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Article Dans Une Revue Bioinformatics Année : 2014

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

Résumé

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.

Dates et versions

hal-01528412 , version 1 (29-05-2017)

Identifiants

Citer

Bogdan Mirauta, Pierre Nicolas, Hugues Richard. Parseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models. Bioinformatics, 2014, 30 (10), pp.1409-1416. ⟨10.1093/bioinformatics/btu042⟩. ⟨hal-01528412⟩
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