title: Predicting functional alternative splicing by measuring RNA selection pressure from multigenome alignments.
abstract: High-throughput methods such as EST sequencing, microarrays and deep sequencing have identified large numbers of alternative splicing (AS) events, but studies have shown that only a subset of these may be functional. Here we report a sensitive bioinformatics approach that identifies exons with evidence of a strong RNA selection pressure ratio (RSPR)--i.e., evolutionary selection against mutations that change only the mRNA sequence while leaving the protein sequence unchanged--measured across an entire evolutionary family, which greatly amplifies its predictive power. Using the UCSC 28 vertebrate genome alignment, this approach correctly predicted half to three-quarters of AS exons that are known binding targets of the NOVA splicing regulatory factor, and predicted 345 strongly selected alternative splicing events in human, and 262 in mouse. These predictions were strongly validated by several experimental criteria of functional AS such as independent detection of the same AS event in other species, reading frame-preservation, and experimental evidence of tissue-specific regulation: 75% (15/20) of a sample of high-RSPR exons displayed tissue specific regulation in a panel of ten tissues, vs. only 20% (4/20) among a sample of low-RSPR exons. These data suggest that RSPR can identify exons with functionally important splicing regulation, and provides biologists with a dataset of over 600 such exons. We present several case studies, including both well-studied examples (GRIN1) and novel examples (EXOC7). These data also show that RSPR strongly outperforms other approaches such as standard sequence conservation (which fails to distinguish amino acid selection pressure from RNA selection pressure), or pairwise genome comparison (which lacks adequate statistical power for predicting individual exons).
authors.affiliation: Molecular Biology Institute, Center for Computational Biology, Institute for Genomics and Proteomics, Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California, USA.
authors.forename: Hongchao
authors.initials: H
authors.lastname: Lu
authors.forename: Lan
authors.initials: L
authors.lastname: Lin
authors.forename: Seiko
authors.initials: S
authors.lastname: Sato
authors.forename: Yi
authors.initials: Y
authors.lastname: Xing
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Lee
mesh_terms: D017398:Alternative Splicing; D000818:Animals; D002352:Carrier Proteins / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D020224:Expressed Sequence Tags; D016678:Genome; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D009419:Nerve Tissue Proteins / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D012313:RNA / Q000235:genetics*; D016194:Receptors, N-Methyl-D-Aspartate / Q000235:genetics; D015203:Reproducibility of Results; D020133:Reverse Transcriptase Polymerase Chain Reaction; D016415:Sequence Alignment / Q000379:methods*; D033921:Vesicular Transport Proteins / Q000235:genetics
chemical_list: D002352:Carrier Proteins; C502251:Exoc7 protein, mouse; C485668:GRIN1 protein, human; C121673:Gprin1 protein, mouse; D009419:Nerve Tissue Proteins; D016194:Receptors, N-Methyl-D-Aspartate; D033921:Vesicular Transport Proteins; D012313:RNA