README.md

In daewon-gong/NanoPlotR: Nanopore Sequencing Based RNA Modification Detection Visualization

NanoPlotR

The purpose of NanoPlotR is to allow visual interpretation of results from nanopore sequencing based RNA modification detection tools such as xPore (Pratanwanich et al., 2021). NanoPlotR enables fast and convenient plotting of RNA modification detection results for visualization/interpretation of the results.

NanoPlotR is unique from other R packages as there does not exist a R package that allows fast/convenient plotting of nanopore sequencing based RNA modification detection results. The package will allow researchers running a nanopore based RNA modification analysis (using xPore or etc.) to quickly visualize the results using the available plotting functions.

Note that currently NanoPlotR assumes modification results formatted according to the xPore (Pratanwanich et al., 2021) output format. Thus, the NanoPlotR works best with RNA modification analysis pipelines that utilize xPore. Support for other output formats may be included in the future. Results from other detection tools must be transformed into the xPore output format unless further support for other formats are released.

NanoplotR was developed in R 4.1.0 and on a Mac platform.

Installation

You can install the development version of NanoPlotR like so:

require("devtools")
devtools::install_github("daewon-gong/NanoPlotR", build_vignettes = TRUE)
library("NanoPlotR")

To run the shinyApp:

runNanoPlotR()

Overview

ls("package:NanoPlotR")
data(package = "NanoPlotR") # optional

There are 4 functions that are available for user access:

1. plotCountMatrix allows users to plot a count matrix of top transcript/gene ids ranked by differencial modification rates. Users have the option to choose which kmers to include from the modSites parameter along with how many transcript/gene ids to include from the numTopIds parameter. The function will output a ggplot.

2. plotTopKmers allows users to plot a bar graph with the top kmers ranked by frequency. Users have the option to choose how many top kmers to plot through the numKmers parameter. The function will output a ggplot.

3. plotModHist allows users to plot histograms to show distribution of modification rates of all replication/conditions.

3. getTopIds allows users to retrieve the top gene/transcript ids ranked by differential modification rates. The function outputs a string vector of the top gene/transcript ids.

For more details about the functions, please take a look at the vignette for this package:

browseVignettes("NanoPlotR")

An overview of the package is illustrated below:

The package tree structure follows:

- microCompet
  |- NanoPlotR.Rproj
  |- DESCRIPTION
  |- NAMESPACE
  |- LICENSE
  |- README
  |- data
    |- RnaModificationResults.rda
  |- inst
    |- extdata
      |- NanoPlotROverview.png
    |- CITATION
    |- shiny-scripts
      |- app.R
      |- modals.R
  |- man
    |- getTopIds.Rd
    |- plotCountMatrix.Rd
    |- plotTopKmers.Rd
    |- plotModHist.Rd
    |- RnaModificationResults.Rd
    |- runNanoPlotR.Rd
  |- R
    |- plotModificationResults.R
    |- data.R
    |- runNanoPlotR.R
  |- tests
    |- testthat.R
    |- testthat
      |- test-getTopIdsR
      |- test-plotCountMatrix.R
      |- test-plotTopKmers.R
      |- test-plotModHist.R
  |- Vignette
    |- NanoPlotRVignette.Rmd
    |- countMatrix.png
    |- kmerFreq.png
    |- modHist.png

Contributions

The author of this package is Dae-won Gong.

For functions plotCountMatrix, plotTopKmers and getTopIds the dplyr and magrittr packages for data processing. The ggplot2 package is used for plotting in the plotCountMatrix and plotTopKmers functions, while the Hmisc package is used for plotting in plotModHist.

References

Frank E Harrell Jr (2021). Hsmisc: Harrell Miscellaneous. R package version 4.6.0. https://CRAN.R-project.org/package=Hmisc

Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. https://CRAN.R-project.org/package=dplyr

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

Pratanwanich, P. N., Yao, F., Chen, Y., Koh, C. W. Q., Wan, Y. K., Hendra, C., Poon, P., Goh, Y. T., Yap, P. M. L., Chooi, J. Y., Chng, W. J., Ng, S. B., Thiery, A., Goh, W. S. S., & Göke, J. (2021). Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nature Biotechnology, 39(11), 1394–1402. https://doi.org/10.1038/s41587-021-00949-w

R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Wickham, H. and Bryan, J. (2019). R Packages (2nd edition). Newton, Massachusetts: O’Reilly Media. https://r-pkgs.org/

Acknowledgements

This package was developed as part of an assessment for 2021 BCB410H: Applied Bioinformatics, University of Toronto, Toronto, CANADA.

daewon-gong/NanoPlotR documentation built on Dec. 19, 2021, 8:02 p.m.