Dataset Information

HiCRep.py : Fast comparison of Hi-C contact matrices in Python.

ABSTRACT:

Motivation

Hi-C is the most widely used assay for investigating genome-wide 3D organization of chromatin. When working with Hi-C data, it is often useful to calculate the similarity between contact matrices in order to asses experimental reproducibility or to quantify relationships among Hi-C data from related samples. The HiCRep algorithm has been widely adopted for this task, but the existing R implementation suffers from run time limitations on high resolution Hi-C data or on large single-cell Hi-C datasets.

Results

We introduce a Python implementation of HiCRep and demonstrate that it is much faster and consume much less memory than the existing R implementation. Furthermore, we give examples of HiCRep's ability to accurately distinguish replicates from non-replicates and to reveal cell type structure among collections of Hi-C data.

Availability

HiCRep.py and its documentation are available with a GPL license at https://github.com/Noble-Lab/hicrep. The software may be installed automatically using the pip package installer.

Supplementary information

Supplementary methods and results are included in an appendix at Bioinformatics online.

SUBMITTER: Lin D

PROVIDER: S-EPMC8479650 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

HiCRep.py: fast comparison of Hi-C contact matrices in Python.

Lin Dejun D Sanders Justin J Noble William Stafford WS

Bioinformatics (Oxford, England) 20210901 18

<h4>Motivation</h4>Hi-C is the most widely used assay for investigating genome-wide 3D organization of chromatin. When working with Hi-C data, it is often useful to calculate the similarity between contact matrices in order to assess experimental reproducibility or to quantify relationships among Hi-C data from related samples. The HiCRep algorithm has been widely adopted for this task, but the existing R implementation suffers from run time limitations on high-resolution Hi-C data or on large s ...[more]

PMID: 33576390

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