Table 1.

Software tools for Hi-C data analysis

Tool	Short-read	Mapping	Read	Read-pair	Normalization	Visualization	Confidence	Implementation
	aligner(s)	improvement	filtering	filtering			estimation	language(s)
HiCUP [46]	Bowtie/Bowtie2	Pre-truncation	✓	✓	−	−	−	Perl, R
Hiclib [47]	Bowtie2	Iterative	✓ a	✓	Matrix balancing	✓	−	Python
HiC-inspector [131]	Bowtie	−	✓	✓	−	✓	−	Perl, R
HIPPIE [132]	STAR	✓ b	✓	✓	−	−	−	Python, Perl, R
HiC-Box [133]	Bowtie2	−	✓	✓	Matrix balancing	✓	−	Python
HiCdat [122]	Subread	−c	✓	✓	Three options d	✓	−	C++, R
HiC-Pro [134]	Bowtie2	Trimming	✓	✓	Matrix balancing	−	−	Python, R
TADbit [120]	GEM	Iterative	✓	✓	Matrix balancing	✓	−	Python
HOMER [62]	−	−	✓	✓	Two options e	✓	✓	Perl, R, Java
Hicpipe [54]	−	−	−	−	Explicit-factor	−	−	Perl, R, C++
HiBrowse [69]	−	−	−	−	−	✓	✓	Web-based
Hi-Corrector [57]	−	−	−	−	Matrix balancing	−	−	ANSI C
GOTHiC [135]	−	−	✓	✓	−	−	✓	R
HiTC [121]	−	−	−	−	Two options f	✓	✓	R
chromoR [59]	−	−	−	−	Variance stabilization	−	−	R
HiFive [136]	−	−	✓	✓	Three options g	✓	−	Python
Fit-Hi-C [20]	−	−	−	−	−	✓	✓	Python

^aHiclib keeps the reads with only one mapped end (single-sided reads) for use in coverage computations

^bHIPPIE states that it rescues chimeric reads. No details are given

^cHiCdat reports no substantial improvement in successfully aligned read pairs when iterative mapping in Hiclib is used for Arabidopsis thaliana Hi-C data

^dHiCdat provides three options for normalization: coverage and distance correction, HiCNorm and ICE

^eHOMER provides two options for normalization: simpleNorm corrects for sequencing coverage only and norm corrects for coverage plus the genomic distance between loci

^fHiTC provides two options for normalization: normLGF implements HiCNorm and normICE implements ICE algorithm from Hiclib

^gHiFive provides three options - Probability, Express, and Binning - for normalization. The Express and Binning algorithms correspond to matrix balancing and explicit-factor correction schemes, respectively