CEN-tools: an integrative platform to identify the contexts of essential genes

Sumana Sharma, Cansu Dincer, Paula Weidemüller, Gavin J Wright, Evangelia Petsalaki

Research output: Contribution to journalArticlepeer-review


An emerging theme from large-scale genetic screens that identify genes essential for cell fitness is that essentiality of a given gene is highly context-specific. Identification of such contexts could be the key to defining gene function and also to develop novel therapeutic interventions. Here, we present Context-specific Essentiality Network-tools (CEN-tools), a website and 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 responses. We show that CEN-tools is suitable for the systematic identification of genetic dependencies and for more targeted queries. The associations between genes and a given context 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.

Original languageEnglish
Pages (from-to)e9698
JournalMolecular systems biology
Issue number10
Publication statusPublished - 16 Oct 2020

Bibliographical note

© 2020 The Authors. Published under the terms of the CC BY 4.0 license.


  • CRISPR-Cas Systems
  • Cell Line, Tumor
  • Computational Biology/methods
  • Gene Expression Regulation, Neoplastic/genetics
  • Gene Knockout Techniques
  • Gene Regulatory Networks
  • Genes, Essential
  • Humans
  • Melanoma/genetics
  • Metabolomics/methods
  • Mutation
  • SOXE Transcription Factors/genetics
  • Serum Response Factor/genetics
  • Signal Transduction/genetics
  • Skin Neoplasms/genetics
  • Software

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