DEPP: Deep Learning Enables Extending Species Trees using Single Genes.
Journal
Systematic biology
ISSN: 1076-836X
Titre abrégé: Syst Biol
Pays: England
ID NLM: 9302532
Informations de publication
Date de publication:
19 05 2023
19 05 2023
Historique:
received:
18
05
2021
revised:
13
04
2022
accepted:
22
04
2022
medline:
22
5
2023
pubmed:
30
4
2022
entrez:
29
4
2022
Statut:
ppublish
Résumé
Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phylogeny. For example, they assume single-gene data (e.g., 16S rRNA amplicons) have evolved under the GTR model on a gene tree. Placement, however, often has a more ambitious goal: extending a (genome-wide) species tree given data from individual genes without knowing the evolutionary model. Addressing this challenging problem requires new directions. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without prespecified models. In simulations and on real data, we show that DEPP can match the accuracy of model-based methods without any prior knowledge of the model. We also show that DEPP can update the multilocus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can combine 16S and metagenomic data onto a single tree, enabling community structure analyses that take advantage of both sources of data. [Deep learning; gene tree discordance; metagenomics; microbiome analyses; neural networks; phylogenetic placement.].
Identifiants
pubmed: 35485976
pii: 6575921
doi: 10.1093/sysbio/syac031
pmc: PMC10198656
doi:
Substances chimiques
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
17-34Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM142725
Pays : United States
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.
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