Estimation of the methylation pattern distribution from deep sequencing data.
BMC Bioinformatics
; 16: 145, 2015 May 06.
Article
em En
| MEDLINE
| ID: mdl-25943746
ABSTRACT
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 .
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Abelhas
/
Algoritmos
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Encéfalo
/
Modelos Estatísticos
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Metilação de DNA
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Sequenciamento de Nucleotídeos em Larga Escala
Tipo de estudo:
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Austrália