Chaudhary N.,Metagenomics and Systems Biology Group |
Sharma A.K.,Metagenomics and Systems Biology Group |
Agarwal P.,Metagenomics and Systems Biology Group |
Agarwal P.,Indian Institute of Science |
And 2 more authors.
PLoS ONE | Year: 2015
The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http:// metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier. © 2015 Chaudhary et al.