Computational biology

Iorio Group

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Iorio group’s Apps, Tools and Computable Manuscripts

The Iorio Group works at the interface of biology, machine learning, statistics and information theory with the goal of understanding and predicting how genomic alterations and molecular traits from other omics contribute to pathological processes, biological circuits’ rewiring and have an impact on therapeutic response in human cancers and other diseases.

Our research aims at advancing human health by designing algorithms, computational tools and novel analytical methods for the integration and the analysis of pharmacogenomics and functional-genomics datasets, with the objective of identifying new therapeutic targets, biomarkers and drug repositioning opportunities.

With our experimental collaborators, we are contributing to the creation of a comprehensive map of all the genetic dependencies occurring in human cancers, and to the development of a computational infrastructure for translating this map into guidelines for early-stage drug development and precision medicine.

The Iorio Group designs, implements and maintains bioinformatics methods and original tools for the assessment of cancer pre-clinical models, the pre-processing, analysis and visualisation of genome-editing screening data, for the in-silico correction of new-technology-specific biases in such data, and for the optimization of single guide RNA libraries for pooled CRISPR-Cas9 screens and other experimental settings.

We are also interested in big-data analytics, the development of biomedical predictive models based on non-biomedical data, and computationally efficient constrained randomization strategies for testing combinatorial properties in large-scale genomic datasets and networks.

 

Group members

Publications

  • 01/2024 - Cancer Cell

    A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization

    Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer […]

  • 01/2024 - The CRISPR Journal

    Benchmark Software and Data for Evaluating CRISPR-Cas9 Experimental Pipelines Through the Assessment of a Calibration Screen

    Genome-wide genetic screens using CRISPR-guide RNA libraries are widely performed in mammalian cells to functionally characterize individual genes and for the discovery of new anticancer therapeutic targets. As the effectiveness of such powerful and precise tools for cancer pharmacogenomics is emerging, tools and methods for their quality assessment are becoming increasingly necessary. Here, we provide […]

  • 11/2023 - Cell Reports Medicine

    RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity

    The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fueled the COVID-19 pandemic with its enduring medical and socioeconomic challenges because of subsequent waves and long-term consequences of great concern. Here, we chart the molecular basis of COVID-19 pathogenesis by analyzing patients’ immune responses at single-cell resolution across disease course and severity. This […]

  • 07/2023 - Febs Letters

    Highlights from the 1st European cancer dependency map symposium and workshop

    The systematic identification of tumour vulnerabilities through perturbational experiments on cancer models, including genome editing and drug screens, is playing a crucial role in combating cancer. This collective effort is known as the Cancer Dependency Map (DepMap). The 1st European Cancer Dependency Map Symposium (EuroDepMap), held in Milan last May, featured talks, a roundtable discussion, and a poster […]

  • 01/2023 - Bioinformatics

    A heuristic algorithm solving the mutual-exclusivity sorting problem

    Motivation Binary (or boolean) matrices provide a common effective data representation adopted in several domains of computational biology, especially for investigating cancer and other human diseases. For instance, they are used to summarise genetic aberrations—copy number alterations or mutations—observed in cancer patient cohorts, effectively highlighting combinatorial relations among them. One of these is the tendency […]