GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data.
PLoS One
; 12(4): e0174744, 2017.
Article
em En
| MEDLINE
| ID: mdl-28379994
ABSTRACT
Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http//www.bioconductor.org/packages/release/bioc/html/GOTHiC.html).
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1
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
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Cromossomos
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Biologia Computacional
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Loci Gênicos
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article