Transcriptional landscape estimation from tiling array data using a model of signal shift and drift
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Transcriptional landscape estimation from tiling array data using a model of signal shift and drift. / Nicolas, Pierre; Leduc, Aurélie; Robin, Stéphane; Rasmussen, Simon; Jarmer, Hanne; Bessières, Philippe.
In: Bioinformatics (Online), Vol. 25, No. 18, 15.09.2009, p. 2341-7.Research output: Contribution to journal › Journal article › Research › peer-review
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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
VL - 25
SP - 2341
EP - 2347
JO - Bioinformatics (Online)
JF - Bioinformatics (Online)
SN - 1367-4811
IS - 18
ER -
ID: 210773887