Probabilistic partitioning methods to find significant patterns in ChIP-Seq data.
Bioinformatics
; 30(17): 2406-13, 2014 Sep 01.
Article
em En
| MEDLINE
| ID: mdl-24812341
ABSTRACT
MOTIVATION We have witnessed an enormous increase in ChIP-Seq data for histone modifications in the past few years. Discovering significant patterns in these data is an important problem for understanding biological mechanisms. RESULTS:
We propose probabilistic partitioning methods to discover significant patterns in ChIP-Seq data. Our methods take into account signal magnitude, shape, strand orientation and shifts. We compare our methods with some current methods and demonstrate significant improvements, especially with sparse data. Besides pattern discovery and classification, probabilistic partitioning can serve other purposes in ChIP-Seq data analysis. Specifically, we exemplify its merits in the context of peak finding and partitioning of nucleosome positioning patterns in human promoters. AVAILABILITY AND IMPLEMENTATION The software and code are available in the supplementary material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Histonas
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Análise de Sequência de DNA
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Imunoprecipitação da Cromatina
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Sequenciamento de Nucleotídeos em Larga Escala
Limite:
Humans
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article