usage.md

Usage

This document present the usage and parameters of the hicpro workflow. To see the parameters of the hicstuff workflow, go here.

Introduction

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:

sample,fastq_1,fastq_2
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz

Full samplesheet

This 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.

sample,fastq_1,fastq_2
SAMPLE_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
SAMPLE_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz
SAMPLE_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz

Column	Description
sample	Custom 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_1	Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".
fastq_2	Full 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 main.nf -profile psmn --workflow hicpro --input samplesheet.csv --outdir <OUTDIR> --fasta <genome.fasta> --digestion <dpnii/hindiii/arima/mboi>

This will launch the pipeline with the psmn 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.

You can edit the nextflow.config file to change the parameters you want. Remember to keep a trace of the default parameters just in case.

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

Core Nextflow arguments

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

-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, 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.

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.

-resume

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.

-c

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.

Parameters

Inputs

--input

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.

--fasta

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

--fasta '[path to Fasta reference]'

--bwt2_index

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]'

--chromosome_size

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]'

--restriction_fragments

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.

--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

--digestion

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'

--restriction_site

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
• 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

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 recommended to use the --min_cis_dist parameter to remove spurious ligation products.

--dnase

HiC-pro processing

--min_restriction_fragment_size

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

--min_restriction_fragment_size '[numeric]'

--max_restriction_fragment_size

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

--max_restriction_fragment_size '[numeric]'

--min_insert_size

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

--min_insert_size '[numeric]'

--max_insert_size

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

--max_insert_size '[numeric]'

--min_cis_dist

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

--min_cis_dist '[numeric]'

--keep_dups

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

--keep_dups

--filter_pcr_picard

If specified, duplicate reads are filtered using PICARD MarkDuplicate method. If true, --keep_dups must also be true. Default:'false'

--filter_pcr_picard

--keep_multi

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.

--keep_multi

--save_bam_intermediates

If specified, save BAM files after PICARD pcr filtering. Default: false

--save_bam_intermediates

--save_pairs

If specified, save pair files. Default: false

--save_pairs

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.

--bin_size

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

--bins_size '[string]'

--res_zoomify

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