Identify alternative splicing events based on position-specific evolutionary conservation.
The evolution of eukaryotes is accompanied by the increased complexity of alternative splicing which greatly expands genome information. One of the greatest challenges in the post-genome era is a complete revelation of human transcriptome with consideration of alternative splicing. Here, we introduce a comparative genomics approach to systemically identify alternative splicing events based on the differential evolutionary conservation between exons and introns and the high-quality annotation of the ENCODE regions. Specifically, we focus on exons that are included in some transcripts but are completely spliced out for others and we call them conditional exons. First, we characterize distinguishing features among conditional exons, constitutive exons and introns. One of the most important features is the position-specific conservation score. There are dramatic differences in conservation scores between conditional exons and constitutive exons. More importantly, the differences are position-specific. For flanking intronic regions, the differences between conditional exons and constitutive exons are also position-specific. Using the Random Forests algorithm, we can classify conditional exons with high specificities (97% for the identification of conditional exons from intron regions and 95% for the classification of known exons) and fair sensitivities (64% and 32% respectively). We applied the method to the human genome and identified 39,640 introns that actually contain conditional exons and classified 8,813 conditional exons from the current RefSeq exon list. Among those, 31,673 introns containing conditional exons and 5,294 conditional exons classified from known exons cannot be inferred from RefSeq, UCSC or Ensembl annotations. Some of these de novo predictions were experimentally verified.
Chen
Liang
L
Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America. liang.chen@usc.edu
Zheng
Sika
S
D000465:Algorithms; D017398:Alternative Splicing; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D019143:Evolution, Molecular; D005091:Exons; D020224:Expressed Sequence Tags; D020869:Gene Expression Profiling; D016678:Genome; D015894:Genome, Human; D006367:HeLa Cells; D006801:Humans; D007438:Introns; D008954:Models, Biological; D015233:Models, Statistical