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Bioinformatics Advance Access originally published online on June 26, 2009
Bioinformatics 2009 25(18):2341-2347; doi:10.1093/bioinformatics/btp395
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Transcriptional landscape estimation from tiling array data using a model of signal shift and drift

Pierre Nicolas 1,*, Aurélie Leduc 1, Stéphane Robin 2, Simon Rasmussen 3, Hanne Jarmer 3 and Philippe Bessières 1

1INRA, Mathématique Informatique et Génome UR1077, 78350 Jouy-en-Josas, 2AgroParisTech/INRA, Mathématiques et Informatique Appliquées UMR518, 16 rue Claude Bernard, 75005 Paris, France and 3Technical University of Denmark, Center for Biological Sequence analysis, Building 208, 2800 Lyngby, Denmark

*To whom correspondence should be addressed.


  Abstract

Motivation: High-density oligonucleotide tiling array technology holds the promise of a better description of the complexity and the dynamics of transcriptional landscapes. In organisms such as bacteria and yeasts, transcription can be measured on a genome-wide scale with a resolution >25 bp. The statistical models currently used to handle these data remain however very simple, the most popular being the piecewise constant Gaussian model with a fixed number of breakpoints.

Results: This article describes a new methodology based on a hidden Markov model that embeds the segmentation of a continuous-valued signal in a probabilistic setting. For a computationally affordable cost, this framework (i) alleviates the difficulty of choosing a fixed number of breakpoints, and (ii) permits retrieving more information than a unique segmentation by giving access to the whole probability distribution of the transcription profile. Importantly, the model is also enriched and accounts for subtle effects such as signal ‘drift’ and covariates. Relevance of this framework is demonstrated on a Bacillus subtilis dataset.

Availability: A software is distributed under the GPL.

Contact: pierre.nicolas{at}jouy.inra.fr

Supplementary information: Supplementary data is available at Bioinformatics online.

Associate Editor: Trey Ideker

Received on January 27, 2009; revised on May 11, 2009; accepted on June 19, 2009

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