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Nucleic Acids Res. 2018 Feb 28;46(4):e23. doi: 10.1093/nar/gkx1209.

Functional sequencing read annotation for high precision microbiome analysis.

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Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ 08873, USA.
Department for Bioinformatics and Computational Biology, Technische Universität München, Boltzmannstr. 3, 85748 Garching/Munich, Germany.
TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Technische Universität München, 85748 Garching/Munich, Germany.
Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany.
State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Institue of Epidemiology, Kiel University, Kiel, Germany.
Canadian Institute for Advanced Research, Toronto, Canada.
Department of Genetics, Rutgers University, Human Genetics Institute, Life Sciences Building, 145 Bevier Road, Piscataway, NJ 08854, USA.
Institute for Advanced Study, Technische Universität München (TUM-IAS), Lichtenbergstrasse 2 a, D-85748 Garching, Germany.


The vast majority of microorganisms on Earth reside in often-inseparable environment-specific communities-microbiomes. Meta-genomic/-transcriptomic sequencing could reveal the otherwise inaccessible functionality of microbiomes. However, existing analytical approaches focus on attributing sequencing reads to known genes/genomes, often failing to make maximal use of available data. We created faser (functional annotation of sequencing reads), an algorithm that is optimized to map reads to molecular functions encoded by the read-correspondent genes. The mi-faser microbiome analysis pipeline, combining faser with our manually curated reference database of protein functions, accurately annotates microbiome molecular functionality. mi-faser's minutes-per-microbiome processing speed is significantly faster than that of other methods, allowing for large scale comparisons. Microbiome function vectors can be compared between different conditions to highlight environment-specific and/or time-dependent changes in functionality. Here, we identified previously unseen oil degradation-specific functions in BP oil-spill data, as well as functional signatures of individual-specific gut microbiome responses to a dietary intervention in children with Prader-Willi syndrome. Our method also revealed variability in Crohn's Disease patient microbiomes and clearly distinguished them from those of related healthy individuals. Our analysis highlighted the microbiome role in CD pathogenicity, demonstrating enrichment of patient microbiomes in functions that promote inflammation and that help bacteria survive it.

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