title: Cross-biome comparison of microbial association networks.
abstract: Clinical and environmental meta-omics studies are accumulating an ever-growing amount of microbial abundance data over a wide range of ecosystems. With a sufficiently large sample number, these microbial communities can be explored by constructing and analyzing co-occurrence networks, which detect taxon associations from abundance data and can give insights into community structure. Here, we investigate how co-occurrence networks differ across biomes and which other factors influence their properties. For this, we inferred microbial association networks from 20 different 16S rDNA sequencing data sets and observed that soil microbial networks harbor proportionally fewer positive associations and are less densely interconnected than host-associated networks. After excluding sample number, sequencing depth and beta-diversity as possible drivers, we found a negative correlation between community evenness and positive edge percentage. This correlation likely results from a skewed distribution of negative interactions, which take place preferentially between less prevalent taxa. Overall, our results suggest an under-appreciated role of evenness in shaping microbial association networks.
authors.affiliation: Center for the Biology of Disease, VIB Leuven, Belgium ; Department of Microbiology and Immunology, REGA Institute, KU Leuven Leuven, Belgium ; Department of Applied Biological Sciences, Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Karoline
authors.initials: K
authors.lastname: Faust
authors.affiliation: Center for the Biology of Disease, VIB Leuven, Belgium ; Department of Microbiology and Immunology, REGA Institute, KU Leuven Leuven, Belgium ; Department of Applied Biological Sciences, Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Gipsi
authors.initials: G
authors.lastname: Lima-Mendez
authors.affiliation: Machine Learning Group, Department of Computer Science, Université Libre de Bruxelles Brussels, Belgium.
authors.forename: Jean-Sébastien
authors.initials: JS
authors.lastname: Lerat
authors.affiliation: 23andMe Inc., Mountain View CA, USA.
authors.forename: Jarupon F
authors.initials: JF
authors.lastname: Sathirapongsasuti
authors.affiliation: Department of Chemistry and Biochemistry and BioFrontiers Institute, University of Colorado, Boulder CO, USA.
authors.forename: Rob
authors.initials: R
authors.lastname: Knight
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston MA, USA.
authors.forename: Curtis
authors.initials: C
authors.lastname: Huttenhower
authors.affiliation: Machine Learning Group, Department of Computer Science, Université Libre de Bruxelles Brussels, Belgium ; Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel Brussels, Belgium ; Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Tom
authors.initials: T
authors.lastname: Lenaerts
authors.affiliation: Center for the Biology of Disease, VIB Leuven, Belgium ; Department of Microbiology and Immunology, REGA Institute, KU Leuven Leuven, Belgium ; Department of Applied Biological Sciences, Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Jeroen
authors.initials: J
authors.lastname: Raes
keywords: 16S rDNA sequencing; co-occurrence; evenness; microbial communities; network comparison; positive edge percentage