usage.md

nf-core/hic: Usage

Table of contents

• Introduction
• Running the pipeline
• Updating the pipeline
• Reproducibility
• Main arguments
  • -profile
    • awsbatch
    • conda
    • docker
    • singularity
    • test
  • --reads
  • --singleEnd
• Reference genomes
  • --genome
  • --fasta
  • --igenomesIgnore
  • --bwt2_index
  • --chromosome_size
  • --restriction_fragments
• Hi-C specific options
  • Reads mapping
    • --bwt2_opts_end2end
    • --bwt2_opts_trimmed
    • --min_mapq
  • Digestion Hi-C
    • --restriction_site
    • --ligation_site
    • --min_restriction_fragment_size
    • --max_restriction_fragment_size
    • --min_insert_size
    • --max_insert_size
  • DNase Hi-C
    • --dnase
  • Hi-C Processing
    • --min_cis_dist
    • --rm_singleton
    • --rm_dup
    • --rm_multi
  • Genome-wide contact maps
    • --bins_size
    • --ice_max_iter
    • --ice_filer_low_count_perc
    • --ice_filer_high_count_perc
    • --ice_eps
  • Inputs/Outputs
    • --splitFastq
    • --saveReference
    • --saveAlignedIntermediates
• Skip options
  • --skip_maps
  • --skip_ice
  • --skip_cool
  • --skip_multiqc
• Job resources
• Automatic resubmission
• Custom resource requests
• AWS batch specific parameters
  • -awsbatch
  • --awsqueue
  • --awsregion
• Other command line parameters
  • --outdir
  • --email
  • -name
  • -resume
  • -c
  • --custom_config_version
  • --max_memory
  • --max_time
  • --max_cpus
  • --plaintext_email
  • --multiqc_config

General Nextflow info

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.

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. Note that multiple profiles can be loaded, for example: -profile docker - the order of arguments is important!

If -profile is not specified at all the pipeline will be run locally and expects all software to be installed and available on the PATH.

• awsbatch
  • A generic configuration profile to be used with AWS Batch.
• conda
  • A generic configuration profile to be used with conda
  • Pulls most software from Bioconda
• docker
  • A generic configuration profile to be used with Docker
  • Pulls software from dockerhub: nfcore/hic
• singularity
  • A generic configuration profile to be used with Singularity
  • Pulls software from DockerHub: nfcore/hic
• 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

Reference genomes and annotation files

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.

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'

Note that multiples restriction motifs can be provided (comma-separated).

--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. Default: 'AAGCTAGCTT'

--ligation_site '[Ligation motif]'

--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