title: Comprehensive Meta-analysis of Ontology Annotated 16S rRNA Profiles Identifies Beta Diversity Clusters of Environmental Bacterial Communities.
abstract: Comprehensive mapping of environmental microbiomes in terms of their compositional features remains a great challenge in understanding the microbial biosphere of the Earth. It bears promise to identify the driving forces behind the observed community patterns and whether community assembly happens deterministically. Advances in Next Generation Sequencing allow large community profiling studies, exceeding sequencing data output of conventional methods in scale by orders of magnitude. However, appropriate collection systems are still in a nascent state. We here present a database of 20,427 diverse environmental 16S rRNA profiles from 2,426 independent studies, which forms the foundation of our meta-analysis. We conducted a sample size adaptive all-against-all beta diversity comparison while also respecting phylogenetic relationships of Operational Taxonomic Units(OTUs). After conventional hierarchical clustering we systematically test for enrichment of Environmental Ontology terms and their abstractions in all possible clusters. This post-hoc algorithm provides a novel formalism that quantifies to what extend compositional and semantic similarity of microbial community samples coincide. We automatically visualize significantly enriched subclusters on a comprehensive dendrogram of microbial communities. As a result we obtain the hitherto most differentiated and comprehensive view on global patterns of microbial community diversity. We observe strong clusterability of microbial communities in ecosystems such as human/mammal-associated, geothermal, fresh water, plant-associated, soils and rhizosphere microbiomes, whereas hypersaline and anthropogenic samples are less homogeneous. Moreover, saline samples appear less cohesive in terms of compositional properties than previously reported.
authors.affiliation: Department of Electrical Engineering and Computer Science/Institute Center Smart Infrastructure (iSmart), Masdar Institute, Abu Dhabi, UAE.
authors.forename: Andreas
authors.initials: A
authors.lastname: Henschel
authors.affiliation: Department of Electrical Engineering and Computer Science/Institute Center Smart Infrastructure (iSmart), Masdar Institute, Abu Dhabi, UAE.
authors.forename: Muhammad Zohaib
authors.initials: MZ
authors.lastname: Anwar
authors.affiliation: Department of Electrical Engineering and Computer Science/Institute Center Smart Infrastructure (iSmart), Masdar Institute, Abu Dhabi, UAE.
authors.forename: Vimitha
authors.initials: V
authors.lastname: Manohar
mesh_terms: D002874:Chromosome Mapping / Q000379:methods; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D017753:Ecosystem; D014644:Genetic Variation / Q000235:genetics*; D016680:Genome, Bacterial / Q000235:genetics*; D054892:Metagenome / Q000235:genetics*; D064307:Microbiota / Q000235:genetics*; D012336:RNA, Ribosomal, 16S / Q000235:genetics*
chemical_list: D012336:RNA, Ribosomal, 16S