preprintbioRxiv (Cold Spring Harbor Laboratory)Sep 9, 2016GREEN OA

SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences

Tiburon Associates (United States)

Indexed incrossref

Abstract

Abstract Metagenomics experiments often characterize microbial communities by sequencing the ribosomal 16S and ITS regions. Taxonomy prediction is a fundamental step in such studies. The SINTAX algorithm predicts taxonomy by using k -mer similarity to identify the top hit in a reference database and provides bootstrap confidence for all ranks in the prediction. SINTAX achieves comparable or better accuracy to the RDP Naive Bayesian Classifier with a simpler algorithm that does not require training. Most tested methods are shown to have high rates of over-classification errors where novel taxa are incorrectly predicted to have known names.

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Authors

1

Topics & keywords

Keywords
  • Classifier (UML)
  • Computer science
  • Artificial intelligence
  • Metagenomics
  • Taxonomy (biology)
  • Bayesian probability
  • Naive Bayes classifier
  • Pattern recognition (psychology)
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