Description: MelonnPan is composed of two high-level workflows: MelonnPan-Predict and MelonnPan-Train .
The MelonnPan-Predict workflow takes a table of microbial sequence features as input (i.e. taxonomic or functional abundances on a per sample basis) and outputs a predicted metabolomic table (i.e. relative abundances of metabolite compounds across samples).
The MelonnPan-Train workflow creates a weight matrix that links an optimal set of sequence features to a subset of predictable metabolites following rigorous internal validation, which is then used to generate a table of predicted metabolite compounds (i.e. relative abundances of metabolite compounds per sample). When sufficiently accurate, these predicted metabolite relative abundances can be used for downstream statistical analysis and end-to-end biomarker discovery.
Himel Mallick, Eric A. Franzosa, Lauren J. McIver, Soumya Banerjee, Alexandra Sirota-Madi, Aleksandar D. Kostic, Clary B. Clish, Hera Vlamakis, Ramnik Xavier, Curtis Huttenhower (2019). Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nature Communications 10(1):3136-3146.