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Metaxa: a software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea, bacteria, eukaryotes, mitochondria, and chloroplasts in metagenomes and environmental sequencing datasets

Journal article
Authors Johan Bengtsson-Palme
Karl Martin Eriksson
Martin Hartmann
Wang Zheng
Belle Damodara Shenoy
Gwen-Aëlle Grelet
Kessy Abarenkov
Anna Petri
Magnus Alm Rosenblad
R. Henrik Nilsson
Published in Antonie van Leeuwenhoek: international journal of general and molecular microbiology
Volume 100
Issue 3
Pages 471-475
ISSN 0003-6072
Publication year 2011
Published at Department of Cell and Molecular Biology
Department of Plant and Environmental Sciences
Institute of Neuroscience and Physiology, Department of Physiology
Pages 471-475
Language en
Keywords Metagenomics, Microbial communities, rRNA libraries, Phylogenetic assignment, Metagenomik, Mikrobiella samhällen, rRNA-bibliotek
Subject categories Microbiology, Ecology, Bioinformatics and Systems Biology, Microbiology


The ribosomal small subunit (SSU) rRNA gene has emerged as an important genetic marker for taxonomic identification in environmental sequencing datasets. In addition to being present in the nucleus of eukaryotes and the core genome of prokaryotes, the gene is also found in the mitochondria of eukaryotes and in the chloroplasts of photosynthetic eukaryotes. These three sets of genes are conceptually paralogous and should in most situations not be aligned and analyzed jointly. To identify the origin of SSU sequences in complex sequence datasets has hitherto been a time-consuming and largely manual undertaking. However, the present study introduces Metaxa (, an automated software tool to extract full-length and partial SSU sequences from larger sequence datasets and assign them to an archaeal, bacterial, nuclear eukaryote, mitochondrial, or chloroplast origin. Using data from reference databases and from full-length organelle and organism genomes, we show that Metaxa detects and scores SSU sequences for origin with very low proportions of false positives and negatives. We believe that this tool will be useful in microbial and evolutionary ecology as well as in metagenomics.

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