MAAMOUL: A method for detecting microbiome-metabolome alterations in disease using metabolic networks
Table of contents:
MAAMOUL is a knowledge-based computational method that integrates metagenomic and metabolomic data to identify custom data-driven microbial metabolic modules associated with disease states. Unlike traditional statistical approaches, MAAMOUL leverages prior biological knowledge about bacterial metabolism to link genes to metabolites through a global, microbiome-wide metabolic network, and then projects genes' and metabolites' disease-association scores onto this network. The identified 'modules' are sub-networks in this graph that are significantly enriched with disease-associated features, both metagenomic and metabolomic.
For further details see: Muller E, Baum S, and Borenstein E. "Detecting Microbiome-Metabolome Alterations in Disease Using Metabolic Networks." In preparation.
MAAMOUL can be installed directly from GitHub, by running the following:
install.packages("devtools")
library(devtools)
install_github("efratmuller/MAAMOUL")
library(MAAMOUL)
Note: The MAAMOUL package is dependant on the installation of the 'BioNet' package [1]. See installation instructions here.
Coming soon...
library(MAAMOUL)
write_test_files()
maamoul(global_network_edges = 'test_input/enzyme_compound_edges_kegg.csv',
ec_pvals = 'test_input/ec_pvals.tsv',
metabolite_pvals = 'test_input/mtb_pvals.tsv',
out_dir = 'test_outputs',
N_REPEATS = 100,
N_VAL_PERM = 9,
N_THREADS = 4
)
For questions about the pipeline, please open an issue (https://github.com/efratmuller/MAAMOUL/issues) or contact Prof. Elhanan Borenstein at elbo@tauex.tau.ac.il.
References
- Beisser D, Klau GW, Dandekar T, Mueller T, Dittrich M (2010). “BioNet: an R-package for the Functional Analysis of Biological Networks.” Bioinformatics, 26(8), 1129-1130.