References

gTranslate manuscript is in progress:

The Genome Taxonomy Database (GTDB) is described in:

Parks DH, et al. 2020. A complete domain-to-species taxonomy for Bacteria and Archaea. Nature Biotechnology, https://doi.org/10.1038/s41587-020-0501-8.

Parks DH, et al. 2018. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nature Biotechnology, http://dx.doi.org/10.1038/nbt.4229.

We strongly encourage you to cite the following 3rd party dependencies:

Software

Reference

Prodigal

Hyatt D, et al. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics, 11:119. doi: 10.1186/1471-2105-11-119.

NumPy

Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 0.1038/s41586-020-2649-2

tqdm

DOI: 10.5281/zenodo.595120

Pandas

McKinney W. 2010. Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 51-56.

scikit-learn

Pedregosa F, et al. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

joblib

Joblib: https://joblib.readthedocs.io/en/latest/

scipy

Virtanen P, et al. 2020. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17, 261–272 (2020). DOI: 10.1038/s41592-019-0686-2

mlxtend

Raschka S. 2018. MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack. Journal of Open Source Software, 3(24), 638, https://doi.org/10.21105/joss.00638

plotly

Plotly Technologies Inc. 2015. Collaborative data science. Montréal, QC: Plotly Technologies Inc. https://plot.ly

xgboost

Chen T, et al. 2016. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI: 10.1145/2939672.2939785

lightgbm

Ke G, et al. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154.

requests

Reitz K. and Kenneth Reitz. 2023. Requests: HTTP for Humans. https://docs.python-requests.org/en/latest/