Related Experiment Video
Updated: May 3, 2026

Comparative RNA Structure Analysis of Nascent and Mature Transcripts in Saccharomyces cerevisiae
Published on: February 27, 2026
Parseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models
Bogdan Mirauta1, Pierre Nicolas, Hugues Richard
1Biologie Computationnelle et Quantitative, UPMC and CNRS UMR7238, Paris, France and Mathématique Informatique et Génome, INRA UR1077, Jouy-en-Josas, France.
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.fr
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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