variantbenchmarking

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variantbenchmarking

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Pipeline to evaluate and validate the accuracy of variant calling methods in genomic research

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## Introduction

**nf-core/variantbenchmarking** is designed to evaluate and validate the accuracy of variant calling methods in genomic research. Initially, the pipeline is tuned well for available gold standard truth sets (for example, Genome in a Bottle and SEQC2 samples) but it can be used to compare any two variant calling results. The workflow provides benchmarking tools for small variants including SNVs and INDELs, Structural Variants (SVs) and Copy Number Variations (CNVs) for germline and somatic analysis.

The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

The workflow involves several key processes to ensure reliable and reproducible results as follows:

### Standardization and normalization of test (query/comparison) variants:

This initial step ensures consistent formatting and alignment of variants in test and truth VCF files for accurate comparison.

- Subsample if input vcf is multisample ([bcftools view](https://samtools.github.io/bcftools/bcftools.html#view))
- Homogenization of multi-allelic variants, MNPs and SVs to BND (including imprecise paired breakends and single breakends) ([variant-extractor](https://github.com/EUCANCan/variant-extractor))
- Reformatting VCF files from different SV callers ([svync](https://github.com/nvnieuwk/svync))
- Standardize SV variants to BND ([SVTK standardize](https://github.com/broadinstitute/gatk-sv/blob/main/src/svtk/scripts/svtk))
- Decompose SVs to BND [rtgtools svdecompose](https://cn.animalgenome.org/bioinfo/resources/manuals/RTGOperationsManual.pdf)
- Rename sample names ([bcftools reheader](https://samtools.github.io/bcftools/bcftools.html#reheader))
- Splitting multi-allelic variants([bcftools norm](https://samtools.github.io/bcftools/bcftools.html#norm))
- Deduplication of variants ([bcftools norm](https://samtools.github.io/bcftools/bcftools.html#norm))
- Left aligning of variants ([bcftools norm](https://samtools.github.io/bcftools/bcftools.html#norm))
- Use prepy in order to normalize. This option is only applicable for happy benchmarking of germline analysis ([prepy](https://github.com/Illumina/hap.py/tree/master))
- Split SNVs and indels if the given test VCF contains both. This is only applicable for somatic analysis ([bcftools view](https://samtools.github.io/bcftools/bcftools.html#view))

### Standardization and normalization of truth (baseline) variants:

- Decompose SVs to BND [rtgtools svdecompose](https://cn.animalgenome.org/bioinfo/resources/manuals/RTGOperationsManual.pdf)
- Rename sample names ([bcftools reheader](https://samtools.github.io/bcftools/bcftools.html#reheader))
- Splitting multi-allelic variants ([bcftools norm](https://samtools.github.io/bcftools/bcftools.html#norm))
- Deduplication of variants ([bcftools norm](https://samtools.github.io/bcftools/bcftools.html#norm))
- Left aligning of variants([bcftools norm](https://samtools.github.io/bcftools/bcftools.html#norm))

### Ensemble (majority rule) approch to prepare truth variants:

When a "Gold Standard" (a high-confidence, validated set of variants) is not available, you can create a Proxy Ground Truth by looking for agreement between different tools. This is Majority Rule approach assumes that if multiple independent variant callers identify the same mutation, it is more likely to be a real biological variant rather than a technical error from a single pipeline. Only variants found by at least the minimum number of callers specified in your threshold are kept as the "truth" for the final benchmark.

If the $--ensemble/_truth$ threshold is set higher than 0, the pipeline performs the following steps:

- Merge small (SNVs and INDELs) using ([bcftools merge](https://samtools.github.io/bcftools/bcftools.html#merge))
- Merge Structual Variants using ([SURVIVOR merge](https://github.com/fritzsedlazeck/SURVIVOR/wiki))
- Consensus filtering the variants according to $--ensemble/_truth$.

### Filtering options:

Applying filtering on the process of benchmarking itself might makes it impossible to compare different benchmarking strategies. Therefore, for whom like to compare benchmarking methods this subworkflow aims to provide filtering options for variants.

- Filtration of contigs ([bcftools view](https://samtools.github.io/bcftools/bcftools.html#view))
- Include or exclude SNVs and INDELs ([bcftools filter](https://samtools.github.io/bcftools/bcftools.html#filter))
- Size and quality filtering for SVs ([SURVIVOR filter](https://github.com/fritzsedlazeck/SURVIVOR/wiki))

### Liftover of vcfs:

This sub-workflow provides option to convert genome coordinates of truth VCF and test VCFs and high confidence BED file to a new assembly. Golden standard truth files are build upon specific reference genomes which makes the necessity of lifting over depending on the test VCF in query. Lifting over one or more test VCFs is also possible.

- Create sequence dictionary for the reference ([picard CreateSequenceDictionary](https://gatk.broadinstitute.org/hc/en-us/articles/360037068312-CreateSequenceDictionary-Picard)). This file can be saved and reused.
- Lifting over VCFs ([picard LiftoverVcf](https://gatk.broadinstitute.org/hc/en-us/articles/360037060932-LiftoverVcf-Picard))
- Lifting over high confidence coordinates ([UCSC liftover](http://hgdownload.cse.ucsc.edu/admin/exe))

### Statistical inference of input test and truth variants:

This step provides insights into the distribution of variants before benchmarking by extracting variant statistics:.

- SNVs, INDELs and complex variants ([bcftools stats](https://samtools.github.io/bcftools/bcftools.html#stats))
- SVs by type ([SURVIVOR stats](https://github.com/fritzsedlazeck/SURVIVOR/wiki))

### Benchmarking of variants:

Actual benchmarking of variants are split between SVs and small variants:

Available methods for germline and somatic _structural variant (SV)_ benchmarking are:

- Truvari ([truvari bench](https://github.com/acenglish/truvari/wiki/bench))
- SVanalyzer ([svanalyzer benchmark](https://github.com/nhansen/SVanalyzer/blob/master/docs/svbenchmark.rst))
- RTGtools (only for BND) ([rtg bndeval](https://realtimegenomics.com/products/rtg-tools))

> [!NOTE]
> Please note that there is no somatic specific tool for SV benchmarking in this pipeline.

Available methods for germline and somatic _CNVs (copy number variations)_ are:

- Truvari ([truvari bench](https://github.com/acenglish/truvari/wiki/bench))
- Wittyer ([witty.er](https://github.com/Illumina/witty.er/tree/master))
- RTG tools ([rtg cnveval](https://realtimegenomics.com/products/rtg-tools))
- Intersection ([bedtools intersect](https://bedtools.readthedocs.io/en/latest/content/tools/intersect.html))

> [!NOTE]
> Please note that there is no somatic specific tool for CNV benchmarking in this pipeline.

> [!NOTE]
> Wittyer does not support BND type of variants. It is recommended to either exclude (filter) them out or convert them to other types before analysis.

Available methods for *small variants: SNVs and INDEL*s:

- Germline variant benchmarking using ([rtg vcfeval](https://realtimegenomics.com/products/rtg-tools))
- Germline variant benchmarking using ([hap.py](https://github.com/Illumina/hap.py/blob/master/doc/happy.md))
- Somatic variant benchmarking using ([rtg vcfeval --squash-ploidy](https://realtimegenomics.com/products/rtg-tools))
- Somatic variant benchmarking using ([som.py](https://github.com/Illumina/hap.py/tree/master?tab=readme-ov-file#sompy))

> [!NOTE]
> Please note that using happ.py and som.py with rtgtools as comparison engine is also possible. Check conf/tests/test_ga4gh.config as an example.

### Intersection of benchmark regions:

Intersecting test and truth BED regions produces benchmark metrics. Intersection analysis is especially recommended for _CNV benchmarking_ where result reports may variate per tool.

- Convert SV or CNV VCF file to BED file, if no regions file is provided for test case using ([SVTK vcf2bed](https://github.com/broadinstitute/gatk-sv/blob/main/src/svtk/scripts/svtk))
- Convert VCF file to BED file, if no regions file is provided for test case using ([Bedops convert2bed](https://bedops.readthedocs.io/en/latest/content/reference/file-management/conversion/convert2bed.html#convert2bed))
- Intersect the regions and gether benchmarking statistics using ([bedtools intersect](https://bedtools.readthedocs.io/en/latest/content/tools/intersect.html))

### Concordance analysis between test VCFs:

- Concordance analysis enables comparison of test VCFs with each other and it can be coupled to benchmarking analysis ([GATK4 concordance](https://gatk.broadinstitute.org/hc/en-us/articles/360040509811-Concordance))

### Comparison of benchmarking results per TP, FP and FN files

It is essential to compare benchmarking results in order to infer uniquely or commonly seen TPs, FPs and FNs.

- Merging TP, FP and FN results for happy, rtgtools and sompy ([bcftools merge](https://samtools.github.io/bcftools/bcftools.html#merge))
- Merging TP, FP and FN results for Truvari and SVanalyzer ([SURVIVOR merge](https://github.com/fritzsedlazeck/SURVIVOR/wiki))
- Conversion of VCF files to CSV to infer common and unique variants per caller (python script)

### Reporting of benchmark results

The generation of comprehensive report that consolidates all benchmarking results.

- Merging summary statistics per benchmarking tool (python script)
- Plotting benchmark metrics per benchmarking tool (R script)
- Create visual HTML report for the integration of NCBENCH ([datavzrd](https://datavzrd.github.io/docs/index.html))
- Apply _MultiQC_ to visualize results

## Usage

> [!NOTE]
> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

`samplesheet.csv`:

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

Each row represents a vcf file (test-query file). For each vcf file and variant calling method (caller) have to be defined.

User _has to provide truth_vcf and truth_id in config files_.

> [!NOTE]
> There are publicly available truth sources. For germline analysis, it is common to use [genome in a bottle (GiAB)](https://www.nist.gov/programs-projects/genome-bottle) variants. There are variate type of golden truths and high confidence regions for hg37 and hg38 references. Please select and use carefully.
> For somatic analysis, [SEQC2 project](https://sites.google.com/view/seqc2/home/data-analysis/high-confidence-somatic-snv-and-indel-v1-2) released SNV, INDEL and CNV regions. One, can select and use those files.

Here you can find example combinations of [truth files](docs/truth.md)

For more details and further functionality, please refer to the [usage documentation](https://nf-co.re/variantbenchmarking/usage) and the [parameter documentation](https://nf-co.re/variantbenchmarking/parameters).

Now, you can run the pipeline using:

```bash
nextflow run nf-core/variantbenchmarking \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR> \
   --genome GRCh37 \
   --analysis germline \
   --truth_id HG002 \
   --truth_vcf truth.vcf.gz
```

> [!WARNING]
> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).
> Conda profile is not available for SVanalyzer (SVBenchmark) tool, if you are planing to use the tool either choose docker or singularity.

### Example usages

This pipeline enables quite a number of subworkflows suitable for different benchmarking senarios. Please go through [this documentation](docs/testcases.md) to learn some example usages which discusses about the test config files under conf/tests and tests/.

## Pipeline output

To see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/variantbenchmarking/results) tab on the nf-core website pipeline page.
For more details about the output files and reports, please refer to the
[output documentation](https://nf-co.re/variantbenchmarking/output).

This pipeline outputs benchmarking results per method besides to the inferred and compared statistics.

## Credits

nf-core/variantbenchmarking was originally written by Kübra Narcı ([@kubranarci](https://github.com/kubranarci)) as a part of benchmarking studies in German Human Genome Phenome Archieve Project ([GHGA](https://www.ghga.de/)).

We thank the following people for their extensive assistance in the development of this pipeline:

- Nicolas Vannieuwkerke ([@nvnienwk](https://github.com/nvnieuwk))
- Maxime Garcia ([@maxulysse](https://github.com/maxulysse))
- Georgia Kesisoglou ([@georgiakes](https://github.com/georgiakes))
- Sameesh Kher ([@khersameesh24](https://github.com/khersameesh24))
- Florian Heyl ([@heylf](https://github.com/heyl))
- Krešimir Beštak ([@kbestak](https://github.com/kbestak))
- Ata Jadidahari ([@AtaJadidAhari](https://github.com/AtaJadidAhari))
- Elad Herz ([@EladH1](https://github.com/EladH1))
- Victor Didier Perez ([@VictorDidier](https://github.com/VictorDidier))

## Acknowledgements

## Contributions and Support

If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).

For further information or help, don't hesitate to get in touch on the [Slack `#variantbenchmarking` channel](https://nfcore.slack.com/channels/variantbenchmarking) (you can join with [this invite](https://nf-co.re/join/slack)).

## Citations

If you use nf-core/variantbenchmarking for your analysis, please cite it using the following doi: [110.5281/zenodo.14916661](https://doi.org/10.5281/zenodo.14916661)

An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.

You can cite the `nf-core` publication as follows:

> **The nf-core framework for community-curated bioinformatics pipelines.**
>
> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
>
> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).

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