Transcriptional landscape estimation from tiling array data using a model of signal shift and drift
Pierre Nicolas,Aurélie Leduc,Stéphane Robin,Simon Rasmussen,Hanne Østergaard Jarmer,Philippe Bessières +5 more
TL;DR: A new methodology based on a hidden Markov model that embeds the segmentation of a continuous-valued signal in a probabilistic setting and permits retrieving more information than a unique segmentation by giving access to the whole probability distribution of the transcription profile.
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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@jouy.inra.fr
Supplementary information: Supplementary data is available at Bioinformatics online.
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Citations
Condition-Dependent Transcriptome Reveals High-Level Regulatory Architecture in Bacillus subtilis
Pierre Nicolas,Ulrike Mäder,Etienne Dervyn,Tatiana Rochat,Aurélie Leduc,Nathalie Pigeonneau,Elena Bidnenko,Elodie Marchadier,Mark Hoebeke,Stéphane Aymerich,Dörte Becher,Paola Bisicchia,Eric Botella,Olivier Delumeau,Geoff Doherty,Emma L. Denham,Mark J. Fogg,Vincent Fromion,Anne Goelzer,Annette Hansen,Elisabeth Härtig,Colin R. Harwood,Georg Homuth,Hanne Østergaard Jarmer,Matthieu Jules,Edda Klipp,Ludovic Le Chat,François Lecointe,Peter J. Lewis,Wolfram Liebermeister,Anika March,Ruben A. T. Mars,Priyanka Nannapaneni,David Noone,Susanne Pohl,Bernd Rinn,Frank Rügheimer,Praveen K. Sappa,Franck Samson,Marc Schaffer,Benno Schwikowski,Leif Steil,Jörg Stülke,Thomas Wiegert,Kevin M. Devine,Anthony J. Wilkinson,Jan Maarten van Dijl,Michael Hecker,Uwe Völker,Philippe Bessières,Philippe Noirot +50 more
TL;DR: The transcriptomes of Bacillus subtilis exposed to a wide range of environmental and nutritional conditions that the organism might encounter in nature are reported, offering an initial understanding of why certain regulatory strategies may be favored during evolution of dynamic control systems.
940
Staphylococcus aureus Transcriptome Architecture: From Laboratory to Infection-Mimicking Conditions
Ulrike Mäder,Pierre Nicolas,Maren Depke,Jan Pané-Farré,Michel Débarbouillé,Magdalena M. van der Kooi-Pol,Cyprien Guérin,Sandra Derozier,Aurélia Hiron,Hanne Østergaard Jarmer,Aurélie Leduc,Stephan Michalik,Ewoud Reilman,Marc Schaffer,Frank Schmidt,Philippe Bessières,Philippe Noirot,Michael Hecker,Tarek Msadek,Uwe Völker,Jan Maarten van Dijl +20 more
TL;DR: This study revealed that Rho-dependent transcription termination suppresses pervasive antisense transcription in S. aureus, presumably originating from abundant spurious transcription initiation in this A+T-rich genome, which would otherwise affect expression of the overlapped genes.
Three essential ribonucleases-RNase Y, J1, and III-control the abundance of a majority of Bacillus subtilis mRNAs.
TL;DR: Although the abundance of a large number of transcripts was altered by depletion of RNase III, this appears to result primarily from indirect transcriptional effects, RNase depletion led to the stabilization of many low-abundance potential regulatory RNAs, both in intergenic regions and in the antisense orientation to known transcripts.
Genome-wide identification of genes directly regulated by the pleiotropic transcription factor Spx in Bacillus subtilis
Tatiana Rochat,Pierre Nicolas,Olivier Delumeau,Alžbeta Rabatinová,Jana Korelusová,Aurélie Leduc,Philippe Bessières,Etienne Dervyn,Libor Krásný,Philippe Noirot +9 more
TL;DR: The study globally characterized the Spx regulatory network, revealing its role in the basal expression of some genes and its complex interplay with other stress responses.
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References
A tutorial on hidden Markov models and selected applications in speech recognition
Lawrence R. Rabiner
- 01 Feb 1989
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
•Book
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
Richard Durbin,Sean R. Eddy,Anders Krogh,Graeme Mitchison +3 more
- 01 Feb 2005
TL;DR: This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis.
4.5K
Multivariate Density Estimation, Theory, Practice and Visualization
TL;DR: Representation and Geometry of Multivariate Data.
4.5K
Variance stabilization applied to microarray data calibration and to the quantification of differential expression.
Wolfgang Huber,Anja von Heydebreck,Holger Sültmann,Annemarie Poustka,Martin Vingron +4 more
- 01 Jul 2002
TL;DR: A statistical model for microarray gene expression data that comprises data calibration, the quantifying of differential expression, and the quantification of measurement error is introduced, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range is derived.
2.7K
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