ChemGAPP: a tool for chemical genomics analysis and phenotypic profiling

Author:

Doherty Hannah M1ORCID,Kritikos George2,Galardini Marco34,Banzhaf Manuel1,Moradigaravand Danesh56

Affiliation:

1. Institute of Microbiology and Infection and School of Biosciences, University of Birmingham , Birmingham B15 2TT, United Kingdom

2. European Molecular Biology Laboratory, Genome Biology Unit , Heidelberg 69117, Germany

3. Institute for Molecular Bacteriology, TWINCORE Centre for Experimental and Clinical Infection Research, A Joint Venture Between the Hannover Medical School (MHH) and the Helmholtz Centre for Infection Research (HZI) , Hannover 30625, Germany

4. Cluster of Excellence RESIST (EXC 2155), Hannover Medical School (MHH) , Hannover 30625, Germany

5. KAUST Smart-Health Initiative and Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST) , Thuwal, Makkah 23955-6900, Saudi Arabia

6. KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST) , Thuwal, Makkah 23955-6900, Saudi Arabia

Abstract

AbstractMotivationHigh-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data.ResultsChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small for small-scale screens; and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the Escherichiacoli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type.Availability and implementationChemGAPP is available at https://github.com/HannahMDoherty/ChemGAPP, as a standalone Python package as well as Streamlit applications.

Funder

Wellcome Trust

Publisher

Oxford University Press (OUP)

Subject

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

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