HiCEnterprise: Identifying long range chromosomal contacts in HiC data
- Published
- Accepted
- Subject Areas
- Bioinformatics
- Keywords
- hi-c data, chromosome contacts, statistical models
- Copyright
- © 2019 Kranas et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. HiCEnterprise: Identifying long range chromosomal contacts in HiC data. PeerJ Preprints 7:e27753v1 https://doi.org/10.7287/peerj.preprints.27753v1
Abstract
Computational analysis of chromosomal capture data is currently gaining popularity with the rapid advance in experimental techniques providing access to growing body of data. An important problem in this area is the identification of long-range contacts be- tween distinct chromatin regions. Such loops were shown to exist at different scales, either mediating interactions between enhancers and promoters or providing much longer interactions between functionally interacting distant chromosome domains. A proper statistical analysis is crucial for accurate identification of such interactions from experi- mental data. We present HiCEnterprise, a software tool for identification of long-range chromatin contacts. It implements three different sta- tistical tests for identification of significant contacts at different scales as well as necessary functions for input, output and visualization of chromosome contact matrices.
Author Comment
This is a preprint version of a manuscript describing the HiCEnterprise bioinformatics software tool published here before submission to a journal.
Supplemental Information
Source code of the HiCEnterprise tool
The sourcecode as downloaded from https://github.com/regulomics/HiCEnterprise