tautyping

This pipeline identifies genes or genomic segments which most closely resemble the genome-wide phylogenetic signal of a given organism using the Kendall Tau rank correlation statistic

https://github.com/hseabolt/tautyping

Science Score: 36.0%

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

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 4 DOI reference(s) in README
  • Academic publication links
    Links to: nature.com
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (13.2%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

This pipeline identifies genes or genomic segments which most closely resemble the genome-wide phylogenetic signal of a given organism using the Kendall Tau rank correlation statistic

Basic Info
  • Host: GitHub
  • Owner: hseabolt
  • License: mit
  • Language: Nextflow
  • Default Branch: main
  • Size: 152 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 5
  • Open Issues: 1
  • Releases: 3
Created over 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

Tau-typing

Introduction

Tau-typing is a bioinformatics analysis pipeline tuned for identifying genes or genomic segments which most closely reflect the genome-wide phylogenetic signal of a given organism using the rank correlation statistics (Kendall's tau or Spearman's rho).

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 installed from nf-core/modules.

Development and testing of this pipeline used singularity as the container technology and Sun Grid Engine (SGE) for testing on cluster environments.

Pipeline summary

  1. Transfer annotations (Liftoff)
  2. Extract features (GFFRead)
  3. Compare genome sequences - ANI or Maximum Likelihood (FastANI, Phangorn)
  4. Compute the core genomes (PIRATE)
  5. Rank individual features against WGS (Custom (R) scripts)
  6. Create sets of features from best-correlating features (Custom (Perl) scripts)
  7. Rank sets against WGS (Custom (R) scripts)
  8. Tabulate results (MultiQC)

Tau-typing

Quick Start

  1. Install Nextflow (>=21.10.3)

  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 the pipeline and test it on a minimal dataset with a single command:

console nextflow run hseabolt/tautyping -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!

console nextflow run hseabolt/tautyping --input samplesheet.csv --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

Documentation

The Tau-typing pipeline comes with documentation about the pipeline usage, parameters and output.

Credits

Tau-typing was originally written by hseabolt.

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

If you use Tau-typing for your analysis, please cite it using the following citation:

Tau-typing: a Nextflow pipeline enabling on-demand, high-resolution molecular typing for pathogen genomics

Matthew H. Seabolt, Arun K. Boddapati, Joshua J. Forstedt, Kostantinos T. Konstantinidis.

Tau-typing: a Nextflow pipeline for finding the best phylogenetic markers in the genome for genomotyping of microbial species

To be submitted to Bioinformatics

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: Hunter Seabolt
  • Login: hseabolt
  • Kind: user
  • Location: Atlanta, GA
  • Company: Leidos contractor for CDC

GitHub Events

Total
  • Fork event: 1
Last Year
  • Fork event: 1

Issues and Pull Requests

Last synced: over 1 year ago

All Time
  • Total issues: 7
  • Total pull requests: 1
  • Average time to close issues: 3 months
  • Average time to close pull requests: 1 minute
  • Total issue authors: 2
  • Total pull request authors: 1
  • Average comments per issue: 0.29
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • hseabolt (5)
  • srusher (2)
Pull Request Authors
  • hseabolt (1)
Top Labels
Issue Labels
enhancement (4) bug (3)
Pull Request Labels

Dependencies

.github/workflows/awstest.yml actions
  • 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
.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
.github/workflows/linting_comment.yml actions
  • dawidd6/action-download-artifact v2 composite
  • marocchino/sticky-pull-request-comment v2 composite
.github/workflows/awsfulltest.yml actions
  • nf-core/tower-action v3 composite