE:: exonic splicing enhancer
ESS:: exonic splicing silencer
EST:: expressed sequence tag
FCS:: functional conservation score
FPR:: false positive rate
LINE:: long interspersed nuclear element
LTR:: long terminal repeat
MS:: mammalian specific
PS:: primate specific
ROC:: receiver operating characteristic
SINE:: short interspersed nuclear element
SRE:: splicing regulatory element
TE:: transposable element
UTR:: untranslated region
VO:: vertebrate and older.

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Acknowledgements

The authors would like to thank J Brosius for useful comments on the manuscript, M Plass (funded by the Spanish Health Institute Carlos III) for EST data handling and R Castelo (funded by the Spanish Ministry of Science) for the splice site position weight matrices. AC received support from the Graduate Program in Areas of Basic and Applied Biology (GABBA) and the Portuguese Foundation for Science and Technology. EE is supported by the Catalan Institution of Research and Advanced Studies (ICREA). This work is partly supported by the grant BIO2005-01287 from the Spanish Ministry of Science and by the project EURASNET from the European Commission.

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Computational Genomics, Universitat Pompeu Fabra, Dr. Aiguader 88, Barcelona, 08003, Spain
André Corvelo & Eduardo Eyras
Graduate Program in Areas of Basic and Applied Biology, Universidade do Porto, Praça Gomes Teixeira, Porto, 4099-002, Portugal
André Corvelo
Catalan Institution for Research and Advanced Studies, Passeig Lluís Companys 23, Barcelona, 08010, Spain
Eduardo Eyras

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Correspondence to Eduardo Eyras.

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AC and EE conceived the project and wrote the manuscript. AC carried out the analyses. All authors read and approved the final manuscript.

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Additional data file 1: Additional figures (Figures A1-6), and additional tables (Tables A1-4) (PDF 373 KB)

Additional data file 2: Exons and Alu pseudo-exons used. (ZIP 972 KB)

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Corvelo, A., Eyras, E. Exon creation and establishment in human genes. Genome Biol 9, R141 (2008). https://doi.org/10.1186/gb-2008-9-9-r141

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Received: 08 August 2008
Revised: 16 August 2008
Accepted: 23 September 2008
Published: 23 September 2008
DOI: https://doi.org/10.1186/gb-2008-9-9-r141

Exon creation and establishment in human genes

Abstract

Background

Results

Conclusions

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Background

Exon creation from repetitive sequences

Analysis of the splicing regulatory content of exons

Study case: why Aluelements are a good substrate for exonization

Discussion

Conclusions

Materials and methods

Datasets

Alignment of exon-intron structures

Classification of exons according to evolutionary ages

Pseudo-exon sets

EST inclusion level

Density of repetitive elements

Splice site strength

Relative abundance of regulatory motifs

Classification of real versus pseudo-exons

SRE functional conservation score

Additional data files

Abbreviations

References

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