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HiNT -- HiC for copy number vairations and translocations detection

Project description


A computational method for detecting copy number variations and translocations from Hi-C data


HiNT (Hi-C for copy Number variation and Translocation detection), a computational method to detect CNVs and Translocations from Hi-C data. HiNT has three main components: HiNT-PRE, HiNT-CNV, and HiNT-TL. HiNT-PRE preprocesses Hi-C data and computes the contact matrix, which stores contact frequencies between any two genomic loci; both HiNT-CNV and HiNT-TL starts with HI-C contact matrix, predicts copy number segments, and inter-chromosomal translocations, respectively

Overview of HiNT workflow:



R and R packages

  1. R >= 3.4
  2. mgcv, strucchange, doParallel, Cairo, foreach

Python and Python packages

  1. python >= 3.5
  2. pyparix >= 0.3.0, cooler >= 0.7.4, pairtools >= 0.2.2, numpy, scipy, pandas, sklearn, multiprocessing

Java and related tools (Optional: required when want to process Hi-C data with juicer tools)

  1. Java (version >= 1.7)
  2. Juicer tools (1.8.9 is recommended)


  1. Perl (version >= 5)

Other dependencies

  1. samtools (1.3.1+)
  2. BIC-seq2 (0.7.3) ! This is optional: if you don't want to run HiNT-CNV, you don't need this package. No need to install, just download BICseq2, unzip it, and give the path where you stored to HiNT.
  3. bwa (0.7.16+) ! This is optional: required only when your input is fastq
  4. tabix (0.2.6)

Install HiNT

  • Method1: Install from PyPI using pip.

    $ pip install HiNT-Packages

  • Method2: Install using conda (highly recommend)

    $ conda install hint

  • Method3: Install manually

    1. Install HiNT dependencies
    2. Download HiNT git clone
    3. Go to HiNT directory, install it by $ python install

*** Type $ hint to test if HiNT successfully installed

Download reference files used in HiNT

  1. Download HiNT references HERE. Only hg19, hg38 and mm10 are available currently. Unzip it $ unzip
  2. Put reference files into the HiNT directory $ mv hg19/* where_you_put_HiNT/HiNT/HiNT/references/

Quick Start

  • Download the test datasets from HERE


HiNT pre: Preprocessing Hi-C data. HiNT pre does alignment, contact matrix creation and normalization in one command line.

$ hint pre -d /path/to/hic_1.fastq.gz,/path/to/hic_2.fastq.gz -i /path/to/bwaIndex --informat fastq --outformat cooler -g hg19 -n test -o /path/to/outputdir --pairsampath /path/to/pairsamtools

see details and more options

$ hint pre -h


HiNT cnv: prediction of copy number information, as well as segmentation from Hi-C.

$ hint cnv -m contactMatrix.mcool -f cooler -r 50 -g hg19 -n test -o /path/to/outputDir

see details and more options

$ hint cnv -h


HiNT tl: interchromosomal translocations and breakpoints detection from Hi-C inter-chromosomal interaction matrices.

$ hint tl -m /path/to/,/path/to/ -c chimericReads.pairsam -f cooler -g hg19 -n test -o /path/to/outputDir

see details and more options

$ hint tl -h

Output of HiNT

HiNT-PRE output

In the HiNT-PRE output directory, you will find

  1. jobname.bam aligned lossless file in bam format
  2. jobname_merged_valid.pairs.gz reads pairs in pair format
  3. jobname_chimeric.sorted.pairsam.gz ambiguous chimeric read pairs used for breakpoint detection in pairsam format
  4. jobname_valid.sorted.deduped.pairsam.gz valid read pairs used for Hi-C contact matrix creation in pairsam format
  5. jobname.mcool Hi-C contact matrix in cool format
  6. jobname.hic Hi-C contact matrix in hic format

HiNT-CNV output

In the HiNT-CNV output directory, you will find

  1. jobname_GAMPoisson.pdf the GAM regression result
  2. segmentation/jobname_bicsq_allchroms.txt CNV segments with log2 copy ratio and p-values in txt file
  3. segmentation/jobname_resolution_CNV_segments.png figure to visualize CNV segments
  4. segmentation/jobname_bicseq_allchroms.l2r.pdf figure to visualize log2 copy ration in each bin (bin size = resolution you set)
  5. segmentation/other_files intermediate files used to run BIC-seq
  6. jonname_dataForRegression/* data used for regression as well as residuals after removing Hi-C biases

HiNT-TL output

In the HiNT-TL output directory, you will find

  1. jobname_Translocation_IntegratedBP.txt the final integrated translocation breakpoint
  2. jobname_chrompairs_rankProduct.txt rank product predicted potential translocated chromosome pairs
  3. otherFolders intermediate files used to identify the translocation breakpoints

Project details

Download files

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Filename, size & hash SHA256 hash help File type Python version Upload date
HiNT_Package-2.0.9-py3-none-any.whl (54.7 kB) Copy SHA256 hash SHA256 Wheel py3
HiNT-Package-2.0.9.tar.gz (47.7 kB) Copy SHA256 hash SHA256 Source None

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