Merge branch 'dev' into nf-core-template-merge-3.0.2

.github/CONTRIBUTING.md

@@ -29,7 +29,7 @@ If you're not used to this workflow with git, you can start with some [docs from
 You have the option to test your changes locally by running the pipeline. For receiving warnings about process selectors and other `debug` information, it is recommended to use the debug profile. Execute all the tests with the following command:
 
 ```bash
-nf-test test --profile debug,test,docker --verbose
+nextflow run . -profile debug,test,docker --outdir <OUTDIR>
 ```
 
 When you create a pull request with changes, [GitHub Actions](https://github.com/features/actions) will run automatic tests.

.github/PULL_REQUEST_TEMPLATE.md

@@ -19,6 +19,7 @@ Learn more about contributing: [CONTRIBUTING.md](https://github.com/nf-core/rare
 - [ ] If necessary, also make a PR on the nf-core/raredisease _branch_ on the [nf-core/test-datasets](https://github.com/nf-core/test-datasets) repository.
 - [ ] Make sure your code lints (`nf-core pipelines lint`).
 - [ ] Ensure the test suite passes (`nextflow run . -profile test,docker --outdir <OUTDIR>`).
+- [ ] Ensure the test suite passes (`nextflow run . -profile test_one_sample,docker --outdir <OUTDIR>`).
 - [ ] Check for unexpected warnings in debug mode (`nextflow run . -profile debug,test,docker --outdir <OUTDIR>`).
 - [ ] Usage Documentation in `docs/usage.md` is updated.
 - [ ] Output Documentation in `docs/output.md` is updated.

.github/workflows/ci.yml

@@ -30,17 +30,15 @@ jobs:
           - "24.04.2"
           - "latest-everything"
         profile:
-          - "conda"
           - "docker"
           - "singularity"
         test_name:
           - "test"
+          - "test_one_sample"
         isMaster:
           - ${{ github.base_ref == 'master' }}
         # Exclude conda and singularity on dev
         exclude:
-          - isMaster: false
-            profile: "conda"
           - isMaster: false
             profile: "singularity"
     steps:
@@ -62,24 +60,9 @@ jobs:
           mkdir -p $NXF_SINGULARITY_CACHEDIR
           mkdir -p $NXF_SINGULARITY_LIBRARYDIR
 
-      - name: Set up Miniconda
-        if: matrix.profile == 'conda'
-        uses: conda-incubator/setup-miniconda@a4260408e20b96e80095f42ff7f1a15b27dd94ca # v3
-        with:
-          miniconda-version: "latest"
-          auto-update-conda: true
-          conda-solver: libmamba
-          channels: conda-forge,bioconda
-
-      - name: Set up Conda
-        if: matrix.profile == 'conda'
-        run: |
-          echo $(realpath $CONDA)/condabin >> $GITHUB_PATH
-          echo $(realpath python) >> $GITHUB_PATH
-
       - name: Clean up Disk space
         uses: jlumbroso/free-disk-space@54081f138730dfa15788a46383842cd2f914a1be # v1.3.1
 
       - name: "Run pipeline with test data ${{ matrix.NXF_VER }} | ${{ matrix.test_name }} | ${{ matrix.profile }}"
         run: |
-          nextflow run ${GITHUB_WORKSPACE} -profile ${{ matrix.test_name }},${{ matrix.profile }} --outdir ./results
+          nextflow run ${GITHUB_WORKSPACE} -profile ${{ matrix.test_name }},${{ matrix.profile }} -stub --outdir ./results

.gitignore

@@ -7,3 +7,4 @@ testing/
 testing*
 *.pyc
 null/
+.prettierignore

CITATIONS.md

@@ -1,23 +1,161 @@
 # nf-core/raredisease: Citations
 
-## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)
+## Nextflow & nf-core
 
-> 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.
+- [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)
 
-## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)
+  > 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.
 
-> 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.
+- [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
+
+- [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
+
 ## Software packaging/containerisation tools
 
 - [Anaconda](https://anaconda.com)