@ARTICLE{TreeBASE2Ref21168,
author = {Irene Newton and Guus Roeselers},
title = {The effect of training set on the classification of honey bee gut microbiota using the Na?ve Baysian Classifier},
year = {2012},
keywords = {honey bee; gut; microbiota; na?ve Bayesian classifier; pyrosequencing; taxonomy},
doi = {},
url = {http://},
pmid = {},
journal = {BMC Microbiology},
volume = {12},
number = {},
pages = {221},
abstract = {Microbial ecologists now routinely utilize next-generation sequencing methods to assess microbial diversity in the environment. One tool heavily utilized by many groups is the Na?ve Bayesian Classifier developed by the Ribosomal Database Project (RDP-NBC) [1]. The consistency of classifications provided by the RDP-NBC is dependent on the training set utilized [2]. We explored the stability of classification of honey bee gut microbiota sequences by the RDP-NBC utilizing three publically available ribosomal RNA sequence databases as training sets: ARB-SILVA, Greengenes and RDP. We found that the inclusion of previously published, high-quality, full-length sequences from 16S rRNA clone libraries improved the precision in classification of novel bee-associated sequences. Results have ramifications for other environments represented by few sequences or bacterial isolates.}
}
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Citation title: "The effect of training set on the classification of honey bee gut microbiota using the Na?ve Baysian Classifier".
Study name: "The effect of training set on the classification of honey bee gut microbiota using the Na?ve Baysian Classifier".
This study is part of submission 13210
(Status: In Progress).
Citation
Newton I., & Roeselers G. 2012. The effect of training set on the classification of honey bee gut microbiota using the Na?ve Baysian Classifier. BMC Microbiology, 12: 221.
Authors
Newton I.
(submitter)
812-855-3883
Roeselers G.
Abstract
Microbial ecologists now routinely utilize next-generation sequencing methods to assess microbial diversity in the environment. One tool heavily utilized by many groups is the Na?ve Bayesian Classifier developed by the Ribosomal Database Project (RDP-NBC) [1]. The consistency of classifications provided by the RDP-NBC is dependent on the training set utilized [2]. We explored the stability of classification of honey bee gut microbiota sequences by the RDP-NBC utilizing three publically available ribosomal RNA sequence databases as training sets: ARB-SILVA, Greengenes and RDP. We found that the inclusion of previously published, high-quality, full-length sequences from 16S rRNA clone libraries improved the precision in classification of novel bee-associated sequences. Results have ramifications for other environments represented by few sequences or bacterial isolates.
@ARTICLE{TreeBASE2Ref21168,
author = {Irene Newton and Guus Roeselers},
title = {The effect of training set on the classification of honey bee gut microbiota using the Na?ve Baysian Classifier},
year = {2012},
keywords = {honey bee; gut; microbiota; na?ve Bayesian classifier; pyrosequencing; taxonomy},
doi = {},
url = {http://},
pmid = {},
journal = {BMC Microbiology},
volume = {12},
number = {},
pages = {221},
abstract = {Microbial ecologists now routinely utilize next-generation sequencing methods to assess microbial diversity in the environment. One tool heavily utilized by many groups is the Na?ve Bayesian Classifier developed by the Ribosomal Database Project (RDP-NBC) [1]. The consistency of classifications provided by the RDP-NBC is dependent on the training set utilized [2]. We explored the stability of classification of honey bee gut microbiota sequences by the RDP-NBC utilizing three publically available ribosomal RNA sequence databases as training sets: ARB-SILVA, Greengenes and RDP. We found that the inclusion of previously published, high-quality, full-length sequences from 16S rRNA clone libraries improved the precision in classification of novel bee-associated sequences. Results have ramifications for other environments represented by few sequences or bacterial isolates.}
}
TY - JOUR
ID - 21168
AU - Newton,Irene
AU - Roeselers,Guus
T1 - The effect of training set on the classification of honey bee gut microbiota using the Na?ve Baysian Classifier
PY - 2012
KW - honey bee; gut; microbiota; na?ve Bayesian classifier; pyrosequencing; taxonomy
UR - http://dx.doi.org/
N2 - Microbial ecologists now routinely utilize next-generation sequencing methods to assess microbial diversity in the environment. One tool heavily utilized by many groups is the Na?ve Bayesian Classifier developed by the Ribosomal Database Project (RDP-NBC) [1]. The consistency of classifications provided by the RDP-NBC is dependent on the training set utilized [2]. We explored the stability of classification of honey bee gut microbiota sequences by the RDP-NBC utilizing three publically available ribosomal RNA sequence databases as training sets: ARB-SILVA, Greengenes and RDP. We found that the inclusion of previously published, high-quality, full-length sequences from 16S rRNA clone libraries improved the precision in classification of novel bee-associated sequences. Results have ramifications for other environments represented by few sequences or bacterial isolates.
L3 -
JF - BMC Microbiology
VL - 12
IS -
ER -
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Citation title: "The effect of training set on the classification of honey bee gut microbiota using the Na?ve Baysian Classifier".
812-855-3883
http://purl.org/phylo/treebase/phylows/study/TB2:S13210
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