https://github.com/brentp/rare-disease-wf

(WIP) best-practices workflow for rare disease
https://github.com/brentp/rare-disease-wf

genomics rare-disease variants

Last synced: 3 months ago
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(WIP) best-practices workflow for rare disease

• Host: GitHub
• URL: https://github.com/brentp/rare-disease-wf
• Owner: brentp
• License: mit
• Created: 2021-04-14T08:21:13.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-07-01T18:07:32.000Z (12 months ago)
• Last Synced: 2025-01-17T20:46:12.674Z (5 months ago)
• Topics: genomics, rare-disease, variants
• Language: Nextflow
• Homepage:
• Size: 557 KB
• Stars: 59
• Watchers: 8
• Forks: 8
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
For rare-disease, the [best practices and expected number of candidate variants for each inheritance mode are known](https://www.biorxiv.org/content/10.1101/2020.08.13.249532v3). The

actual filtering is easily done with a tool like [slivar](https://github.com/brentp/slivar/wiki/rare-disease). 

This is a necessary first step with the following limitations:

 1. it leaves an analyst or clinician with choices on how to prioritize the 10-15 candidates variants or ~100 for autosomal (non *de novo*) dominant.

    - This is quite a small number, but the prioritization after this is highly variable across tools and analysts.

 2. it is limited text/spreadsheet output

 3. it assumes a high-quality, jointly-called VCF is already available

 4. it leaves the analyst with the chore of getting IGV set up, and browsing each candidate for each family.

## Quickstart 

Note, it is early days for the project. It will produce high-quality SNP/indel candidates

but you may need experience with [nextflow](https://nextflow.io) to run it easily.

This project **currently** has workflow that can be run as:

```

# NOTE that you need to remove everything after \ on each line for the command to work

# the comments here are just for documentation purposes.

nextflow run -resume -profile slurm rare-disease.nf \

    -config nextflow.config \    # a starting config is included in this repo. adjust from there.

    --xams "/path/to/*/*.cram" \ # NOTE that this is a string glob

    --ped $pedigree_file \       # see: https://gatk.broadinstitute.org/hc/en-us/articles/360035531972-PED-Pedigree-format

    --fasta $reference_fasta \

    --gff $gff \                   # e.g. from: ftp://ftp.ensembl.org/pub/current_gff3/homo_sapiens/

    --slivarzip gnomad.hg38.zip  \  # from: https://github.com/brentp/slivar#gnotation-files

    --cohort_name my_rare_disease

```

## Output

See [this wiki page](https://github.com/brentp/rare-disease-wf/wiki/Workflow-Output) for more information about how to use the output.

This does:

 1. Run [DeepVariant](https://github.com/google/deepvariant) and [GLNexus](https://github.com/dnanexus-rnd/GLnexus) (we have shown these tools to give higher quality results for trios) in an efficient nextflow workflow that can be easily run in the cloud or on a cluster.

 1. Decompose and normalize variants.

 1. Annotate with [bcftools csq](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870570/) and [snpEff](https://pcingola.github.io/SnpEff/adds/SnpEff_paper.pdf) 

 1. Annotate with allele frequency and inheritance modes using [slivar](https://github.com/brentp/slivar)

 1. Annotate with gene-based annotations:

    - clinvar-gene-phenotype

    - loss-of-function intolerance

 1. Output high-quality calls from [slivar](https://github.com/brentp/slivar) for recessive, dominant, x-linked, compound-het and other

    inheritance modes.

 1. Generates and links pre-made, standalone [igv.js](https://github.com/igvteam/igv.js)/[jigv](https://github.com/brentp/jigv) outputs for each candidate.

And the key output will be in: `results-rare-disease/${cohort_name}.slivar.candidates.tsv` which is something one can easily view in excel or other spreadsheet software.

In addition, it will create: `results-rare-disease/${cohort_name}.jigv.html` and `results-rare-disease/jigv_plots/*` which together provide an HTML table and interactive [igv.js](https://github.com/brentp/igvteam/igv.js) views of each variant and associated alignments that do not rely on the original alignment files.

In coming releases, this will:

 1. Output QC with [somalier](https://github.com/brentp/somalier) and other tools to be shown in [multiQC](https://multiQC.info)

 1. Output high-quality SVs (using manta-> graphtyper)

## Octopus

currently, [octopus](https://doi.org/10.1038/s41587-021-00861-3) is included as

a separate workflow. This octopus.nf pipeline will detect trios and families

and run them together and then iteratively merge across families using the

`n+1` schema [described in the octopus

docs](https://luntergroup.github.io/octopus/docs/guides/models/population)

Finally, the workflow will do the forest filtering as recommended by the

octopus documentation.

We plan to integrate the octopus and deepvariant calls in the future.

## Future Development

Development and research is underway so that it will:

 1. Add a high-quality set of SV/CNVs

    - [Manta](https://github.com/Illumina/manta) + SVchannels and [duphold](https://github.com/brentp/duphold) filtering

 1. Add some **prioritization** of variants

    - For example, lower priority to variants filtered in gnomAD

 1. Integrate SV/CNV calls with the snp/indels to find, for example compound heterozygotes with a snp:SV pair.

 1. Evaluate use of [octopus](https://github.com/luntergroup/octopus) to find large indels (and/or SNPs and indels).

 1. Use GTex + phenotypes to further prioritize variants in a family and phenotype-specific way, such that, for example

    variants in genes that are not expressed in relevant tissues are down-weighted.

 1. Provide a graphical-user-interface so that sorting, filtering, note-taking, sharing is simplified

## Software Used

+ [DeepVariant](https://github.com/google/deepvariant) Variant Calling with Deep Learning. https://doi.org/10.1038/nbt.4235

+ [GLNexus](https://github.com/dnanexus-rnd/GLnexus) Joint variant calling. http://dx.doi.org/10.1101/343970

+ [octopus](https://github.com/luntergroup/octopus) haplotype-based mutation caller. https://doi.org/10.1038/s41587-021-00861-3

+ [bcftools](https://github.com/samtools/bcftools) BCF/VCF manipulation. https://doi.org/10.1093/gigascience/giab008

+ [bcftools csq](https://github.com/samtools/bcftools) variant consequence annotation. https://doi.org/10.1093/bioinformatics/btx100

+ [htslib](https://github.com/samtools/htslib) C libary for genomics data. https://doi.org/10.1093/gigascience/giab007

+ [slivar](https://github.com/brentp/slivar) variant filtering and annotation. https://doi.org/10.1101/2020.08.13.249532

+ [igv.js](https://github.com/igvteam/igv.js/). javascript genomics viewer. https://doi.org/10.1101/2020.05.03.075499

+ [nextflow](https://nextflow.io/) scientific workflows. https://doi.org/10.1038/nbt.3820

+ [manta](https://github.com/Illumina/manta) structural variant caller. https://doi.org/10.1093/bioinformatics/btv710

+ [dysgu](https://github.com/kcleal/dysgu) structural variant caller. https://doi.org/10.1101/2021.05.28.446147 

+ [paragraph](https://github.com/Illumina/paragraph) structural variant genotyper. https://doi.org/10.1186/s13059-019-1909-7

+ [jasmine](https://github.com/mkirsche/Jasmine) structural variant merging. https://doi.org/10.1101/2021.05.27.445886

+ [duphold](https://github.com/brentp/duphold) structural variant depth annotation. https://doi.org/10.1093/gigascience/giz040

+ [snpEff](http://pcingola.github.io/SnpEff/) variant consequence annotation. https://doi.org/10.4161/fly.19695

+ [svpack](https://github.com/amwenger/svpack/) structural variant annotation.