Biology subjects

Vinceti, A.

Publications and source records attributed to Vinceti, A..

3 recordsLinked to original sources

bioRxiv2022

CRISPRcleanR WebApp: an interactive web application for processing genome-wide pooled CRISPR-Cas9 viability screen

A limitation of pooled CRISPR-Cas9 viability screens is the high false-positive rate in detecting essential genes arising from copy number-amplified (CNA) regions of the genome. To solve this issue, we 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 false-positive signals, while maintaining sensitivity in identifying relevant genetic dependencies. Here, we present CRISPRcleanRWebApp, a web-based application enabling access to CRISPRcleanR through an intuitive graphical web-interface. CRISPRcleanRWebApp removes the complexity of low-level R/python-language user interactions; it provides a user-friendly access to a complete analytical pipeline, not requiring any data pre-processing, and generating gene-level summaries of essentiality with associated statistical scores; it offers a range of interactively explorable plots, while supporting a wider range of CRISPR guide RNAs libraries with respect to the original package. CRISPRcleanRWebApp is freely available at: https://crisprcleanr-webapp.fht.org/. HighlightsO_LICRISPR-Cas9 screens are widely used for the identification of cancer dependencies C_LIO_LIIn such screens, false-positives arise from targeting copy number amplified genes C_LIO_LICRISPRcleanR corrects this bias in an unsupervised fashion C_LIO_LICRISPRcleanRWebApp is a web user-friendly front-end for CRISPRcleanR C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/483924v2_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@739480org.highwire.dtl.DTLVardef@1a6c0aorg.highwire.dtl.DTLVardef@ab2b8eorg.highwire.dtl.DTLVardef@1b8a623_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Reduced gene templates for supervisedanalysis of scale-limited CRISPR-Cas9 fitnessscreens

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, individual biological pathways, or portions of the druggable genome - constitute an optimal setting for investigating narrow hypotheses and they are easier to execute on complex models, such as organoids and in vivo models. Different supervised methods are used for the computational analysis of genome-wide CRISPR-Cas9 screens; most are not well suited for scale-limited screens as they require large sets of positive/negative control genes (gene templates) to be included among the screened ones. We have developed a computational framework identifying optimal subsets of known essential and nonessential genes (at different subsampling percentages) that can be used as templates for supervised analyses of scale-limited CRISPR-Cas9 screens, while having a reduced impact on the size of the employed library. HighlightsO_LIScale-limited CRISPR-Cas9 screens are experimentally easier than genome-wide screens C_LIO_LIReference gene templates are used for supervised analyses of genome-wide screens C_LIO_LIReduced templates allow supervised analyses of scale-limited CRISPR-Cas9 screens C_LIO_LIWe present optimal reduced templates and a computational method to assemble them C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/482271v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1dcf4corg.highwire.dtl.DTLVardef@1149e3borg.highwire.dtl.DTLVardef@a04788org.highwire.dtl.DTLVardef@b84fc9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

CoRe: A robustly benchmarked R package for the identification of core fitness genes in genome-wide pooled CRISPR knock-out screens

CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. Identifying from these screens the genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), is of paramount importance to assess the safety profile of candidate therapeutic targets and for elucidating mechanisms involved in tissue-specific genetic diseases. We present CoRe: An R package implementing novel methods for identifying core-fitness genes from joint analyses of multiple CRISPR-Cas9 screens. We demonstrate that CoRe outperforms state-of-the-art tools, yielding more reliable sets of core-fitness genes than existing and widely used reference sets.

bioinformatics↗