GuidePro: a multi-source ensemble predictor for prioritizing sgRNAs in CRISPR/Cas9 protein knockouts

Wei He, Helen Wang, Yanjun Wei, Zhiyun Jiang, Yitao Tang, Yiwen Chen, Han Xu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Motivation: The efficiency of CRISPR/Cas9-mediated protein knockout is determined by three factors: sequence-specific sgRNA activity, frameshift probability and the characteristics of targeted amino acids. A number of computational methods have been developed for predicting sgRNA efficiency from different perspectives. However, an integrative method that combines all three factors for rational sgRNA selection is still lacking. Results: We developed GuidePro, a two-layer ensemble predictor that enables the integration of multiple factors for the prioritization of sgRNAs in protein knockouts. Tested on independent datasets, GuidePro outperforms existing methods and demonstrates consistent superior performance in predicting phenotypes caused by protein loss-of-function, suggesting its robustness for prioritizing sgRNAs in various applications of CRISPR/Cas9 knockouts.

Original languageEnglish (US)
Pages (from-to)134-136
Number of pages3
JournalBioinformatics
Volume37
Issue number1
DOIs
StatePublished - Jan 1 2021

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

MD Anderson CCSG core facilities

  • Bioinformatics Shared Resource

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