Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens.

Area of Science:

• Genomics
• Microbiology
• Computational Biology

Background:

• Microbial fitness screens are essential for functional genomics research.
• Analyzing large-scale fitness data requires robust and automated pipelines.

Purpose of the Study:

• To introduce pyphe, an integrated software solution for automating and enhancing the analysis of microbial fitness screening data.
• To provide a versatile tool for processing diverse fitness metrics including colony size, viability, and growth curves.

Main Methods:

• pyphe automates image acquisition, quantification, data normalization, and statistical analysis.
• The software processes data from colony sizes, phloxine B staining (viability), and growth curves using flatbed scanners.
• It was applied to phenotype gene-deletion strains of fission yeast across 70 conditions.

Main Results:

• Fitness information from late colony size measurements is comparable to growth slopes from time-series data.
• Colony size and viability assays provide complementary and independent functional genomic information.
• Phloxine B staining accurately quantifies live cell fractions within colonies.

Conclusions:

• pyphe offers a user-friendly, open-source, and comprehensive solution for microbial fitness data analysis.
• The tool enhances the efficiency and accuracy of large-scale functional genomics studies.
• pyphe demonstrates the utility of inexpensive flatbed scanners for high-throughput phenotyping.