Our main focus is Integromics - integrative systems-level network-based single and multi-OMICS Data analysis, starting from pair-wise integrations, e.g. via quantitative trait loci (QTL) mapping, linking Genomics data to Transcriptomics or other `OMICS' data (i.e. Metabolomics or Microbiome), followed by multi-level network and pathway analysis, resulting in biomarker, candidate gene or drug target discovery, biological hypothesis generation and insightful data interpretation.

INTEGROMICS net-OMICS we provide pair wise data integration via expression protein metabolite quantitative trait loci mapping and correlation analysis

Pair-wise OMICS data integromics

<a "” target="_blank">Quantitative trait loci (QTL) mapping, integrating genetic variants with e.g. transcript expression levels (eQTLs), protein levels (pQTLs) or metabolite levels (mQTLs)

Genetic variant set enrichment analysis in predefined molecular pathways and biological processes

Correlations of "down-stream" omics data, e.g. transcript expression levels and protein or metabolite levels

INTEGROMICS net-OMICS we provide network-based omics and phenotype data integration module-detection for candidate gene selection biomarker discovery predictive-modeling

Network-based OMICS data integromics

Multi-level networks integrating genetic variants with transcript co-expression patterns, protein and metabolite levels, phenotypic traits, experimental read-outs or clinical patient data

Protein-protein interaction (PPI) networks, transcription factor (TF)-target gene networks, micro-RNA (miRNA) and long non-coding RNA (lncRNA)-target gene networks, kinase-target gene networks, drug-target networks

Network topology analysis, temporal pattern and module (sub-network) detection, dynamics modeling (e.g. Boolean networks)

Data interpretation, candidate gene, biomarker, or drug target discovery and prioritization, biological hypothesis generation that can be validated experimentally