nf-core/tumourevo

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Introduction

tumourevo is a workflow to infer a tumour evolution model from whole-genome sequencing (WGS) data.

Through the analysis of variant and copy-number calls, it reconstructs the evolutionary process leading to the observed tumour genome. Most of the analyses can be done at mutliple levels: single sample, multiple samples from the same patient (multi-region/longitudinal assays), and multiple patients from distinct cohorts.

Running the pipeline

Quickstart

The typical command for running the pipeline is as follows:

nextflow run nf-core/tumourevo \
 -r <VERSION> \
 -profile <PROFILE> \
 --input <INPUT CSV> \
 --outdir ./results
 --tools <TOOLS> \
 --filter #check the correct name of the flag

-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 documentation imply that any nextflow run nf-core/tumourevo command is run with the appropriate -r and -profile commands.

This will launch the pipeline and perform variant calling with the tools specified in --tools, see the parameter section for details on the available tools.

Unless running with the test profile, the paths of input files must be provided within the <INPUT CSV> file specified in --input, see the input section for input requirements.

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.

Input: Sample sheet configurations

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use the parameter --input to specify its location. It has to be a comma-separated file with at least 5 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.

For the joint analysis of multiple samples, a tumor BAM file is required for each sample, such that the number of reads of a private mutation can be retrieved for all the samples thorugh mpileup.

Multiple samples from the same patient must be specified with the same dataset ID, patient ID, and a different tumour_sample ID. normal_sample columns is required.

Multiple patients from the same dataset must be specified with the same dataset ID, and a different patient ID.

tumourevo will output sample-specific results in a different directory for each sample, patient-specific results in a common directory for each patient, and dataset-specific results in a common directory for each dataset.

Output from different workflows, subworkflows and modules will be in a specific directory for each dataset, patient, sample and tool configuration.

Overview: Samplesheet Columns

An example samplesheet has been provided with the pipeline.

Pipeline modalities

The tumourevo pipeline supports variant annotation, driver annotation, quality control processes, subclonal deconvolution and signature deconvolution analysis through various tools. It can be used to analyse both single sample experiments and longitudinal/multi-region assays, in which multiple samples of the same patient are avaiable. As input, you must provide at least information on the samples, the VCF file from one of the supported callers and the output of one of the supported copy number caller. By default, if multiple samples from the same patient are provided, they will be analysed in a multivariate framework (which affects in particular the subclonal deconvolution deconvolution steps) to retrieve information useful in the reconstruction of the evolutionary process. Depending on the variant calling strategy (single sample or multi sample) and the provided input files, different strategies will be applied.

Variant calling

1. Multi-sample variant calling

Modern tools (ie: Platypus and Mutect2) allow to perform variant calling directly in multisample mode. If the VCFs provided as input are already multisample, no additional step is required.

Examples

Running the pipeline

nextflow run nf-core/tumourevo \
 -r <VERSION> \
 -profile <PROFILE> \
 --input <INPUT CSV> \
 --outdir results \
 --tools pyclonevi,mobster,viber,sparsesignature,sigprofiler

Minimal input file, two samples from the same patient:

dataset,patient,sample,normal_sample,vcf,vcf_tbi,cna_segments,cna_extra,cna_caller,cancer_type
dataset1,patient1,S1,N1,patient1_S1.vcf.gz,patient1_S1.vcf.gz.tbi,/CNA/patient1/S1/segments.txt,/CNA/patient1/S1/purity_ploidy.txt,caller,PANCANCER
dataset1,patient1,S2,N1,patient1_S1.vcf.gz,patient1_S1.vcf.gz.tbi,/CNA/patient1/S2/segments.txt,/CNA/patient1/S2/purity_ploidy.txt,caller,PANCANCER

2. Single sample variant calling

If the variant calling performed independently on each sample, even if coming from the same patient, you can provide the BAM and BAI files from each tumor sample. In this way, a classical pileup strategy will be used in order to retrieve the depth for all samples of private mutations, in order to correctly perform the subclonal deconvolution analysis.

Input file for two patients without joint variant calling, bam files available:

dataset,patient,sample,normal_sample,vcf,vcf_tbi,cna_segments,cna_extra,cna_caller,cancer_type,tumour_bam,tumour_bai
dataset1,patient1,S1,N1,patient1_S1.vcf.gz,patient1_S1.vcf.gz.tbi,/CNA/patient1/S1/segments.txt,/CNA/patient1/S1/purity_ploidy.txt,caller,PANCANCER,patient1/BAM/S1.bam,patient1/BAM/S1.bam.bai 
dataset1,patient1,S2,N1,patient1_S2.vcf.gz,patient1_S2.vcf.gz.tbi,/CNA/patient1/S2/segments.txt,/CNA/patient1/S2/purity_ploidy.txt,caller,PANCANCER,,patient1/BAM/S2.bam,patient1/BAM/S2.bam.bai  

If you can not include the bam files in the input csv, the pipeline will run anyway, treating each sample as independent.

Filtering data

Using the --filter flag it is possible to control how the pipeline handles different steps, in particular regarding the QC workflow. Setting --filter TRUE will

Subclonal deconvolution

The remove_tail parameter

Examples

Running the pipeline without mobster

nextflow run nf-core/tumourevo \
 -r <VERSION> \
 -profile <PROFILE> \
 --samples <INPUT CSV> \
 --publish_dir ./results \
 --mode multisample \
 --tools pyclone,viber,ctree,sparsesignature,sigprofiler

Running the pipeline with mobster

nextflow run nf-core/tumourevo \
 -r <VERSION> \
 -profile <PROFILE> \
 --samples <INPUT CSV> \
 --publish_dir ./results \
 --mode multisample \
 --tools mobster,pyclone,viber,ctree,sparsesignature,sigprofiler

Minimal input file (two samples from the same patient):

dataset,patient,sample,vcf,vcf_tbi,cna_dir,cna_caller
dataset1,patient1,S1,patient1_S1.vcf.gz,patient1_S1.vcf.gz.tbi,/CNA/patient1/S1,caller
dataset1,patient1,S2,patient1_S2.vcf.gz,patient1_S2.vcf.gz.tbi,/CNA/patient1/S2,caller

Driver annotation

You can retrieve tumor-specific drivers in the driver annotation step by specifying the tumor type in the input csv. Pan-cancer drivers will be retrieved by specifying PANCANCER as tumour type in the input csv file. For this step, we currently refer to IntOGen latest release, but you can also provide a custom driver table that will be used in the analysis. Please note that the tumor types reported in the input file must correspond to those present in the table used for the annotation (default driver table used can be found here)

Input file:

dataset,patient,sample,vcf,vcf_tbi,cna_dir,cna_caller,tumour_bam,tumour_bai,tumour_type
dataset1,patient1,S1,patient1_S1.vcf.gz,patient1_S1.vcf.gz.tbi,/CNA/patient1/S1,caller,patient1/BAM/S1,patient1/BAM/S1.bam.bai,BRCA
dataset1,patient1,S2,patient1_S2.vcf.gz,patient1_S2.vcf.gz.tbi,/CNA/patient1/S2,caller,patient1/BAM/S2,patient1/BAM/S2.bam.bai,BRCA 
dataset1,patient1,S1,patient1_S1.vcf.gz,patient1_S1.vcf.gz.tbi,/CNA/patient2/S1,caller,patient1/BAM/S1,patient1/BAM/S1.bam.bai,BRCA 
dataset1,patient2,S2,patient2_S2.vcf.gz,patient2_S2.vcf.gz.tbi,/CNA/patient2/S2,caller,patient2/BAM/S2,patient2/BAM/S2.bam.bai,BRCA 

Available tools

We report the different tools included in the pipeline.

1. Gene annotation - EnsemblVEP

2. Driver annotation

3. Quality control - CNAqc

4. Subclonal deconvolution - MOBSTER - PyClone-VI - VIBER - Ctree

5. Signature deconvolution - SparseSignatures - SigProfiler

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/tumourevo

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

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.

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.

--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.