Open AccessComputational tools for Hi‐C data analysis
Author(s) -
Han Zhijun,
Wei Gang
Publication year - 2017
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-017-0113-6
Subject(s) - chromatin , workflow , computational biology , genome , chromosome conformation capture , computer science , pipeline (software) , biology , function (biology) , dna , genetics , transcription factor , gene , database , enhancer , programming language
Background In eukaryotic genome, chromatin is not randomly distributed in cell nuclei, but instead is organized into higher‐order structures. Emerging evidence indicates that these higher‐order chromatin structures play important roles in regulating genome functions such as transcription and DNA replication. With the advancement in 3C (chromosome conformation capture) based technologies, Hi‐C has been widely used to investigate genome‐wide long‐range chromatin interactions during cellular differentiation and oncogenesis. Since the first publication of Hi‐C assay in 2009, lots of bioinformatic tools have been implemented for processing Hi‐C data from mapping raw reads to normalizing contact matrix and high interpretation, either providing a whole workflow pipeline or focusing on a particular process. Results This article reviews the general Hi‐C data processing workflow and the currently popular Hi‐C data processing tools. We highlight on how these tools are used for a full interpretation of Hi‐C results. Conclusions Hi‐C assay is a powerful tool to investigate the higher‐order chromatin structure. Continued development of novel methods for Hi‐C data analysis will be necessary for better understanding the regulatory function of genome organization.