AbunRNA

Description

AbunRNA is an R package focuses on the commonly used RNAseq analysis pipelines including indexing, quantification, and to the downstream expression analysis. This packages is friendly to users with lack or no experience in command line usage, but can still access the high-efficiency sequence mapping software i.e. Salmon. The R-based pipeline can access the available cDNA, DNA and transcriptome data from Ensembl, generate a count matrix indicating the expression abundance of each genes within each sample, plot a heatmap to visualize the abundance among wild type and mutants, and finally perform principle component analysis on the count matrix for more exploratory information. The AbunRNA package is developed using R version 4.2.1 (2022-06-23), Platform: x86_64-apple-darwin17.0 (64-bit) and Running under: macOS Ventura 13.0.

Installation

To install the latest version of the package:

require("devtools")
devtools::install_github("zhangchengyue/AbunRNA", build_vignettes = TRUE)
library("AbunRNA")

To run shinyApp:

AbunRNA::runAbunRNA()

Sometimes errors about dependencies are showing up, usually that would happen for the following packages not previously downloaded in your RStudio: tximport, biomaRt, GenomicFeatures, AnnotationDbi.

To solve this, run the following code to download these packages first, then re-install AbunRNA.

if (! requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}

if (! requireNamespace("tximport", quietly = TRUE)) {
  BiocManager::install("tximport")
}

if (! requireNamespace("biomaRt", quietly = TRUE)) {
  BiocManager::install("biomaRt")
}

if (! requireNamespace("GenomicFeatures", quietly = TRUE)) {
  BiocManager::install("GenomicFeatures")
}

if (! requireNamespace("AnnotationDbi", quietly = TRUE)) {
  BiocManager::install("AnnotationDbi")
}

if (! requireNamespace("ggfortify", quietly = TRUE)) {
  install.packages("ggfortify")
}

Overview

ls("package:AbunRNA")
data(package = "AbunRNA") 

AbunRNA contains functions to:

1. install the popularly used Salmon software through bioconda (See function installSalmon)
2. directly obtain reference transcriptome from Ensembl database (See function obtainCDNA, obtainDNA, and obtainGTF)
3. run quantification through Salmon (See function quantification)
4. generate TxDb object for storing transcript annotations (See function txdbObj)
5. generate the count matrix from TxDb and optionally plot the heatmap to visualize abundance (See function generateMatrix)
6. performs principle component analysis for the matrix, and plot the graph to further visualize the variation in expression abundance among wild type and mutant samples.(See function plotPCA)

browseVignettes("AbunRNA")

An overview of the package is illustrated below.

Contributions

The author of the package is ChengYue Zhang. The obtainGTF(), obtainDNA() and obtainCDNA() functions makes use of rvest package to web scrape from online database. The txdbObj function makes use of makeTxDbFromGFF() function from GenomicFeatures package to create a txdb object from GTF file. The generateMatrix() function uses pheatmap function from pheatmap R package to plot the heatmap. The stats and ggplot2 R packages are used for principle component analysis in plotPCA() function. The rstudioapi CRAN package is used to send commands to terminal for downloading Salmon software in installSalmon() function, as well as running Salmon commands for indexing and quantification in quantification() function.

References

Patro, R., Duggal, G., Love, M. et al. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14, 417–419 (2017).https://doi.org/10.1038/nmeth.4197

Ushey K, Allaire J, Wickham H, Ritchie G (2022). rstudioapi: Safely Access the RStudio API. https://CRAN.R-project.org/package=rstudioapi

Ensembl 2022, Nucleic Acids Research, Volume 50, Issue D1,7 January 2022, Pages D988–D995, https://doi.org/10.1093/nar/gkab1049

Durinck S, Spellman P, Birney E, Huber W (2009). “Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt.” Nature Protocols, 4, 1184–1191.

Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W (2005). “BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis.” Bioinformatics, 21, 3439–3440.

Wickham H, François R, Henry L, Müller K (2022). dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr

Müller K, Wickham H (2022). tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble

Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan M, Carey V (2013). “Software for Computing and Annotating Genomic Ranges.” PLoS Computational Biology, 9. doi: 10.1371/journal.pcbi.1003118, http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118

Wickham H (2022). rvest: Easily Harvest (Scrape) Web Pages. https://CRAN.R-project.org/package=rvest

Wickham H (2022). stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4. https://CRAN.R-project.org/package=ggplot2

R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.http://www.R-project.org/

Acknowledgements

This package was developed as part of an assessment for 2022 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. AbunRNA welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues.