Recognition of Unknown Conserved Alternatively Spliced Exons

Abstract

The split structure of most mammalian protein-coding genes allows for the potential to produce multiple different mRNA and protein isoforms from a single gene locus through the process of alternative splicing (AS). We propose a computational approach called UNCOVER based on a pair hidden Markov model to discover conserved coding exonic sequences subject to AS that have so far gone undetected. Applying UNCOVER to orthologous introns of known human and mouse genes predicts skipped exons or retained introns present in both species, while discriminating them from conserved noncoding sequences. The accuracy of the model is evaluated on a curated set of genes with known conserved AS events. The prediction of skipped exons in the ~1% of the human genome represented by the ENCODE regions leads to more than 50 new exon candidates. Five novel predicted AS exons were validated by RT-PCR and sequencing analysis of 15 introns with strong UNCOVER predictions and lacking EST evidence. These results imply that a considerable number of conserved exonic sequences and associated isoforms are still completely missing from the current annotation of known genes. UNCOVER also identifies a small number of candidates for conserved intron retention. Alternative splicing is a process in which more than one protein variant can be produced from one gene: Specific parts of the mRNA precursor are included or excluded during the processing into the mature transcript. It is very prevalent in mammalian genomes, and variants are often specific for particular cell types, developmental states, or environmental changes. The identification of such variants has until recently relied solely on the sequencing and comparison of expressed sequence tags (ESTs), but the number of available ESTs is not large enough to cover all variants under all conditions. Ohler et al. have now devised a comparative genomics algorithm based on a pair hidden Markov model, which identifies parts of genes that are alternatively spliced and have not been observed in ESTs. Starting from known annotated genes conserved in human and mouse, they scan corresponding intron pairs of these genes to identify conserved sequences that match the model. Experimental validation of a number of new predictions show that the approach can successfully uncover splice variants that are as yet unknown and not part of the large libraries of ESTs. Together with recently proposed complementary computational methods, this approach helps us to complete our knowledge about the transcript diversity created by alternative splicing.