Computational Multiomics

Our metagenomics activities focus on the characterization of microbial communities and their functional potential. We provide end-to-end analysis of amplicon sequencing data (16S rRNA and ITS), including quality control, read trimming and filtering, denoising, ASV/OTU generation, taxonomic assignment, and alpha- and beta-diversity analysis.

For shotgun metagenomics, we perform high-resolution taxonomic profiling, metabolic pathway reconstruction, differential analysis, and longitudinal studies. Additional activities include genome assembly from metagenomic or isolate-derived datasets, functional annotation, variant calling, and analysis of SNPs and INDELs. Multi-omics integration, biomarker discovery, correlation and network analysis, as well as biological interpretation and publication-oriented reporting, complement these workflows.

• 16S / ITS amplicons
• Shotgun profiling
• Genome assembly
• Longitudinal studies

Genomics encompasses the analysis of genetic variation across microarrays, SNP arrays, short-read NGS (targeted sequencing, WES and WGS), and long-read sequencing. Our research combines variant calling, annotation, filtering, and prioritization to investigate the genetic basis of rare and common diseases, as well as the molecular mechanisms underlying cancer through oncogenomics, integrating germline and somatic variation.

We also focus on mitochondrial genomics, developing variant-calling and annotation pipelines, machine-learning pathogenicity predictors (APOGEE2, nAPOGEE), and ACMG-based variant assessment for human mitochondrial DNA. Additional activities include genetic association studies, polygenic risk scores, and pharmacogenomics.

• Oncogenomics
• Rare & common disease
• Mitochondrial genomics
• APOGEE2 · nAPOGEE
• Polygenic risk scores
• Pharmacogenomics

Our transcriptomics research covers bulk and single-cell/spatial transcriptomic data, from quality control and preprocessing to advanced biological interpretation. Analyses include differential expression, biomarker discovery, clinical associations, and gene co-expression analysis such as WGCNA.

For single-cell and spatial transcriptomics, we investigate cell populations and tissue organization through cluster annotation, cell-type deconvolution, nearest-neighbor analysis, cell–cell interaction studies, and trajectory analysis. These approaches enable the characterization of cellular heterogeneity, spatial organization, and dynamic biological processes.

• Differential expression
• WGCNA
• Single-cell
• Spatial
• Cell–cell interaction
• Trajectory analysis

This line focuses on the characterization of molecular phenotypes at the protein and metabolite levels. Activities include bulk proteomics, Olink-based proteomic profiling, spatial proteomics, and metabolomics analyses, supported by customized consulting and collaborative projects.

These datasets can be integrated with genomic, transcriptomic, and metagenomic information to identify molecular signatures, characterize biological mechanisms, and develop multi-omics models and biomarkers with potential applications in translational and clinical research.

• Bulk proteomics
• Olink
• Spatial proteomics
• Metabolomics
• Multi-omics models