MMUPHin
MMUPHin is an R package implementing meta-analysis methods for microbial community profiles. It has interfaces for: a) covariate-controlled batch and study effect adjustment, b) meta-analytic differential abundance testing, and meta-analytic discovery of c) discrete (cluster-based) or d) continuous unsupervised population structure. Overall, MMUPHin enables the normalization and combination of multiple microbial community studies. It can then help in identifying microbes, genes, or pathways that are differential with respect to combined phenotypes. Finally, it can find clusters or gradients of sample types that reproduce consistently among studies.
https://huttenhower.sph.harvard.edu/mmuphin/
MetaWIBELE
MetaWIBELE (Workflow to Identify novel Bioactive Elements in the microbiome) is a workflow to efficiently and systematically identify potentially bioactive (and often uncharacterized) gene products in microbial communities. It prioritizes candidate gene products from assembled metagenomes using a combination of sequence homology, secondary-structure-based functional annotations, phylogenetic binning, ecological distribution, and association with environmental parameters or phenotypes to target candidate bioactives.
https://huttenhower.sph.harvard.edu/metawibele/
MACARRoN
MACARRoN (Metabolome Analysis and Combined Annotation Ranks for pRediction of Novel bioactives) is a method for the systematic analysis and prioritization of potentially bioactive small molecules from (microbial community) metabolomes. It leverages covariance i.e. correlation of abundances to associate unknown metabolic features with annotated metabolites and transfer biological or functional annotations. For each metabolic feature, MACARRoN evaluates indicators of bioactivity such as ecological relevance (abundance relative to a covarying annotated metabolite) and association with a phenotype or environmental condition (effect size and q-value of differential abundance). Ranks from the aforementioned indicators of bioactivity are integrated into a meta-rank which is used to prioritize metabolic features as potential bioactives.