Research group in Computational Transcriptomics - Coordinator: Federico M. Giorgi

Study gene transcription in diseases such as cancer, metabolic disorders, drug abuse and viral infections. Infer architecture and evolution of gene networks, and their rewiring in pathologic progression. Develop algorithms for biomarker prediction in personalized medicine, using artificial intelligence approaches.

Research themes

Gene Networks. The lab develops algorithms for the reverse engineering of gene networks, studying their rewiring in pathological contexts, such as: cancer, neurodegenerative diseases, genetic dysfunctions, viral infections. Gene networks are also studied in general, even in non-human contexts, such as Arabidopsis thaliana.

Human Pathology Transcriptomics. The lab studies gene transcription via a quantitative approach leveraging on expression data derived from high-throughput technologies, such as microarrays and next generation sequencing. The main research areas are transcriptomes of human pathologies, such as cancer, metabolic genetic disorders, substance abuse, and viral infections.

Phylogenomics. The lab builds evolutionary models based on sequence comparison and gene expression comparison, using classic (phylogenetic trees) and innovative approaches (evolutionary networks and principal component analysis).

Omics machine learning. The lab develops algorithms for the prediction of genetic phenomena (e.g. somatic mutations, copy number alterations), metabolic levels, clinical features (survival and pharmacological response) in human data through the interrogation of gene networks.

Lab members

Prof. Federico M. Giorgi, PI

Dott. Daniele Mercatelli, Postdoc

Dott. Nicola Balboni, PhD student

Main publications:

Paull EO, Aytes A, Jones SJ, SubramaniProf. Federico M. Giorgi, Coordinatoream PS, Giorgi FM, Douglass EF, Tagore S, Chu B, Vasciaveo A, Zheng S, Verhaak R, Abate-Shen C, Alvarez MJ, Califano A. A modular master regulator landscape controls cancer transcriptional identity. Cell 2021. https://pubmed.ncbi.nlm.nih.gov/33434495/

Mercatelli D, Bortolotti M, Giorgi FM. Transcriptional network inference and master regulator analysis of the response to ribosome-inactivating proteins in leukemia cells. Toxicology 2020. https://pubmed.ncbi.nlm.nih.gov/32593706/

Mercatelli D, Giorgi FM. Geographic and Genomic Distribution of SARS-CoV-2 Mutations. Frontiers in Microbiology 2020. https://pubmed.ncbi.nlm.nih.gov/32793182/

Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM. Gene regulatory network inference resources: A practical overview. Biochimica et Biophysica Acta GRM 2020. https://pubmed.ncbi.nlm.nih.gov/31678629/

Mercatelli D, Ray F, Giorgi FM. Pan-Cancer and Single-Cell modelling of genomic alterations through gene expression. Frontiers in Genetics 2019. https://pubmed.ncbi.nlm.nih.gov/31379928/