SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences
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.
02 /Specification & Classification
Click any tag to browse matching papers- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- HiFiTaxa: a reproducible analysis pipeline for full length 16S rRNA gene and ITS amplicon sequencing from PacBio reads
Alessandro N. Garritano, Fluvio Modolon, Charles Robert Warren·bioRxiv·Nov 4, 2026
- raxtax: A k-mer-based non-Bayesian Taxonomic Classifier
Noah A Wahl, Georgios Koutsovoulos, … Alexandros Stamatakis·BioinformaticsPeer-reviewed·Nov 17, 2025
- Taxonomic classification method for metagenomics based on core protein families with Core-Kaiju
Anna Tovo, Peter Menzel, … Samir Simon Suweis·Nucleic Acids ResearchPeer-reviewed·Jun 24, 2020
- Gist: an ensemble approach to the taxonomic classification of metatranscriptomic sequence data.
Samantha Halliday, John Parkinson·bioRxiv·Oct 28, 2016
- Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data
Tyler Faits, Aubrey R. Odom-Mabey, … William Evan Johnson·bioRxiv·Jul 27, 2022
- MT-MAG: Accurate and interpretable machine learning for complete or partial taxonomic assignments of metagenome-assembled genomes
Wanxin Li, Lila Kari A, … Laura A. Hug·bioRxiv·Jan 12, 2022