GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data
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© 2017 Mifsud et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.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).
AuthorsMifsud, B; Martincorena, I; Darbo, E; Sugar, R; Schoenfelder, S; Fraser, P; Luscombe, NM
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