MayoMate

MayoMate is a Python package designed to facilitate analysis and interpretation of meiotic mutagenesis yeast data. MayoMate toolkit allows for discovery and visualization of recombination events, mutations, and mutation clusters in Whole Genome Sequencing (WGS) data of Saccharomyces cerevisiae meiotic outcomes.

MayoMate processes Illumina read alignments and variant calls from CLC Genomics Workbench 20 to identify high-confidence de novo SNVs and mutation clusters. It provides tools to create a Parental Reference Genome for calling parental SNPs using a single modified reference genome. MayoMate's predefined classes store sample information, including SNVs, mutation clusters, parental SNPs, recombination events, sample names, and genotypes. These properties enable event classification, genomic feature correlation, hypermutable ssDNA estimation, mutation rate normalization, statistical analysis, and the creation of customized plots, summaries, and tables.

Features

• Identifying and visualizing recombination events based on a panel of parental SNPs.
• Identifying, visualizing, and classifying mutation clusters.
• Associating mutation clusters with identified recombination events.
• Inferring the length of hypermutable single-stranded DNA (ssDNA).
• Identifying promoter mutation rates.
• Conducting statistical comparisons between various mutants.

Requirements

This package was developed and tested on a standard desktop computer with 16GB of RAM using Python 3.10.4. The following packages are required to run MayoMate:

• betterbeeswarm==0.2.0
• bio-aid==0.3.1
• matplotlib==3.5.2
• natsort==8.3.1
• numpy==1.22.3
• pandas==1.4.2
• plotly==5.11.0
• scipy==1.8.1
• seaborn==0.13.2

You can install quckly these packages using the following command:

pip install -r requirements.txt

Installation

To install MayoMate, you can clone this repository by running the command below. This step shouldn't take more than a few minutes depending on your internet connection.

git clone https://github.com/malkovalab/MayoMate.git

Usage

To use MayoMate, please use the main_playground_clean.ipynb Jupyter notebook. This notebook provides a step-by-step guide on how to use MayoMate's functions to help analyze and interpret meiotic mutagenesis yeast data. The repository contains a small portion of the dataset used in the full study. You can use that data to test that you have properly configured your environment and that MayoMate is working properly. You can see example outputs from the reduced dataset and their compute times within the notebook and within the log files. The outputs created by this notebook can be used for further downstream analysis and visualizations, for example, using scripts below.

In addition to the main_playground_clean.ipynb a few other standalone scripts are provided to help with specific tasks and post-processing of the outputs. These scripts are:

• cluster_analysis.py: This script is used to conduct analysis on called mutation clusters and compare cluster statistics between different mutants.
• cluster_coordination.py: This script is used to identify and visualize the classess of mutation clusters (e.g. "single-switch", "non-switching", "multiple-switch", etc.) and their ssDNA contributions.
• remake_parental_reference.py: This script is used to create a Parental Reference Genome for calling parental SNPs with CLC Genomics Workbench.
• find_imbalanced_snps.py: This script is used to identify and visualize poorly resolved SNPs within the Parental Reference Genome, enabling their exclusion from the analysis.
• freqgraph.py: This script is used to create mutation frequency bar graphs for the ura3-29 reporter.
• csv_to_paraBed.py: This script is was used to convert the CLC Genomics Workbench variant call output to a format that can be used with NIEHS/P-MACD package. Not used in this version of MayoMate.
• SNP_dens_map.py: This script is used to create a graph detailing distances between SNPs in the Parental Reference Genome.
• tracks.ipynb: This Jupyter notebook is used to reconstruct the ssDNA tracks based on the identified recombination events, mutations, and mutation clusters. It also conducts statistical analysis on the ssDNA tracks between different mutants.
• transcription_graph: This script is used to create a graph summarizing sA3A mutation rates for tRNA and protein-coding genes.
• run_association_simulation_parallel.py: This script is used to run association simulations between recombination events and mutation clusters on a computing cluster for parallel processing and faster results.

MayoMate Pipeline Overview

Data

The data used in the MayoMate package is not provided in this repository. The data used in the MayoMate package is generated from Whole Genome Sequencing (WGS) data of Saccharomyces cerevisiae meiotic outcomes. The data is first processed using CLC Genomics Workbench 20 and then used as input for the MayoMate package to identify recombination events, mutations, and mutation clusters. Raw reads data can be found in the BioProject and the Sequence Read Archive (SRA) under the accession number PRJNA1225307

Before you start

Before you start using MayoMate, please make sure you have updated the config.py file with the correct paths to your input files and directories. The config.py file is located in the MayoMate directory.

In addition, please make sure other settings files are also properly configured. These files are within mayo/settings directory.

Contributing

We welcome contributions to MayoMate! If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a new Pull Request.

License

MayoMate is licensed under the MIT License.