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Phylogeny-aware Identification and Correction of Taxonomically Mislabeled Sequences

View ORCID ProfileAlexey M. Kozlov, Jiajie Zhang, View ORCID ProfilePelin Yilmaz, View ORCID ProfileFrank Oliver Glöckner, View ORCID ProfileAlexandros Stamatakis
doi: https://doi.org/10.1101/042200
Alexey M. Kozlov
*The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany Email: ,
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  • For correspondence: alexey.kozlov@h-its.org alexandros.stamatakis@h-its.org
Jiajie Zhang
*The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany Email: ,
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  • For correspondence: alexey.kozlov@h-its.org alexandros.stamatakis@h-its.org
Pelin Yilmaz
†Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Bremen, Germany
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Frank Oliver Glöckner
†Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Bremen, Germany
‡Jacobs University Bremen gGmbH, Bremen, Germany
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Alexandros Stamatakis
*The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany Email: ,
§Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Germany
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  • For correspondence: alexey.kozlov@h-its.org alexandros.stamatakis@h-its.org
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Abstract

Molecular sequences in public databases are mostly annotated by the submitting authors without further validation. This procedure can generate erroneous taxonomic sequence labels. Mislabeled sequences are hard to identify, and they can induce downstream errors because new sequences are typically annotated using existing ones. Furthermore, taxonomic mislabelings in reference sequence databases can bias metagenetic studies which rely on the taxonomy. Despite significant efforts to improve the quality of taxonomic annotations, the curation rate is low because of the labour-intensive manual curation process.

Here, we present SATIVA, a phylogeny-aware method to automatically identify taxonomically mislabeled sequences (‘mislabels’) using statistical models of evolution. We use the Evolutionary Placement Algorithm (EPA) to detect and score sequences whose taxonomic annotation is not supported by the underlying phylogenetic signal, and automatically propose a corrected taxonomic classification for those. Using simulated data, we show that our method attains high accuracy for identification (96.9% sensitivity / 91.7% precision) as well as correction (94.9% sensitivity / 89.9% precision) of mislabels. Furthermore, an analysis of four widely used microbial 16S reference databases (Greengenes, LTP, RDP and SILVA) indicates that they currently contain between 0.2% and 2.5% mislabels. Finally, we use SATIVA to perform an in-depth evaluation of alternative taxonomies for Cyanobacteria.

SATIVA is freely available at https://github.com/amkozlov/sativa.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted March 04, 2016.
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Phylogeny-aware Identification and Correction of Taxonomically Mislabeled Sequences
Alexey M. Kozlov, Jiajie Zhang, Pelin Yilmaz, Frank Oliver Glöckner, Alexandros Stamatakis
bioRxiv 042200; doi: https://doi.org/10.1101/042200
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Phylogeny-aware Identification and Correction of Taxonomically Mislabeled Sequences
Alexey M. Kozlov, Jiajie Zhang, Pelin Yilmaz, Frank Oliver Glöckner, Alexandros Stamatakis
bioRxiv 042200; doi: https://doi.org/10.1101/042200

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