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

  • 03/2021 - Nature Communications

    Integrated cross-study datasets of genetic dependencies in cancer

    CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated […]

  • 02/2021 - Nature Communications

    Combinatorial CRISPR screen identifies fitness effects of gene paralogues

    Genetic redundancy has evolved as a way for human cells to survive the loss of genes that are single copy and essential in other organisms, but also allows tumours to survive despite having highly rearranged genomes. In this study we CRISPR screen 1191 gene pairs, including paralogues and known and predicted synthetic lethal interactions to […]

  • 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.