hlatyping

Precision HLA typing from next-generation sequencing data

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

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 10 DOI reference(s) in README
  • Academic publication links
  • Committers with academic emails
    7 of 17 committers (41.2%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
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    Low similarity (9.7%) to scientific vocabulary

Keywords

dna hla hla-typing immunology nextflow nf-core optitype personalized-medicine pipeline rna workflow

Keywords from Contributors

metagenomics pipelines workflows bioinformatics illumina smrna-seq small-rna rna-seq pacbio metabarcoding
Last synced: 6 months ago · JSON representation ·

Repository

Precision HLA typing from next-generation sequencing data

Basic Info
  • Host: GitHub
  • Owner: nf-core
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Homepage: https://nf-co.re/hlatyping
  • Size: 39.4 MB
Statistics
  • Stars: 70
  • Watchers: 168
  • Forks: 33
  • Open Issues: 11
  • Releases: 11
Topics
dna hla hla-typing immunology nextflow nf-core optitype personalized-medicine pipeline rna workflow
Created over 7 years ago · Last pushed 7 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/hlatyping

GitHub Actions CI Status GitHub Actions Linting StatusAWS CICite with Zenodo nf-test

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Introduction

nf-core/hlatyping is a bioinformatics pipeline that can be used to perform HLA typing from next-generation sequencing data. The pipeline does next-generation sequencing-based Human Leukocyte Antigen (HLA) typing using OptiType. OptiType is a HLA genotyping algorithm based on integer linear programming. Reads of whole exome/genome/transcriptome sequencing data are mapped against a reference of known MHC class I alleles. To produce accurate 4-digit HLA genotyping predictions, all major and minor HLA-I loci are considered simultaneously to find an allele combination that maximizes the number of explained reads.

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

  1. Merge FastQ files (cat)
  2. Read QC (FastQC)
  3. Generate reference indices (yara)
  4. Map reads to reference (yara)
  5. Run HLA typing (OptiType)
  6. Present QC for raw reads (MultiQC)

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,fastq_1,fastq_2,seq_type CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,dna

Each row represents a sample and its fastq file (single-end) or a pair of fastq files (paired end) and the sequencing type. The sample identifiers have to be specified with the fastq files and the sequencing type, i.e. dna or rna. It is also possible to provide reads as bam file by adding a column bam in which the file path will be specified. The fastq columns have to be kept.

The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet.

Now, you can run the pipeline using:

bash nextflow run nf-core/hlatyping \ -profile <docker/singularity/.../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

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/hlatyping was originally written by Christopher Mohr from Medical Data Integration Center and Quantitative Biology Center, Alexander Peltzer from Boehringer Ingelheim, and Sven Fillinger from Quantitative Biology Center.

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 #hlatyping channel (you can join with this invite).

Citations

If you use nf-core/hlatyping for your analysis, please cite it using the following doi: 10.5281/zenodo.1401039

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.

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/hlatyping: Citations

## [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

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

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

- [SAMtools](https://pubmed.ncbi.nlm.nih.gov/19505943/)

  > Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9. doi: 10.1093/bioinformatics/btp352. Epub 2009 Jun 8. PMID: 19505943; PMCID: PMC2723002.

- [Yara](https://www.seqan.de/apps/yara.html)

  > Siragusa E, Weese D, Reinert K. Fast and accurate read mapping with approximate seeds and multiple backtracking. Nucleic Acids Res. 2013 Apr;41(7):e78. doi: 10.1093/nar/gkt005. Epub 2013 Jan 28. PMID: 23358824; PMCID: PMC3627565.

  > Siragusa, E. Approximate string matching for high-throughput sequencing. Diss. 2015.

- [OptiType](https://pubmed.ncbi.nlm.nih.gov/25143287/)

  > Szolek A, Schubert B, Mohr C, Sturm M, Feldhahn M, Kohlbacher O. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics. 2014 Dec 1;30(23):3310-6. doi: 10.1093/bioinformatics/btu548. Epub 2014 Aug 20. PMID: 25143287; PMCID: PMC4441069.

- [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.

## 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

Total
  • Create event: 7
  • Release event: 1
  • Issues event: 11
  • Watch event: 7
  • Delete event: 1
  • Issue comment event: 35
  • Push event: 16
  • Pull request review comment event: 29
  • Pull request review event: 34
  • Pull request event: 28
  • Fork event: 1
Last Year
  • Create event: 7
  • Release event: 1
  • Issues event: 11
  • Watch event: 7
  • Delete event: 1
  • Issue comment event: 35
  • Push event: 16
  • Pull request review comment event: 29
  • Pull request review event: 34
  • Pull request event: 28
  • Fork event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 345
  • Total Committers: 17
  • Avg Commits per committer: 20.294
  • Development Distribution Score (DDS): 0.603
Past Year
  • Commits: 47
  • Committers: 2
  • Avg Commits per committer: 23.5
  • Development Distribution Score (DDS): 0.064
Top Committers
Name Email Commits
sven1103 s****r@q****e 137
Christopher Mohr c****r@u****e 92
Alexander Peltzer a****r@g****m 39
nf-core-bot c****e@n****e 15
Tobias t****h@s****e 13
Alexander Peltzer a****r@u****m 10
Travis CI User t****s@e****g 8
Christopher Mohr c****r@q****e 6
ggabernet g****t@q****e 6
Alexander Peltzer a****r@u****e 5
nvk747 n****7@g****m 5
MaxUlysse m****a@g****m 3
mohr m****r@i****e 2
kevinmenden k****n@t****e 1
Paolo Di Tommaso p****o@g****m 1
Alexander Peltzer a****r@b****m 1
KochTobi K****i@u****m 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 46
  • Total pull requests: 78
  • Average time to close issues: 11 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 29
  • Total pull request authors: 16
  • Average comments per issue: 2.93
  • Average comments per pull request: 1.49
  • Merged pull requests: 45
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 7
  • Pull requests: 15
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 19 days
  • Issue authors: 6
  • Pull request authors: 5
  • Average comments per issue: 0.86
  • Average comments per pull request: 1.4
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • apeltzer (8)
  • szymonwieloch (4)
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  • sven1103 (3)
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Pull Request Authors
  • nf-core-bot (36)
  • christopher-mohr (25)
  • apeltzer (5)
  • ggabernet (4)
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  • maxulysse (2)
  • pditommaso (1)
  • vsmalladi (1)
  • nanjalaruth (1)
  • phue (1)
Top Labels
Issue Labels
enhancement (18) bug (10) good first issue (4) question (1) upstream (1)
Pull Request Labels
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Dependencies

.github/workflows/awsfulltest.yml actions
  • actions/upload-artifact v3 composite
  • nf-core/tower-action v3 composite
.github/workflows/awstest.yml actions
  • actions/upload-artifact v3 composite
  • nf-core/tower-action v3 composite
.github/workflows/branch.yml actions
  • mshick/add-pr-comment v1 composite
.github/workflows/ci.yml actions
  • actions/checkout v2 composite
  • nf-core/setup-nextflow v1 composite
.github/workflows/fix-linting.yml actions
  • actions/checkout v3 composite
  • actions/setup-node v2 composite
.github/workflows/linting.yml actions
  • actions/checkout v2 composite
  • actions/setup-node v2 composite
  • actions/setup-python v3 composite
  • actions/upload-artifact v2 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/nf-core/custom/dumpsoftwareversions/meta.yml cpan
modules/nf-core/fastqc/meta.yml cpan
modules/nf-core/gunzip/meta.yml cpan
modules/nf-core/multiqc/meta.yml cpan
modules/nf-core/optitype/meta.yml cpan
modules/nf-core/samtools/collatefastq/meta.yml cpan
modules/nf-core/samtools/view/meta.yml cpan
modules/nf-core/yara/index/meta.yml cpan
modules/nf-core/yara/mapper/meta.yml cpan
pyproject.toml pypi