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README.md
# MutSeqR <a href="https://ehsrb-bsrse-bioinformatics.github.io/MutSeqR/"><img src="man/figures/MutSeqR_hex_logo.png" align="right" height="138" style="float:right; height:138px;" alt="The MutSeqR logo."></a> <!-- badges: start --> [![R-CMD-check](https://github.com/EHSRB-BSRSE-Bioinformatics/MutSeqR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/EHSRB-BSRSE-Bioinformatics/MutSeqR/actions/workflows/R-CMD-check.yaml) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) <!-- badges: end --> ## Overview MutSeqR is an open-source R package to analyze error-corrected Next-Generation Sequencing (ECS) data, empowering users with flexibility during exploratory analyses while ensuring compatibility across technologies. <img src="man/figures/MutSeqR_overview.png" style="display:block; margin:auto; max-width:100%;" alt="A Flowchart showing MutSeqR's function utility and workflow: Data Import, Data Processing, Statistical Analyses, Visualization, Output. Includes a visual of a woman working at a computer."> <details> <summary> <strong> Figure transcript </strong> </summary> <em>1. Data Import: Imports mutation data into the R environment. Binds data from multiple libraries into a single object. Joins sample and target region metadata to the mutation data. Retrieves trinucleotide context. 2. Data Processing: Calculates mutation frequencies for groups of interest. Calculates frequencies and proportions of mutation subtypes. Optional Variant filtering: eliminates putative germline variants, removes variants outside of specified regions, quality assurance filtering. 3. Statistical Analyses: Generalized linear modeling. Benchmark Dose Modeling. COSMIC signature analysis. Spectra comparison between groups. Unsupervised clustering based on mutation spectra. 4. Visualization: Create figures to display mutation frequencies and the proportions of mutation subtypes. Visualise statistical results. Visualise mutation distribution across genomic loci. View clonal expansion of mutations. 5. Output: Summary report RMarkdown file will faciliatte the generation of results. Output mutation data as VCF. Output sequences in FASTA format. Output spectra data in SigProfiler format. Export results to Excel workbook. </em> </details> ## Vignette See the [vignette](https://ehsrb-bsrse-bioinformatics.github.io/MutSeqR/articles/MutSeqR_introduction.html#introduction) for details on function utility. ## Changelog See the [release notes on the pkgdown site](https://ehsrb-bsrse-bioinformatics.github.io/MutSeqR/news/index.html) for version history. You can also view [GitHub releases](https://github.com/EHSRB-BSRSE-Bioinformatics/MutSeqR/releases). ## Installation Install the package from github: ``` r # install.packages("devtools") devtools::install_github("EHSRB-BSRSE-Bioinformatics/MutSeqR") ``` Load the package ``` r library(MutSeqR) ``` ## Getting Help If you encounter a clear bug, please file an issue with a minimal reproducible example on [Github](https://github.com/EHSRB-BSRSE-Bioinformatics/MutSeqR/issues). ## Citation To cite this package in publications use: Dodge A, Williams A, LeBlanc D, Schuster D, Esina E, Valentine C, Salk J, Maslov A, Bradley C, Yauk C, Marchetti F, Meier M (2025). *MutSeqR: Analysis of Error-Corrected Sequencing Data for Mutation Detection*. R package version 0.99.0, <https://ehsrb-bsrse-bioinformatics.github.io/MutSeqR/>.