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 data-sets. Availability: A program named Parseq is available at: http://www.lgm.upmc.fr/parseq/.
['Bogdan Mirauta', 'Pierre Nicolas', 'Hugues Richard']
Parseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models