nf-core/methylseq

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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'

Bismark and bwa-meth workflow

The nf-core/methylseq package is actually two pipelines in one. The default workflow uses Bismark with Bowtie2 as alignment tool: unless specified otherwise, nf-core/methylseq will run this pipeline.

Since bismark v0.21.0 it is also possible to use HISAT2 as alignment tool. To run this workflow, invoke the pipeline with the command line flag --aligner bismark_hisat. HISAT2 also supports splice-aware alignment if analysis of RNA is desired (e.g. SLAMseq experiments), a file containing a list of known splicesites can be provided with --known_splices.

The second workflow uses BWA-Meth and MethylDackel instead of Bismark. To run this workflow, run the pipeline with the command line flag --aligner bwameth.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/methylseq --reads '*_R{1,2}.fastq.gz' -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/methylseq

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

You can find the keys to specify the genomes in the iGenomes config file. Common genomes that are supported are:

• Human
  • --genome GRCh37
  • --genome GRCh38
• 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 index given
    }
    // Any number of additional genomes, key is used with --genome
  }
}

Supplying reference indices

If you don’t want to use the Illumina iGenomes references, you can supply your own reference genome.

The minimum requirement is just a FASTA file - the pipeline will automatically generate the relevant reference index from this. You can use the command line option --save_reference to keep the generated references so that they can be added to your config and used again in the future. The bwa-meth workflow always needs a FASTA file, for methylation calling.

--fasta

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

# Single multifasta for genome
--fasta /path/to/genome.fa
 
# Bismark index directory
--bismark_index /path/to/ref/BismarkIndex/
 
# bwa-meth index filename base
# where for example the index files are called:
# /path/to/ref/genome.fa.bwameth.c2t.bwt
--bwa_meth_index /path/to/ref/genome.fa
 
# Genome Fasta index file
--fasta_index /path/to/genome.fa.fai

--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.

--save_reference

Supply this parameter to save any generated reference genome files to your results folder. These can then be used for future pipeline runs, reducing processing times.

Additional parameters

Adapter Trimming

Bisulfite libraries often require additional base pairs to be removed from the ends of the reads before alignment. You can specify these custom trimming parameters as follows:

• --clip_r1 <NUMBER>
  • Instructs Trim Galore to remove bp from the 5’ end of read 1 (or single-end reads).
• --clip_r2 <NUMBER>
  • Instructs Trim Galore to remove bp from the 5’ end of read 2 (paired-end reads only).
• --three_prime_clip_r1 <NUMBER>
  • Instructs Trim Galore to remove bp from the 3’ end of read 1 AFTER adapter/quality trimming has been
• --three_prime_clip_r2 <NUMBER>
  • Instructs Trim Galore to re move bp from the 3’ end of read 2 AFTER adapter/quality trimming has been performed.

The pipeline also accepts a number of presets for common bisulfite library preparation methods:

--rrbs

Specifying --rrbs will pass on the --rrbs parameter to TrimGalore! See the TrimGalore! documentation to read more about the effects of this option.

This parameter also makes the pipeline skip the deduplication step.

--skip_trimming

Specifying --skip_trimming will skip the adapter trimming step. Use this if your input FastQ files have already been trimmed outside of the workflow.

--skip_deduplication

By default, the pipeline includes a deduplication step after alignment. Use --skip_deduplication on the command line to skip this step. This is automatically set if using --rrbs for the workflow.

--pbat

Using the --pbat parameter will affect the trimming (see above) and also set the --pbat flag when aligning with Bismark. It tells Bismark to align complementary strands (the opposite of --directional).

--non_directional

By default, Bismark assumes that libraries are directional and does not align against complementary strands. If your library prep was not directional, use --non_directional to align against all four possible strands.

Note that the --single_cell and --zymo parameters both set the --non_directional workflow flag automatically.

--comprehensive

By default, the pipeline only produces data for cytosine methylation states in CpG context. Specifying --comprehensive makes the pipeline give results for all cytosine contexts. Note that for large genomes (e.g. Human), these can be massive files. This is only recommended for small genomes (especially those that don’t exhibit strong CpG context methylation specificity).

If specified, this flag instructs the Bismark methylation extractor to use the --comprehensive and --merge_non_CpG flags. This produces coverage files with information from about all strands and cytosine contexts merged into two files - one for CpG context and one for non-CpG context.

If using the bwa-meth workflow, the flag makes MethylDackel report CHG and CHH contexts as well.

--cytosine_report

By default, Bismark does not produce stranded calls. With this option the output considers all Cs on both forward and reverse strands and reports their position, strand, trinucleotide context and methylation state.

--relax_mismatches and --num_mismatches

By default, Bismark is pretty strict about which alignments it accepts as valid. If you have good reason to believe that your reads will contain more mismatches than normal, these flags can be used to relax the stringency that Bismark uses when accepting alignments. This can greatly improve the number of aligned reads you get back, but may negatively impact the quality of your data.

--num_mismatches is 0.2 by default in Bismark, or 0.6 if --relax_mismatches is specified. 0.6 will allow a penalty of bp * -0.6 - for 100bp reads, this is -60. Mismatches cost -6, gap opening -5 and gap extension -2. So, -60 would allow 10 mismatches or ~ 8 x 1-2bp indels.

--unmapped

Use the --unmapped flag to set the --unmapped flag with Bismark align and save the unmapped reads to FastQ files.

--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.

--save_align_intermeds

By default intermediate BAM files will not be saved. The final BAM files created after the deduplication step are always. Set to true to also copy out BAM files from the initial Bismark alignment step. If --skip_deduplication or --rrbs is specified then BAMs from the initial alignment will always be saved.

--min_depth

Specify to specify a minimum read coverage for MethylDackel to report a methylation call.

--meth_cutoff

Use this to specify a minimum read coverage to report a methylation call during Bismark’s bismark_methylation_extractor step.

--ignore_flags

Specify to run MethylDackel with the --ignore_flags flag to ignore SAM flags.

--methyl_kit

Specify to run MethylDackel with the --methyl_kit flag to produce files suitable for use with the methylKit R package.

--known_splices

Specify to run Bismark with the --known-splicesite-infile flag to run splice-aware alignment using HISAT2. A .gtf file has to be provided from which a list of known splicesites is created by the pipeline. (only works with --aligner bismark_hisat)

--slamseq

Specify to run Bismark with the --slam flag to run bismark in SLAM-seq mode (only works with --aligner bismark_hisat)

--local_alignment

Specify to run Bismark with the --local flag to allow soft-clipping of reads. This should only be used with care in certain single-cell applications or PBAT libraries, which may produce chimeric read pairs. (See Wu et al. (doesn’t work with --aligner bwameth)

--bismark_align_cpu_per_multicore

The pipeline makes use of the --multicore option for Bismark align. When using this option, Bismark uses a large number of CPUs for every --multicore specified. The pipeline calculates the number of --multicore based on the resources available to the task. It divides the available CPUs by 3, or by 5 if any of --single_cell, --zymo or --non_directional are specified. This is based on usage for a typical mouse genome.

You may find when running the pipeline that Bismark is not using this many CPUs. To fine tune the usage and speed, you can specify an integer with --bismark_align_cpu_per_multicore and the pipeline will divide the available CPUs by this value instead.

See the bismark documentation for more information.

--bismark_align_mem_per_multicore

Exactly as above, but for memory. By default, the pipeline divides the available memory by 13.GB, or 18.GB if any of --single_cell, --zymo or --non_directional are specified.

Note that the final --multicore value is based on the lowest limiting factor of both CPUs and memory.

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.

--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.

--multiqc_config

If you would like to supply a custom config file to MultiQC, you can specify a path with --multiqc_config. This is used instead of the config file that comes with the pipeline.

--monochrome_logs

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

--project

UPPMAX profile only: Cluster project for SLURM job submissions.

--clusterOptions

UPPMAX profile only: Submit arbitrary SLURM options.