Computational discovery of human coding and non-coding transcripts with conserved splice sitesстатья из журнала
Аннотация: Abstract Motivation: Long non-coding RNAs (lncRNAs) resemble protein-coding mRNAs but do not encode proteins. Most lncRNAs are under lower sequence constraints than protein-coding genes and lack conserved secondary structures, making it hard to predict them computationally. Results: We introduce an approach to predict spliced lncRNAs in vertebrate genomes combining comparative genomics and machine learning. It is based on detecting signatures of characteristic splice site evolution in vertebrate whole genome alignments. First, we predict individual splice sites, then assemble compatible sites into exon candidates, and finally predict multi-exon transcripts. Using a novel method to evaluate typical splice site substitution patterns that explicitly takes the species phylogeny into account, we show that individual splice sites can be accurately predicted. Since our approach relies only on predicted splice sites, it can uncover both coding and non-coding exons. We show that our predicted exons and partial transcripts are mostly non-coding and lack conserved secondary structures. These exons are of particular interest, since existing computational approaches cannot detect them. Transcriptome sequencing data indicate tissue-specific expression patterns of predicted exons and there is evidence that increasing sequencing depth and breadth will validate additional predictions. We also found a significant enrichment of predicted exons that form multi-exon transcript parts, and we experimentally validate such a novel multi-exon gene. Overall, we obtain 336 novel multi-exon transcript predictions from human intergenic regions. Our results indicate the existence of novel human transcripts that are conserved in evolution and our approach contributes to the completion of the human transcript catalog. Availability and Implementation: Predicted human splice sites, exons and gene structures together with a Perl implementation of the tree-based log-odds scoring and a supplementary PDF file containing additional figures and tables are available at: http://www.bioinf.uni-leipzig.de/publications/supplements/10-010. The five experimentally confirmed partial transcript isoforms have been deposited in GenBank under accession numbers HM587422-HM587426. Contact: dominic@bioinf.uni-leipzig.de Supplementary Information: Supplementary data are available at Bioinformatics online.
Год издания: 2011
Авторы: Dominic Rose, Michael Hiller, Katharina Schütt, Jörg Hackermüller, Rolf Backofen, Peter F. Stadler
Издательство: Oxford University Press
Источник: Bioinformatics
Ключевые слова: Cancer-related molecular mechanisms research, RNA modifications and cancer, RNA and protein synthesis mechanisms
Другие ссылки: Bioinformatics (PDF)
Bioinformatics (HTML)
PubMed (HTML)
Bioinformatics (HTML)
PubMed (HTML)
Открытый доступ: bronze
Том: 27
Выпуск: 14
Страницы: 1894–1900