Methods to Assess the Reproducibility and Similarity of Hi-C Data

Tao Yang; Xi He; Lin An; Qunhua Li

doi:10.1007/978-1-0716-1390-0_2

Methods to Assess the Reproducibility and Similarity of Hi-C Data

Methods Mol Biol. 2022:2301:17-37. doi: 10.1007/978-1-0716-1390-0_2.

Authors

Tao Yang^#¹, Xi He^#¹, Lin An^#¹, Qunhua Li^#²

Affiliations

¹ Bioinformatics and Genomics Program, Pennsylvania State University, University Park, PA, USA.
² Department of Statistics, Pennsylvania State University, University Park, PA, USA. qunhua.li@psu.edu.

^# Contributed equally.

PMID: 34415529
DOI: 10.1007/978-1-0716-1390-0_2

Abstract

Hi-C experiments are costly to perform and involve multiple complex experimental steps. Reproducibility of Hi-C data is essential for ensuring the validity of the scientific conclusions drawn from the data. In this chapter, we describe several recently developed computational methods for assessing reproducibility of Hi-C replicate experiments. These methods can also be used to assess the similarity between any two Hi-C samples.

Keywords: GenomeDISCO; Hi-C data; HiC-spector; HiCRep; Quality control; Reproducibility; Similarity.

MeSH terms

Reproducibility of Results*
Software