nf-core/chipseq

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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/chipseq --input design.csv --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/chipseq

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/chipseq 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/chipseq
• singularity
  • A generic configuration profile to be used with Singularity
  • Pulls software from DockerHub: nfcore/chipseq
• 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

--input

You will need to create a design file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 6 columns, and a header row as shown in the examples below.

--input '[path to design file]'

Multiple replicates

The group identifier should be identical when you have multiple replicates from the same experimental group, just increment the replicate identifier appropriately. The first replicate value for any given experimental group must be 1.

The antibody column is required to separate the downstream consensus peak merging and differential analysis for different antibodies. Its not advisable to generate a consensus peak set across different antibodies especially if their binding patterns are inherently different e.g. narrow transcription factors and broad histone marks.

The control column should be the group identifier for the controls for any given IP. The pipeline will automatically pair the inputs based on replicate identifier (i.e. where you have an equal number of replicates for your IP’s and controls), alternatively, the first control sample in that group will be selected.

In the single-end design below there are triplicate samples for the WT_BCATENIN_IP group along with triplicate samples for their corresponding WT_INPUT samples.

group,replicate,fastq_1,fastq_2,antibody,control
WT_BCATENIN_IP,1,BLA203A1_S27_L006_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,2,BLA203A25_S16_L002_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,3,BLA203A49_S40_L001_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_INPUT,1,BLA203A6_S32_L006_R1_001.fastq.gz,,,
WT_INPUT,2,BLA203A30_S21_L002_R1_001.fastq.gz,,,
WT_INPUT,3,BLA203A31_S21_L003_R1_001.fastq.gz,,,

Multiple runs of the same library

Both the group and replicate identifiers should be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will perform the alignments in parallel, and subsequently merge them before further analysis. Below is an example where the second replicate of the WT_BCATENIN_IP and WT_INPUT groups has been re-sequenced multiple times:

group,replicate,fastq_1,fastq_2,antibody,control
WT_BCATENIN_IP,1,BLA203A1_S27_L006_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,2,BLA203A25_S16_L001_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,2,BLA203A25_S16_L002_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,2,BLA203A25_S16_L003_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,3,BLA203A49_S40_L001_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_INPUT,1,BLA203A6_S32_L006_R1_001.fastq.gz,,,
WT_INPUT,2,BLA203A30_S21_L001_R1_001.fastq.gz,,,
WT_INPUT,2,BLA203A30_S21_L002_R1_001.fastq.gz,,,
WT_INPUT,3,BLA203A31_S21_L003_R1_001.fastq.gz,,,

Full design

A final design file may look something like the one below. This is for two antibodies and associated controls in triplicate, where the second replicate of the WT_BCATENIN_IP and NAIVE_BCATENIN_IP group has been sequenced twice:

group,replicate,fastq_1,fastq_2,antibody,control
WT_BCATENIN_IP,1,BLA203A1_S27_L006_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,2,BLA203A25_S16_L001_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,2,BLA203A25_S16_L002_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP,3,BLA203A49_S40_L001_R1_001.fastq.gz,,BCATENIN,WT_INPUT
NAIVE_BCATENIN_IP,1,BLA203A7_S60_L001_R1_001.fastq.gz,,BCATENIN,NAIVE_INPUT
NAIVE_BCATENIN_IP,2,BLA203A43_S34_L001_R1_001.fastq.gz,,BCATENIN,NAIVE_INPUT
NAIVE_BCATENIN_IP,2,BLA203A43_S34_L002_R1_001.fastq.gz,,BCATENIN,NAIVE_INPUT
NAIVE_BCATENIN_IP,3,BLA203A64_S55_L001_R1_001.fastq.gz,,BCATENIN,NAIVE_INPUT
WT_TCF4_IP,1,BLA203A3_S29_L006_R1_001.fastq.gz,,TCF4,WT_INPUT
WT_TCF4_IP,2,BLA203A27_S18_L001_R1_001.fastq.gz,,TCF4,WT_INPUT
WT_TCF4_IP,3,BLA203A51_S42_L001_R1_001.fastq.gz,,TCF4,WT_INPUT
NAIVE_TCF4_IP,1,BLA203A9_S62_L001_R1_001.fastq.gz,,TCF4,NAIVE_INPUT
NAIVE_TCF4_IP,2,BLA203A45_S36_L001_R1_001.fastq.gz,,TCF4,NAIVE_INPUT
NAIVE_TCF4_IP,3,BLA203A66_S57_L001_R1_001.fastq.gz,,TCF4,NAIVE_INPUT
WT_INPUT,1,BLA203A6_S32_L006_R1_001.fastq.gz,,,
WT_INPUT,2,BLA203A30_S21_L001_R1_001.fastq.gz,,,
WT_INPUT,3,BLA203A31_S21_L003_R1_001.fastq.gz,,,
NAIVE_INPUT,1,BLA203A12_S3_L001_R1_001.fastq.gz,,,
NAIVE_INPUT,2,BLA203A48_S39_L001_R1_001.fastq.gz,,,
NAIVE_INPUT,3,BLA203A49_S1_L006_R1_001.fastq.gz,,,

Example design files have been provided with the pipeline for paired-end and single-end data.

Generic arguments

--single_end

By default, the pipeline expects paired-end data. If you have single-end data, specify --single_end on the command line when you launch the pipeline.

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

--seq_center

Sequencing center information that will be added to read groups in BAM files.

--fragment_size

Number of base pairs to extend single-end reads when creating bigWig files (Default: 200).

--fingerprint_bins

Number of genomic bins to use when generating the deepTools fingerprint plot. Larger numbers will give a smoother profile, but take longer to run (Default: 500000).

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.

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 star index given
    }
    // Any number of additional genomes, key is used with --genome
  }
}

--fasta

Full path to fasta file containing reference genome (mandatory if --genome is not specified). If you don’t have a BWA index available this will be generated for you automatically. Combine with --save_reference to save BWA index for future runs.

--fasta '[path to FASTA reference]'

--gtf

The full path to GTF file for annotating peaks (mandatory if --genome is not specified). Note that the GTF file should resemble the Ensembl format.

--gtf '[path to GTF file]'

--bwa_index

Full path to an existing BWA index for your reference genome including the base name for the index.

--bwa_index '[directory containing BWA index]/genome.fa'

--gene_bed

The full path to BED file for genome-wide gene intervals. This will be created from the GTF file if not specified.

--gene_bed '[path to gene BED file]'

--macs_gsize

Effective genome size parameter required by MACS2. These have been provided when --genome is set as GRCh37, GRCh38, GRCm38, WBcel235, BDGP6, R64-1-1, EF2, hg38, hg19 and mm10. For other genomes, if this parameter is not specified then the MACS2 peak-calling and differential analysis will be skipped.

--macs_gsize 2.7e9

--blacklist

If provided, alignments that overlap with the regions in this file will be filtered out (see ENCODE blacklists). The file should be in BED format. Blacklisted regions for GRCh37, GRCh38, GRCm38, hg19, hg38, mm10 are bundled with the pipeline in the blacklists directory, and as such will be automatically used if any of those genomes are specified with the --genome parameter.

--blacklist '[path to blacklisted regions]'

--save_reference

If the BWA index is generated by the pipeline use this parameter to save it to your results folder. These can then be used for future pipeline runs, reducing processing times (Default: false).

--igenomes_ignore

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 (Default: false).

Adapter trimming

The pipeline accepts a number of parameters to change how the trimming is done, according to your data type. You can specify custom trimming parameters as follows:

• --clip_r1 [int]
  • Instructs Trim Galore to remove [int] bp from the 5’ end of read 1 (for single-end reads).
• --clip_r2 [int]
  • Instructs Trim Galore to remove [int] bp from the 5’ end of read 2 (paired-end reads only).
• --three_prime_clip_r1 [int]
  • Instructs Trim Galore to remove [int] bp from the 3’ end of read 1 AFTER adapter/quality trimming has been
• --three_prime_clip_r2 [int]
  • Instructs Trim Galore to remove [int] bp from the 3’ end of read 2 AFTER adapter/quality trimming has been performed.
• --trim_nextseq [int]
  • This enables the option Cutadapt --nextseq-trim=3'CUTOFF option via Trim Galore, which will set a quality cutoff (that is normally given with -q instead), but qualities of G bases are ignored. This trimming is in common for the NextSeq- and NovaSeq-platforms, where basecalls without any signal are called as high-quality G bases.

--skip_trimming

Skip the adapter trimming step. Use this if your input FastQ files have already been trimmed outside of the workflow or if you’re very confident that there is no adapter contamination in your data (Default: false).

--save_trimmed

By default, trimmed FastQ files will not be saved to the results directory. Specify this flag (or set to true in your config file) to copy these files to the results directory when complete (Default: false).

Alignments

--bwa_min_score

Don’t output BWA MEM alignments with score lower than this parameter (Default: false).

--keep_dups

Duplicate reads are not filtered from alignments (Default: false).

--keep_multi_map

Reads mapping to multiple locations in the genome are not filtered from alignments (Default: false).

--save_align_intermeds

By default, intermediate BAM files will not be saved. The final BAM files created after the appropriate filtering step are always saved to limit storage usage. Set to true to also save other intermediate BAM files (Default: false).

Peaks

--narrow_peak

MACS2 is run by default with the --broad flag. Specify this flag to call peaks in narrowPeak mode (Default: false).

--broad_cutoff

Specifies broad cut-off value for MACS2. Only used when --narrow_peak isnt specified (Default: 0.1).

--macs_fdr

Minimum FDR (q-value) cutoff for peak detection, --macs_fdr and --macs_pvalue are mutually exclusive (Default: false).

--macs_pvalue

p-value cutoff for peak detection, --macs_fdr and --macs_pvalue are mutually exclusive (Default: false). If --macs_pvalue cutoff is set, q-value will not be calculated and reported as -1 in the final .xls file.

--min_reps_consensus

Number of biological replicates required from a given condition for a peak to contribute to a consensus peak . If you are confident you have good reproducibility amongst your replicates then you can increase the value of this parameter to create a “reproducible” set of consensus of peaks. For example, a value of 2 will mean peaks that have been called in at least 2 replicates will contribute to the consensus set of peaks, and as such peaks that are unique to a given replicate will be discarded (Default: 1).

-- min_reps_consensus 1

--save_macs_pileup

Instruct MACS2 to create bedGraph files using the -B --SPMR parameters (Default: false).

--skip_peak_qc

Skip MACS2 peak QC plot generation (Default: false).

--skip_peak_annotation

Skip annotation of MACS2 and consensus peaks with HOMER (Default: false).

--skip_consensus_peaks

Skip consensus peak generation, annotation and counting (Default: false).

Differential analysis

--deseq2_vst

Use vst transformation instead of rlog with DESeq2. See DESeq2 docs (Default: false).

--skip_diff_analysis

Skip differential binding analysis with DESeq2 (Default: false).

Skipping QC steps

The pipeline contains a large number of quality control steps. Sometimes, it may not be desirable to run all of them if time and compute resources are limited. The following options make this easy:

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 are likely to be running nf-core pipelines 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 (see definition below). 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.

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.

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.

--publish_dir_mode

Value passed to Nextflow publishDir directive for publishing results in the output directory. Available: ‘symlink’, ‘rellink’, ‘link’, ‘copy’, ‘copyNoFollow’ and ‘move’ (Default: ‘copy’).

--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.

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.