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PLoS One. 2010 Nov 8;5(11):e13875. doi: 10.1371/journal.pone.0013875.

HMMSplicer: a tool for efficient and sensitive discovery of known and novel splice junctions in RNA-Seq data.

Author information

  • 1Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America.

Abstract

BACKGROUND:

High-throughput sequencing of an organism's transcriptome, or RNA-Seq, is a valuable and versatile new strategy for capturing snapshots of gene expression. However, transcriptome sequencing creates a new class of alignment problem: mapping short reads that span exon-exon junctions back to the reference genome, especially in the case where a splice junction is previously unknown.

METHODOLOGY/PRINCIPAL FINDINGS:

Here we introduce HMMSplicer, an accurate and efficient algorithm for discovering canonical and non-canonical splice junctions in short read datasets. HMMSplicer identifies more splice junctions than currently available algorithms when tested on publicly available A. thaliana, P. falciparum, and H. sapiens datasets without a reduction in specificity.

CONCLUSIONS/SIGNIFICANCE:

HMMSplicer was found to perform especially well in compact genomes and on genes with low expression levels, alternative splice isoforms, or non-canonical splice junctions. Because HHMSplicer does not rely on pre-built gene models, the products of inexact splicing are also detected. For H. sapiens, we find 3.6% of 3' splice sites and 1.4% of 5' splice sites are inexact, typically differing by 3 bases in either direction. In addition, HMMSplicer provides a score for every predicted junction allowing the user to set a threshold to tune false positive rates depending on the needs of the experiment. HMMSplicer is implemented in Python. Code and documentation are freely available at http://derisilab.ucsf.edu/software/hmmsplicer.

PMID:
21079731
[PubMed - indexed for MEDLINE]
PMCID:
PMC2975632
Free PMC Article

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