The Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far, finding the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals.
This Review describes how metagenomics and 16S pyrosequencing techniques are opening the way towards global ecosystem network prediction and the development of ecosystem-wide dynamic models.
An ensemble method based on multiple similarity measures in combination with generalized boosted linear models (GBLMs) to taxonomic marker (16S rRNA gene) profiles of this cohort resulted in a global network of 3,005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome.
Resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times are presented, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far.
Stool consistency showed the largest effect size, whereas medication explained largest total variance and interacted with other covariate-microbiota associations, and proposed disease marker genera associated to host covariates were found associated to microbiota compositional variation with a 92% replication rate.
The Cytoscape app version of the association network inference tool CoNet, designed to be generic and can detect associations in any data set where biological entities have been observed repeatedly, is presented.
This work benchmarks the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts.
Analysis of 820 phages with annotated hosts shows how current knowledge and insights about the interaction mechanisms and ecology of coevolving phages and bacteria can be exploited to predict phage–host relationships, with potential relevance for medical and industrial applications.
The need for robust microbial network inference is highlighted and strategies to infer networks more reliably are suggested and shown in a simulation how network properties are affected by tool choice and environmental factors.
It is found that environmental factors are incomplete predictors of community structure and associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns.