nf-core/methylseq

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Introduction

The nf-core/methylseq pipeline provides two distinct workflows for DNA methylation analysis. These workflows support different aligners and cater to a range of computational requirements.

Read more about Bisulfite Sequencing & Three-Base Aligners used in this pipeline here

Workflow: Bismark

By default, the nf-core/methylseq pipeline uses Bismark with Bowtie2 as the alignment tool. This configuration is optimized for most DNA methylation workflows and will run unless an alternative aligner is specified.

Starting with Bismark v0.21.0, the pipeline also supports HISAT2 as an alternative aligner. To activate this option, use the command-line flag --aligner bismark_hisat.

HISAT2 offers splice-aware alignment, making it suitable for RNA-based analyses (e.g., SLAMseq experiments). For such cases, you can supply a file with known splice sites using the --known_splices parameter.

Workflow: BWA-Meth

The second workflow uses BWA-Meth as the alignment tool and MethylDackel for post-processing.

bwa-meth aligner options:

• Standard bwa-meth (CPU-based): This option can be invoked via --aligner bwameth and uses the traditional BWA-Meth aligner and runs on CPU processors.

• Parabricks/FQ2BAMMETH (GPU-based): For higher performance, the pipeline can leverage the Parabricks implementation of bwa-meth (fq2bammeth), which implements the baseline tool bwa-meth in a performant method using fq2bam (BWA-MEM + GATK) as a backend for processing on GPU. To use this option, include the --use_gpu flag along with --aligner bwameth.

Samplesheet input

Before running the pipeline, you must create a samplesheet containing information about the samples to be analyzed. Use the appropriate parameter to specify the location of this file.

The samplesheet must be a comma-separated file (CSV) with four columns and a header row, formatted as shown in the examples below:

--input '[path to samplesheet file]'

sample,fastq_1,fastq_2,genome

Multiple runs of the same sample

When a sample has been re-sequenced multiple times (e.g., to increase sequencing depth), the sample identifiers must remain the same across all runs. This ensures that the pipeline concatenates the raw reads from all runs before proceeding with downstream analysis.

Below is an example where the same sample (single-end) has been sequenced across three lanes:

sample,fastq_1,fastq_2,genome
SRR389222,SRR389222_sub1.fastq.gz,,
SRR389222,SRR389222_sub2.fastq.gz,,
SRR389222,SRR389222_sub3.fastq.gz,,
Ecoli_10K_methylated,Ecoli_10K_methylated_R1.fastq.gz,Ecoli_10K_methylated_R2.fastq.gz,

Full samplesheet

The pipeline automatically detects whether a sample is single- or paired-end based on the information provided in the samplesheet. While additional columns can be included for metadata or other purposes, the first three columns must strictly adhere to the format described in the table below.

A completed samplesheet containing both single- and paired-end data might look like the example below. In this case, the sheet includes six samples, with TREATMENT_REP3 sequenced twice:

sample,fastq_1,fastq_2,genome
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,
CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz,
CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz,
TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,,
TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,,
TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,,
TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,,

An example samplesheet has been provided with the pipeline.

Parameters

Check out the full list of parameters required, available for multiple aligners on nf-core/methylseq pipeline parameters page.

• Input/output options
• Save intermediate files
• Reference genome options
• Alignment options
• Special library types
• Adapter Trimming
• Bismark options
• bwa-meth options
• Qualimap Options
• Skip pipeline steps
• Run pipeline steps

It is mandatory to provide --fasta along with --bismark_index/--bwameth_index parameters

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/methylseq --input ./samplesheet.csv --outdir ./results --genome GRCh38 -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.

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.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/methylseq -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

Providing ext.args to Tools

Additional arguments can be appended to a command in a module by specifying them within the module’s custom configuration. The configurations for modules and subworkflows used in the pipeline can be found in conf/modules or conf/subworkflows. A module’s publishDir path can also be customized in these configurations.

For example, users working with unfinished genomes containing tens or even hundreds of thousands of scaffolds, contigs, or chromosomes often encounter errors when pre-sorting reads into individual chromosome files.

These errors are typically caused by the operating system’s limit on the number of file handles that can be open simultaneously (usually 1024; to find out this limit on Linux, use the command: ulimit -a).

To bypass this limitation, the --scaffolds option can be added as an additional ext.args in conf/modules/bismark_methylationextractor.config.

This prevents methylation calls from being pre-sorted into individual chromosome files.

Instead, all input files are temporarily merged into a single file (unless there is only one file), which is then sorted by both chromosome and position using the Unix sort command.

For a detailed list of different options available, please refer to the official docs of:

• Bismark
• bwa-meth

Running the test profile

Every nf-core pipeline comes with test data than can be run using -profile test. This test profile is useful for testing whether a user’s environment is properly setup.

nextflow run nf-core/methylseq \
  --input samplesheet.csv \
  --outdir <OUTDIR> \
  --genome GRCh38 \
  -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>

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/methylseq

Reproducibility

It is a good idea to specify the 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/methylseq 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.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow 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, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

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 check if your system is supported, 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 environment.

• 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
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• 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, Charliecloud, or Apptainer.

-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 pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (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 the container or conda environment used by a pipeline steps 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.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

Command error:
    .command.sh: line 9:  30 Killed    STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. <TRUNCATED>
Work dir:
    /home/pipelinetest/work/9d/172ca5881234073e8d76f2a19c88fb
 
Tip: you can replicate the issue by changing to the process work dir and entering the command `bash .command.run`

Resource Limits

In addition to the executor, you may find that pipeline runs occasionally fail due to a particular step of the pipeline requesting more resources than you have on your system.

To avoid these failures, you can tell Nextflow to set a cap pipeline-step resource requests against a list called resourceLimits specified in Nextflow config file. These should represent the maximum possible resources of a machine or node.

Specify the maximum resources that can be used (cpus, memory, time) for all processes by default or for a specific process using withName or withLabel selectors as shown below:

Global resource limits

process {
    resourceLimits = [
        cpus: 4,
        memory: '15.GB',
        time: '1.h'
    ]
}

Process-specific resource limits

process {
  withName: 'BISMARK_ALIGN' {
    resourceLimits = [
        cpus: 4,
        memory: '15.GB',
        time: '1.h'
    ]
  }
}

Advanced option on process level

To find out exactly what resources have been set for a process, for example., the BISMARK_ALIGN process. Navigate to the BISMARK_ALIGN module used in the workflow.

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/bismark/align/main.nf.

In the module main.nf, 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 organize 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 72.GB.

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 BISMARK_ALIGN process failure by creating a custom config file that sets at least 72.GB of memory, in this case increased to 100.GB.

The custom config below can then be provided to the pipeline via the -c parameter as highlighted in previous sections.

process {
  withName: 'BISMARK_ALIGN' {
    memory = 100.GB
  }
}

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 every time 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 Quay.io

3. Create the custom config accordingly:

• For Docker:

  process {
      withName: PANGOLIN {
          container = 'quay.io/biocontainers/pangolin:3.0.5--pyhdfd78af_0'
      }
  }

• For Singularity:

  process {
      withName: PANGOLIN {
          container = 'https://depot.galaxyproject.org/singularity/pangolin:3.0.5--pyhdfd78af_0'
      }
  }

• 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.

nf-core/configs

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.

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'

Nextflow edge releases

Stable releases will be becoming more infrequent as Nextflow shifts its development model to becoming more dynamic via the usage of plugins. This will allow functionality to be added as an extension to the core codebase with a release cycle that could potentially be independent to that of Nextflow itself. As a result of the reduction in stable releases, some pipelines may be required to use Nextflow edge releases in order to be able to exploit cutting “edge” features e.g. version 3.0 of the nf-core/rnaseq pipeline requires Nextflow >=20.11.0-edge in order to be able to directly download Singularity containers over http (see nf-core/rnaseq#496).

There are a number of ways you can install Nextflow edge releases, the main difference with stable releases being that you have to export the version you would like to install before issuing the appropriate installation/execution commands as highlighted below.

• If you have Nextflow installed already, you can issue the version you would like to use on the same line as the pipeline command and it will be fetched if required before the pipeline execution.

NXF_VER="20.11.0-edge" nextflow run nf-core/rnaseq -profile test,docker -r 3.0

• If you have Nextflow installed already, another alternative to the option above is to export it as an environment variable before you run the pipeline command:

export NXF_VER="20.11.0-edge"
nextflow run nf-core/rnaseq -profile test,docker -r 3.0

• If you would like to download and install a Nextflow edge release from scratch with minimal fuss:

export NXF_VER="20.11.0-edge"
wget -qO- get.nextflow.io | bash
sudo mv nextflow /usr/local/bin/
nextflow run nf-core/rnaseq -profile test,docker -r 3.0

Note if you don’t have sudo privileges required for the last command above then you can move the nextflow binary to somewhere else and export that directory to $PATH instead. One way of doing that on Linux would be to add export PATH=$PATH:/path/to/nextflow/binary/ to your ~/.bashrc file so that it is available every time you login to your system.

• Manually download and install Nextflow from the available assets on Github. See Nextflow installation docs.