rseAnalysis (RNA structure and Expression analysis) package includes
series of utility function including stander file reader, structural
prediction, RNA distance calculation, and analysis package.
rseAnalysis provides an all-in-one solution for gene expression and
secondary structure mutation correlation analysis by automating the data
processing and analysis of gene expression and mutation data from the
well-knowleged database such as mirBase and TCGA.
You can install the development version from GitHub with:
# install.packages("devtools")
require("devtools")
devtools::install_github("JackXu2333/rseAnalysis", build_vignettes = TRUE)
library("rseAnalysis")To run the rseAnalysis Shiny app:
rseAnalysis::runRSEAnalysis()To list all functions available in the package:
ls("package:rseAnalysis")rseAnalysis consisted of 8 public functions. Functions vcf2df.,
fasta2df., bed2df. loads gene mutation, gene sequence and
location data from vcf, fasta and bed file respectively; Functions
predictStructure. utilize command-line software RNAstructure to
predict RNA secondary structure in dot-bracket form; Functions
predictDistance. utilize command-line software RNAdistance to
calculate gene distance based on structure; Working with gene mutation
data, function RNA.validate. applies mutation to RNA sequence
after validation on the genome assembly between files, DNA2RNA.
maps DNA sequence to RNA after validation. Function
Analysis.DISEXP. and runRSEAnalysis. use regression to
analyze genetic distance and gene expression data, offering the use of
linear or logistic models.
An overview of the package is illustrated below:
Arrow illustration used in image: Arrow vector created by starline - www.freepik.com
For tutorials and plot interpretation, refer to the vignette:
browseVignettes("rseAnalysis")Or via link here: https://github.com/JackXu2333/rseAnalysis/blob/new_branch/vignettes/Introduction_rseAnalysis.md
rseAnalysis is initialized, developed and maintained by Sijie Xu sijie.xu@mail.utoronto.ca. Functions vcf2df., fasta2df., bed2df. uses import mechanism from package vcfR and Biostrings and tailor to the desired object form. Function predictStructure. uses RNA structure from users operating machine to generate results. Function predictDistance. utilize command-tool RNAdistance and package gscVisualizer in the RNA distance calculation. Note that gscVisualizer is a developer version of R package originated by Zhiwen Tan, a student of BCB410Fall 2020, link: https://github.com/Deemolotus/gscVisualizer. DNA2RNA use the s2c from seqinr R package, function Analysis.DISEXP. make use of glm and lm modelling provided by R stats package and ggplot from ggplot2 package to visualize the result. Function predictDistance. and RNA.validate. includes utilities function pBar., written by Professor Steipe in the .utilities.R file under project ABC-units. Last but not less, the key algorithm from function RNA.validate. and the shiny app runRSEAnalysis. is inspired by a project partnered with Zhiwen Tan, mainly contributed by Sijie Xu in 2020; shiny library are used by the shiny app and its app loader function runRSEAnalysis.
A file including human miRNA sequence, BRCA gene expression data used in both example and the test is sourced from miRBase and TCGA, all references are listed below.
Baptiste Auguie (2017). gridExtra: Miscellaneous Functions for “Grid” Graphics. R package version 2.3. https://CRAN.R-project.org/package=gridExtra
Charif D, Lobry J (2007). “SeqinR 1.0-2: a contributed package to the R project for statistical computing devoted to biological sequences retrieval and analysis.” In Bastolla U, Porto M, Roman H, Vendruscolo M (eds.), Structural approaches to sequence evolution: Molecules, networks, populations, series Biological and Medical Physics, Biomedical Engineering, 207-232. Springer Verlag, New York. ISBN : 978-3-540-35305-8. http://seqinr.r-forge.r-project.org/
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Hadley Wickham (2020). tidyr: Tidy Messy Data. R package version 1.1.2. https://CRAN.R-project.org/package=tidyr
Kozomara, A., & Griffiths-Jones, S. (2011). miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic acids research, 39(Database issue), D152–D157. https://doi.org/10.1093/nar/gkq1027
Knaus BJ, Grünwald NJ (2016). “VcfR: an R package to manipulate and visualize VCF format data.” BioRxiv. <URL: http://dx.doi.org/10.1101/041277>.
Pagès H, Aboyoun P, Gentleman R, DebRoy S (2020). Biostrings: Efficient manipulation of biological strings. R package version 2.58.0, https://bioconductor.org/packages/Biostrings
Wickham, H. and Bryan, J. (2019). R Packages (2nd edition). Newton, Massachusetts: O’Reilly Media. https://r-pkgs.org/
TCGA Research Network: https://www.cancer.gov/tcga.
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Steipe B., ABC project (.utility 4.07) A Bioinformatics Course: Applied Bioinformatics http://steipe.biochemistry.utoronto.ca/abc/index.php/Bioinformatics_Main_Page
Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2020). shiny: Web Application Framework for R. R package version 1.5.0. https://CRAN.R-project.org/package=shiny
Tan Z., Xu S. (2020) miRNA Motif Analysis https://github.com/Deemolotus/BCB330Y-and-BCB430Y/tree/master/Main
Tan Z. (2020). gscVisualizer: Visualize The Difference among Gene Sequences. R package version 0.1.0.
This package was developed as part of an assessment for 2020BCB410H: Applied Bioinformatics, University of Toronto, Toronto,CANADA.