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Identify real loops from Hi-C data.

Project description


hicpeaks provide a Python CPU-based implementation for BH-FDR and HiCCUPS, two peak calling algorithms for Hi-C data, proposed by Rao et al [1].


hicpeaks is developed and tested on UNIX-like operating systems, and the following packages or softwares are required:

Python requirements:

  1. Python 2.7/3.5+

  2. Multiprocess

  3. Numpy

  4. Scipy

  5. Matplotlib

  6. Pandas

  7. Statsmodels

  8. Scikit-Learn

  9. H5py

  10. Cooler

Other requirements:

  • ucsc-fetchchromsizes

conda, an excellent package manager, can be used to install all requirements above.

Install Requirements Through Conda

All above requirements can be installed through the conda package manager.

Choose an appropriate Miniconda installer for your system, then in your terminal window type the following and follow the prompts on the installer screens:

$ bash

After that, update the environment variables to finish the Conda installation:

$ source ~/.bashrc

Next, you need to set up channels to make all packages listed above accessible (note that the order is important to guarantee the correct priority):

$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forge

Then type and execute the commands below to satisfy the requirements:

$ conda create -n HiCPeaks numpy scipy matplotlib pandas statsmodels scikit-learn h5py multiprocess cooler ucsc-fetchchromsizes
$ conda activate HiCPeaks

Install hicpeaks

Finally, hicpeaks can be installed from PyPI using pip:

$ pip install -U hicpeaks


hicpeaks comes with 6 scripts: toCooler, pyBHFDR, pyHICCUPS, combine-resolutions, peak-plot, and apa-analysis.

  • toCooler

    Store TXT/NPZ bin-level Hi-C data into the cooler container.

    1. I have included a sample data with the hicpeaks source code to illustrate how you should prepare your data in TXT format. It’s quite easy, just remember 3 points: 1. the file name should follow this pattern “chrom1_chrom2.txt” (remove prefix from your chromosome labels, i.e. “chr1” should be “1”, and “chrX” should be “X”); 2. each file should only contain 3 columns, corresponding to “bin1” of “chrom1”, “bin2” of “chrom2”, and “contact frequency” (don’t perform any normalization processes); 3. all files at the same resolution should be placed in the same folder.

    2. NPZ format is another bin-level Hi-C data container which can extremely speed up data loading. hicpeaks supports NPZ files generated by old versions of runHiC (<0.8.0) and TADLib (<0.4.0).

  • pyBHFDR

    A CPU-based python implementation for the BH-FDR algorithm. Rao et al (2014) stated in their supplementary material that this algorithm is robust enough to obtain all main results of their paper. Compared with HiCCUPS, BH-FDR doesn’t use λ-chunk in multiple hypothesis tests, and only considers the Donut background region when calculating the expected values.


    A CPU-based python implementation for the HiCCUPS algorithm. Besides the donut region, HiCCUPS also considers the lower-left, the vertical, and the horizontal backgrounds when calculating the expected values. And λ-chunk is used to overcome several multiple hypothesis testing challenges for Hi-C data. Finally, while BH-FDR can only detect chromatin interactions near the diagonal (<2Mb), HiCCUPS is able to detect super long-range interactions. Here, pyHICCUPS keeps all main concepts of the original algorithm except for these points:

    1. pyHICCUPS excludes vertical and horizontal backgrounds from its calculation.

    2. There are two critical parameters related to the loop definition in HiCCUPS: the peak width p and the donut width w. In original implementation, they are set exclusively for each certain resolution, specifically, p=1 and w=3 at 25Kb, p=2 and w=5 at 10Kb, and p=4 and w=7 at 5Kb. To improve the sensitivity, pyHICCUPS can calculate and output the union peak calls from all parameter combinations (1,3), (2,5), (4,7) in a single run.

    3. Due to computational complexity, the search space still need to be limited, for example, within 5Mb/10Mb.

  • combine-resolutions

    Combine peak calls from different resolutions in a way similar to original HiCCUPS. Briefly, it excludes redundant lower resolution peaks while filters out low-confidence high resolution peaks.

  • peak-plot

    Visualize peaks (or chromatin loops) on a local contact matrix.

  • apa-analysis

    Perform Aggregate Peak Analysis (APA).


This tutorial will guide you through the basic usage of all scripts distributed with hicpeaks.


If you have already created a cooler file for your Hi-C data, skip to the next section pyBHFDR and pyHICCUPS, go on otherwise.

First, you should store your TXT/NPZ bin-level Hi-C data into a cooler file by using toCooler. Let’s begin with our sample data below. Suppose you are in the hicpeaks source code root folder: change your current working directory to the sub-folder example:

$ cd example
$ ls -lh *

-rw-r--r-- 1 xtwang  18 May  4 18:00 datasets
-rw-r--r-- 1 xtwang 293 May  4 18:00 hg38.chromsizes

total 12M
-rw-r--r-- 1 xtwang 12M May  4 18:00 21_21.txt

There is a sub-directory called 25K and a metadata file called datasets. The 25K folder contains chromatin interactions of chromosome 21 of the K562 cell line at the 25K resolution, and the datasets describes the data that need to be transformed:

$ cd 25K
$ head -5 21_21.txt

201 703     1
201 1347    1
201 1351    1
201 1524    1
201 1691    1

$ cd ..
$ cat datasets


You should construct your TXT files (no head, no tail) with 3 columns, which indicate “bin1 of the 1st chromosome”, “bin2 of the 2nd chromosome”, and “contact frequency” respectively. See Overview above.

To transform this data to the cooler format, just run the command below:

$ toCooler -O -d datasets --assembly hg38 --nproc 1

toCooler routinely fetch sizes of each chromosome from UCSC with the provided genome assembly name (here hg38). However, if your reference genome is not holded in UCSC, you can also build a file like “hg38.chromsizes” in current working directory, and pass the file path to the argument “–chromsizes-file”.

Type toCooler with no arguments on your terminal to print detailed help information for each parameter.

For this dataset, toCooler will create a cooler file named “”, and your data will be stored under the URI “”.

This tutorial only illustrates a very simple case, in fact the metadata file may contain list of resolutions (if you have data at different resolutions for the same cell line) and corresponding folder paths (both relative and absolute path are accepted, and if your data are in the NPZ format, this path should point to the NPZ file):




Then toCooler will generate a single cooler file storing all the specified data under different cooler URI. Suppose your cool file is named “specified_cooler_path”, the above data will be stored at “specified_cooler_path::10000”, “specified_cooler_path::25000”, and “specified_cooler_path::40000”, respectively.


After you have obtained a cool file, you can call peaks or chromatin loops using pyBHFDR or pyHICCUPS:

$ pyBHFDR -O K562-MboI-BHFDR-loops.txt -p -C 21 --pw 1 --ww 3


$ pyHICCUPS -O K562-MboI-HICCUPS-loops.txt -p --pw 1 2 4 --ww 3 5 7 --only-anchors

Type pyBHFDR or pyHICCUPS on your terminal to print detailed help information for each parameter.

Before step to the next section, let’s list the contents under current working directory again:

$ ls -lh

total 852K
drwxr-xr-x 4 xtwang  128 May  4 18:21 25K/
-rw-r--r-- 1 xtwang  17K May  4 18:23 K562-MboI-BHFDR-loops.txt
-rw-r--r-- 1 xtwang  15K May  4 18:23 K562-MboI-HICCUPS-loops.txt
-rw-r--r-- 1 xtwang 723K May  4 18:22
-rw-r--r-- 1 xtwang   18 May  4 18:21 datasets
-rw-r--r-- 1 xtwang  293 May  4 18:21 hg38.chromsizes
-rw-r--r-- 1 xtwang 2.2K May  4 18:23 pyBHFDR.log
-rw-r--r-- 1 xtwang 8.5K May  4 18:23 pyHICCUPS.log
-rw-r--r-- 1 xtwang  17K May  4 18:22 tocooler.log

The detected loops are reported in a customized bedpe format. The first 10 columns are identical to the official definition, and the additional fields are:

  1. Fold enrichment score calculated from the donut background.

  2. The p value calculated from the donut background.

  3. The q value calculated from the donut background.

  4. Fold enrichment score calculated from the lower-left background.

  5. The p value calculated from the lower-left background.

  6. The q value calculated from the lower-left background.

Peak Visualization

Now, you can visualize the detected peaks/loops using peak-plot:

$ peak-plot -O test-HICCUPS.png -p -I K562-MboI-HICCUPS-loops.txt \
  -C 21 -S 25000000 -E 29500000 --balance-type ICE --vmin 0 --vmax 0.008

The output figure should look like this:


Aggregate Peak Analysis

To inspect the overall loop patterns of the detected peaks, you can use the apa-analysis script:

$ apa-analysis -O apa.png -p -I K562-MboI-HICCUPS-loops.txt -U --vmax 2

The output plot should look like this:


Combine different resolutions

The inputs to combine-resolutions are loop annotation files (bedpe) at different resolutions. If an interaction is detected as a peak in both resolutions, this script records the precise coordinates in finer resolutions and discards the coarser resolution one. And a long-range (determined by the --min-dis parameter) peak call at high resolutions (any resolutions lower than the --good-res cutoff, note that lower values correspond to higher resolutions) will be treated as a false positive if it could not be identified at lower resolutions (any resolutions equal to or greater than the --good-res cutoff). Here’s a pseudo command with 3 loop files at 5Kb, 10Kb, and 20Kb respectively:

$ combine-resolutions -O K562-MboI-pyHICCUPS-combined.bedpe -p K562-MboI-pyHICCUPS-5K.txt K562-MboI-pyHICCUPS-10K.txt K562-MboI-pyHICCUPS-20K.txt -R 5000 10000 20000 -G 20000 -M 100000


The table below shows a performance test for the toCooler, pyBHFDR , and pyHICCUPS scripts:

  • Processor: 2.6 GHz Intel Core i7, Memory: 16 GB 2400 MHz DDR4

  • Software version: hicpeaks 0.3.0

  • At the 40Kb resolution, --pw and --ww are set to 1 and 3, respectively; at the 10Kb resolution, they are set to 2 and 5, respectively.

  • The original Hi-C data is stored in TXT

  • Number of proccesses assigned: 1

  • Valid contacts: total number of non-zero pixels in intra-chromosomal matrices

  • Running time format: hr: min: sec


Valid contacts




Memory Usage

Running time

Memory Usage

Running time

Memory Usage

Running time

T47D (40K)








K562 (40K)








K562 (10K)








Release Notes

Version 0.3.5 (08/28/2022)

  • Added parameters to peak-plot and apa-analysis so that the output figures can be more finely tuned

Version 0.3.4 (05/04/2019)

  • Improved the efficiency of the local clustering algorithm

  • Changed the output loop format to bedpe

Version 0.3.3 (03/08/2019)

  • Made toCooler support the float data type

  • Removed ticklabels in APA plot

Version 0.3.2 (03/03/2019)

  • Supported combination of different resolutions

  • Improved the local clustering algorithm

  • Added the APA analysis module

  • Dealed with the compatiblility with cooler 0.8

Version 0.3.0 (09/03/2018)

  • Removed the horizontal and vertical backgrounds

  • Supported multiple combinations of the pw and ww parameters

  • Migrated to Python 3

  • Optimized the calculation efficiency

  • Fixed bugs when external .cool files are provided.

Version 0.2.0-r1 (08/26/2018)

  • Speeded up the program by dynamically limiting the donut widths

Version 0.2.0 (08/25/2018)

  • Added the vertical and horizontal backgrounds

  • Added additional filtering procedures based on the dbscan clusters and more stringent q value cutoffs

  • Fixed bugs of toCooler in storing the inter-chromosomal data

Version 0.1.1 (08/24/2018)

  • Lower memory usage and more efficient calculation

Version 0.1.0 (08/22/2018)

  • The first release.

  • Added toCooler and peak-plot.

  • Added support for multiple processing.

Pre-Release (05/04/2015)

  • Implemented core algorithms of BH-FDR and HICCUPS


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