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 |