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Table 2 Normalization tools for Hi-C data

From: Molecular and computational approaches to map regulatory elements in 3D chromatin structure

Software Function How to run
Binless [225] Normalize in resolution-agnostic way and adapt to quality and quantity of available data R package
hicapp/caICB [167] Normalize genomic DNA copy number bias in tumor cells, as well as fragment length, GC bias, and mappability Linux command line
HiCorr [226] Normalize GC bias, mappability, fragment explicitly, and visibility implicitly Linux command line
HiFive [155] Normalize through binning, matrix-balancing, and multiplicative-probability model Linux command line/Python package
HiCNorm [164] Explicitly normalize fragment length, GC bias, and mappability Linux command line through R script
Hicpipe [165] Explicitly normalize fragment length, GC bias, and mappability Linux command line
multiHiCcompare [168] Normalize across multiple Hi-C datasets R package
oneD [227] Normalize copy number variation bias, especially for biological samples with aberrant karyotype R package