BMC Genomics 2006, 7:273 http://www.biomedcentral.com/1471-2164/7/273 complementary information that is not contained in the number of alternatively spliced genes. As far as we are aware, the question of assessing the number of alternative exons was never addressed in the literature before. We have recently described an accurate machine-learning method allowing the detection of conserved alternatively spliced exons without using ESTs [10,11]. This approach relies on specific features of the exon and its genomic vicinity that distinguish alternatively spliced exons from constitutive ones. Since this method does not rely on ESTs, it avoids the problems of sampling errors and noise inherent to ESTs, and can therefore provide robust assess- ment on the absolute number of conserved alternatively spliced exons in the human genome. Results Classifying ancestral alternative exons Human internal exons (excluding the first and last ones) can roughly be divided into three categories, based on comparison to their counterparts in mouse: (i) constitu- tive exons, i.e., exons that occur in the same form in all (one or more) splice variants of the gene they belong to; (ii) ancestral alternative exons, which are exons that are alternatively spliced both in human and in mouse, i.e., were alternative in the common ancestor of primates and rodents, and remained so in both species; (iii) human- specific alternative exons, which are exons that have alternative splicing pattern in human but not in mouse [12]. To distinguish between ancestral alternative exons and other exons we used a classifier trained by Support Vector Machine (SVM), a machine learning algorithm [11]. For each exon, the features taken into account were (i) exon length, (ii) exon length divisibility by three, (iii) percent identity when aligned to the mouse counterpart, (iv) con- servation in the upstream and downstream flanking intronic sequences (4 features), (v) 5' splice-site composi- tion (28 features), (vi) poly-pyrimidine tract intensity, and (vii) 3-tuple counts in the exon and the flanking introns (192 features). These features were all previously shown to differ between alternative and constitutive exons [10,11,13,14]. For detailed description of our clas- sifier see ref [11]. To obtain an accurate classifier we first collected pre-calcu- lated training sets of each of the three groups of exons: (i) 5069 constitutive exons from Yeo et al [13]; (ii) 241 ancestral alternative exons compiled by the same authors [13] and (iii) 212 human-specific alternative exons com- piled by Pan et al. [12]. After filtering (see Methods), the final sets contained 4655 constitutive exons, 221 ancestral alternative exons, and 182 human specific alternative exons. We also used a second set of 243 (215 after filter- ing) ancestral alternative exons compiled in [15] as an independent training set. Next, we trained the classifier to distinguish between the ancestral alternative exons (221 exons) and the 'other' exons, i.e., the combined group of constitutive and human-specific alternative (4655+182 = 4837) exons. For each exon, the trained classifier outputs an 'alternativity score', a number that is indicative to whether that exon is ancestrally alternative or not. Using ten-fold cross valida- tion we estimated the distributions of these scores for alternative and other exons. Figure 1 presents these score distributions. As seen in the figure, the scores significantly distinguish ancestral alternative exons from the other exons. It is noteworthy that we could not find a set of parameters that distinguishes well between the constitu- tive and the human-specific alternative exons, indicating that these two sets of exons have very similar characteris- tics. Genome-wide collection of human exons To assess the fraction of conserved, ancestral alternative exons out of the entire human exon population, we first used ESTs and cDNAs from Genbank version 136 aligned to the human genome (version hg16) to identify internal human exons. We collected 120,964 internal exons for which we were able to identify the mouse ortholog exon as described in ref [10]. These exons constitute the set of "conserved exons". This set of exons is the basis for our analysis, as our classifier relies on human-mouse homol- ogy-based features. Fraction of ancestral alternative cassette exons Next, we employed our classifier on the entire set of 120,964 conserved exons (Figure 2). The resulting score distribution represents a combination between "ancestral alternative" and "other" exon scores, with the observed right tail clearly representing the contribution of ancestral alternative exons (Figure 2). To decompose the distribu- tion into its two components we used a simple mixture model: Assume that the distributions of scores of "ances- tral alternative" and "other" exons are represented by the functions fa(x) and fo(x) respectively. Then the distribu- tion of the entire population of 120,964 exons composed of these two components is modeled by f(x) = p fa(x) + (1- p) fo(x), where p is the relative fraction of ancestral alterna- tive exons. Estimating the three distributions fa, fo and f using standard Maximum Likelihood estimation (see Methods), we calculated that p equals 3.6% (± 0.5%). To check the possibility that this estimator was biased by the training set of ancestral alternative exons, we repeated our analysis using the second training set of 215 ancestral alternative exons from Sorek et al [15]. This exon set over- laps the Yeo et al set by only 38 exons, and can therefore Page 2 of 8 (page number not for citation purposes)