Introduction Usage Output Results Releases

Introduction

Nextflow handles job submissions on SLURM or other environments, and supervises running the jobs. Thus the Nextflow process must run until the pipeline is finished. We recommend that you put the process running in the background through screen / tmux or similar tool. Alternatively you can run nextflow within a cluster job submitted your job scheduler.

It is recommended to limit the Nextflow Java virtual machines memory. We recommend adding the following line to your environment (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/hic --reads '*_R{1,2}.fastq.gz' --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
results         # Finished results (configurable, see below)
.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

Reproducibility

It’s 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.

It’s 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 version number - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) eg. -r 1.3.1.

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.

Main arguments

-profile

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, 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 https://github.com/nf-core/configs 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.

  • docker
    • A generic configuration profile to be used with Docker
    • Pulls software from dockerhub: nfcore/hic
  • singularity
  • conda
    • Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker or Singularity.
    • A generic configuration profile to be used with Conda
    • Pulls most software from Bioconda
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters

--reads

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

--reads '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

--single_end

By default, the pipeline expects paired-end data. If you have single-end data, you need to specify --single_end on the command line when you launch the pipeline. A normal glob pattern, enclosed in quotation marks, can then be used for --reads. For example:

--single_end --reads '*.fastq'

It is not possible to run a mixture of single-end and paired-end files in one run.

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

There are 31 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. Common genomes that are supported are:

  • Human
    • --genome GRCh37
  • Mouse
    • --genome GRCm38
  • Drosophila
    • --genome BDGP6
  • S. cerevisiae
    • --genome 'R64-1-1'

There are numerous others - check the config file for more.

Note that you can use the same configuration setup to save sets of reference files for your own use, even if they are not part of the iGenomes resource. See the Nextflow documentation for instructions on where to save such a file.

The syntax for this reference configuration is as follows:

params {
  genomes {
    'GRCh37' {
      fasta   = '<path to the genome fasta file>' // Used if no annotations are given
      bowtie2 = '<path to bowtie2 index files>'
    }
    // Any number of additional genomes, key is used with --genome
  }
}

--fasta

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

--fasta '[path to Fasta reference]'

--igenomesIgnore

Do not load igenomes.config when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in igenomes.config.

--bwt2_index

The bowtie2 indexes are required to run the Hi-C pipeline. If the --bwt2_index is not specified, the pipeline will either use the igenome bowtie2 indexes (see --genome option) or build the indexes on-the-fly (see --fasta option)

--bwt2_index '[path to bowtie2 index (with basename)]'

--chromosome_size

The Hi-C pipeline will also requires a two-columns text file with the chromosome name and its 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]'

--restriction_fragments

Finally, Hi-C experiments based on restriction enzyme digestion requires 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 pipline. In this case, the --fasta reference genome will be used. Note that the --restriction_site parameter is mandatory to create this file.

Hi-C specific options

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

Reads 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 aligned are then trimmed at the ligation site, and their 5’ end is re-aligned to the reference genome. Note that the default option are quite stringent, and can be updated according to the reads quality or the reference genome.

--bwt2_opts_end2end

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]'

--bwt2_opts_trimmed

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]'

--min_mapq

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

--min_mapq '[Minimum quality value]'

Digestion Hi-C

--restriction_site

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
  • BglII: A^GATCT
  • HindIII: A^AGCTT
  • ARIMA kit: ^GATC,G^ANTC

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

--restriction_size '[Cutting motif]'

--ligation_site

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]'

Exemple of the ARIMA kit: GATCGATC,GANTGATC,GANTANTC,GATCANTC

--min_restriction_fragment_size

Minimum size of restriction fragments to consider for the Hi-C processing. Default: ”

--min_restriction_fragment_size '[numeric]'

--max_restriction_fragment_size

Maximum size of restriction fragments to consider for the Hi-C processing. Default: ”

--max_restriction_fragment_size '[numeric]'

--min_insert_size

Minimum reads insert size. Shorter 3C products are discarded. Default: ”

--min_insert_size '[numeric]'

--max_insert_size

Maximum reads insert size. Longer 3C products are discarded. Default: ”

--max_insert_size '[numeric]'

DNAse Hi-C

--dnase

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

--dnase'

Hi-C processing

--min_cis_dist

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

--min_cis_dist '[numeric]'

--rm_singleton

If specified, singleton reads are discarded at the mapping step.

--rm_singleton

--rm_dup

If specified, duplicates reads are discarded before building contact maps.

--rm_dup

--rm_multi

If specified, reads that aligned multiple times on the genome are discarded. Note the default mapping options are based on random hit assignment, meaning that only one position is kept per read.

--rm_multi

Genome-wide contact maps

--bin_size

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

--bins_size '[numeric]'

--ice_max_iter

Maximum number of iteration for ICE normalization. Default: 100

--ice_max_iter '[numeric]'

--ice_filer_low_count_perc

Define which pourcentage of bins with low counts should be force to zero. Default: 0.02

--ice_filter_low_count_perc '[numeric]'

--ice_filer_high_count_perc

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

--ice_filter_high_count_perc '[numeric]'

--ice_eps

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

--ice_eps '[numeric]'

Inputs/Outputs

--splitFastq

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 --splitFastq option allows to automatically split input read pairs into chunks of reads. 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.

--splitFastq '[Number of reads per chunk]'

--saveReference

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

--saveReference

--saveAlignedIntermediates

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

--saveReference

--saveInteractionBAM

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

Skip options

--skipMaps

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

--skipMaps

--skipIce

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

--skipIce

--skipCool

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

--skipCool

--skipMultiQC

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

--skipMultiQC

Job resources

Automatic resubmission

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 an error code of 143 (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.

Custom resource requests

Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files hosted at nf-core/configs for examples.

If you have any questions or issues please send us a message on Slack.

AWS Batch specific parameters

Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use -profile awsbatch and then specify all of the following parameters.

--awsqueue

The JobQueue that you intend to use on AWS Batch.

--awsregion

The AWS region in which to run your job. Default is set to eu-west-1 but can be adjusted to your needs.

--awscli

The AWS CLI path in your custom AMI. Default: /home/ec2-user/miniconda/bin/aws.

The AWS region to run your job in. Default is set to eu-west-1 but can be adjusted to your needs.

Please make sure to also set the -w/--work-dir and --outdir parameters to a S3 storage bucket of your choice - you’ll get an error message notifying you if you didn’t.

Other command line parameters

--outdir

The output directory where the results will be saved.

--email

Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don’t need to specify this on the command line for every run.

--email_on_fail

This works exactly as with --email, except emails are only sent if the workflow is not successful.

--max_multiqc_email_size

Threshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB).

-name

Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.

Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.

This is used in the MultiQC report (if not default) and in the summary HTML / e-mail (always).

NB: Single hyphen (core Nextflow option)

-resume

Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

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.

NB: Single hyphen (core Nextflow option)

-c

Specify the path to a specific config file (this is a core NextFlow command).

NB: Single hyphen (core Nextflow option)

Note - you can use this to override pipeline defaults.

--custom_config_version

Provide git commit id for custom Institutional configs hosted at nf-core/configs. This was implemented for reproducibility purposes. Default: master.

## Download and use config file with following git commid id
--custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96

--custom_config_base

If you’re running offline, nextflow will not be able to fetch the institutional config files from the internet. If you don’t need them, then this is not a problem. If you do need them, you should download the files from the repo and tell nextflow where to find them with the custom_config_base option. For example:

## Download and unzip the config files
cd /path/to/my/configs
wget https://github.com/nf-core/configs/archive/master.zip
unzip master.zip
 
## Run the pipeline
cd /path/to/my/data
nextflow run /path/to/pipeline/ --custom_config_base /path/to/my/configs/configs-master/

Note that the nf-core/tools helper package has a download command to download all required pipeline files + singularity containers + institutional configs in one go for you, to make this process easier.

--max_memory

Use to set a top-limit for the default memory requirement for each process. Should be a string in the format integer-unit. eg. --max_memory '8.GB'

--max_time

Use to set a top-limit for the default time requirement for each process. Should be a string in the format integer-unit. eg. --max_time '2.h'

--max_cpus

Use to set a top-limit for the default CPU requirement for each process. Should be a string in the format integer-unit. eg. --max_cpus 1

--plaintext_email

Set to receive plain-text e-mails instead of HTML formatted.

--monochrome_logs

Set to disable colourful command line output and live life in monochrome.

--multiqc_config

Specify a path to a custom MultiQC configuration file.
On this page