SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences
Tiburon Associates (United States)
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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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Topics
Keywords
- Classifier (UML)
- Computer science
- Artificial intelligence
- Metagenomics
- Taxonomy (biology)
- Bayesian probability
- Naive Bayes classifier
- Pattern recognition (psychology)
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