In kairenchen721/interpretproteinidentification: Visually Interprets Protein Identification

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interpretproteinidentification

The goal of interpretproteinidentification is to improve the interpretbility of protein inference

#plot(pressure)

Description

The purpose of this package is to visualize the connection between peptide identified from mass spectrometry, and the protein that the peptide can map to, and to improve interpertability of a proteome.

In proteomics, bottom-up proteomics is used more often, where the protein first undergoes protease digestion into smaller piece of protein known as “peptides”, it is then the peptides that the mass spectrometer identifies. Then each peptide is determined to be part which protein based on sequence. This package is not trying to show a plot that visualizes the performance of protein inference, nor is it trying to visualize the connection between mass spectrometry spectra and peptides.

The main reason why this is interesting is because one peptide sequence can map to multiple proteins sequence (because or else if one peptide can only map to one protein, visualization of this kind would not provide much information compare to text files), so this means that this package can show the what conclusion (or protein) has been drawn (or inferred) given the evidences (or peptide), and what other conclusion could have been drawn (or other protein that could possibly be inferred).

In theory, cytoscape [4] can do this, (since it is a visualizer for any network), but file reading (idXML or osw [1] is not an supported file format) and processing (having to click many thing in the graphical user interface, or run a few command in the command line interface) may a slight annoyance. This package solve cytoscape's limitation through its simplicity and short learning phase.

The reason why an idXML is not supported by cytoscape is because it is not a file represent a network, it is just a file represent what peptide are identified from mass spectra and which protein has a sequence that also is a supersequence of those peptide's sequence

The R version is 4.0.2 and the platform used to develop this package is Mac.

Installation

You can install the development version of interpretproteinidentification from GitHub with:

require("devtools")
devtools::install_github("kairenchen721/interpretproteinidentification", build_vignettes = TRUE)
library("interpretproteinidentification")

To run the shinyApp:

runinterpretproteinidentification()

Overview

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

interpretproteinidentification contains 4 functions to visualize protein inference. The readSQLiteFil} read the osw file and make a character vector representing the edges of a graph. The generateBipartiteGraph function generate the graph. The displayComponent allows the user to choose a component of the graph to display. selectProtein is a function that draw a sub-graph consisting of the selected protein/peptide and all its adjacent vertices. The runinterpretproteinidentification is the function that launches the shiny app for this package. The package also contains a sample idXML and osw data in inst/extdata Refer to package vignettes for more details.

browseVignettes("interpretproteinidentification")

The package tree structure is provided below

- interpretProteinIdentification
  |- interpretProteinIdentification.Rproj
  |- DESCRIPTION
  |- NAMESPACE
  |- LICENSE
  |- README
  |- inst
    |-extdata
      |-BSA1_OMSSA.idXML
  |- doc
    |-my-vignette.html
  |- man
    |- displayComponent.Rd
    |- findNumEdges.Rd
    |- generateBipartiteGraph.Rd
    |- interpretProteinIdentitification.Rd
    |- loadFileIntoVector.Rd
    |- selectProtein.Rd
  |- R
    |- displayComponent.R
    |- searchForAnnotations.R
    |- generateBipartiteGraph.R
    |- interpretProteinIdentitification-package.R
    |- selectProtein.R
  |- vignettes
  |- tests
    |- testthat.R
    |- testthat
      |- test-displayComponent.R
      |- test-searchForAnnotations.R
      |- test-generateBipartiteGraph.R
      |- test-selectProtein.R

Contributions

The author of this package is Kai Ren Chen. The \code{generateBipartiteGraph} function make use of load function from pyopenms python package [3] to read idXML files, through reticulate. [7] \code{readSQLiteFile} uses DBI [9] and RSQLite [10] to execute the SQL queries. \code{generateBipartiteGraph} uses igraph R package [2] to make the graph and simplify the graph. \code{displayComponents} also uses the igraph R package [2] to decompose the graph into components and draw the component. \code{selectProtein} uses the igraph R package [2] to search for relevant vertices (those connected to the selected protein/peptide), induce a sub-graph, then plot it. The data set BSA1_OMSSA.idXML is obtained from the OpenMS github. [1]. The data set test_data.osw is obtain from the pyprophet github. [8]

References

1. Röst, H. L., Sachsenberg, T., Aiche, S., Bielow, C., Weisser, H., Aicheler, F., Andreotti, S., Ehrlich, H. C., Gutenbrunner, P., Kenar, E., Liang, X., Nahnsen, S., Nilse, L., Pfeuffer, J., Rosenberger, G., Rurik, M., Schmitt, U., Veit, J., Walzer, M., Wojnar, D., … Kohlbacher, O. (2016). OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nature methods, 13(9), 741–748. https://doi.org/10.1038/nmeth.3959

2. Csardi G, Nepusz T (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org.

3. Röst, H. L., Schmitt, U., Aebersold, R., & Malmström, L. (2014). pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics, 14(1), 74–77. https://doi.org/10.1002/pmic.201300246

4. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303

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

6. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

7. Ushey, K., Allaire, JJ., and Tang, Y. (2021). reticulate: Interface to 'Python'. R package version 1.22. https://CRAN.R-project.org/package=reticulate

8. Teleman, J., Röst, H. L., Rosenberger, G., Schmitt, U., Malmström, L., Malmström, J., & Levander, F. (2015). DIANA--algorithmic improvements for analysis of data-independent acquisition MS data. Bioinformatics (Oxford, England), 31(4), 555–562. https://doi.org/10.1093/bioinformatics/btu686

9. R Special Interest Group on Databases (R-SIG-DB), Hadley Wickham and Kirill Müller (2021). DBI: R Database Interface. R package version 1.1.1. https://CRAN.R-project.org/package=DBI

10. Kirill Müller, Hadley Wickham, David A. James and Seth Falcon (2021). RSQLite: 'SQLite' Interface for R. R package version 2.2.8. https://CRAN.R-project.org/package=RSQLite

Acknowledgements

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

Bugs to fix/Feature to implement

1. How do I compare edges in an igraph object?, it seems to be unsupported
2. Change plot to include inferred protein information
3. In select protein add gene ontology labels and pathology
4. Implemented search for annotations, maybe use match
5. Consider using cytoscape [4] http://cytoscape.org/RCy3/articles/Cytoscape-and-iGraph.html

kairenchen721/interpretproteinidentification documentation built on Dec. 21, 2021, 5:15 a.m.