The effect of training set on the classification of honey bee gut microbiota using the Naive Bayesian Classifier.

ABSTRACT:

Background

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 Naïve Bayesian Classifier developed by the Ribosomal Database Project (RDP-NBC). However, the consistency and confidence of classifications provided by the RDP-NBC is dependent on the training set utilized.

Results

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 nove

SUBMITTER: Newton IL 

PROVIDER: S-EPMC3520854 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature