DOI: 10.25377/sussex.29459084
title: Mismatch repair disturbs meiotic crossover control: data and code

description: Data and code for the manuscript "Mismatch repair disturbs meiotic crossover control"Data from experiments in <i>S. cerevisiae</i>Includes positions of crossover and noncrossover events detected in sequencing dataIncludes simulator used to model crossover interference, along with code used to plot the figures of the paper.Abstract:Crossover formation during meiosis generates genetic diversity. In many species most crossovers display interference, meaning they are spaced more evenly than expected by chance, and are called class I crossovers. Class II crossovers, a minority pathway, are believed to lack substantial interference. Here, using whole-genome recombination maps, we examine the impact of mismatch repair (MMR) on the formation and distribution of crossovers in <i>Saccharomyces cerevisiae</i>. Loss of the MMR protein Msh2 increases the uniformity of crossover distributions—an effect that is independent of changes in crossover frequency. Simulations indicate that this effect is driven by increases in the class I crossover proportion without any change in interference strength. Consistent with this view, distributions of Zip3 foci, specific markers of class I crossovers, are unchanged by <i>MSH2 </i>deletion. Notably, in wild-type cells, fewer crossovers arise in regions of higher polymorphism density—a skew that depends on both Msh2 and Zip3. Taken together, our results indicate an unexpected influence of Msh2 on recombination: suppression of class I crossovers in regions of higher polymorphism density, whilst promoting class II crossover formation. Our findings highlight how MMR shapes the landscape of genetic exchange, and links recombination to sequence divergence and its role in speciation.##how to use##Figures_script, Density_COs_DSBs and Migration_testing R scripts:1. Ensure data folder is present in same directory as script, along with a folder titled "Output" (as provided here)2. Run the script. The scripts will search for the required R packages and install them if any are not installed.<br>COsim:1. Load the COsim script2. Run the script. This will store COsim() as a function in the R environment3. Call COsim() with the desired parameters. See the description at the top of the script for more details.<br>###Contents###<br>#ScriptsCOsim- The crossover interference simulatorFigures_script- script used to produce figures used in the studyMigration_testing- script used to displace crossovers randomly using user-defined parametersDensity_COs_DSBs- script used to test effects of possible confounders on observed crossover distributions<br>#DataChrSizesS288cH4L2_L2HG_edited.txt- sizes of S. cerevisiae chromosomes (in S288c strain)fail_fits- results of Kolmogorov-Smirnov tests between different genotypes and simulated data with variable class II crossover fractions and class I crossover failure ratesfail_sim- example output of COsim, used to plot some panels of the figuresfail_sim_model_parameters.csv- results of fitting gamma models to synthetic crossover distributions.gamma_sampling_simplemix.csv- tests of mixed gamma models on sampled gamma distributionsICDs_calc_CO+NCO.csv- inter-CO and NCO distances calculated from file SK1_Crawford&Llorente_MasterEventTable1500NEW.csvICDs_calc_NCO.csv- inter-NCO distances calculated from file SK1_Crawford&Llorente_MasterEventTable1500NEW.csvICDs_calc_YJM_CO+NCO.csv- inter-CO and NCO distances calculated from file YJM_Fung&Mancera_MasterEventTable1500NEW.txtICDs_calc_YJM_NCO.csv- inter-NCO distances calculated from file YJM_Fung&Mancera_MasterEventTable1500NEW.txtICDs_calc_YJM.csv- inter-CO distances calculated from file YJM_Fung&Mancera_MasterEventTable1500NEW.txtICDs_calc.csv- inter-CO distances calculated from file SK1_Crawford&Llorente_MasterEventTable1500NEW.csvIF_3_scale_380_105CO- example simulated ICDs from simulator, 105COs to match SK1 msh2 dataminority_models- results of fitting gamma models to simulations of two independent, interfering classes of crossoverPan.Hotspots.IGR.SacCer3_2016.08.10.txt- DSB hotspot data from Pan et al. 2011, used as control to asses polymorphism densities around COs and NCOspolymorphism_density- results of tests of polymorphism density around COs and NCOsshape_fits- results of testing simulations which vary strength of interference and class II CO proportion on biological datashape_sim- example simulation where strength of interference and class II CO proportion are allowed to varyshape_sim_model_parameters.csv- results of fitting gamma models to simulations in which strength of interference and class II CO proportion are allowed to varySK1_Crawford&Llorente_MasterEventTable1500NEW.csv- crossover and non crossover positions in SK1 strains (Crawford et al 2024)VariantTable_SK1.txt- list of polymorphisms between SK1 and S288c cerevisiae backgroundsYJM_Fung&Mancera_MasterEventTable1500NEW.txt- crossover and non crossover positions in YJM strains (Mancera et al 2008)zip3_event_fits- results of testing simulations on zip3D biological datazip3_microscopy- data processed from images of zip1 and zip3-stained cells<br>#OutputEach file in this folder is a separate panel from the study. Running the scripts Figures_script, Migration_testing and Density_COs_DSBs will reproduce these files.