ChemGAPP: a tool for chemical genomics analysis and phenotypic profiling.

Area of Science:

• Genomics
• Computational Biology
• Chemical Biology

Background:

• High-throughput chemical genomic screens generate valuable data for genome-wide gene function discovery.
• A lack of comprehensive, publicly available analytical packages hinders the full utilization of this data.

Purpose of the Study:

• To develop ChemGAPP, a user-friendly, integrated analytical package for chemical genomic screening data.
• To provide robust quality control and streamline data curation for improved analysis.

Main Methods:

• ChemGAPP integrates multiple analysis steps into a streamlined workflow.
• The package includes three sub-packages: ChemGAPP Big (large-scale screens), ChemGAPP Small (small-scale screens), and ChemGAPP GI (genetic interaction screens).
• Rigorous quality control measures are incorporated for data curation.

Main Results:

• ChemGAPP Big successfully identified reliable fitness scores and biologically relevant phenotypes in the *Escherichia coli* KEIO collection.
• ChemGAPP Small detected significant phenotypic changes in small-scale screening experiments.
• ChemGAPP GI accurately reproduced known epistasis interactions when benchmarked against gene sets with defined interaction types.

Conclusions:

• ChemGAPP effectively bridges the gap in analytical tools for chemical genomic screens.
• The package provides a reliable and versatile solution for analyzing diverse chemical genomic datasets, enhancing the discovery of gene function.
• ChemGAPP is accessible as a standalone Python package and Streamlit applications.