Alu Exonization Events Reveal Features Required for Precise Recognition of Exons by the Splicing Machinery
How are short exons , embedded within vast intronic sequences , precisely recognized and processed by the splicing machinery ?, Despite decades of molecular and bioinformatic research , the features that allow recognition of exons remain poorly understood ., Various factors are thought to be of importance ., These include the splicing signals flanking the exon at both ends , known as the 5′ and 3′ splice sites ( 5′ss and 3′ss , respectively ) , auxiliary cis-elements known as exonic and intronic splicing enhancers and silencers ( ESE/Ss and ISE/S ) that promote or repress splice-site selection , respectively 1 , 2 , and exon 3 and intron length 4 ., There is an increasing body of evidence that secondary structure is a powerful modifier of splicing events 5–12 ., Secondary structure is thought to present binding sites for auxiliary splicing factors , correctly juxtapose widely separated cis-elements , and directly affect the accessibility of the splice sites ., However , only very few studies have used bioinformatic approaches to broadly study the effects of secondary structure on splicing 13–15 ., Many of the above-listed factors have been subjected to analysis in the context of comparison between constitutively and alternatively spliced exons ., It has been found , for example , that constitutively spliced exons are flanked by stronger splicing signals , that they contain more ESEs but fewer ESSs , and are longer but flanked by shorter introns with respect to their alternatively spliced counterparts ( reviewed in 16 ) ., However , to what extent do these features contribute to the selection of exons and allow discrimination between true exons and “non-exons” , i . e . sequences resembling exons but not recognized by the splicing machinery ?, This question is fundamental for understanding the process of exon selection by the spliceosome , and yet has not been subjected to much analysis ., This is presumably because unlike alternatively and constitutively spliced exons , both of which are relatively easy to define computationally , defining a non-exon or a pseudo-exon is more of a challenge ., One approach is to compare exons to sequences of up to a certain length which are flanked by splicing signals exceeding a certain threshold 17 , 18 ., Although this approach is powerful and has contributed to the discovery of the “vocabulary” of exons , it is also limited ., The primary limitation is that it is circular: For the mere definition of pseudo-exons , we are forced to fix various features—such as minimal splice site strength and exon length—that we would prefer to infer ., To circumvent these obstacles , we have studied Alu exonization events ., Alu elements are primate-specific retroelements present at about 1 . 1 million copies in the human genome ., A large portion of Alu elements reside within introns 19 ., Alus are dimeric , with two homologous but distinct monomers , termed left and right arms 20–22 ., During evolution , some intronic Alus accumulated mutations that led the splicing machinery to select them as internal exons , a process termed exonization 23–25 ., Such exonization events may occur either from the right or the left arm of the Alu sequence , but are observed predominantly in the antisense orientation relative to the mRNA precursor ., Almost invariably , such events give birth to an alternatively spliced exon , as a constitutively spliced exon would compromise the original transcriptomic repertoire and hence probably be deleterious 19 , 24 , 26 , 27 ., The fact that exonizing and non-exonizing Alus have retained high sequence similarity but are perceived as different by the splicing machinery makes them excellent candidates for studying the factors required for precise recognition of exons by the spliceosome ., The natural control group of non-exonizing Alus obviates the need to fix different parameters in the control set , and the high degree of sequence similarity shared by all Alus , regardless of whether they do or do not undergo exonization , enables direct comparison of a wide array of features ., Based on the comparison between Alu exons and their non-exonizing counterparts , we were able to identify several key features that characterize Alu exons and to determine the relative importance of these features in the process of Alu exonization ., A novel result of this comparison was the importance of pre-mRNA secondary structure: More thermodynamically stable predicted secondary structure in an Alu arm harboring a potential Alu exon decreases the probability of an exonization event originating from this Alu ., Thus , this study is among the first to provide wide-scale statistical proof of the importance of secondary structure in the context of exon selection ., We identified numerous further factors differentiating between Alu exons and non-exons , and integrated them in a machine learning classification model ., This model displayed a high performance in classifying Alu exons and non-exons ., Moreover , the strength of predictions by this model correlated with biological inclusion levels , and higher probabilities of exonization were given by the model to constitutive exons than to alternative ones ., These findings indicate that the features identified in this study may form the basis for precise exon selection , and make the difference between a non-selected element , an alternatively-selected element , and a constitutively selected one ., We set out to determine the features underlying the recognition of Alu exons by the splicing machinery ., We therefore required datasets of Alus that undergo and that do not undergo exonization ., We took advantage of the fact that Alu elements may exonize either from the right or from the left arm , and composed three core datasets ( Figure 1A ) : ( 1 ) A dataset of 313 Alu exons ( AEx ) that are exonized from the right Alu arm , termed AEx-R; ( 2 ) A dataset of 77 Alus that undergo exonization in the left arm , termed AEx-L; ( 3 ) A dataset of 74 , 470 intronic Alus lacking any evidence of exonization , called No AEx ., In all these datasets , Alus had to be embedded in the antisense orientation within genes , since most exonization events of Alus occur in this orientation 19 , 23 , 28 ., Finally , to allow direct comparison between parallel positions in different Alus , we used pairwise alignments to align each Alu in each of the datasets against an Alu consensus sequence ., We next computationally searched for the optimal borders , or splice sites , of non-exons within both the right arm and the left arm of the sequences in the No AEx dataset ., This was done in two steps: ( 1 ) We first empirically determined the positional windows in which the selected 3′ss and 5′ss appeared within exonizing Alus; ( 2 ) We next searched the above-determined positional windows for the highest scoring splicing signals ( see Materials and Methods ) ., We found that computational selection of the highest scoring splicing signal yielded a high extent of congruence ( ranging between 74%–96% , depending on the arm and on the signal ) with the “true” splicing signal based on EST data ., Since the congruence was not perfect , we created two control datasets based on the AEx-R and AEx-L group , termed AEx-R ( c ) and AEx-L ( c ) , respectively , in which exon borders were searched for computationally as in the No AEx dataset ., These two subsets were used to verify that differences between the exonizing and non-exonizing datasets were not due to the manner in which exons and non-exons were derived ( ESTs versus computational predictions ) ., To complete the picture , we computationally searched for non-exons within the right arm of the AEx-L group and in the left arm of the AEx-R group ., Notably , we demanded that all exons within all datasets have a minimal potential 3′ss ( AG ) and 5′ss ( GT/GC ) , because lacking such minimal conditions Alus cannot undergo exonization at all ., Thus , our analyses are based on three core and two control sets of Alus with two sets of start and end coordinates mapped for each Alu—one in the right arm and one in the left ( see Materials and Methods for further details ) ., Previous studies , based on much smaller datasets , implicated the 3′ss 24 and the 5′ss 26 splicing signals as major factors determining exonization events ., To assess whether this held for our dataset as well , we calculated the strength of the 5′ss and 3′ss of the exons/non-exons in the right and in the left arms in each of the five datasets ., Indeed , we found that in the right arms the 3′ss and the 5′ss scores were highest among those Alus that underwent exonization ( Figure 1B and 1C , respectively ) ., Similarly , in the left arms , the scores of the 3′ss and the 5′ss are highest among the exonizing Alus ( Figure 1D and 1 E , respectively ) ., These results were highly statistically significant ( see Text S1 ) ., Moreover , these differences are even more pronounced when comparing the two control datasets to their non-exonizing counterparts ( compare the results for AEx-R and AEx-L to AEx-R ( c ) and AEx-L ( c ) , respectively , in Figure 1B–E ) ., Thus , these analyses fit in with previous analyses emphasizing the role of the two major splicing signals ., We were interested in assessing the role of secondary structure in the context of Alu exonization events ., We therefore began by computing the thermodynamic stabilities of the secondary structures predicted for the Alus in each of the core datasets ., We used RNAfold 29 to calculate the secondary structure partition function; but rather than use this metric directly , we used a dinucleotide randomization approach to yield a Z-score that is not sensitive to sequence length or nucleotide composition ( see Materials and Methods ) ., We found that Alus that gave rise to exonization events , regardless of whether from the left or from the right arm , were characterized by weaker secondary structures than Alus that do not undergo exonization ( Figure 2A ) ., This was highly significant in the case of exonizations originating from the right arm ( AEx-R vs . No AEx p\u200a=\u200a9 . 8E−12 ) and of borderline significance for the left arm exonizations ( AEx-L vs . No AEx p\u200a=\u200a0 . 07 ) ., This provided the first indication that strong secondary structures might prevent Alu exonizations ., To pinpoint the subsequences to which the differences in strength of secondary structure could be attributed , we next calculated secondary structure Z-scores for each of the two Alu arms separately ., We found that the secondary structures of right and the left arms were weakest in cases in which these arms undergo exonization ( Figure 2B and 2C , respectively ) ., These changes relative to the No AEx group were highly significant ( p\u200a=\u200a2E−15 and p\u200a=\u200a1 . 08E−5 , respectively ) ., Interestingly , the non-exonizing arm tended to have weaker secondary structure in those cases in which the opposite arm underwent exonization ( p\u200a=\u200a0 . 001 when comparing the left arm of the AEx-R to the No AEx dataset , and p\u200a=\u200a0 . 055 when comparing the right arm of the AEx-L to the No AEx dataset ) ., These observations suggested that secondary structures have a detrimental effect on the recognition of Alu exons primarily when the structure incorporates sequence from the exon itself , but also when stable structures are located in relative proximity to the exon ., Secondary structure has been shown to impair exon recognition by affecting the accessibility of splice sites 8 , 9 , 11 , 12 , 30 ., To examine whether sequestration of splice sites within secondary structures plays a role in the context of Alu exonizations , we used a measure indicating the probability that all bases in a motif are unpaired ( denoted probability unpaired or PU value ) 31 ., Briefly , this measure indicates the probability that a motif , located within a longer sequence , is participating in a secondary structure ., Higher values indicate that the motif is more likely to be single stranded and lower values indicate a greater likelihood of participating in a secondary structure ( see Materials and Methods ) ., We assessed the single strandedness of the two most frequently selected 5′ss in the right arm located at positions 156 and 176 relative to the consensus ( also termed sites B and C 28 ) and the most frequently selected 5′ss of the left arm , located at position 291 ( see Figure 2A ) ., We found that 5′ss selected in exonization events are characterized by significantly higher PU values than their non-exonizing counterparts , indicating that selected 5′ss have a lower tendency to participate in secondary structures ( see Figure 2E–G ) ., We repeated this analysis for the two most frequently selected 3′ss in the right arm and the most frequently selected 3′ss in the left arm , but did not observe higher single-strandedness in the selected 3′ss with respect to their non-selected counterparts ( data not shown ) ., However , this finding may also be attributed to the fact that all Alus , regardless of whether they undergo exonization or not , are characterized by relatively strong 3′ss , due to the poly-T stretch characterizing them ( see Discussion ) ., See Text S1 for description of a control analysis ., Intron-exon architecture has well-documented effects on splicing ., Therefore , we compared the lengths of the Alu exons to their counterpart non-exons ( diagram in Figure 3A ) ., We found that exons were ∼10 nt longer than their non-exonizing counterparts ( Figure 3C and 3D ) ., Exons in the right arm of the AEx-R dataset were 112 nt long , on average , whereas non-exons were only 102 nt long in the No AEx dataset ., The same trend was observed in the AEx-L dataset: Exons in the left arm of the AEx-L dataset were 88 nt long , whereas the non-exons in the No AEx group were 78 nt long ., In both cases , the differences were highly statistically significant ( see Text S1 ) ., This indicates that increased exon length is an advantage in terms of exonization of Alu elements ., Analyzing the lengths of the flanking introns , we found that introns flanking Alu exons were almost 50% shorter than those flanking their non-exonizing counterparts ., Introns upstream of Alu exons in the AEx-R or AEx-L dataset were 7 , 216 and 9 , 497 nt long , respectively , on average ( Figure 3B ) , but 14 , 458 nt long upstream of the non-exons in the No AEx group ., These differences were highly significant ( No AEx vs . AEx-R p\u200a=\u200a1 . 38E−13 , No AEx vs . AEx-L p\u200a=\u200a0 . 0047 ) ., Highly significant findings were observed in the downstream intron as well ., These introns were 7 , 844 and 9 , 210 nt long for exons in the AEx-R and AEx-L dataset , respectively , but 14 , 808 nt long for Alus in the No AEx dataset ( Figure 3E ) ., Taken together , these results indicate that recognition of exons by the splicing machinery correlates positively with exon length but negatively with intron length , yielding insight on the constraints and the mechanism of the splicing machinery ( see Discussion ) ., Based on both biologic and bioinformatic methodologies , datasets of exonic splicing enhancers ( ESEs ) and silencers ( ESSs ) have been compiled; these sequences are believed to increase or decrease , respectively , the spliceosomes ability to recognize exons ., Indeed , exons were found to be enriched in ESRs with respect to pseudo-exons or exons 32–34 ., Thus , our next step was to determine the densities of ESEs and ESSs in exons and non-exons ., We made use of four datasets of exonic splicing regulators ( ESRs ) : the groups of SR-protein binding sites in ESEfinder 35 , the dataset of ESEs from Fairbrother et al . 36 , the exonic splicing regulatory sequences compiled by Goren et al . that consists mostly of ESEs 37 , and the ESS dataset compiled by Wang et al . 38 ., For each exon ( or non-exon ) in the two Alu arms ( Figure 4A ) in the three core and two control datasets , we calculated the ESR density for the four groups of ESRs ., The ESR density was calculated as the total number of nucleotides within an exon that overlap with motifs from a given dataset divided by the length of the exon ., We found that Alu exons showed a marked tendency for enrichment in ESEs and depletion in ESSs with respect to their non-exonizing counterparts ., Right arm Alu exons had significantly higher densities of ESEfinder ESEs than their counterparts in the No AEx group ( Figure 4B , p\u200a=\u200a0 . 00007 ) and higher densities of ESEs from Fairbrother et al . ( Figure 4C , p\u200a=\u200a0 . 00009 ) ., Higher densities were also observed in terms of ESEs found in Goren et al . ( Figure 4D ) , whereas slightly lower densities were observed for the ESSs of Wang et al ( Figure 4E ) ; However , the trends for the latter two datasets were not statistically significant ., In the left arms , similar tendencies were observed: Exons originating from this arm were highly enriched in ESEs of Goren et al . ( Figure 4H , p\u200a=\u200a0 . 0001 ) and depleted in ESSs of Wang et al . ( Figure 4I , p\u200a=\u200a0 . 0003 ) ., They also tended to be enriched in ESEs of Fairbrother et al . ( Figure 4G ) , although this was not significant ( p\u200a=\u200a0 . 12 ) ; and in this arm no differences were found in terms of ESEs of ESEfinder ( Figure 4F , p\u200a=\u200a0 . 72 ) ., To summarize , in all cases in which significant differences were observed , these differences reflect an increase in ESE densities in parallel with a decrease in ESS densities in exons relative to non-exons ., Since the splicing machinery is able to differentiate between exonizing and non-exonizing Alus , we were interested in discovering whether the features identified here can give rise to such precise classification ., Toward these aims , we used Support Vector Machine ( SVM ) machine learning , which has shown excellent empirical performance in a wide range of applications in science , medicine , engineering , and bioinformatics 39 ., We created two classifiers: One discriminating between non-exonizing Alus and Alus exonizing from the right arm and one discriminating between non-exonizing Alus and Alus exonizing from the left arm ., Receiver-operator curves ( ROC curves ) were used to test performance ., Briefly , ROC curves measure the tradeoff between sensitivity and specificity of a given classification ., A perfect classification with 100% sensitivity and 100% specificity will yield an area under the curve ( AUC ) of 1 , whereas a random classification will yield an AUC of 0 . 5 ( see Materials and Methods for complete details of the SVM protocol used ) ., 14 features were selected for the machine learning ., These were divided into 5 clusters: 5′ss strength ( 1 feature: 5′ss score ) , 3′ss strength ( 1 feature: 3′ss score ) , secondary structure ( 5 features: z-scores for the stability of secondary structure of the entire Alu and of each of the two Alu arms , PU values of the 5′ss , and PU values of the 3′ss ) , exon-intron architecture ( 3 features: lengths of upstream intron , of Alu exon , and of downstream intron ) , and ESRs ( 4 features: density in terms of each of the 4 groups of ESRs ) ., Based on the above-described features , we were able to achieve a high degree of classification between exonizing and non-exonizing Alus ., Figure 5A presents the ROC curves and AUC values for the classification between Alus exonizing from the right arm and non-exonizing Alus and Figure 5B presents these values for the classification between the Alus exonizing from the left arm and the non-exonizing ones ., The AUC values of ∼0 . 91 , demonstrate that our features achieve a high degree of accuracy in discriminating between true exons and non-exons , thus mimicking the role of the splicing machinery ., If selection of an Alu exon is indeed determined by this set of features , then this same set of features may well also determine the inclusion level of an Alu exon ., A “strong” set of features will lead to a high selection rate by the spliceosome , and hence to high inclusion levels , whereas “weaker” features may lead to a more reduced selection rate by the spliceosome and to lower inclusion levels ., Indeed , we found a positive , highly significant correlation between probabilities of exonization based on the SVM model and between inclusion levels of exons based on EST data in the case of right arm Alu exons ( Pearson , r\u200a=\u200a0 . 28 , p\u200a=\u200a6 . 35e−07 ) ., For the sake of comparison , the correlation between 5′ss scores and inclusion levels is considerably lower and less significant ( r\u200a=\u200a0 . 15 , p\u200a=\u200a0 . 007 ) ., Thus , although the computational model was explicitly trained on the basis of a dichotomous input ( Alus were labeled either as exonizing or as non-exonizing ) , the model managed to capture the more stochastic nature of the spliceosomal recognition of exons ., A positive correlation existed in the left arm as well , but this correlation was not significant presumably due to the fewer number of Alus in the AEx-L dataset ., Although our model was trained on Alus , and specifically on comparing non-exonizing Alus to mostly alternatively recognized Alus , we reasoned that the same set of features which make the difference between a non-recognized and an alternatively-recognized Alu exon might also make the difference between an alternatively recognized exon and a constitutively recognized one ., We therefore applied the SVM model to datasets of constitutive and cassette exons ., For this purpose , we generated a dataset of 55 , 037 constitutive and 3 , 040 cassette exons based on EST-data ( see Materials and Methods ) ., For each of these exons , we first extracted all above-described features , and then applied the SVM model to them ., Our model classified constitutive and alternative exons as different in a highly statistically significant manner ., The mean probability of undergoing exonization , provided by the logistic regression transformed SVM model , was 73% for the constitutive exons , but only 60% for the alternative ones ( Mann-Whitney , p<2 . 2e−16 ) ., In addition , 82% of the constitutive exons were classified as “exonizing” , in comparison to only 63% of the alternative exons ., These results demonstrate that the features learned by the SVM model are relevant for exonization in general , and control not only the shift of non-exons to alternative ones , but also of alternative exons to constitutive ones ., Finally , we were interested in assessing the importance of different features in allowing correct discrimination between exonizing and non-exonizing elements ., For this purpose , we used ΔAUC to measure the contribution of each feature cluster ., This measure compares the performance of the classification with and without each cluster of features , with greater differences indicating greater contribution of a given cluster of features to precise classification ., The feature with the highest contribution , both in the right arm ( Figure 5C ) and in the left arm ( Figure 5D ) , was the strength of the 5′ss , in concordance with previous bioinformatic findings 26 ., However , much information is included in the other features as well ., The second most important feature both in the left and in the right arm was exon-intron architecture ., Secondary structure and the 3′ss had a comparable contribution in the right and left arm ., Despite the differences in terms of ESR densities between the different datasets , this feature cluster had a negligent contribution to classification in the right arm , and a slightly higher one in the left arm ., Using a mutual information based metric to measure the contribution of the different features , yielded similar , consistent results ( see Text S1 ) ., In this study , we sought to determine how the splicing machinery distinguishes true exons from non-exons ., Alu exonization provided a powerful model for approaching this question ., Exonizing Alus have retained high sequence similarity to their non-exonizing counterparts but are perceived differently by the splicing machinery ., Past studies have emphasized mainly the splice sites , but our results indicate the importance additional features that lead to exonization ., These features , which include splicing signals ( splice sites and ESRs ) , exon-intron architecture , and secondary structural features , achieved a high degree of classification between true Alu exons and non-exons , demonstrating the biological relevance of these layers in determining and controlling exonization events ., Perhaps the most interesting result to emerge from this study is that secondary structure is critical for exon recognition ., It has been assumed that pre-RNA is coated in vivo by proteins 10 and that these RNA-protein interactions either prevent pre-mRNAs from folding into stable secondary structures 40 or provide pre-mRNAs with a limited time span for folding 41 ., However , an increasing number of studies are finding that secondary structure plays a crucial role in the regulation of splicing ., Secondary structures involving entire exons ( e . g . , 5–7 ) , the splice sites only ( e . g . , 8 , 11 , 12 ) , or specific regulatory elements 42 , 43 were shown to be involved in the regulation of alternative splicing ., Hiller et al . 14 recently found that regulatory elements within their natural pre-mRNA context were significantly more single stranded than controls ., Our current study puts these findings into a broad context , and provides bioinformatic evidence for the notion that the structural context of splicing motifs is part of the splicing code ., Such a structure , as we have shown , is detrimental for exonization in general , and specifically if it overlaps the 5′ss ., Several intriguing observations can be made when merging our results based on the exonizing and non-exonizing Alus with those of the alternative and constitutive datasets ., In terms of inclusion level , these four groups form a continuum , with non-exonizing Alus having a 0% inclusion level , exonizing Alus having a mean inclusion level of 10% , cassette exons having a mean inclusion level of 25% , and constitutive exons being included in 100% of the cases ., Gradual changes when moving from non-exonizing Alus , to exonizing Alus , to alternative exons , to constitutive ones are observed in several additional features: The strength of the 5′ss gradually increases from non-exonizing Alus to constitutive exons , the strength of the secondary structure gradually decreases , lengths of the upstream and downstream introns gradually decrease while length of the exons gradually increase ( see Figure 6 for detailed values ) ., These gradual changes are all coherent in biological terms: Stronger 5′ splice sites allow higher affinity of binding between the spliceosomal snRNAs and the 5′ss , and have well documented effects in increasing exon selection 28 , 44; stronger secondary structure can sequester binding sites of spliceosomal components; And it has been previously shown that longer flanking introns profoundly increase the likelihood that an exon is alternatively spliced 4 , and that alternative exons tend to be shorter than their constitutive counterparts ( reviewed by 16 ) , presumably due to spliceosomal constraints ., In addition , our finding that selective constraints are simultaneously applied both on the lengths of the exons and of their flanking introns suggests that the exon and its flanking introns are recognized , to some extent , as a unit ., This challenges the more traditional exon-definition and intron-definition models 3 , 45 , according to which either the exon , or its flanking introns , but not both , are recognized by the splicing machinery ., Notably , in our search for features differentiating between exonizing and non-exonizing Alus , we focused only on features which can potentially be mechanistically employed by the splicing machinery to differentiate between exons and introns ., For this reason , we did not use phylogenetic conservation , nor the age of the Alu exons , nor the location of the exonization event ( CDS vs . UTR ) as features ., Although these features are informative as well ( see Text S1 , and 32 ) , and thus may potentially boost the performance of our classifier , these cannot be directly sensed by the spliceosome ., Rather , these elements reflect the evolutionary pressures to which an exonizing Alu element is subjected ., In our study we found that introns flanking exonizing Alus are dramatically shorter than the introns flanking their non-exonizing counterparts ., These results appear to contradict recent results 46 according to which there is a tendency for new exons to form within longer introns ., However , two points must be borne in mind in this context: First , the introns flanking exonizing Alus are longer than average introns , and thus our results are consistent with the above study in that exonizations occur in longer introns ., Second , our findings may reflect an upper bound in terms of intron length within which exonization optimally occurs , and introns longer than a certain threshold may cease to be good candidates for exonization ., Our results indicate that the Alu-trained model could be applied to a more general context of alternative and constitutive exons , where it yielded coherent results ., This does not , however , imply that all findings made in the context of Alus can be directly extrapolated to exons in general ., For Alu sequences , we found the 5′ss to be the most informative feature for correctly predicting exonization events , in agreement with previous findings 26 , 28 ., We found , however , that the 3′ss , which was also found to play a major role in exonization 24 , is less critical ., This finding may not necessarily hold for all exons ., The relatively low contribution of the 3′ss to Alu exonization may reflect the general tendency of Alus to have relatively strong splice signals at their 3′ end , regardless of whether they undergo exonization or not ., This is since the poly-T track , present in all Alus in the antisense orientation , serves as a strong polypyrimidine tract 24 , 47 ., On the other hand , our results regarding the importance of ESRs are consistent with several previous studies that have found exons to be enriched in ESRs with respect to pseudo-exons , more poorly recognized exons , and introns 32–34 ., Thus , while the importance of different features may vary from one exon to another , our results provide a general understanding of the features impacting on exon recognition ., It is noteworthy , that the majority of Alu exonization events in our two exonizing datasets presumably reflect either errors of the splicing machinery or newly born exons , which presumably do not give rise to functional proteins ( see also 48 ) ., This is indicated by the low inclusion level of the Alu exons , averaging 13% and 10% in the AEx-R and AEx-L groups , respectively ., In addition , the symmetry of the Alu exons ( i . e . , divisibility-by-three ) , at least in the AEx-R dataset , is very low: Only 23% of the exons are symmetric ( in the AEx-L dataset 55% of the Alus are symmetric ) ., Thus , the majority of Alus in this dataset insert a frame-shift mutation ., These numbers contrast with the 73% symmetry found in alternative events conserved between human and mouse 49 ., However , since our objective in this research was to understand the requirements of the spliceosome , the potential function of the transcript is irrelevant ., Moreover , newly born alternatively spliced Alu exons are the raw materials for future evolution: Given the right conditions and time , further mutations might generate a functional reading frame ., The features identified here provided good , but not perfect , classification using machine learning ., A number of factors underlie the non-perfect classification: For example , EST data is very noisy and far from providing a comprehensive coverage of all genes in all tissues 50 ., Therefore , many Alus categorized as non-e
Despite decades of research , the question of how the mRNA splicing machinery precisely identifies short exonic islands within the vast intronic oceans remains to a large extent obscure ., In this study , we analyzed Alu exonization events , aiming to understand the requirements for correct selection of exons ., Comparison of exonizing Alus to their non-exonizing counterparts is informative because Alus in these two groups have retained high sequence similarity but are perceived differently by the splicing machinery ., We identified and characterized numerous features used by the splicing machinery to discriminate between Alu exons and their non-exonizing counterparts ., Of these , the most novel is secondary structure: Alu exons in general and their 5′ splice sites ( 5′ss ) in particular are characterized by decreased stability of local secondary structures with respect to their non-exonizing counterparts ., We detected numerous further differences between Alu exons and their non-exonizing counterparts , among others in terms of exon–intron architecture and strength of splicing signals , enhancers , and silencers ., Support vector machine analysis revealed that these features allow a high level of discrimination ( AUC\u200a=\u200a0 . 91 ) between exonizing and non-exonizing Alus ., Moreover , the computationally derived probabilities of exonization significantly correlated with the biological inclusion level of the Alu exons , and the model could also be extended to general datasets of constitutive and alternative exons ., This indicates that the features detected and explored in this study provide the basis not only for precise exon selection but also for the fine-tuned regulation thereof , manifested in cases of alternative splicing .
A typical human gene consists of 9 exons around 150 nucleotides in length , separated by introns that are ∼3 , 000 nucleotides long ., The challenge of the splicing machinery is to precisely identify and ligate the exons , while removing the introns ., We aimed to understand how the splicing machinery meets this momentous challenge , based on Alu exonization events ., Alus are transposable elements , of which approximately one million copies exist in the human genome , a large portion of which within introns ., Throughout evolution , some intronic Alus accumulated mutations and became recognized by the splicing machinery as exons , a process termed exonization ., Such Alus remain highly similar to their non-exonizing counterparts but are perceived as different by the splicing machinery ., By comparing exonizing Alus to their non-exonizing counterparts , we were able to identify numerous features in which they differ and which presumably lead to the recognition only of the former by the splicing machinery ., Our findings reveal insights regarding the role of local RNA secondary structures , exon–intron architecture constraints , and splicing regulatory signals ., We integrated these features in a computational model , which was able to successfully mimic the function of the splicing machinery and discriminate between true Alu exons and their intronic counterparts , highlighting the functional importance of these features .
computational biology/alternative splicing