The CrossPlan source code for systematically planning genetic cross experiments
CrossPlan is a novel methodology for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. We base our approach on a generic experimental workflow used in performing genetic crosses in budding yeast. CrossPlan uses an integer-linear-program (ILP) formulation to maximize the number of target mutants that we can make under certain experimental constraints. Specifically, CrossPlan computes a sequence of genetic cross experiments organized into batches such that we can perform the crosses in each batch in parallel. CrossPlan takes as input a source set S of mutants that are available in the lab, a set T of target mutants whose phenotypes we are interested in characterizing experimentally, and the number k of batches (which reflects the experimental budget) with at most s crosses per batch. The plan computed by CrossPlan maximizes the number of target mutants that can be made from the source set in k batches.
Python packages:
Mutant Information file: A tab-delimited file with one mutant per line. Each line must contain a mutant id, mutant name, genes mutated (ids) in that mutant, mutant viability, whether the mutant is source or target. An example file can be found under inputs/example-infoFile.txt
python master-script.py --mutantInfoFile=inputs/example-infoFile.txt
For help on other options use:
python master-script.py -h
GNU GPLv3.0
We will be very glad to hear from you if you use CrossPlan in your work. If you publish a paper that uses CrossPlan, please cite:
- CrossPlan: Systematic Planning of Genetic Crosses to Validate Mathematical Models. Aditya Pratapa, Neil Adames, Pavel Kraikivski , Nicholas Franzese, John J Tyson, Jean Peccoud, TM Murali. Bioinformatics, 2018.