%0 Journal Article %A Borbala Mifsud %A Inigo Martincorena %A Elodie Darbo %A Robert Sugar %A Stefan Schoenfelder %A Peter Fraser %A Nicholas M. Luscombe %T GOTHiC, a simple probabilistic model to resolve complex biases and to identify real interactions in Hi-C data %D 2015 %R 10.1101/023317 %J bioRxiv %P 023317 %X 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 user-friendly BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html). %U https://www.biorxiv.org/content/biorxiv/early/2015/07/27/023317.full.pdf