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* Add JOSS paper and citations
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@software{gtnet, | ||
author = {Tritt, Andrew}, | ||
title = {{The Genome Taxonomy Network}}, | ||
url = {https://github.com/exabiome/gtnet}, | ||
version = {0.0.6}, | ||
year = {2024} | ||
} |
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@article{GTDB, | ||
doi = {10.1093/nar/gkab776}, | ||
url = {https://doi.org/10.1093%2Fnar%2Fgkab776}, | ||
year = 2021, | ||
month = {sep}, | ||
publisher = {Oxford University Press ({OUP})}, | ||
volume = {50}, | ||
number = {D1}, | ||
pages = {D785--D794}, | ||
author = {Donovan H Parks and Maria Chuvochina and Christian Rinke and Aaron J Mussig and Pierre-Alain Chaumeil and Philip Hugenholtz}, | ||
title = {{GTDB}: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized~and complete genome-based taxonomy}, | ||
journal = {Nucleic Acids Research} | ||
} | ||
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@software{gtnet, | ||
author = {Tritt, Andrew}, | ||
title = {{The Genome Taxonomy Network}}, | ||
url = {https://github.com/exabiome/gtnet}, | ||
version = {0.0.6}, | ||
year = {2024} | ||
} |
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--- | ||
title: 'gtnet: A Python package for taxonomic labelling with the Genome Taxonomy Network' | ||
tags: | ||
- Python | ||
- metagenomics | ||
- deep learning | ||
authors: | ||
- name: Andrew J. Tritt | ||
orcid: 0000-0002-1617-449X | ||
equal-contrib: false | ||
affiliation: "1, 2" # (Multiple affiliations must be quoted) | ||
- name: Kristofer Bouchard | ||
orcid: 0000-0002-1974-4603 | ||
equal-contrib: true # (This is how you can denote equal contributions between multiple authors) | ||
affiliation: 2 | ||
- name: Author with no affiliation | ||
corresponding: true # (This is how to denote the corresponding author) | ||
affiliation: 3 | ||
- given-names: Ludwig | ||
dropping-particle: van | ||
surname: Beethoven | ||
affiliation: 3 | ||
affiliations: | ||
- name: Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA | ||
index: 1 | ||
- name: Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. | ||
index: 2 | ||
- name: Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA | ||
index: 3 | ||
- name: Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, University of California Berkeley, Berkeley, CA, USA. | ||
index: 4 | ||
date: 13 August 2017 | ||
bibliography: paper.bib | ||
--- | ||
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# Summary | ||
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The field of metagenomics seeks to understand the genomic and functional diversity of microbial | ||
communities. Modern metagenomic sequencing pipelines produce unlabelled genomic sequences at an | ||
unprecedented rate. Processing of these sequences, i.e. contigs, involves labelling the taxonomy of these | ||
contigs. In recent years, the metagenomics fields has been coalescing around the use of the Genome | ||
Taxonomy Database [@GTDB], a phylogenetically informed taxonomy for consistently labelling microbial taxa. | ||
The Genome Taxonomy Network, `GTNet`, is a neural network capable of classifying metagenomic | ||
contigs with taxonomic labels from the Genome Taxonomy Database. | ||
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# Statement of need | ||
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`gtnet` [@gtnet] is a Python package and command-line utility built on top of `GTNet`. The purpose of this software | ||
is to make the predictive capabilities of the GTNet easily accessible to the meteagenomics community. | ||
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In addition to deploying GTNet, the `gtnet` software seeks to address other outstanding issues in the | ||
field. Many taxonomic classification tools are still released as source code in tarball formats, require | ||
installation of third-party software that may no longer be maintained, or use application-specific output formats. | ||
These issues make existing tools cumbersome and difficult to use. By leveraging the existing Python ecosystem, we | ||
seek to make a tool that is easier to use and version for the sake if user-friendliness and reproducibility. | ||
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By releasing easily-installable and user-friendly software capable of generating GTDB taxonomies, we | ||
hope to lower the technical barrier to wide adoption of standardized taxonomy across the metagenomics field. | ||
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# References |
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