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.

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.

Genome, metagenome assembly and annotation

Alignment-based or de novo genome assembly for isolate genomes or metagenomes from Illumina or PacBio platforms. 

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.

Antibiotic resistance and virulence gene profiling

Whole-genome profiling for antibiotic resistance and virulence genes against public databases and putative phage identification. 

2D/3D molecular and microbial mapping

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

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.

Cloud application development for microbiome analysis

Porting microbiome-based analysis workflows or applications to the cloud, such as data quality control, genome assembly or annotation.

Pan-genome and phylogeny analysis

Pan-genome analysis (core, accessory genes) and phylogenetic tree generation based on protein coding sequences.