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


BACKGROUND
A program named Parseq is available at: http://www.lgm.upmc.fr/parseq/.


BACKGROUND
bodgan.mirauta@upmc.fr


BACKGROUND
Supplementary data are available at Bioinformatics online.
Biologie Computationnelle et Quantitative, UPMC and CNRS UMR7238, Paris, France and Mathématique Informatique et Génome, INRA UR1077, Jouy-en-Josas, France.
Bogdan
B
Mirauta
Pierre
P
Nicolas
Hugues
H
Richard
D000465:Algorithms; D004926:Escherichia coli / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D012313:RNA / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*; D014158:Transcription, Genetic
D012313:RNA