Identification of molecules or species predictive of response

Statistical analysis to identify microbes, metabolites or proteins predictive of metadata responses (such as disease phenotype, sample time collection, protein measurements in blood). Microbe data can be in form of 16S, shotgun metagenomics, RNA-Seq or microarray.

2D/3D molecular and microbial mapping

2D/3D molecular or microbiome cartography to view spatial distribution of molecules or microbes on the sample surface.

Untargeted metabolomics for novel molecule biomarker leads

Raw tandem MS/MS spectra from untargeted liquid or gas chromatography (LC/GC) mass spectrometry are converted into analyte feature tables and molecular annotations are explored beyond simple library matching using molecular networking and in silico screening.

Machine learning on control and disease cohorts

Predictive analytics models using machine learning and covariate analysis on public and private cohorts for specific disease types.

Microbiome-metabolome or proteome correlation analysis

Paired samples are analyzed to identify significant correlations between microbes, metabolites or proteins, yielding insight into microbial-derived metabolites and their metabolic pathways. Microbe data can be in form of 16S, shotgun metagenomics, RNA-Seq or microarray.

Small molecule profiling of microbial cultures

Small molecule profiling of microbial cultures via mass spectrometry for metabolite provenance identification in molecular networking.