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Table 1 Software tools for Hi-C data analysis

From: Analysis methods for studying the 3D architecture of the genome

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
  1. aHiclib keeps the reads with only one mapped end (single-sided reads) for use in coverage computations
  2. bHIPPIE states that it rescues chimeric reads. No details are given
  3. cHiCdat reports no substantial improvement in successfully aligned read pairs when iterative mapping in Hiclib is used for Arabidopsis thaliana Hi-C data
  4. dHiCdat provides three options for normalization: coverage and distance correction, HiCNorm and ICE
  5. eHOMER provides two options for normalization: simpleNorm corrects for sequencing coverage only and norm corrects for coverage plus the genomic distance between loci
  6. fHiTC provides two options for normalization: normLGF implements HiCNorm and normICE implements ICE algorithm from Hiclib
  7. 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