MaxHiC: robust estimation of chromatin interaction frequency in Hi-C and capture Hi-C experiments

Abstract

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions, and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than random (background) interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools and demonstrate using published Hi-C and capture Hi-C datasets that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase 2 sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, and 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and more likely to identify known enhancer-promoter pairs than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distribution only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C and capture Hi-C data.

Seeing the forest through the trees: Identifying functional interactions from Hi-C

“…Recently, another tool called MaxHiC also based on negative binomial distribution was developed [103]. Compared to other tools, all parameters of the background model in MaxHiC are established by using the ADAM algorithm [104] to maximize the logarithm of likelihood of the observed Hi-C interactions.…”

“…Compared to other tools, all parameters of the background model in MaxHiC are established by using the ADAM algorithm [104] to maximize the logarithm of likelihood of the observed Hi-C interactions. Significant interactions identified by MaxHiC were shown to outperform tools such as Fit-Hi-C/FitHiC2 and GOTHiC in identifying significant interactions enriched between known regulatory regions [103].…”

“…ChiCMaxima's approach is more stringent and exhibits a more robust performance when comparing biological replicates datasets [107]. As well as being applicable to conventional HiC data, MaxHiC is also able to identify significant interactions in CHi-C data [103] and offers robust performance to identify regulatory areas compared to CHi-C-specific tools including…”

Somatic point mutations are enriched in long non-coding RNAs with possible regulatory function in breast cancer

“…MaxHiC [47] to analyze Hi-C raw data and identify statistically significant interactions, respectively. We identified 188,982 statistically significant interactions (P-value < 0.01 and read-count ≥ 10 -see method section) in the Hi-C library 1.…”

A Multi-Object Simulated Annealing Approach to Identify Likely Functional Genomic Pairs

“…Using Hi-C, many studies have engaged in detecting promoters-enhancers pairs and how they regulate the genes and how they interact with each other [7,8,9]. During the past decade, a wide range of tools have been proposed to effectively analysis Hi-C data and detect informative interacting pairs in the genome.…”