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Parseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models.

TitleParseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models.
Publication TypeJournal Article
Year of Publication2014
AuthorsMirauta, B, Nicolas, P, Richard, H
JournalBioinformatics
Volume30
Issue10
Pagination1409-16
Date Published2014 May 15
ISSN1367-4811
KeywordsAlgorithms, Escherichia coli, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Markov Chains, Models, Genetic, Monte Carlo Method, RNA, Saccharomyces cerevisiae, Sequence Analysis, RNA, Transcription, Genetic
Abstract

MOTIVATION: The most common RNA-Seq strategy consists of random shearing, amplification and high-throughput sequencing of the RNA fraction. Methods to analyze transcription level variations along the genome from the read count profiles generated by the RNA-Seq protocol are needed.RESULTS: We developed a statistical approach to estimate the local transcription levels and to identify transcript borders. This transcriptional landscape reconstruction relies on a state-space model to describe transcription level variations in terms of abrupt shifts and more progressive drifts. A new emission model is introduced to capture not only the read count variance inside a transcript but also its short-range autocorrelation and the fraction of positions with zero counts. The estimation relies on a particle Gibbs algorithm whose running time makes it more suited to microbial genomes. The approach outperformed read-overlapping strategies on synthetic and real microbial datasets.AVAILABILITY: A program named Parseq is available at: http://www.lgm.upmc.fr/parseq/.CONTACT: bodgan.mirauta@upmc.frSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI10.1093/bioinformatics/btu042
Alternate JournalBioinformatics
PubMed ID24470570