MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks.

ABSTRACT: Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group 'meta-data' and 'inter-omic' functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts requ

SUBMITTER: Nagpal S 

PROVIDER: S-EPMC7319469 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature