nf-core/rnadnavar

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Introduction

RaVEX is an end-to-end workflow designed for detecting somatic mutations in bulk RNA sequencing data, with the option to apply it to whole genome or whole exome sequencing data. It follows GATK best practices for data cleanup, utilises Mutect2, Strelka2 and SAGE for variant calling, and includes post-processing steps such as VCF normalization, consensus of called variants (resulting in a MAF file), and filtering. Additionally, an optional realignment with HISAT2 is performed only in the coordinates where mutations were detected during the variant calling subworkflow.

Originally, this pipeline was developed for human and mouse data.

Quickstart

The typical command for running the pipeline is as follows:

nextflow run nf-core/rnadnavar -r <VERSION> -profile <PROFILE> --input ./samplesheet.csv --outdir ./results --tools <TOOLS>

-r <VERSION> is optional but strongly recommended for reproducibility and should match the latest version.

-profile <PROFILE> is mandatory and should reflect either your own institutional profile or any pipeline profile specified in the profile section.

This will launch the pipeline and perform variant calling, normalisation, consensus and filtering if specified in --tools, see the [parameter section](https://nf-co. re/rnadnavar/latest/parameters#tools) for details on the tools. In the above example the pipeline runs with the docker configuration profile. See below for more information about profiles.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. It has to be a comma-separated file with columns, and a header row as shown in the examples below. It is recommended to use the absolute path of the files, but a relative path should also work.

An RNA tumor sample must be associated to a DNA normal sample at the moment, specified with the same patient ID. The sample type can be specified with the status: 0 (normal DNA), 1 (tumour DNA) and 2 (tumour RNA). An additional tumor sample (such as a relapse for example), can be added if specified with the same patient ID but different sample name, and the status value 1 or 2.

The pipeline will output results in a different directory for each sample.

These are the accepted columns for the sample sheet, although not all of them are mandatory at the same time:

An example samplesheet has been provided with the pipeline.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/rnadnavar --input ./samplesheet.csv --outdir ./results --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
<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/rnadnavar -profile docker -params-file params.yaml

with params.yaml containing:

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

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

Starting from different steps

The pipeline has the flexibility to start from different steps but tools need to be specified to run. If you are planning to run the pipeline by stages please read relevant information below on how to run from different steps:

• mapping will require: patient,status,sample,lane,bam,bai or patient,status,sample,lane,cram,crai or patient,status,sample,lane,bam,bai,cram,crai. When BAM and CRAM files are mixed BAMs will be converted to CRAMs to save on space.

  • tools is set to null by default but all GATK pre-processing will run regardless unless skipped with skip_tools.

• variant_calling will require: patient,status,sample,lane,bam,bai or patient,status,sample,lane,cram,crai or patient,status,sample,lane,bam,bai,cram,crai. When BAM and CRAM files are mixed BAMs will be converted to CRAMs to save on space.

• variant_calling will require: patient,status,sample,lane,bam,bai or patient,status,sample,lane,cram,crai or patient,status,sample,lane,bam,bai,cram,crai. When BAM and CRAM files are mixed BAMs will be converted to CRAMs to save on space.

  • we recommend adding mutect2,strelka,sage to tools option for this step.

• norm will require: patient,status,sample,vcf,variantcaller,normal_id if not realignment step in tools.

  • normal_id is needed to properly create the id tag, which is going to be tumour_vs_normal style to match it with the rest of the pipeline (note that this will be simplified in the future to just id but it is not yet implemented).
  • we recommend adding vep,norm,consensus to tools to get annotated vcfs and consensus maf with this option.

• filtering

  • If you use other variant callers that are not mutect2, strelka or SAGE you will need to change in config/modules/filtering/maf_filtering.config and add to args --vc_priority caller1 caller2 caller3
  • If you use other variant callers that are not mutect2, strelka or SAGE you will need to change in config/modules/filtering/maf_filtering.config and add to args --vc_priority caller1 caller2 caller3. Do not forget to add consensus to callers if you are running DNA and RNA in parallel to annotate mutations found in both data types. Disclaimer: this has not been tested thoroughly, therefore please ask in our slack channel or open an issue in github describing your issue. Be aware that even if processes run, you will need to review your results
  • At the end of the RNA filtering you will have two entries per mutation, the extra one is the annotation from realignment mode. If you want to remove these entried, they can be filtered through the column Tumor_Sample_Barcode and remove all entries with the suffix _realign.

• realignment: this step is slightly different from the rest as it requires both results from variant calling (vcf/maf) and alignment files (bam,cram). Reason being that it will convert coordinates from vcf/maf to bed and extract reads from the alignment files of those regions of interest where a mutation was found. With those reads a re-alignment is performed using HISAT2. requires patient,status,sample,normal_id,vcf/maf,variantcaller,bam/cram,bai/crai.

  • Realignment can be activated from any step as long as it is specified in params.tools, alignment files are provided and a maf/vcf file is provided or produced by the pipeline.

• realigns regions where mutations in RNA samples (status=2) where found using HISAT2 instead of STAR. If you want to activate this step it needs to be specified in tools and CRAM/BAM must be specified.

• rescue will require:patient,status,sample,vcf,variantcaller,normal_id if not realignment step in tools. If realignment step then: patient,status,sample,vcf,variantcaller,normal_id,cram,crai if not realignment step in tools.

If you think there is a step of a workflow missing that we haven’t think about yet please contact us in the slack channel.

Note that if you get this error TypeError: '<=' not supported between instances of 'str' and 'int' in the FILTERING process it might be that vcf2maf failed to pass the information from vcf to maf because the id in the vcf differs from sample id provided in samplesheet. Please make sure they both match.

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/rnadnavar

Reproducibility

It is 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/rnadnavar 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 reproducbility, you can use share and re-use 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 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, since it can lead to different results on different machines dependent on the computer enviroment.

• 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 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.

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.

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.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

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'