CEN-tools: An integrative platform to identify the ‘contexts’ of essential genes

Abstract

An emerging theme from large-scale genetic screens that identify genes essential for fitness of a cell, is that essentiality of a given gene is highly context-specific and depends on a number of genetic and environmental factors. Identification of such contexts could be the key to defining the function of the gene and also to develop novel therapeutic interventions. Here we present CEN-tools (Context-specific Essentiality Network-tools), a website and an accompanying python package, in which users can interrogate the essentiality of a gene from large-scale genome-scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels, and drug response levels. We show that CEN-tools is suitable for both the systematic identification of genetic dependencies as well as for targeted queries into the dependencies of specific user-selected genes. The associations between genes and a given context within CEN-tools are represented as dependency networks (CENs) and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein-protein interaction networks to reveal context-dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN-tools in aiding the current efforts to define the human cellular dependency map.

CoRe: A robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens

“…We compared the sets of CFGs and CEGs predicted by CoRe (through ADaM and all the FiPer variants) when applied to the largest integrative dataset of cancer dependency assembled to date, accounting for 17,486 genes and 855 cell lines from 30 different tissue-lineages and 43 cancer types (the DepMap dataset, Fig. 2AB) [19], with state-of-the-art sets of core-fitness genes derived from recent functional genetic screening datasets [10,12,33,34]. We also included in the comparison the output of a logistic-regression based method, part of the recent CEN-tools software proposed in [33] applied to the DepMap dataset (Tables 1 and 2).…”

“…2AB) [19], with state-of-the-art sets of core-fitness genes derived from recent functional genetic screening datasets [10,12,33,34]. We also included in the comparison the output of a logistic-regression based method, part of the recent CEN-tools software proposed in [33] applied to the DepMap dataset (Tables 1 and 2).…”

“…A set of 519 genes presented in [33]. Logistic regression approach (part of the CEN-tools software), which uses the BAGEL essential/never-essential genes as training, respectively the Hart2017 set and a set of 927 never-essential genes [10,26].…”

“…Finally, a logistic regression based method for classifying genes into CFGs or context-specific essentials has been recently introduced by Sharma and colleagues [33] as part of the CEN-tools suite, using reference sets of essential and non-essential genes for the training phase [34].…”

“…We present CoRe: an R package implementing recently proposed as well as novel versions of algorithms for the identification of CFGs from a joint analysis of multiple genome-wide pooled CRISPR-Cas9 knock-out screens. Furthermore, we present results from a comparison of CoRe's output (when applied to the largest integrative cancer dependency dataset generated to date [19]) against widely used [10,34], or more recent [33] sets of CFGs obtained via an alternative approach (which we have also tested on the same recent cancer dependency dataset). We report an increased coverage of prior known human essential genes, new potential core-fitness genes, and lower false positive rates for CoRe's methods with respect to other state-of-the-art core-fitness sets and available methods.…”