Francesco Iorio

Francesco Iorio

Francesco Iorio is a computer scientist, currently Group Leader at HT’s Computational Biology Research Centre and team leader at the Wellcome Sanger Institute in Hixton (UK). He works on analytical methods for pharmacogenomics, therapeutic target discovery, drug repositioning and biomedical big-data mining, with a specific focus on cancer, rare diseases and neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease. Francesco is building up his group and research activity, dividing his time between Milan and Cambdrige.

Publications

  • 01/2021 - Nature

    Cancer research needs a better map

    It is time to move beyond tumour sequencing data to identify vulnerabilities in cancers.

  • 01/2021 - Genome Biology

    Minimal genome-wide human CRISPR-Cas9 library

    CRISPR guide RNA libraries have been iteratively improved to provide increasingly efficient reagents, although their large size is a barrier for many applications. We design an optimised minimal genome-wide human CRISPR-Cas9 library (MinLibCas9) by mining existing large-scale gene loss-of-function datasets, resulting in a greater than 42% reduction in size compared to other CRISPR-Cas9 libraries while […]

  • 01/2021 - Nature Computational Science

    Redefining false discoveries in cancer data analyses

    The nature of biological networks still brings challenges related to computational complexity, interpretable results and statistical signifcance. Recent work proposes a new method that paves the way for addressing these issues when analyzing cancer genomic data.

  • 10/2020 - Nucleic Acid Research

    Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets

    CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR–Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score […]

  • 08/2020 - Patterns

    Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens

    High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations […]