HiC on Accessible Regulatory DNA (HiCAR) is a feasible tool to study the long-range interactions of the cis- and trans-regulatory elements (cREs/TREs) especially for the low input samples and the samples with no available antibodies. To facilitate the analysis of HiCAR data, the Nextflow platform based nf-core/hicar pipeline can detect the chromatin interaction in single nucleotide resolution within and among chromosomes.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analzse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 4 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Multiple runs of the same sample

The sample identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:


Please Note that the dots (’.’) and space (’ ’) in the group names will be replaced by ’_’ to avoid potential issues in python scripts. In the pipeline, the group names will be used as the prefix for filenames. Many tools developed in python will consider the first dot as the sign of file extension and this will throw errors when the program does not expected the extra dots in the filenames. If you want to keep consistent of file naming system with the group names, please consider to replace all the dots and spaces in the samplesheet.

Full samplesheet

HiCAR requires paired-end sequencing. Both fastq_1 and fastq_2 must be provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 4 columns to match those defined in the table below.

If md5_1/2 is provided, the pipeline will check the checksums.

A final samplesheet file consisting of two groups of paired-end data may look something like the one below. This is for 6 samples, where TREATMENT_REP3 has been sequenced twice.

groupCustom group name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_).
replicateBiological replicates of the samples.
techniquereplicatetechnique replicates of the samples. Default is 1.
fastq_1Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
fastq_2Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
md5_1Checksum for fastq_1. The checksums of the files will be check to make sure the file is not truncated if provided.
md5_2Checksum for fastq_2. The checksums of the files will be check to make sure the file is not truncated if provided.

An example samplesheet has been provided with the pipeline.

Different levels of 3D organization of chromatin

Schematic representation of the 3D arrangement of chromatin

Call A/B compartments and TADs

On a large scale, the arrangement of chromosomes are organised into two compartments labelled A (“active”) and B (“inactive”). A/B compartment-associated regions are on the multi-Mb scale and correlate with either open and expression-active chromatin (“A” compartments) or closed and expression inactive chromatin (“B” compartments). A compartments tend to be gene-rich, have high GC-content, contain histone markers for active transcription, and usually displace the interior of the nucleus. The regions in compartment A tend to interact preferentially with A compartment-associated regions than B compartment-associated ones. B compartments, on the other hand, tend to be gene-poor, compact, contain histone markers for gene silencing, and lie on the nuclear periphery.

A topologically associating domain (TAD) is a smaller size genomic region compared to A/B compartments. It is a self-interacting genomic region. Most of the studies indicate TADs regulate gene expression by limiting the enhancer-promoter interaction to each TAD. A number of proteins are known to be associated with TAD formation. The most studied proteins are the protein CCCTC-binding factor (CTCF) and the protein complex cohesin. It has been shown that the TAD boundaries have high levels of CTCF binding and cohesin/lamina shifting edges.

There are multiple available modules to call A/B compartments and TADs.

  • For A/B Compartments calling, available tools are ‘cooltools’, ‘hicExplorer’, ‘Homer’, and ‘juicer_tools’.
  • For TADs calling, available tools are ‘cooltools’, ‘hicexplorer’, and ‘Homer’.

Here is a short introduction about the tools:

  • The cooltools leverages cooler format to enable flexible and reproducible analysis of high-resolution data. insulation tool will be used for TADs calling.
  • The HiCExplorer is a set of programs to process, normalize, analyze and visualize Hi-C and cHi-C data. The hicFindTADs will be used to call TADs.
  • The Homer is a software for motif discovery and next-gen sequencing analysis.
  • The juicer_tools is a platform for analyzing bin sized Hi-C data. The HiCTools will be used to create .hic files, and call compartments. Please note that by default the normalization method for compartment calling was set as SCALE. You may want to try different parameters for the normalization method such as KR.

Call interactions/loops

Chromatin loops (or significant interactions), represent two inter/intra chromosome regions that interact at a high frequency with one another (high reads density in sequence data). Different from HiC, HiCAR data are biased with one ends or both ends in the open chromatin. The MAPS are designed to remove this kind of biases introduced by the ChIP or Tn5-transposition procedure. However, many tools are hesitant to introduce this kind of model-based analysis for interaction analysis since high frequency interactions must happen within the highly opened chromatin regions. Here nf-core/hicar provides multiple choices for interactions calling. Available tools are ‘MAPS’, ‘HiC-DC+’ and ‘peakachu’.

In downstream, differential analysis available for called interactions. Available tools are Bioconductor packages such as edgeR, and diffhic, and HiCExplorer. We borrowed capture Hi-C analysis pipeline from HiCExplorer to do the differential analysis. Different from edgeR and diffhic pipeline, HiCExplorer pipeline does not require the replicates. A simple differential analysis by set operation are also available.

For annotation, we will use Bioconductor package ChIPpeakAnno. Please note that, the involved genes are not only distance based annotation. The most of the interaction calling tools are bin-based caller, and the bin size are kilo-base or even more, which make the annotation difficult. For HiCAR data, the R2 reads are Tn5 insertion site of the open chromatin. And most of the R2 reads will be an anchor of annotation for the gene promoters. We will annotate the interactions by the annotation of called ATAC (R2) peaks located within the interaction regions.

Call high resolution interactions

The high resolution interaction caller is also available for confident transcription factor enrichment analysis. However, please note that the high resolution interaction caller are not bin-based but peak based analysis, which uses high computational resources.

Aggregate peak analysis

Aggregate peak analysis (APA) plots the pileup signals detected by high-resolution interaction data. It is a kind of 2 dimension meta-gene analysis. By providing a list of interested genomic coordinates, the pileup signal will present the enrichment between the interactions and the target interested region. Current available tools for APA are cooltools, HiCExplorer and JuicerTools.

Virtual 4C

Circularized Chromosome Conformation Capture (4C) is a powerful technique for studying the interactions of a specific genomic region with the rest of the genome. Visualize Hi-C data in a virtual 4C (v4c) format can help user to zoom in the interactions within a specific viewpoint. Current available tools for v4c are cooltools, HiCExplorer and trackViewer.

Available tools

Toolsas paramerterA/B compartmentsTADsInteractionsDifferential analysisAPAV4C

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/hicar --input ./samplesheet.csv --outdir ./results --genome GRCh37 -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

⚠️ Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/hicar -profile docker -params-file params.yaml

with params.yaml containing:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'

You can also generate such YAML/JSON files via nf-core/launch.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/hicar


It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/hicar releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducbility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

💡 If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

Core Nextflow arguments

NB: These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).


Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer enviroment.

  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters
  • docker
    • A generic configuration profile to be used with Docker
  • singularity
    • A generic configuration profile to be used with Singularity
  • podman
    • A generic configuration profile to be used with Podman
  • shifter
    • A generic configuration profile to be used with Shifter
  • charliecloud
    • A generic configuration profile to be used with Charliecloud
  • apptainer
    • A generic configuration profile to be used with Apptainer
  • conda
    • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.


Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.


Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.


In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'


  • Error: The exit status of the task that caused the workflow execution to fail was: null.

Check the files are readable for the workflow.

  • Error: Session aborted -- Cause: Unable to execute HTTP request:

The internet connection reached the limitation. Try to resume the analysis one hour later.

  • Error: PaddingError: Placeholder of length '80' too short in package

There is no easy answer here. The new conda packages should having a longer prefix (255 characters). The possible solution now is that try to run the pipeline in a shorter folder path, if at all possible.

  • Error: Not a conda environment or command not found

There is something going wrong with the conda environment building. Just try to remove the conda environment folder and resume the run.

  • Error: unable to load shared object 'work/conda/env-xxxxxx/lib/R/library/rtracklayer/libs/rtracklayer.dylib', dlopen(rtracklayer.dylib, 6) Library not loaded: @rpath/libssl.1.1.dylib

The openssl installation have issues for conda. Try to reinstall it by conda activate work/conda/env-xxxxxx && conda install --force-reinstall -y openssl

  • Error: error Can't locate Statistics/

The perl-statistics-basic installed in wrong location. Try to reinstall it by conda activate work/conda/env-xxxxx && perl -MCPAN -e 'CPAN::install(Statistics::Basic)'

  • Error in result[[njob]] <- value : attempt to select less than one element in OneIndex

The error may caused by out of memory (although the error message seems to be unrelated to memory). Try to set --peak_pair_block to a smaller number less than 1e9.

  • [CRITICAL] The sample sheet **must** contain these column headers: replicate, group, fastq_2, fastq_1.

The error may caused by the improper formtated .csv file (the file contain header info before the column names) by Microsoft Excel. Please use a text editor to reprepare the .csv sample files.

  • line 2: gtf2bed: command not found or line 1: command not found.

When you are using container and your nextflow home folder is a symlink, the bin folder in the source code will be not available in the container.

Known issue with Juicer_tools

If you are using Juicer_tools with GPU supported, it is not supported by the containers. We are using Juicer Tools Pre to create the hic files from aligned HiCAR reads. We recommend having at least 4GB free RAM to generate the hic files.

Tips for HPC Users

When using an HPC system you should specify the executor matching your system. Check available executors to use the correct executor and parameters. This instructs Nextflow to submit pipeline tasks as jobs into your HPC workload manager. Take SLURM workload manager system as an example for the minimal test, this can be done adding the following lines to the nextflow.config.

process.executor = 'slurm' // the workload manager name
executor {// the job queue size
    name = 'slurm'
    queueSize = 10
process.clusterOptions = "-J nextFlowHiCAR -p scavenger" // the options, here -p request a specific partition for the resource allocation. It will be different in your cluster.

Useful resources