Welcome to the netome tools and tutorials wiki, which provides software, documentation, and tutorials for methods for metabolite profiling and network-based analyses using omics data developed by the metabolomics platform at the Broad Institute of MIT and Harvard. Most tools are supported both as individual software packages (typically Python or R), using our webserver, netome café, and within the netome virtual image, a pre-built platform that provides meta’omic analysis tools already installed with dependencies and configuration.
Citation: Rahnavard et al., netome: a computational framework for metabolite profiling and omics network analysis. bioRxiv 443903; doi: https://doi.org/10.1101/443903
This set of methods generally provide Liquid chromatography tandem mass spectrometry (LC-MS) based profiles of metabolite features, e.g. known metabolites or predicted profiles. They apply broadly to mass spectrometry data (spectrum and chromatogram), with some methods applying to other types of omics data such as metagenomics data.
The methods in this section generally provide quantitative models for interpreting metabolite profiles as generated by the profilling methods above. These methods also include identifying significant associations of sample metadata (phenotype, environment, health status, etc.) with metabolites and microbiome, metabolic pathway enrichment analysis for metabolites and microbiome.