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).
Highlights
Hi-C is a high-throughput technique based on chromosome conformation capture to detect the spatial proximity between pairs of genomic loci [1,2]
It is routinely used to study the three-dimensional folding of genomes [3,4,5,6,7]
For a given pair of genomic loci, GOTHiC calculates: (i) the probability of observing a given number of read-pairs between two loci through random ligations; and (ii) the effect size, "strength” or “frequency", of interaction measured as the ratio of observed-over-expected numbers of interactions
Summary
Hi-C is a high-throughput technique based on chromosome conformation capture to detect the spatial proximity between pairs of genomic loci [1,2]. It is routinely used to study the three-dimensional folding of genomes [3,4,5,6,7]. A sequenced Hi-C read-pair should directly represent an interaction between two loci, with the number of mapped read-pairs corresponding to the frequency of interactions in the sample cell population. Two challenges must be resolved in order to extract the true signal from Hi-C data. The first is to identify and resolve systematic biases.
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