References
Citing
gPlace reproduces pplacer’s placements on the GTDB reference trees. If you use gPlace, please cite pplacer and GTDB:
Matsen FA, Kodner RB, Armbrust EV. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics, 11:538. https://doi.org/10.1186/1471-2105-11-538 |
Parks DH, et al. 2022. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research, 50:D785–D794. https://doi.org/10.1093/nar/gkab776 |
Parks DH, et al. 2020. A complete domain-to-species taxonomy for Bacteria and Archaea. Nature Biotechnology, 38:1079–1086. https://doi.org/10.1038/s41587-020-0501-8 |
Software gPlace builds on
For reference, the tools, models and libraries behind gPlace and the GTDB-Tk reference packages:
Software |
Reference |
|---|---|
GTDB-Tk |
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. 2022. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics, 38:5315–5316. https://doi.org/10.1093/bioinformatics/btac672 Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. 2020. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics, 36:1925–1927. https://doi.org/10.1093/bioinformatics/btz848 |
FastTree |
Price MN, Dehal PS, Arkin AP. 2010. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE, 5:e9490. https://doi.org/10.1371/journal.pone.0009490 |
WAG |
Whelan S, Goldman N. 2001. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular Biology and Evolution, 18:691–699. https://doi.org/10.1093/oxfordjournals.molbev.a003851 |
GSL |
Galassi M, et al. GNU Scientific Library Reference Manual (3rd Ed.), ISBN 0954612078. https://www.gnu.org/software/gsl/ |
CORE-MATH |
Sibidanov A, Zimmermann P, Glondu S. 2022. The CORE-MATH Project. 2022 IEEE 29th Symposium on Computer Arithmetic (ARITH), 26–34. https://doi.org/10.1109/ARITH54963.2022.00014 |
NumPy |
Harris CR, et al. 2020. Array programming with NumPy. Nature, 585:357–362. https://doi.org/10.1038/s41586-020-2649-2 |
tqdm |
da Costa-Luis C, et al. tqdm: A fast, Extensible Progress Bar for Python and CLI. https://github.com/tqdm/tqdm |