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Estimation of the methylation pattern distribution from deep sequencing data
BACKGROUND: Bisulphite sequencing enables the detection of cytosine methylation. The sequence of the methylation states of cytosines on any given read forms a methylation pattern that carries substantially more information than merely studying the average methylation level at individual positions. I...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4428226/ https://www.ncbi.nlm.nih.gov/pubmed/25943746 http://dx.doi.org/10.1186/s12859-015-0600-6 |
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author | Lin, Peijie Forêt, Sylvain Wilson, Susan R Burden, Conrad J |
author_facet | Lin, Peijie Forêt, Sylvain Wilson, Susan R Burden, Conrad J |
author_sort | Lin, Peijie |
collection | PubMed |
description | BACKGROUND: Bisulphite sequencing enables the detection of cytosine methylation. The sequence of the methylation states of cytosines on any given read forms a methylation pattern that carries substantially more information than merely studying the average methylation level at individual positions. In order to understand better the complexity of DNA methylation landscapes in biological samples, it is important to study the diversity of these methylation patterns. However, the accurate quantification of methylation patterns is subject to sequencing errors and spurious signals due to incomplete bisulphite conversion of cytosines. RESULTS: A statistical model is developed which accounts for the distribution of DNA methylation patterns at any given locus. The model incorporates the effects of sequencing errors and spurious reads, and enables estimation of the true underlying distribution of methylation patterns. CONCLUSIONS: Calculation of the estimated distribution over methylation patterns is implemented in the R Bioconductor package MPFE. Source code and documentation of the package are also available for download at http://bioconductor.org/packages/3.0/bioc/html/MPFE.html. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0600-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4428226 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44282262015-05-13 Estimation of the methylation pattern distribution from deep sequencing data Lin, Peijie Forêt, Sylvain Wilson, Susan R Burden, Conrad J BMC Bioinformatics Methodology Article BACKGROUND: Bisulphite sequencing enables the detection of cytosine methylation. The sequence of the methylation states of cytosines on any given read forms a methylation pattern that carries substantially more information than merely studying the average methylation level at individual positions. In order to understand better the complexity of DNA methylation landscapes in biological samples, it is important to study the diversity of these methylation patterns. However, the accurate quantification of methylation patterns is subject to sequencing errors and spurious signals due to incomplete bisulphite conversion of cytosines. RESULTS: A statistical model is developed which accounts for the distribution of DNA methylation patterns at any given locus. The model incorporates the effects of sequencing errors and spurious reads, and enables estimation of the true underlying distribution of methylation patterns. CONCLUSIONS: Calculation of the estimated distribution over methylation patterns is implemented in the R Bioconductor package MPFE. Source code and documentation of the package are also available for download at http://bioconductor.org/packages/3.0/bioc/html/MPFE.html. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0600-6) contains supplementary material, which is available to authorized users. BioMed Central 2015-05-06 /pmc/articles/PMC4428226/ /pubmed/25943746 http://dx.doi.org/10.1186/s12859-015-0600-6 Text en © Lin et al.; licensee BioMed Central. 2015 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Lin, Peijie Forêt, Sylvain Wilson, Susan R Burden, Conrad J Estimation of the methylation pattern distribution from deep sequencing data |
title | Estimation of the methylation pattern distribution from deep sequencing data |
title_full | Estimation of the methylation pattern distribution from deep sequencing data |
title_fullStr | Estimation of the methylation pattern distribution from deep sequencing data |
title_full_unstemmed | Estimation of the methylation pattern distribution from deep sequencing data |
title_short | Estimation of the methylation pattern distribution from deep sequencing data |
title_sort | estimation of the methylation pattern distribution from deep sequencing data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4428226/ https://www.ncbi.nlm.nih.gov/pubmed/25943746 http://dx.doi.org/10.1186/s12859-015-0600-6 |
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