nf-core/variantbenchmarking

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Introduction

Given test vcfs in samplesheet.csv, this pipelines compares them to truth vcf provided with params.truth_vcf.

params.varianttype can be “small” or “structural” for params.analysis of “germline” or params.variant_type can be “snv”, “indel” or “structural” for params.analysis of “somatic”. Please be aware that _only one type of varian_analysis is possible for each run.

Samplesheet input

You will need to create a samplesheet with information about the test vcf you would like to analyze before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Full samplesheet

The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below.

id,test_vcf,caller
test1,test1.vcf.gz,delly
test2,test2.vcf,gatk
test3,test3.vcf.gz,cnvkit

An example samplesheet has been provided with the pipeline.

Truth samples

Please find the detailed information about truth samples here.

Lifting over truth sets

This workflow comes with a liftover option for truth sets. In order to activate liftover use --liftover "truth".

• --chain: This workflow uses picard tools for lifting over and a chain file has to be provided specific to the input truth vcf. Some examples can be found here
• --rename_chr: Renaming chromosomes is required after liftover process. Some examples can be found under assets/rename_contigs directory.
• --dictionary: .dict file is required to run liftover process. If dictionary file is not provided, picard createsequencedictionary will create and use the file.

Lifting over test sets

Lifting over test samples is also possible through this pipeline, if you want to liftover at least one of the samples first use --liftover "test" and add liftover option to samplesheet:

id,test_vcf,caller,liftover
test1,test1.vcf.gz,delly,true
test2,test2.vcf,gatk,false
test3,test3.vcf.gz,cnvkit,true

Please note that you should still provide chain and reame_chr files, and lifting over truth and test samples simultaneously is not possible.

Standardization and normalization parameters

Consistent formatting and alignment of variants in test and truth VCF files for accurate comparison is controlled by sv_standardization and preprocesses.

• --sv_standardization: The standardization methods to perform on the input files. Should be a comma-separated list of one or more of the following options: homogenize,svync.

  • homogenize: makes use of variant-extractor
  • svync: makes use of svync

• --preprocesses: The preprocessing steps to perform on the input files. Should be a comma-separated list of one or more of the following options: normalization,deduplication,prepy,filter_contigs

  • normalization: Splits multi-allelic variants in test and truth VCF files (bcftools norm)
  • deduplication: Deduplicates variants in test and truth VCF files (bcftools norm)
  • prepy: Uses prepy in order to normalize test files. This option is only applicable for happy benchmarking of germline analysis (prepy)
  • filter_contigs: Filter out extra contigs. It is common for truth files not to include extra contigs.

Using multi-sample vcf inputs

If the input test vcf contains more than one sample, then user has to define which sample name to use. subsample will added to the samplesheet as an additional column as follows:

id,test_vcf,caller,subsample
test1,test1.vcf.gz,delly,"TUMOR"
test2,test2.vcf,gatk,"NA128120"
test3,test3.vcf.gz,cnvkit,

Note that, this option can be inevitable for somatic analysis since most of the callers reports both normal and tumor genotypes in the same vcf file.

Optional benchmarking parameters

Benchmarking parameters may vary between the tools and for callers. In order to use the same parameters for all callers be sure to write the same value for all. If noting provided, deafault values will be used.

SVbenchmark

id,test_vcf,caller,normshift,normdist,normsizediff,maxdist
test1,test1.vcf.gz,delly,0.7,0.7,0.7,100000
test2,test2.vcf,gatk,0.6,0.5,0.7,110000

• normshift: Has to be between 0-1. Disallow matches if alignments between alternate alleles have normalized shift greater than normshift (default 0.2)
• normdist: Has to be between 0-1. Disallow matches if alternate alleles have normalized edit distance greater than normdist (default 0.2)
• normsizediff: Has to be between 0-1. Disallow matches if alternate alleles have normalized size difference greater than normsizediff (default 0.2)
• maxdist: Disallow matches if positions of two variants are more than maxdist bases from each other (default 100,000)

Truvari

id,test_vcf,caller,pctsize,pctseq,pctovl,refdist,chunksize,dup_to_ins,typeignore
test1,test1.vcf.gz,delly,0.7,0.7,0.7,100000,50000,true,true
test2,test2.vcf,gatk,0.6,0.5,0.7,110000,40000,false,true

• pctsize: Has to be between 0-1. Ratio of min(base_size, comp_size)/max(base_size, comp_size)
• pctseq: Has to be between 0-1. Edit distance ratio between the REF/ALT haplotype sequences of base and comparison call. Turn it off (0) for no sequence comparison.
• pctovl: Has to be between 0-1. Ratio of two calls’ (overlapping bases)/(longest span)
• refdist: Maximum distance comparison calls must be within from base call’s start/end
• chunksize: Create chunks of all calls overlapping within ±chunksize basepairs
• dup_to_ins: Converts DUP to INS type (boolean)
• typeignore: Ignore SVTYPE matching (boolean)

Wittyer

id,test_vcf,caller,vartype,bpDistance,percentThreshold,absoluteThreshold,maxMatches,evaluationmode
test1,test1.vcf.gz,delly,sv,200,0.5,17000,100,sc
test2,test2.vcf,gatk,sv,100,0.5,11000,-1,cts

• bpDistance: Upper bound of boundary distance when comparing truth and query. By default it is 500bp for all types except for Insertions, which are 100bp.Please note that if you set this value in the command line, it overrides all the defaults, so Insertions and other types will have the same bpd.
• percentThreshold: This is used for percentage thresholding. For CopyNumberTandemRepeats, this determines how large of a RepeatUnitCount (RUC) threshold to use for large tandem repeats. For all other SVs, in order to match between query and truth, the distance between boundaries should be within a number thats proportional to total SV (default 0.25)
• absoluteThreshold: This is used for absolute thresholding. For CopyNumberTandemRepeats, this determines how large of a RepeatUnitCount (RUC) threshold to use. For all other SVs, this is the upper bound of boundary distance when comparing truth and query. (default 10000)
• maxMatches: axMatches is a wittyer parameter. This is used for matching behaviour. Negative value means to match any number (for large SVs it is not recommended).
• evaluationmode: It is by default requires genotype matching. simpleCounting
  , CrossTypeAndSimpleCounting
  , genotypematch

Filtering parameters

• --exclude_expression: Use bcftools expressions to exclude variants. Default
• --include_expression: Use bcftools expressions to include variants. Default

Parameters applicable only to Structural Variants

• --min_sv_size: Minimum SV size of variants to benchmark, 0 to disable , Default:30
• --max_sv_size: Maximum SV size of variants to benchmark, -1 to disable , Default:-1
• --min_allele_freq: Minimum Alele Frequency of variants to benchmark, Use -1 to disable , Default:-1
• --min_num_reads: Minimum number of read supporting variants to benchmark, Use, -1 to disable , Default:-1

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/variantbenchmarking --input ./samplesheet.csv --outdir ./results -profile docker --genome GRCh37 --sample HG002 --analysis germline --variant_type small

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/variantbenchmarking -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
sample: 'HG002'
analysis: 'germline'
<...>

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

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/variantbenchmarking

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/variantbenchmarking 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
• test_full
  • A profile with a complete configuration for full size of sample testing
  • Includes links to test data so needs no other parameters
• liftover_test
  • A profile with a complete configuration for using liftover of HG002 hg38 test set to hg37
  • Includes links to test data so needs no other parameters
• liftover_truth
  • A profile with a complete configuration for using liftover of HG002 hg37 truth set to hg38
  • Includes links to test data so needs no other parameters
• germline_small
  • A profile with a complete configuration for germline analysis with small variat type of data
  • Includes links to test data so needs no other parameters
• germline_structural
  • A profile with a complete configuration for germline analysis with structural variat type of data
  • Includes links to test data so needs no other parameters
• somatic_structural
  • A profile with a complete configuration for somatic analysis with structural variat type of data
  • Includes links to test data so needs no other parameters
• somatic_snv
  • A profile with a complete configuration for somatic analysis with snv variat type of data
  • Includes links to test data so needs no other parameters
• somatic_indel
  • A profile with a complete configuration for somatic analysis with indel variat type of data
  • 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.

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'