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

TY - JOUR

T1 - Transcriptional landscape estimation from tiling array data using a model of signal shift and drift

AU - Nicolas, Pierre

AU - Leduc, Aurélie

AU - Robin, Stéphane

AU - Rasmussen, Simon

AU - Jarmer, Hanne

AU - Bessières, Philippe

PY - 2009/9/15

Y1 - 2009/9/15

N2 - 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.

AB - 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.

KW - Bacillus subtilis/genetics

KW - Computational Biology/methods

KW - Gene Expression Profiling/methods

KW - Genome

KW - Oligonucleotide Array Sequence Analysis/methods

KW - Sequence Analysis, DNA/methods

KW - Transcription, Genetic

U2 - 10.1093/bioinformatics/btp395

DO - 10.1093/bioinformatics/btp395

M3 - Journal article

C2 - 19561016

SN - 1367-4811

VL - 25

SP - 2341

EP - 2347

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

IS - 18

ER -