QFA screens to search for genetic interaction with a bir1-17 mutation
Quantitative Fitness Analysis (QFA) is a set of experimental and computational techniques for analysing the growth of microbial colonies arrayed on the surface of solid agar plates. QFA is usually used to search for genetic interactions with a query mutation. In this case, we look for genetic interactions with bir1-17, a temperature sensitive allele of BIR1.
To find genetic interactions with bir1-17 we carried out a screen of the yeast knockout collection crossed with a bir1-17 query strain to give ~5,000 double mutant strains. We also generated a second set of ~5,000 control strains by crossing the same knockout collection with a wild-type strain (labelled cSGA).
This repository contains four directories whose contents are described below. An R script file (updateFiles.R) and this file (README.md) can be found in the root directory.
Fitness estimates generated from raw growth curve data in IMAGELOGS directory by fitting the logistic model to data and by smoothing raw data, using the QFA R package.
Raw Colonyzer image analysis output for four replicate bir1-17 screens.
Fitness estimates generated from raw growth curve data in IMAGELOGS directory by fitting the logistic model to data and by smoothing raw data, using the QFA R package.
Raw Colonyzer image analysis output for four replicate cSGA screens.
Files for building the QFA visualisation tool for this project.
Output report tables reporting Genetic Interaction Strengths (GIS.txt), fitness plots for visualising the evidence for genetic interaction (.pdf)