Recognition of Unknown Conserved Alternatively Spliced Exons
Uwe Ohler,
Noam Shomron and
Christopher B Burge
PLOS Computational Biology, 2005, vol. 1, issue 2, 1-
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.
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.0010015 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 10015&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:0010015
DOI: 10.1371/journal.pcbi.0010015
Access Statistics for this article
More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().