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Department of Pharmacy and Biotechnology - FABIT

Computational Genomics and Transcriptomics. Coordinator: Giorgi

Studying pathological gene transcription in cancer, metabolic disorders, drug abuse, and viral infections. Inferring the architecture and evolution of gene networks and their involvement in pathological progression. Developing algorithms for the prediction of personalized biomarkers using artificial intelligence techniques. AREAS: Cancer Biology Genomics.

Areas: Cancer biology Genomics

Research themes

Gene networks

The group develops algorithms for constructing models of gene networks and studies their involvement in pathological contexts such as cancer, neurodegenerative diseases, genetic dysfunctions, and viral infections. The group also works on the study of gene networks in general, including in non-human contexts such as the model plant Arabidopsis thaliana.

Transcriptomics of human pathologies

The group studies the phenomenon of gene transcription quantitatively through the analysis of expression data from high-throughput technologies such as microarrays and next-generation sequencing. The analysis contexts predominantly relate to human pathologies such as cancer, metabolic genetic diseases, drug abuse, and viral infections.

Phylogenomics

The group produces models of molecular evolution based on the comparison of sequences and gene expression, using classical phylogenetic techniques (evolutionary trees) and innovative ones (evolutionary networks and principal component analysis).

Omics machine learning

The group develops algorithms for predicting genetic phenomena (e.g., somatic mutations), metabolic levels, and clinical characteristics (estimation of course and pharmacological response) in patient samples through the interrogation of gene networks.

Lab Members

Federico M. Giorgi,  Associate Professor

Fabrizio Ferrè, Associate Professor

Chiara Canrelle, Research Fellow

Job Openings or Internship Projects 

No positions are available at the moment.

Main Publications

  • Giorgi FM, Pozzobon D, Di Meglio A, Mercatelli D. Genomic and transcriptomic analysis of the recent Mpox outbreak. Vaccine. 2024 Feb 3:S0264-410X(24)00125-7. doi: 10.1016/j.vaccine.2023.12.086. Epub ahead of print. PMID: 38311533.
  • Beccacece L, Costa F, Pascali JP, Giorgi FM. Cross-Species Transcriptomics Analysis Highlights Conserved Molecular Responses to Per- and Polyfluoroalkyl Substances. Toxics. 2023 Jun 29;11(7):567. doi: 10.3390/toxics11070567. PMID: 37505532; PMCID: PMC10385990.
  • Giorgi FM, Ceraolo C, Mercatelli D. The R Language: An Engine for Bioinformatics and Data Science. Life (Basel). 2022 Apr 27;12(5):648. doi: 10.3390/life12050648. PMID: 35629316; PMCID: PMC9148156.
  • Cavicchioli MV, Santorsola M, Balboni N, Mercatelli D, Giorgi FM. Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines. Int J Mol Sci. 2022 Mar 31;23(7):3867. doi: 10.3390/ijms23073867. PMID: 35409231; PMCID: PMC8998886.
  • Paull EO, Aytes A, Jones SJ, Subramaniam 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 Jan 21;184(2):334-351.e20. doi: 10.1016/j.cell.2020.11.045. Epub 2021 Jan 11. PMID: 33434495; PMCID: PMC8103356.

Contacts