Science Score: 57.0%
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✓CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
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✓DOI references
Found 8 DOI reference(s) in README -
○Academic publication links
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○Scientific vocabulary similarity
Low similarity (13.3%) to scientific vocabulary
Keywords
Repository
Drug Resistance Prediction with Reference Graphs
Basic Info
- Host: GitHub
- Owner: mbhall88
- License: mit
- Language: Rust
- Default Branch: main
- Homepage: https://mbh.sh/drprg/
- Size: 18.1 MB
Statistics
- Stars: 21
- Watchers: 6
- Forks: 2
- Open Issues: 3
- Releases: 2
Topics
Metadata Files
README.md
👩⚕Dr. PRG - Drug resistance Prediction with Reference Graphs️👨⚕️
Full documentation: https://mbh.sh/drprg/
As the name suggests, Dr. PRG (pronounced "Doctor P-R-G") is a tool for predicting drug resistance from sequencing data. It can be used for any species, provided an index is available for that species. The documentation outlines which species have prebuilt indices and also a guide for how to create your own.
Quick Installation
conda install -c bioconda drprg
Linux is currently the only supported platform; however, there is a Docker container that can be used on other platforms.
See the installation guide for more options.
Quick usage
Download the latest M. tuberculosis prebuilt index
drprg index --download mtb
Predict resistance from an Illumina fastq
drprg predict -x mtb -i reads.fq --illumina -o outdir/
Help
``` $ drprg -h Drug Resistance Prediction with Reference Graphs
Usage: drprg [OPTIONS]
Commands: build Build an index to predict resistance from predict Predict drug resistance index Download and interact with indices help Print this message or the help of the given subcommand(s)
Options:
-v, --verbose Use verbose output
-t, --threads
Citation
Hall MB, Lima L, Coin LJM, Iqbal Z (2023) Drug resistance prediction for Mycobacterium tuberculosis with reference graphs. Microbial Genomics 9:001081. doi: 10.1099/mgen.0.001081
bib
@article{hall_drug_2023,
title = {Drug resistance prediction for {Mycobacterium} tuberculosis with reference graphs},
volume = {9},
copyright = {All rights reserved},
issn = {2057-5858},
url = {https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.001081},
doi = {10.1099/mgen.0.001081},
number = {8},
journal = {Microbial Genomics},
author = {Hall, Michael B. and Lima, Leandro and Coin, Lachlan J. M. and Iqbal, Zamin},
year = {2023},
pages = {001081},
}
Owner
- Name: Michael Hall
- Login: mbhall88
- Kind: user
- Location: Sunshine Coast, Australia
- Company: University of Queensland | UQCCR
- Website: https://mbh.sh
- Repositories: 130
- Profile: https://github.com/mbhall88
Postdoc @ University of Queensland with @LeahRoberts Bioinformatics | Nanopore | Microbial Genomics | Software Dev.
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Drug resistance prediction for Mycobacterium tuberculosis
with reference graphs
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Michael
name-particle: B
family-names: Hall
email: michael.hall2@unimelb.edu.au
affiliation: University of Melbourne
orcid: 'https://orcid.org/0000-0003-3683-6208'
- given-names: Leandro
family-names: Lima
affiliation: EMBL-EBI
orcid: 'https://orcid.org/0000-0001-8976-2762'
- given-names: Lachlan
name-particle: J.M
family-names: Coin
orcid: 'https://orcid.org/0000-0002-4300-455X'
affiliation: University of Melbourne
- given-names: Zamin
family-names: Iqbal
affiliation: EMBL-EBI
orcid: 'https://orcid.org/0000-0001-8466-7547'
identifiers:
- type: doi
value: 10.1101/2023.05.04.539481
description: The bioRxiv deposit of the accompanying paper
repository-code: 'https://github.com/mbhall88/drprg/'
url: 'https://mbh.sh/drprg/'
abstract: >-
The dominant paradigm for analysing genetic variation
relies on a central idea: all genomes in a species can be
described as minor differences from a single reference
genome. However, this approach can be problematic or
inadequate for bacteria, where there can be significant
sequence divergence within a species.
Reference graphs are an emerging solution to the reference
bias issues implicit in the “single-reference” model. Such
a graph represents variation at multiple scales within a
population – e.g., nucleotide- and locus-level.
The genetic causes of drug resistance in bacteria have
proven comparatively easy to decode compared with studies
of human diseases. For example, it is possible to predict
resistance to numerous anti-tuberculosis drugs by simply
testing for the presence of a list of single nucleotide
polymorphisms and insertion/deletions, commonly referred
to as a catalogue.
We developed DrPRG (Drug resistance Prediction with
Reference Graphs) using the bacterial reference graph
method Pandora. First, we outline the construction of a
Mycobacterium tuberculosis drug resistance reference
graph, a process that can be replicated for other species.
The graph is built from a global dataset of isolates with
varying drug susceptibility profiles, thus capturing
common and rare resistance- and susceptible-associated
haplotypes. We benchmark DrPRG against the existing
graph-based tool Mykrobe and the pileup-based approach of
TBProfiler using 44,709 and 138 publicly available
Illumina and Nanopore datasets with associated phenotypes.
We find DrPRG has significantly improved sensitivity and
specificity for some drugs compared to these tools, with
no significant decreases. It uses significantly less
computational memory than both tools, and provides
significantly faster runtimes, except when runtime is
compared to Mykrobe on Illumina data.
We discover and discuss novel insights into
resistance-conferring variation for M. tuberculosis -
including deletion of genes katG and pncA – and suggest
mutations that may warrant reclassification as associated
with resistance.
keywords:
- bioinformatics
- genome graphs
- antimicrobial resistance
- resistance prediction
- software
license: MIT
version: 0.1.1
date-released: '2023-04-06'
GitHub Events
Total
- Watch event: 2
- Fork event: 1
Last Year
- Watch event: 2
- Fork event: 1
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 24
- Total pull requests: 11
- Average time to close issues: about 1 month
- Average time to close pull requests: 2 days
- Total issue authors: 6
- Total pull request authors: 2
- Average comments per issue: 4.96
- Average comments per pull request: 4.09
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 8
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
- mbhall88 (16)
- tseemann (2)
- erinyoung (2)
- arnoldbain (1)
- belarus1941 (1)
- ptrmtb (1)
Pull Request Authors
- dependabot[bot] (7)
- mbhall88 (3)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cargo 3,195 total
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 2
- Total maintainers: 1
crates.io: drprg
Drug resistance prediction with reference graphs
- Homepage: https://github.com/mbhall88/drprg
- Documentation: https://docs.rs/drprg/
- License: MIT
-
Latest release: 0.1.1
published almost 3 years ago
Rankings
Maintainers (1)
Dependencies
- 130 dependencies
- anyhow 1.0.58
- bstr 0.2.17
- clap 3.2.7
- csv 1.1.6
- env_logger 0.9.0
- float-cmp 0.9.0
- fs_extra 1.2.0
- lazy_static 1.4.0
- log 0.4.17
- noodles 0.24.0
- rayon 1.5.3
- regex 1.5.6
- rust-htslib 0.39.5
- serde 1.0.137
- serde_derive 1.0.137
- serde_json 1.0.82
- strum 0.24.1
- strum_macros 0.24.2
- tempfile 3.3.0
- thiserror 1.0.31
- uuid 1.1.2
- actions-rs/toolchain v1 composite
- actions/cache v2 composite
- actions/checkout v2 composite
- codecov/codecov-action v1 composite
- taiki-e/install-action just composite
- taiki-e/install-action v2 composite
- rust 1.65 build
- actions/checkout v3 composite
- peaceiris/actions-gh-pages v3 composite
- peaceiris/actions-mdbook v1 composite
- actions/checkout v3 composite
- actions/upload-artifact master composite
- dtolnay/rust-toolchain stable composite
- softprops/action-gh-release v1 composite
- taiki-e/install-action v2 composite