De Lucia, R. R. | Search bioRxiv

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↗

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↗