SGAtools

SGAtools analyzes images of agar plates to quantify colony sizes and compute fitness scores from genome-wide mutant screens in microorganisms such as yeast, supporting inference of gene function, genetic interactions, and mechanisms of drug action.

Key Features:

• Quantitative Data Extraction: Processes images of agar plates to measure colony sizes as quantitative proxies for growth.
• Systematic Bias Correction: Corrects systematic biases and plate position effects to reduce experimental artifacts.
• Fitness Scoring: Calculates fitness scores by comparing mutant colony sizes to controls to identify growth defects.
• Data Visualization and Exploration: Provides visualization of colony sizes and distributions of fitness scores for exploratory analysis.
• Gene Ontology Enrichment Analysis: Performs Gene Ontology enrichment to associate gene sets with biological processes, cellular components, or molecular functions.

Scientific Applications:

• Genome-wide mutant screens: Analysis of genome-wide sets of mutants in yeast and other microorganisms with comprehensive mutant collections.
• Chemical-genetic interaction screens: Quantification of colony size changes in response to chemical perturbations to identify genes affecting drug response.
• Genetic network and gene function inference: Identification of genetic interactions and inference of gene function from fitness profiles.
• Mechanisms of drug action: Detection of mutants with specific sensitivity or resistance patterns to probe drug mechanisms.

Methodology:

Image processing to quantify colony sizes; normalization and filtering to adjust for position effects and confounding factors; systematic bias correction followed by calculation of fitness scores relative to controls.