Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 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. Below is an example for the same sample sequenced across 3 lanes:


Full samplesheet

The nf-core-hic pipeline is designed to work only with paired-end data. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below.

sampleCustom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_).
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”.

An example samplesheet has been provided with the pipeline.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/hic --input samplesheet.csv --outdir <OUTDIR> --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.

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/hic


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/hic 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.

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, 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
  • 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 or Charliecloud.


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.

For example, if the nf-core/rnaseq pipeline is failing after multiple re-submissions of the STAR_ALIGN process due to an exit code of 137 this would indicate that there is an out of memory issue:

[62/149eb0] NOTE: Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Caused by:
    Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137)
Command executed:
    STAR \
        --genomeDir star \
        --readFilesIn WT_REP1_trimmed.fq.gz  \
        --runThreadN 2 \
        --outFileNamePrefix WT_REP1. \
Command exit status:
Command output:
Command error: line 9:  30 Killed    STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. <TRUNCATED>
Work dir:
Tip: you can replicate the issue by changing to the process work dir and entering the command `bash`

For beginners

A first step to bypass this error, you could try to increase the amount of CPUs, memory, and time for the whole pipeline. Therefor you can try to increase the resource for the parameters --max_cpus, --max_memory, and --max_time. Based on the error above, you have to increase the amount of memory. Therefore you can go to the parameter documentation of rnaseq and scroll down to the show hidden parameter button to get the default value for --max_memory. In this case 128GB, you than can try to run your pipeline again with --max_memory 200GB -resume to skip all process, that were already calculated. If you can not increase the resource of the complete pipeline, you can try to adapt the resource for a single process as mentioned below.

Advanced option on process level

To bypass this error you would need to find exactly which resources are set by the STAR_ALIGN process. The quickest way is to search for process STAR_ALIGN in the nf-core/rnaseq Github repo. We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the modules/ directory and so, based on the search results, the file we want is modules/nf-core/star/align/ If you click on the link to that file you will notice that there is a label directive at the top of the module that is set to label process_high. The Nextflow label directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements. The default values for the process_high label are set in the pipeline’s base.config which in this case is defined as 72GB. Providing you haven’t set any other standard nf-core parameters to cap the maximum resources used by the pipeline then we can try and bypass the STAR_ALIGN process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB. The custom config below can then be provided to the pipeline via the -c parameter as highlighted in previous sections.

process {
        memory = 100.GB

NB: We specify the full process name i.e. NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN in the config file because this takes priority over the short name (STAR_ALIGN) and allows existing configuration using the full process name to be correctly overridden.

If you get a warning suggesting that the process selector isn’t recognised check that the process name has been specified correctly.

Updating containers (advanced users)

The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the process name and override the Nextflow container definition for that process using the withName declaration. For example, in the nf-core/viralrecon pipeline a tool called Pangolin has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn’t make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via -c custom.config.

  1. Check the default version used by the pipeline in the module file for Pangolin

  2. Find the latest version of the Biocontainer available on

  3. Create the custom config accordingly:

    • For Docker:

      process {
          withName: PANGOLIN {
              container = ''
    • For Singularity:

      process {
          withName: PANGOLIN {
              container = ''
    • For Conda:

      process {
          withName: PANGOLIN {
              conda = 'bioconda::pangolin=3.0.5'

NB: If you wish to periodically update individual tool-specific results (e.g. Pangolin) generated by the pipeline then you must ensure to keep the work/ directory otherwise the -resume ability of the pipeline will be compromised and it will restart from scratch.


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'

Use case

Hi-C digestion protocol

Here is an command line example for standard DpnII digestion protocols. Alignment will be performed on the mm10 genome with default parameters. Multi-hits will not be considered and duplicates will be removed. Note that by default, no filters are applied on DNA and restriction fragment sizes.

nextflow run --input './*_R{1,2}.fastq.gz' --genome 'mm10' --digestion 'dnpii'

DNase Hi-C protocol

Here is an command line example for DNase protocol. Alignment will be performed on the mm10 genome with default paramters. Multi-hits will not be considered and duplicates will be removed. Contacts involving fragments separated by less than 1000bp will be discarded.

nextflow run --input './*_R{1,2}.fastq.gz' --genome 'mm10' --dnase --min_cis 1000



Use this to specify the location of your input FastQ files. For example:

--input 'path/to/data/sample_*_{1,2}.fastq'

Please note the following requirements:

  1. The path must be enclosed in quotes
  2. The path must have at least one * wildcard character
  3. When using the pipeline with paired end data, the path must use {1,2} notation to specify read pairs.

If left unspecified, a default pattern is used: data/*{1,2}.fastq.gz

Note that the Hi-C data analysis workflow requires paired-end data.

Reference genomes

The pipeline config files come bundled with paths to the Illumina iGenomes reference index files. If running with docker or AWS, the configuration is set up to use the AWS-iGenomes resource.

--genome (using iGenomes)

There are many different species supported in the iGenomes references. To run the pipeline, you must specify which to use with the --genome flag.

You can find the keys to specify the genomes in the iGenomes config file.


If you prefer, you can specify the full path to your reference genome when you run the pipeline:

--fasta '[path to Fasta reference]'


The bowtie2 indexes are required to align the data with the HiC-Pro workflow. If the --bwt2_index is not specified, the pipeline will either use the iGenomes bowtie2 indexes (see --genome option) or build the indexes on-the-fly (see --fasta option)

--bwt2_index '[path to bowtie2 index]'


The Hi-C pipeline also requires a two-column text file with the chromosome name and the chromosome size (tab-separated). If not specified, this file will be automatically created by the pipeline. In the latter case, the --fasta reference genome has to be specified.

   chr1    249250621
   chr2    243199373
   chr3    198022430
   chr4    191154276
   chr5    180915260
   chr6    171115067
   chr7    159138663
   chr8    146364022
   chr9    141213431
   chr10   135534747
--chromosome_size '[path to chromosome size file]'


Finally, Hi-C experiments based on restriction enzyme digestion require a BED file with coordinates of restriction fragments.

   chr1   0       16007   HIC_chr1_1    0   +
   chr1   16007   24571   HIC_chr1_2    0   +
   chr1   24571   27981   HIC_chr1_3    0   +
   chr1   27981   30429   HIC_chr1_4    0   +
   chr1   30429   32153   HIC_chr1_5    0   +
   chr1   32153   32774   HIC_chr1_6    0   +
   chr1   32774   37752   HIC_chr1_7    0   +
   chr1   37752   38369   HIC_chr1_8    0   +
   chr1   38369   38791   HIC_chr1_9    0   +
   chr1   38791   39255   HIC_chr1_10   0   +

If not specified, this file will be automatically created by the pipeline. In this case, the --fasta reference genome will be used. Note that the --digestion or --restriction_site parameter is mandatory to create this file.

Hi-C specific options

The following options are defined in the nextflow.config file, and can be updated either using a custom configuration file (see -c option) or using command line parameters.

HiC-pro mapping

The reads mapping is currently based on the two-steps strategy implemented in the HiC-pro pipeline. The idea is to first align reads from end-to-end. Reads that do not align are then trimmed at the ligation site, and their 5’ end is re-aligned to the reference genome. Note that the default options are quite stringent, and can be updated according to the reads quality or the reference genome.


Bowtie2 alignment option for end-to-end mapping. Default: ‘—very-sensitive -L 30 —score-min L,-0.6,-0.2 —end-to-end —reorder’

--bwt2_opts_end2end '[Options for bowtie2 step1 mapping on full reads]'


Bowtie2 alignment option for trimmed reads mapping (step 2). Default: ‘—very-sensitive -L 20 —score-min L,-0.6,-0.2 —end-to-end —reorder’

--bwt2_opts_trimmed '[Options for bowtie2 step2 mapping on trimmed reads]'


Minimum mapping quality. Reads with lower quality are discarded. Default: 10

--min_mapq '[Minimum quality value]'

Digestion Hi-C


This parameter allows to automatically set the --restriction_site and --ligation_site parameter according to the restriction enzyme you used. Available keywords are ‘hindiii’, ‘dpnii’, ‘mboi’, ‘arima’.

--digestion 'hindiii'


If the restriction enzyme is not available through the --digestion parameter, you can also define manually the restriction motif(s) for Hi-C digestion protocol. The restriction motif(s) is(are) used to generate the list of restriction fragments. The precise cutting site of the restriction enzyme has to be specified using the ’^’ character. Default: ‘A^AGCTT’ Here are a few examples:

  • MboI: ^GATC
  • DpnII: ^GATC
  • HindIII: A^AGCTT

Note that multiples restriction motifs can be provided (comma-separated) and that ‘N’ base are supported.

--restriction_size '[Cutting motif]'


Ligation motif after reads ligation. This motif is used for reads trimming and depends on the fill in strategy. Note that multiple ligation sites can be specified (comma-separated) and that ‘N’ base is interpreted and replaced by ‘A’,‘C’,‘G’,‘T’. Default: ‘AAGCTAGCTT’

--ligation_site '[Ligation motif]'


DNAse Hi-C


In DNAse Hi-C mode, all options related to digestion Hi-C (see previous section) are ignored. In this case, it is highly recommended to use the --min_cis_dist parameter to remove spurious ligation products.


HiC-pro processing


Minimum size of restriction fragments to consider for the Hi-C processing. Default: ‘0’ - no filter

--min_restriction_fragment_size '[numeric]'


Maximum size of restriction fragments to consider for the Hi-C processing. Default: ‘0’ - no filter

--max_restriction_fragment_size '[numeric]'


Minimum reads insert size. Shorter 3C products are discarded. Default: ‘0’ - no filter

--min_insert_size '[numeric]'


Maximum reads insert size. Longer 3C products are discarded. Default: ‘0’ - no filter

--max_insert_size '[numeric]'


Filter short range contact below the specified distance. Mainly useful for DNase Hi-C. Default: ‘0’

--min_cis_dist '[numeric]'


If specified, duplicate reads are not discarded before building contact maps.



If specified, reads that aligned multiple times on the genome are not discarded. Note the default mapping options are based on random hit assignment, meaning that only one position is kept per read. Note that in this case the --min_mapq parameter is ignored.


Genome-wide contact maps

Once the list of valid pairs is available, the standard is now to move on the cooler framework to build the raw and balanced contact maps in txt and (m)cool formats.


Resolution of contact maps to generate (comma-separated). Default:‘1000000,500000’

--bins_size '[string]'


Define the maximum resolution to reach when zoomify the cool contact maps. Default:‘5000’

--res_zoomify '[string]'

HiC-Pro contact maps

By default, the contact maps are now generated with the cooler framework. However, for backward compatibility, the raw and normalized maps can still be generated by HiC-pro if the --hicpro_maps parameter is set.


If specified, the raw and ICE normalized contact maps will be generated by HiC-Pro.



Maximum number of iteration for ICE normalization. Default: 100

--ice_max_iter '[numeric]'


Define which percentage of bins with low counts should be forced to zero. Default: 0.02

--ice_filter_low_count_perc '[numeric]'


Define which percentage of bins with low counts should be discarded before normalization. Default: 0

--ice_filter_high_count_perc '[numeric]'


The relative increment in the results before declaring convergence for ICE normalization. Default: 0.1

--ice_eps '[numeric]'

Downstream analysis

Additional quality controls


Generates distance vs Hi-C counts plots at a given resolution using HiCExplorer. Several resolutions can be specified (comma-separeted). Default: ‘250000’

--res_dist_decay '[string]'

Compartment calling

Call open/close compartments for each chromosome, using the cooltools command.


Resolution to call the chromosome compartments (comma-separated). Default: ‘250000’

--res_compartments '[string]'

TADs calling


TADs calling can be performed using different approaches. Currently available options are insulation and hicexplorer. Note that all options can be specified (comma-separated). Default: ‘insulation’

--tads_caller '[string]'


Resolution to run the TADs calling analysis (comma-separated). Default: ‘40000,20000’

--res_tads '[string]'



By default, the nf-core Hi-C pipeline expects one read pairs per sample. However, for large Hi-C data processing single fastq files can be very time consuming. The --split_fastq option allows to automatically split input read pairs into chunks of reads of size --fastq_chunks_size (Default : 20000000). In this case, all chunks will be processed in parallel and merged before generating the contact maps, thus leading to a significant increase of processing performance.

--split_fastq --fastq_chunks_size '[numeric]'


If specified, annotation files automatically generated from the --fasta file are exported in the results folder. Default: false



If specified, all intermediate mapping files are saved and exported in the results folder. Default: false



If specified, write a BAM file with all classified reads (valid pairs, dangling end, self-circle, etc.) and its tags.


Skip options


If defined, the workflow stops with the list of valid interactions, and the genome-wide maps are not built. Useful for capture-C analysis. Default: false



If defined, the contact maps normalization is not run on the raw contact maps. Default: false



If defined, cooler files are not generated. Default: false



Do not run distance decay plots. Default: false



Do not call compartments. Default: false



Do not call TADs. Default: false



If defined, the MultiQC report is not generated. Default: false