HMCan

HMCan (Histone Modifications in Cancer) is a software tool that was developed to analyze histone modification ChIP-seq data produced from cancer genomes. The motivation behind the development of this tool was the observation that cancer cells are often characterized by epigenetic changes, which include aberrant histone modifications. In particular, cancer cells often use local or regional epigenetic silencing as a mechanism to silence the expression of tumor suppressor genes.

Several tools have been developed to enable the detection of histone marks in ChIP-seq data from normal samples. However, it is unclear whether these tools can be efficiently applied to ChIP-seq data generated from cancer samples. This is because cancer genomes are often characterized by frequent copy number alterations, which are gains and losses of large regions of chromosomal material. These copy number alterations may create a substantial statistical bias in evaluating histone mark signal enrichment. As a result, it can lead to an underdetection of the signal in the regions of loss and an overdetection of the signal in the regions of gain.

HMCan addresses these challenges by correcting for the GC content and copy number bias in the data. It then applies Hidden Markov Models to detect the signal from the corrected data. On simulated data, HMCan outperformed several commonly used tools developed to analyze histone modification data produced from genomes without copy number alterations.

Additionally, HMCan showed superior results on a ChIP-seq dataset generated for the repressive histone mark H3K27me3 in a bladder cancer cell line. HMCan's predictions matched well with experimental data (qPCR validated regions) and included, for example, the previously detected H3K27me3 mark in the promoter of the DLEC1 gene.