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raredisease

Call and score variants from WGS/WES of rare disease patients.

https://github.com/nf-core/raredisease

Science Score: 44.0%

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    Found 14 DOI reference(s) in README
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Keywords

diagnostics nextflow nf-core pipeline rare-disease snv structural-variants variant-annotation variant-calling wes wgs workflow
Last synced: 6 months ago · JSON representation ·

Repository

Call and score variants from WGS/WES of rare disease patients.

Basic Info
Statistics
  • Stars: 103
  • Watchers: 172
  • Forks: 46
  • Open Issues: 42
  • Releases: 9
Topics
diagnostics nextflow nf-core pipeline rare-disease snv structural-variants variant-annotation variant-calling wes wgs workflow
Created over 4 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/raredisease

GitHub Actions CI Status

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TOC

Introduction

nf-core/raredisease is a best-practice bioinformatic pipeline for calling and scoring variants from WGS/WES data from rare disease patients. This pipeline is heavily inspired by MIP.

[!NOTE] Right now, we only support paired-end data from Illumina. If you've got other types of data and the pipeline doesn't work for you, just open an issue. We'd be happy to chat about a solution.

The pipeline is built using Nextflow, 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 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 in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

nf-core/raredisease workflow

1. Metrics:

2. Alignment:

3. Variant calling - SNV:

4. Variant calling - SV:

5. Annotation - SNV:

6. Annotation - SV:

7. Mitochondrial analysis:

8. Variant calling - repeat expansions:

9. Variant calling - mobile elements:

10. Rank variants - SV and SNV:

11. Variant evaluation:

Note that it is possible to include/exclude certain tools or steps.

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup 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 sample,lane,fastq_1,fastq_2,sex,phenotype,paternal_id,maternal_id,case_id hugelymodelbat,1,reads_1.fastq.gz,reads_2.fastq.gz,1,2,,,justhusky

Each row represents a pair of fastq files (paired end).

Second, ensure that you have defined the path to reference files and parameters required for the type of analysis that you want to perform. More information about this can be found here.

Now, you can run the pipeline using:

bash nextflow run nf-core/raredisease \ -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \ --input samplesheet.csv \ --outdir <OUTDIR>

[!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.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/raredisease was written in a collaboration between the Clinical Genomics nodes in Sweden, with major contributions from Ramprasad Neethiraj, Anders Jemt, Lucia Pena Perez, and Mei Wu at Clinical Genomics Stockholm.

Additional contributors were Sima Rahimi, Gwenna Breton and Emma Västerviga (Clinical Genomics Gothenburg); Halfdan Rydbeck and Lauri Mesilaakso (Clinical Genomics Linköping); Subazini Thankaswamy Kosalai (Clinical Genomics Örebro); Annick Renevey, Peter Pruisscher and Eva Caceres (Clinical Genomics Stockholm); Ryan Kennedy (Clinical Genomics Lund); Anders Sune Pedersen (Danish National Genome Center) and Lucas Taniguti.

We thank the nf-core community for their extensive assistance in the development of this pipeline.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #raredisease channel (you can join with this invite).

Citations

If you use nf-core/raredisease for your analysis, please cite it using the following doi: 10.5281/zenodo.7995798

An extensive list of references for the tools used by the pipeline can be found in the 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.

You can read more about MIP's use in healthcare in,

Stranneheim H, Lagerstedt-Robinson K, Magnusson M, et al. Integration of whole genome sequencing into a healthcare setting: high diagnostic rates across multiple clinical entities in 3219 rare disease patients. Genome Med. 2021;13(1):40. doi:10.1186/s13073-021-00855-5

Owner

  • Name: nf-core
  • Login: nf-core
  • Kind: organization
  • Email: core@nf-co.re

A community effort to collect a curated set of analysis pipelines built using Nextflow.

Citation (CITATIONS.md) 

# nf-core/raredisease: Citations

## Nextflow & nf-core

- [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

  > Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

- [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

  > Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [BCFtools](https://academic.oup.com/gigascience/article/10/2/giab008/6137722) & [SAMtools](https://academic.oup.com/bioinformatics/article/25/16/2078/204688)

  > Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10(2):giab008. doi:10.1093/gigascience/giab008

- [BEDTools](https://academic.oup.com/bioinformatics/article/26/6/841/244688)

  > Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841-842. doi:10.1093/bioinformatics/btq033

- [BWA-MEM](https://arxiv.org/abs/1303.3997)

  > Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Published online May 26, 2013. Accessed March 14, 2023. http://arxiv.org/abs/1303.3997

- [BWA-MEM2](https://ieeexplore.ieee.org/abstract/document/8820962)

  > Vasimuddin Md, Misra S, Li H, Aluru S. Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems. In: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE; 2019:314-324. doi:10.1109/IPDPS.2019.00041

- [BWA-MEME](https://academic.oup.com/bioinformatics/article/38/9/2404/6543607)

  > Jung Y, Han D. BWA-MEME: BWA-MEM emulated with a machine learning approach. Bioinformatics. 2022;38(9):2404-2413. doi:10.1093/bioinformatics/btac137

- [CADD<sup>1</sup>](https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-021-00835-9)<sup>,</sup> [<sup>2</sup>](https://academic.oup.com/nar/article/47/D1/D886/5146191)

  > Rentzsch P, Schubach M, Shendure J, Kircher M. CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 2021;13(1):31. doi:10.1186/s13073-021-00835-9

  > Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Research. 2019;47(D1):D886-D894. doi:10.1093/nar/gky1016

- [DeepVariant](https://www.nature.com/articles/nbt.4235)

  > Poplin R, Chang PC, Alexander D, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983-987. doi:10.1038/nbt.4235

- [eKLIPse](https://www.nature.com/articles/s41436-018-0350-8)

  > Goudenège D, Bris C, Hoffmann V, et al. eKLIPse: a sensitive tool for the detection and quantification of mitochondrial DNA deletions from next-generation sequencing data. Genet Med 21, 1407–1416 (2019). doi:10.1038/s41436-018-0350-8

- [Ensembl VEP](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0974-4)

  > McLaren W, Gil L, Hunt SE, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17(1):122. doi:10.1186/s13059-016-0974-4

- [ExpansionHunter](https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btz431/5499079)

  > Dolzhenko E, Deshpande V, Schlesinger F, et al. ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions. Birol I, ed. Bioinformatics. 2019;35(22):4754-4756. doi:10.1093/bioinformatics/btz431

- [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

> Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].

- [Fastp](https://github.com/OpenGene/fastp)

  > Shifu, C. (2023). Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2: e107. https://doi.org/10.1002/imt2.107

- [GATK](https://genome.cshlp.org/content/20/9/1297)

  > McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297-1303. doi:10.1101/gr.107524.110

- [Genmod](https://github.com/Clinical-Genomics/genmod)

  > Magnusson M, Hughes T, Glabilloy, Bitdeli Chef. genmod: Version 3.7.3. Published online November 15, 2018. doi:10.5281/ZENODO.3841142

- [Gens](https://github.com/Clinical-Genomics-Lund/gens)

- [GLnexus](https://academic.oup.com/bioinformatics/article/36/24/5582/6064144)

  > Yun T, Li H, Chang PC, Lin MF, Carroll A, McLean CY. Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Robinson P, ed. Bioinformatics. 2021;36(24):5582-5589. doi:10.1093/bioinformatics/btaa1081

- [Haplocheck](https://genome.cshlp.org/content/31/2/309.long)

  > Weissensteiner H, Forer L, Fendt L, et al. Contamination detection in sequencing studies using the mitochondrial phylogeny. Genome Res. 2021;31(2):309-316. doi:10.1101/gr.256545.119

- [HaploGrep 2](https://academic.oup.com/nar/article/44/W1/W58/2499296)

  > Weissensteiner H, Pacher D, Kloss-Brandstätter A, et al. HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing. Nucleic Acids Res. 2016;44(W1):W58-W63. doi:10.1093/nar/gkw233

- [Hmtnote](https://doi.org/10.1101/600619)

  > Preste R, Clima R, Attimonelli M. Human mitochondrial variant annotation with HmtNote. bioRxiv 600619; doi:10.1101/600619

- [Manta](https://academic.oup.com/bioinformatics/article/32/8/1220/1743909?login=true)

  > Chen X, Schulz-Trieglaff O, Shaw R, et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2016;32(8):1220-1222. doi:10.1093/bioinformatics/btv710

- [Mosdepth](https://academic.oup.com/bioinformatics/article/34/5/867/4583630?login=true)

  > Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Hancock J, ed. Bioinformatics. 2018;34(5):867-868. doi:10.1093/bioinformatics/btx699

- [ngs-bits-samplegender](https://github.com/imgag/ngs-bits/tree/master)

- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)

> Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

- [Peddy](<https://www.cell.com/action/showFullTextImages?pii=S0002-9297(17)30017-4>)

  > Pedersen BS, Quinlan AR. Who’s Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy. The American Journal of Human Genetics. 2017;100(3):406-413. doi:10.1016/j.ajhg.2017.01.017

- [Picard](https://broadinstitute.github.io/picard/)

- [Qualimap](https://academic.oup.com/bioinformatics/article/32/2/292/1744356?login=true)

  > Okonechnikov K, Conesa A, García-Alcalde F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics. 2016;32(2):292-294. doi:10.1093/bioinformatics/btv566

- [RetroSeq](https://academic.oup.com/bioinformatics/article/29/3/389/257479)

  > Thomas M. Keane, Kim Wong, David J. Adams, RetroSeq: transposable element discovery from next-generation sequencing data. Bioinformatics.2013 Feb 1;29(3):389-90. doi: 10.1093/bioinformatics/bts697

- [rhocall](https://github.com/dnil/rhocall)

- [RTG Tools (vcfeval)](https://github.com/RealTimeGenomics/rtg-tools)

  > John G. Cleary, Ross Braithwaite, Kurt Gaastra, Brian S. Hilbush, Stuart Inglis, Sean A. Irvine, Alan Jackson, Richard Littin, Mehul Rathod, David Ware, Justin M. Zook, Len Trigg, and Francisco M. De La Vega. "Comparing Variant Call Files for Performance Benchmarking of Next-Generation Sequencing Variant Calling Pipelines." bioRxiv, 2015. doi:10.1101/023754.

- [Sentieon DNAscope](https://www.biorxiv.org/content/10.1101/2022.05.20.492556v1.abstract)

  > Freed D, Pan R, Chen H, Li Z, Hu J, Aldana R. DNAscope: High Accuracy Small Variant Calling Using Machine Learning. Bioinformatics; 2022. doi:10.1101/2022.05.20.492556

- [Sentieon DNASeq](https://www.frontiersin.org/articles/10.3389/fgene.2019.00736/full)

  > Kendig KI, Baheti S, Bockol MA, et al. Sentieon DNASeq Variant Calling Workflow Demonstrates Strong Computational Performance and Accuracy. Front Genet. 2019;10:736. doi:10.3389/fgene.2019.00736

- [SMNCopyNumberCaller](https://www.nature.com/articles/s41436-020-0754-0)

  > Chen X, Sanchis-Juan A, French CE, Connel AJ, Delon I, Kingsbury Z, Chawla A, Halpern AL, Taft RJ, NIHR BioResource, Bentley DR, Butchbach MER, Raymond FL, Eberle MA. Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data. Genet Med. February 2020:1-9. doi:10.1038/s41436-020-0754-0

- [stranger](https://github.com/Clinical-Genomics/stranger)

  > Nilsson D, Magnusson M. moonso/stranger v0.7.1. Published online February 18, 2021. doi:10.5281/ZENODO.4548873

- [svdb](https://github.com/J35P312/SVDB)

  > Eisfeldt J, Vezzi F, Olason P, Nilsson D, Lindstrand A. TIDDIT, an efficient and comprehensive structural variant caller for massive parallel sequencing data. F1000Res. 2017;6:664. doi:10.12688/f1000research.11168.2

- [Tabix](https://academic.oup.com/bioinformatics/article/27/5/718/262743)

  > Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011;27(5):718-719. doi:10.1093/bioinformatics/btq671

- [TIDDIT](https://f1000research.com/articles/6-664/v2)

  > Eisfeldt J, Vezzi F, Olason P, Nilsson D, Lindstrand A. TIDDIT, an efficient and comprehensive structural variant caller for massive parallel sequencing data. F1000Res. 2017;6:664. doi:10.12688/f1000research.11168.2

- [UCSC Bigwig and Bigbed](https://academic.oup.com/bioinformatics/article/26/17/2204/199001?login=true)

  > Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics. 2010;26(17):2204-2207. doi:10.1093/bioinformatics/btq351

- [vcf2cytosure](https://github.com/NBISweden/vcf2cytosure)

- [Vcfanno](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0973-5)

  > Pedersen BS, Layer RM, Quinlan AR. Vcfanno: fast, flexible annotation of genetic variants. Genome Biol. 2016;17(1):118. doi:10.1186/s13059-016-0973-5

- [VerifyBamID2]()

  > Zhang F, Flickinger M, Taliun SAG, Consortium IPG, Abecasis GR, Scott LJ, McCaroll SA, Pato CN, Boehnke M, & Kang HM. (2020). Ancestry-agnostic estimation of DNA sample contamination from sequence reads. Genome Research, 30(2), 185–194. https://doi.org/10.1101/gr.246934.118

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

GitHub Events

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Last Year
  • Create event: 50
  • Release event: 4
  • Issues event: 23
  • Watch event: 16
  • Delete event: 46
  • Issue comment event: 107
  • Push event: 197
  • Pull request review comment event: 104
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  • Fork event: 12

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 1,314
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  • Avg Commits per committer: 42.387
  • Development Distribution Score (DDS): 0.511
Past Year
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  • Avg Commits per committer: 35.4
  • Development Distribution Score (DDS): 0.403
Top Committers
Name Email Commits
Ramprasad Neethiraj 2****n 642
Mei Wu 2****d 102
Mei Wu s****i@g****m 89
Emil Bertilsson e****n@h****e 66
lucpen l****z@h****l 56
sima-r s****i@g****e 52
jemten j****n 38
Lucpen l****z@s****e 36
Gwenna Breton G****n@g****e 36
lucpen l****z@h****e 31
peterpru p****r@s****e 27
LJMesi 3****i 20
ryanjameskennedy r****y@i****m 20
Gwenna Breton g****n@g****e 17
nf-core-bot c****e@n****e 14
Thankaswamy Kosalai Subazini, Labmed USÖ (sth036) s****d@g****m 11
Lucia Pena Perez l****z@h****e 8
Subazini TK s****k@g****m 8
Lauri Mesilaakso j****o@g****m 8
halfdan rydbeck h****k@g****m 6
Annick Renevey 4****k 5
Lucas Taniguti l****i@m****r 5
Marius Bjørnstad p****b@f****t 3
asp8200 a****s@m****m 3
rannick a****y@s****e 3
EmmaCAndersson e****n@g****m 2
sysbiocoder s****6@o****e 2
EmmaCAndersson 4****n 1
Adam Talbot 1****t 1
Peter Pruisscher p****r@h****e 1
and 1 more...
Committer Domains (Top 20 + Academic)
scilifelab.se: 3 gu.se: 3 hasta.scilifelab.se: 3 t-online.de: 1 orcus.orebroll.se: 1 mynucleus.com: 1 fa2k.net: 1 mendelics.com.br: 1 hasta-login.scilifelab.se: 1 nf-co.re: 1

Issues and Pull Requests

Last synced: 6 months ago

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Past Year
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Dependencies

.github/workflows/awsfulltest.yml actions
  • actions/upload-artifact v3 composite
  • seqeralabs/action-tower-launch v2 composite
.github/workflows/awstest.yml actions
  • actions/upload-artifact v3 composite
  • seqeralabs/action-tower-launch v2 composite
.github/workflows/branch.yml actions
  • mshick/add-pr-comment v1 composite
.github/workflows/ci.yml actions
  • actions/checkout v3 composite
  • nf-core/setup-nextflow v1 composite
.github/workflows/clean-up.yml actions
  • actions/stale v7 composite
.github/workflows/fix-linting.yml actions
  • actions/checkout v3 composite
  • actions/setup-node v3 composite
.github/workflows/linting.yml actions
  • actions/checkout v3 composite
  • actions/setup-node v3 composite
  • actions/setup-python v4 composite
  • actions/upload-artifact v3 composite
  • mshick/add-pr-comment v1 composite
  • nf-core/setup-nextflow v1 composite
  • psf/black stable composite
.github/workflows/linting_comment.yml actions
  • dawidd6/action-download-artifact v2 composite
  • marocchino/sticky-pull-request-comment v2 composite
modules/local/ensemblvep/meta.yml cpan
modules/local/gatk4/collectreadcounts/meta.yml cpan
modules/local/gatk4/denoisereadcounts/meta.yml cpan
modules/local/gens/meta.yml cpan
modules/nf-core/bcftools/annotate/meta.yml cpan
modules/nf-core/bcftools/concat/meta.yml cpan
modules/nf-core/bcftools/filter/meta.yml cpan
modules/nf-core/bcftools/merge/meta.yml cpan
modules/nf-core/bcftools/norm/meta.yml cpan
modules/nf-core/bcftools/reheader/meta.yml cpan
modules/nf-core/bcftools/roh/meta.yml cpan
modules/nf-core/bcftools/view/meta.yml cpan
modules/nf-core/bwa/index/meta.yml cpan
modules/nf-core/bwamem2/index/meta.yml cpan
modules/nf-core/bwamem2/mem/meta.yml cpan
modules/nf-core/cadd/meta.yml cpan
modules/nf-core/cat/cat/meta.yml cpan
modules/nf-core/chromograph/meta.yml cpan
modules/nf-core/custom/dumpsoftwareversions/meta.yml cpan
modules/nf-core/deepvariant/meta.yml cpan
modules/nf-core/eklipse/meta.yml cpan
modules/nf-core/expansionhunter/meta.yml cpan
modules/nf-core/fastqc/meta.yml cpan
modules/nf-core/gatk4/bedtointervallist/meta.yml cpan
modules/nf-core/gatk4/collectreadcounts/meta.yml cpan
modules/nf-core/gatk4/createsequencedictionary/meta.yml cpan
modules/nf-core/gatk4/determinegermlinecontigploidy/meta.yml cpan
modules/nf-core/gatk4/filtermutectcalls/meta.yml cpan
modules/nf-core/gatk4/germlinecnvcaller/meta.yml cpan
modules/nf-core/gatk4/intervallisttools/meta.yml cpan
modules/nf-core/gatk4/mergebamalignment/meta.yml cpan
modules/nf-core/gatk4/mergevcfs/meta.yml cpan
modules/nf-core/gatk4/mutect2/meta.yml cpan
modules/nf-core/gatk4/postprocessgermlinecnvcalls/meta.yml cpan
modules/nf-core/gatk4/preprocessintervals/meta.yml cpan
modules/nf-core/gatk4/printreads/meta.yml cpan
modules/nf-core/gatk4/revertsam/meta.yml cpan
modules/nf-core/gatk4/samtofastq/meta.yml cpan
modules/nf-core/gatk4/selectvariants/meta.yml cpan
modules/nf-core/gatk4/shiftfasta/meta.yml cpan
modules/nf-core/gatk4/splitintervals/meta.yml cpan
modules/nf-core/gatk4/variantfiltration/meta.yml cpan
modules/nf-core/genmod/annotate/meta.yml cpan
modules/nf-core/genmod/compound/meta.yml cpan
modules/nf-core/genmod/models/meta.yml cpan
modules/nf-core/genmod/score/meta.yml cpan
modules/nf-core/glnexus/meta.yml cpan
modules/nf-core/haplocheck/meta.yml cpan
modules/nf-core/haplogrep2/classify/meta.yml cpan
modules/nf-core/hmtnote/annotate/meta.yml cpan
modules/nf-core/manta/germline/meta.yml cpan
modules/nf-core/mosdepth/meta.yml cpan
modules/nf-core/multiqc/meta.yml cpan
modules/nf-core/peddy/meta.yml cpan
modules/nf-core/picard/addorreplacereadgroups/meta.yml cpan
modules/nf-core/picard/collecthsmetrics/meta.yml cpan
modules/nf-core/picard/collectmultiplemetrics/meta.yml cpan
modules/nf-core/picard/collectwgsmetrics/meta.yml cpan
modules/nf-core/picard/liftovervcf/meta.yml cpan
modules/nf-core/picard/markduplicates/meta.yml cpan
modules/nf-core/picard/renamesampleinvcf/meta.yml cpan
modules/nf-core/picard/sortvcf/meta.yml cpan
modules/nf-core/qualimap/bamqc/meta.yml cpan
modules/nf-core/rhocall/annotate/meta.yml cpan
modules/nf-core/samtools/faidx/meta.yml cpan
modules/nf-core/samtools/index/meta.yml cpan
modules/nf-core/samtools/merge/meta.yml cpan
modules/nf-core/samtools/sort/meta.yml cpan
modules/nf-core/samtools/stats/meta.yml cpan
modules/nf-core/samtools/view/meta.yml cpan
modules/nf-core/smncopynumbercaller/meta.yml cpan
modules/nf-core/stranger/meta.yml cpan
modules/nf-core/svdb/merge/meta.yml cpan
modules/nf-core/svdb/query/meta.yml cpan
modules/nf-core/tabix/bgziptabix/meta.yml cpan
modules/nf-core/tabix/tabix/meta.yml cpan
modules/nf-core/tiddit/cov/meta.yml cpan
modules/nf-core/tiddit/sv/meta.yml cpan
modules/nf-core/ucsc/wigtobigwig/meta.yml cpan
modules/nf-core/untar/meta.yml cpan
modules/nf-core/upd/meta.yml cpan
modules/nf-core/vcfanno/meta.yml cpan
pyproject.toml pypi