DrugSeekR is an R package that analyzes genomic data in the format of
MAF files to identify potential drug targets for personalized medicine
through mutational frequency analysis, gene annotation, and drug target
matching. It aims to provide a user-friendly and comprehensive workflow
for finding personalized treatments based on genomic samples from
patients. It leverages existing tools and databases, such as maftools,
biomaRt, and Drug Gene Interaction Database, to perform various steps of
the analysis. The package was developed using
R version 4.3.1 (2023-06-16), Platform: x86_64-apple-darwin20
running under macOS: Monterey 12.6.8.
You can install the development version of DrugSeekR from GitHub with:
require("devtools")
devtools::install github("this-wei/DrugSeekR", build vignettes = TRUE)
library("DrugSeekR")To run the shinyApp:
runDrugSeekR()DrugSeekR is designed to analyze MAF file inputs. MAF files for
analysis can be found at the repository of the The Cancer Genome Atlas
Program (TCGA), which provides publicly available genomic, epigenomic,
transcriptomic, and proteomic data.
Note you must unzip and provide the path to the maf.gz file to the maftools::read.maf function.
ls("package:DrugSeekR")
browseVignettes("DrugSeekR")DrugSeekR contains 4 functions: 1. calculateMutationFrequency
for calculating the frequency and percentage of observed mutations
within a sample. 2. annotateGenes for annotating the genes with
information from the ensembl database and returns the top genes with the
highest number of mutations. Note this function must use dbplyr version
2.3.4 due to a known bug 3. getGeneDrugTargets for finding the
most druggable genes and the drugs that target them from the Drug Gene
Interaction Database. 4. plotDruggableGenes for ploting the number
of drugs for each gene using ggplot2.
The author of the package is Wei Jin Luo. The author wrote the calculate_mutation_frequency function, which calculates the frequency and percentage of observed mutations within a sample. The calculate_mutation_frequency function makes use of the maftools R package to get a summary of mutation data.
The author wrote the annotate_genes function, which annotates the genes with information from the ensembl database and returns the top genes with the highest number of mutations. The functions make use of the biomaRt R package to query the ensembl database.
The author wrote the get_gene_drug_targets function, which finds the most druggable genes and the drugs that target them from the Drug Gene Interaction Database. The functions makes use of the maftools R package to query the Drug Gene Interaction Database.
The author wrote the plot_druggable_genes function, which plots the number of drugs for each gene. The plot_druggable_genes function makes use of the ggplot2 R package to create the plot.
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Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP (2018). Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Research. http://dx.doi.org/10.1101/gr.239244.118
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Cancer Genome Atlas Research, N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 368, 2059-74. 2013
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Steffen Durinck, Paul T. Spellman, Ewan Birney and Wolfgang Huber (2009). Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols 4, 1184-1191 .
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Steffen Durinck, Yves Moreau, Arek Kasprzyk, Sean Davis, Bart De Moor, Alvis Brazma and Wolfgang Huber (2005). BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439-3440
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Griffith, M., Griffith, O., Coffman, A. et al (2013). DGIdb: mining the druggable genome. Nat Methods 10, 1209–1210. https://doi.org/10.1038/nmeth.2689
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H. Wickham (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York
This package was developed as part of an assessment for 2022 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. DrugSeekR welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues.