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gtseqdesign

aCaMEL NextFlow DSL2 pipeline for designing GTSeq assays from genotyped SNP data

https://github.com/uark-acamel/gtseqdesign

Science Score: 57.0%

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Repository

aCaMEL NextFlow DSL2 pipeline for designing GTSeq assays from genotyped SNP data

Basic Info
  • Host: GitHub
  • Owner: UARK-aCaMEL
  • License: other
  • Language: Nextflow
  • Default Branch: dev
  • Homepage:
  • Size: 9.84 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 1
  • Open Issues: 3
  • Releases: 0
Created about 1 year ago · Last pushed 12 months ago
Metadata Files
Readme Changelog Contributing License Citation

README.md

aCaMEL/gtseqdesign

GitHub Actions CI Status GitHub Actions Linting StatusCite with Zenodo nf-test

Nextflow run with conda run with docker run with singularity Launch on Seqera Platform

Introduction

aCaMEL/gtseqdesign is a Nextflow pipeline for GT-seq panel design. It filters and scores SNPs based on their utility for distinguishing population structure, using metrics from Rosenberg et al. (2003) and structure results from ADMIXTURE.

The pipeline takes VCF and population map inputs and produces filtered SNP panels, summary figures, and MultiQC reports.

📊 Example MultiQC Report

🚀 Example Report: Click here to view the full MultiQC report

Overview

aCaMEL/gtseqdesign overview

Main Steps

  1. Optional psuedo-reference generation from reduced-representation catalogs (when FASTA reference unavailable)
  2. SNP filtering using SNPio
  3. Population inference using ADMIXTURE via AdmixPipe
  4. Cluster alignment with CLUMPAK and visualization with Distruct
  5. Locus ranking with infocalc (Rosenberg et al. 2003)
  6. Panel selection and downstream re-analysis
  7. Summary output with MultiQC

Usage

Quick Start

  1. Install Nextflow (>=22.10.1)

  2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

  3. Download and test the pipeline on a minimal dataset with a single command:

bash nextflow run UARK-aCaMEL/gtseqdesign -profile test,YOURPROFILE --outdir <OUTDIR>

Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

  • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
  • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
  • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
  • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.
  1. Start running your own analysis!

bash nextflow run main.nf --input genotypes.vcf --popmap popmap.tsv --reference genome.fasta --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

[!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.

[!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.

Credits

aCaMEL/gtseqdesign was originally written by Tyler K. Chafin.

We thank the following people 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.

Citations

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

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

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: Arkansas Conservation and Molecular Ecology Lab
  • Login: UARK-aCaMEL
  • Kind: organization
  • Email: tkchafin@uark.edu

Citation (CITATIONS.md) 

# aCaMEL/acamel-gtseqdesign: 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

## Pipeline tools

### [ADMIXTURE](https://dalexander.github.io/admixture/)

> Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009 Sep;19(9):1655-64. doi: 10.1101/gr.094052.109. Epub 2009 Jul 13. PubMed PMID: 19648217; PubMed Central PMCID: PMC2752134.

### [AdmixPipe](https://github.com/stevemussmann/admixturePipeline)

> Mussmann SM, Douglas MR, Chafin TK, Douglas ME. AdmixPipe: population analyses in Admixture for non-model organisms. BMC Bioinformatics. 2020 Aug 31;21(1):337. doi: 10.1186/s12859-020-03701-4. PubMed PMID: 32867706; PubMed Central PMCID: PMC7460838.

### [BCFtools](https://pubmed.ncbi.nlm.nih.gov/21903627/)

> Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011 Nov 1;27(21):2987-93. doi: 10.1093/bioinformatics/btr509. Epub 2011 Sep 8. PubMed PMID: 21903627; PubMed Central PMCID: PMC3198575.

### [CLUMPAK](https://clumpak.tau.ac.il)

> Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour. 2015 Sep;15(5):1179-91. doi: 10.1111/1755-0998.12387. Epub 2015 Feb 13. PubMed PMID: 25684545; PubMed Central PMCID: PMC4817459.

### [Distruct](https://rosenberglab.stanford.edu/distruct.html)

> Rosenberg NA. DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2004 Mar;4(1):137-138. doi: 10.1046/j.1471-8286.2003.00566.x.

### [HTSlib](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. PubMed PMID: 19505943; PubMed Central PMCID: PMC2723002.

### [infocalc](https://rosenberglab.stanford.edu/infocalc.html)

> Rosenberg NA, Li LM, Ward R, Pritchard JK. Informativeness of genetic markers for inference of ancestry. Am J Hum Genet. 2003 Dec;73(6):1402-22. doi: 10.1086/380416. Epub 2003 Nov 6. PubMed PMID: 14631557; PubMed Central PMCID: PMC1180497.

### [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.

### [PLINK](https://www.cog-genomics.org/plink/)

> Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015 Feb 25;4:7. doi: 10.1186/s13742-015-0047-8. PubMed PMID: 25722852; PubMed Central PMCID: PMC4342193.

> Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007 Sep;81(3):559-75. doi: 10.1086/519795. Epub 2007 Jul 25. PubMed PMID: 17701901; PubMed Central PMCID: PMC1950838.

### [Plotly](https://plotly.com/python/)

> Plotly Technologies Inc. Collaborative data science. Montreal, QC: Plotly Technologies Inc.; 2015. Available from: <https://plot.ly>

### [SNPio](https://github.com/btmartin721/SNPio)

> Martin BT. SNPio: A Python package for easy manipulation and analysis of SNP data. 2023. Available from: <https://github.com/btmartin721/SNPio>

### [VCFtools](https://pubmed.ncbi.nlm.nih.gov/21653522/)

> Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R; 1000 Genomes Project Analysis Group. The variant call format and VCFtools. Bioinformatics. 2011 Aug 1;27(15):2156-8. doi: 10.1093/bioinformatics/btr330. Epub 2011 Jun 7. PubMed PMID: 21653522; PubMed Central PMCID: PMC3137218.


## 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.

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