Umberto Perron
- Postdoctoral Researcher, Iorio Group
Publications
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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 […]
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01/2023 - Cell Reports Methods
An interactive web application for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens
A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing […]
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07/2022 - Cell Reports
Reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 fitness screens
Pooled genome-wide CRISPR-Cas9 screens are furthering our mechanistic understanding of human biology and have allowed us to identify new oncology therapeutic targets. Scale-limited CRISPR-Cas9 screens—typically employing guide RNA libraries targeting subsets of functionally related genes, biological pathways, or portions of the druggable genome—constitute an optimal setting for investigating narrow hypotheses and are easier to execute […]
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11/2021 - BMC Genomics
CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Background CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from such screens is to identify genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), and to distinguish them from […]
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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.