scHiCluster v0.1.1 – Single-cell Clustering algorithm for Hi-C Contact Matrices

scHiCluster v0.1.1

:: DESCRIPTION

scHiCluster is a comprehensive python package for single-cell chromosome contact data analysis. It includes the identification of cell types (clusters), loop calling in cell types, and domain and compartment calling in single cells.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

scHiCluster

:: MORE INFORMATION

Citation:

Zhou J, Ma J, Chen Y, Cheng C, Bao B, Peng J, Sejnowski TJ, Dixon JR, Ecker JR.
Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation.
Proc Natl Acad Sci U S A. 2019 Jul 9;116(28):14011-14018. doi: 10.1073/pnas.1901423116. Epub 2019 Jun 24. PMID: 31235599; PMCID: PMC6628819.

SALSA v2.3 – Scaffold long read Assemblies with Hi-C data

SALSA v2.3

:: DESCRIPTION

SALSA is a tool to scaffold long read assemblies with Hi-C.

::DEVELOPER

MarBL (Maryland Bioinformatics Labs)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

SALSA

:: MORE INFORMATION

Citation

Ghurye J, Pop M, Koren S, Bickhart D, Chin CS.
Scaffolding of long read assemblies using long range contact information.
BMC Genomics. 2017 Jul 12;18(1):527. doi: 10.1186/s12864-017-3879-z. PMID: 28701198; PMCID: PMC5508778.

HiNT v2.2.7 – Hi-C for Copy Number Variation and Translocation Detection

HiNT v2.2.7

:: DESCRIPTION

HiNT is a computational method to detect CNVs and Translocations from Hi-C data.

::DEVELOPER

Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
Perl
R package
Python

:: DOWNLOAD

HiNT

:: MORE INFORMATION

Citation

Wang S, Lee S, Chu C, Jain D, Kerpedjiev P, Nelson GM, Walsh JM, Alver BH, Park PJ.
HiNT: a computational method for detecting copy number variations and translocations from Hi-C data.
Genome Biol. 2020 Mar 23;21(1):73. doi: 10.1186/s13059-020-01986-5. PMID: 32293513; PMCID: PMC7087379.

FisHiCal 1.1 – Iterative FISH-based Calibration of Hi-C Data

FisHiCal 1.1

:: DESCRIPTION

FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis.

::DEVELOPER

Yoli Shavit, Fiona Kathryn Hamey and Pietro Lio’

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ Windows/MacOsX
R

:: DOWNLOAD

FisHiCal

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jul 23. pii: btu491.
FisHiCal: an R package for iterative FISH-based calibration of Hi-C data.
Shavit Y, Hamey FK, Lio P.

HiCat – Hi-C data analysis tool

HiCat

:: DESCRIPTION

HiCat is an Hi-C data analysis tool. Importantly, HiCat is focussed on analysis of larger structural features of chromosomes and on comparative studies.

::DEVELOPER

Ueli Grossniklaus’s group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ WIndows/MacOsX

:: DOWNLOAD

HiCat

:: MORE INFORMATION

Citation

Grob S, Schmid MW, Grossniklaus U. (2014)
HiC Analysis in Arabidopsis Identifies the KNOT, a Structure with Similarities to the flamenco Locus of Drosophila.
Molecular Cell, 55(5): 678-693

BACH / BACH-MIX – Bayesian 3D constructor for Hi-C data / Characterize Structural Variations of Chromatin Folding

BACH / BACH-MIX

:: DESCRIPTION

BACH is a novel Bayesian probabilistic approach for analyzing Hi-C data. BACH takes the Hi-C contact matrix and local genomic features (restriction enzyme cutting frequencies, GC content and sequence uniqueness) as input and produces, via MCMC computation, the posterior distribution of three-dimensional (3D) chromosomal structure

BACH-MIX is an extended BACH algorithm to characterize structural variations of chromatin folding

::DEVELOPER

Jun Liu

:: SCREENSHOTS

:: REQUIREMENTS

Windows / Linux
R

:: DOWNLOAD

BACH , BACH-MIX

:: MORE INFORMATION

Citation

Hu M, Deng K, Qin Z, Dixon J, Selvaraj S, Fang J, Ren B, Liu JS. (2013)
Bayesian Inference of Spatial Organizations of Chromosomes.
PLoS Computational Biology 9(1): e1002893.

HiCNorm – Removing Biases in Hi-C data via Poisson Regression

HiCNorm

:: DESCRIPTION

HiCNorm is a parametric model to remove systematic biases in the raw Hi-C contact maps. It relates chromatin interactions and systemic biases at the desired resolution level,resulting in a simple, yet accurate normalization procedure. Compared to the exiting Hi-C normalization method, our model has only a few parameters, is much easier to implement, can be interpreted intuitively, and achieves higher reproducibility in real Hi-C data

::DEVELOPER

Jun Liu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux
R

:: DOWNLOAD

HiCNorm

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Dec 1;28(23):3131-3. doi: 10.1093/bioinformatics/bts570. Epub 2012 Sep 27.
HiCNorm: removing biases in Hi-C data via Poisson regression.
Hu M1, Deng K, Selvaraj S, Qin Z, Ren B, Liu JS.

HiCNN 2 – Enhance the Resolution of Hi-C data

HiCNN 2

:: DESCRIPTION

HiCNN is a computational method for resolution enhancement of Hi-C data. It uses a very deep convolutional neural network (54 layers) to learn the mapping between low-resolution and high-resolution Hi-C contact matrices.

HiCNN2 is an improved version of our previously developed tool HiCNN for enhancing resolution of Hi-C data and uses three architectures to learn the mapping between low-resolution and high-resolution Hi-C contact matrices.

::DEVELOPER

Z. WANG LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Perl

:: DOWNLOAD

HiCNN2

:: MORE INFORMATION

Citation

Tong Liu and Zheng Wang.
HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks.
Genes, 2019, 10(11):862.

Tong Liu and Zheng Wang.
HiCNN: A very deep convolutional neural network to better enhance the resolution of Hi-C data.
Bioinformatics, 2019, 35(21):4222-4228.

SCL 1.0 – Three-dimensional Chromosome Structures from Single-cell Hi-C data

SCL 1.0

:: DESCRIPTION

SCL (Single-Cell Lattice) is a computational method to reconstruct high-resolution 3D chromosome structures based on the zero-inflated single-cell Hi-C data.

::DEVELOPER

Z. WANG LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Perl

:: DOWNLOAD

SCL

:: MORE INFORMATION

Citation

Zhu H, Wang Z.
SCL: a lattice-based approach to infer 3D chromosome structures from single-cell Hi-C data.
Bioinformatics. 2019 Oct 15;35(20):3981-3988. doi: 10.1093/bioinformatics/btz181. PMID: 30865261; PMCID: PMC6792089.

HiCNet – Reconstruct High-resolution Chromosome 3D Structures based on Hi-C Complex Networks

HiCNet

:: DESCRIPTION

HiCNet is an approach for establishing small-world networks for individual chromosomes using Hi-C data. It is a tool capable of converting Hi-C contacts to spatial distances. And it can also infer three-dimensional structures using spatial distances and multidimensional scaling methods.

::DEVELOPER

Z. WANG LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Perl

:: DOWNLOAD

HiCNet

:: MORE INFORMATION

Citation

Liu T, Wang Z.
Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks.
BMC Bioinformatics. 2018 Dec 28;19(Suppl 17):496. doi: 10.1186/s12859-018-2464-z. PMID: 30591009; PMCID: PMC6309071.