2024
DOI: 10.1128/mra.01063-23
|View full text |Cite
|
Sign up to set email alerts
|

Updated RDP taxonomy and RDP Classifier for more accurate taxonomic classification

Qiong Wang,
James R. Cole

Abstract: The RDP Classifier is one of the most popular machine learning approaches for taxonomic classification due to its robustness and relatively high accuracy. Both the RDP taxonomy and RDP Classifier have been updated to incorporate newly described taxa and recent changes to prokaryotic nomenclature.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2025
2025

Publication Types

Select...
3
2

Relationship

0
5

Authors

Journals

citations
Cited by 7 publications
(1 citation statement)
references
References 10 publications
0
1
0
Order By: Relevance
“…To account for index bleed between samples, read counts in the OTU□×□sample matrices were adjusted using 1% as the filter percentage. To assign taxonomy, we used a hybrid algorithm integrating results from a USEARCH global alignment against the UNITE (v8, Nilsson et al 2019) and RDP (training set 19, Wang and Cole 2024) databases, including both UTAX and SINTAX classifiers. Data are deposited in the NCBI Short Read Archive (Bioproject # for bacteria PRJNA1094420 ; for fungi PRJNA1094416 ).…”
Section: Methodsmentioning
confidence: 99%
“…To account for index bleed between samples, read counts in the OTU□×□sample matrices were adjusted using 1% as the filter percentage. To assign taxonomy, we used a hybrid algorithm integrating results from a USEARCH global alignment against the UNITE (v8, Nilsson et al 2019) and RDP (training set 19, Wang and Cole 2024) databases, including both UTAX and SINTAX classifiers. Data are deposited in the NCBI Short Read Archive (Bioproject # for bacteria PRJNA1094420 ; for fungi PRJNA1094416 ).…”
Section: Methodsmentioning
confidence: 99%