BMC Genomics 2006, 7:273 http://www.biomedcentral.com/1471-2164/7/273 Methods Data To compile the "conserved exons" set, internal human exons were identified by aligning ESTs and cDNAs from Genbank version 136 [23] to the human genome (version hg16) downloaded from UCSC genome browser [24]. Internal exons were considered as such if they had valid flanking 3' and 5' splice sites. The resulting exons were compared to the mouse genome as described in ref [10]. Classifying features were calculated for each of the exons as described in ref [11]. Training set exons were downloaded from refs [12,13,15] and searched for matches in the "conserved exons" set (120,964 exons) using BLASTn. Exons with no perfect match were filtered out of the training set. Exoniphy exons were downloaded from the UCSC hg16 genome version. EST/cDNA coverage of Exoniphy exons was also derived from the UCSC annotation. SVM training and hyper-parameter selection To identify the ancestral alternative exons from species- specific alternative and constitutive exons, an SVM classi- fier with a Gaussian kernel was trained using the datasets described above. Soft-margin SVM implemented in SVM- light [25] was used throughout the research. SVM training involves fixing several hyper-parameters, the values of which has a crucial effect on the performance of the trained classifier. A grid search combined with five- fold cross validation was used to evaluate the performance over various values of the slack parameter C, the width the kernel σ and the number of features. The latter was con- trolled by standard filtering using Pearson correlation as an association measure between a feature and the target. The final model, obtained by optimizing the area under the ROC curve, used all available features and its hyper- parameters were C = 0.1 and σ = 1.4. The "alternativity score" of an exon is the output of the trained SVM classi- fier when applied to its feature vector (For details see ref [11]). Likelihood maximization Using the trained SVM we obtained several distributions of alternativity scores: the scores distribution of the ances- tral alternative exons, the scores distribution of the 'other' exons, the scores distribution of all human exons, and the scores distribution of Exoniphy exons. The latter two pop- ulations contain unknown fractions of ancestral alterna- tive exons, hence the distribution of each one of them can be decomposed into two components: f(x) = pfa(x) + (1- p)fo(x) where fa and fo represent the probability distribu- tion function of the SVM score x for the ancestral alterna- tive exons and for the 'other' exons (ancestral constitutive and human-specific alternative), respectively. p is the frac- tion of ancestral alternative exons (0 ≤ p ≤ 1). f represents the distribution function of a mixed population, either all human exons or Exoniphy exons. p was computed by maximizing the log likelihood func- tion L = log Πif(xi) = Σi log(pfa(xi) + (1-p)fo(xi)), where the summation is over the SVM scores of all exons in the test set. As L is a function of a single parameter, finding its glo- bal maximum is straightforward. The Fisher information F was used to estimate a confidence interval for p. In sub- sequent analysis we used one standard deviation Δ = 1/√F as a measure of the uncertainty in p. Parzen smoothing fa and fowere extracted from the score distribution of the ancestral alternative exons and the 'other' exons of the training set, respectively, using 200-fold cross validation to avoid over-fitting. The score distributions are only insignificantly different if a 'leave-one-out' cross-valida- tion is performed instead. Since a normal approximation of these distributions devi- ated considerably from the observed distributions, Parzen smoothing was used instead to model them as continuous probability distribution functions. To determine the Gaussian width for Parzen smoothing, each population, ancestral alternative exons and 'other' exons, was split into two equal disjoint sets. One set was used to produce a probability distribution function using a fixed Gaussian width, and the second set was used to calculate the log- likelihood function La = Σi log(fa(xi)) and L = Σi log(fo(xi)) o for the ancestral alternative exons and 'other' exons, respectively, where the summation is over the examples of the second set. This process was repeated 100 time for each Gaussian width. The widths that gave the highest average log-likelihood values were chosen for Parzen smoothing. These widths were 0.0111 and 0.0117 for the ancestral alternative and the 'other' exons, respectively, in the Yeo set, and 0.0159 and 0.0145 for the ancestral alter- native and the 'other' exons, respectively, in the Sorek set. Authors' contributions R. Sorek participated in the study design and performed the data collection and analysis. GD participated in the study design and performed the SVM and statistical anal- yses. R Shamir participated in the design and coordina- tion of the study. All authors read and approved the final manuscript. Acknowledgements We thank Adam Siepel for assistance with the Exoniphy data and for an insightful discussion. We also thank Eli Eisenberg for a fruitful discussion. R. Sorek was supported by the Clore Israel Foundation. R. Shamir was sup- ported by the Israeli Science Foundation (grant no. 309/02). Page 6 of 8 (page number not for citation purposes)