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Bioinformatics. 2018 Nov 30. doi: 10.1093/bioinformatics/bty954. [Epub ahead of print]

DiTaxa: Nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection.

Author information

1
Department of Bioengineering, University of California, Berkeley, CA, USA.
2
Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany.
3
Max von Pettenkofer-Institute of Hygiene and Medical Microbiology, Faculty of Medicine, LMU Munich, Munich, Germany.
4
Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, CA, USA.

Abstract

Summary:

Identifying distinctive taxa for microbiome-related diseases is considered key to the establishment of diagnosis and therapy options in precision medicine and imposes high demands on the accuracy of microbiome analysis techniques. We propose an alignment- and reference- free subsequence based 16S rRNA data analysis, as a new paradigm for microbiome phenotype and biomarker detection. Our method, called DiTaxa, substitutes standard OTU-clustering by segmenting 16S rRNA reads into the most frequent variable-length subsequences. We compared the performance of DiTaxa to the state-of-the-art methods in phenotype and biomarker detection, using human-associated 16S rRNA samples for periodontal disease, rheumatoid arthritis, and inflammatory bowel diseases, as well as a synthetic benchmark dataset. DiTaxa performed competitively to the k-mer based state-of-the-art approach in phenotype prediction while outperforming the OTU-based state-of-the-art approach in finding biomarkers in both resolution and coverage evaluated over known links from literature and synthetic benchmark datasets.

Availability:

DiTaxa is available under the Apache 2 license at http://llp.berkeley.edu/ditaxa.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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