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A hidden Markov model approach for determining expression from genomic tiling micro arrays

BACKGROUND: Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. RESULTS: We present a probabilistic procedure, ExpressHMM, that adaptive...

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Detalles Bibliográficos
Autores principales: Munch, Kasper, Gardner, Paul P, Arctander, Peter, Krogh, Anders
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1481622/
https://www.ncbi.nlm.nih.gov/pubmed/16672042
http://dx.doi.org/10.1186/1471-2105-7-239
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author Munch, Kasper
Gardner, Paul P
Arctander, Peter
Krogh, Anders
author_facet Munch, Kasper
Gardner, Paul P
Arctander, Peter
Krogh, Anders
author_sort Munch, Kasper
collection PubMed
description BACKGROUND: Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. RESULTS: We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes [1]. Results can be downloaded and viewed from our web site [2]. CONCLUSION: The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity.
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spelling pubmed-14816222006-06-22 A hidden Markov model approach for determining expression from genomic tiling micro arrays Munch, Kasper Gardner, Paul P Arctander, Peter Krogh, Anders BMC Bioinformatics Methodology Article BACKGROUND: Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. RESULTS: We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes [1]. Results can be downloaded and viewed from our web site [2]. CONCLUSION: The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity. BioMed Central 2006-05-03 /pmc/articles/PMC1481622/ /pubmed/16672042 http://dx.doi.org/10.1186/1471-2105-7-239 Text en Copyright © 2006 Munch et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Munch, Kasper
Gardner, Paul P
Arctander, Peter
Krogh, Anders
A hidden Markov model approach for determining expression from genomic tiling micro arrays
title A hidden Markov model approach for determining expression from genomic tiling micro arrays
title_full A hidden Markov model approach for determining expression from genomic tiling micro arrays
title_fullStr A hidden Markov model approach for determining expression from genomic tiling micro arrays
title_full_unstemmed A hidden Markov model approach for determining expression from genomic tiling micro arrays
title_short A hidden Markov model approach for determining expression from genomic tiling micro arrays
title_sort hidden markov model approach for determining expression from genomic tiling micro arrays
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1481622/
https://www.ncbi.nlm.nih.gov/pubmed/16672042
http://dx.doi.org/10.1186/1471-2105-7-239
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