microCompet is for identifying and visualizing potential microbial
competitions for nutrition, mainly simple sugars such as pentoses and
pyranoses. This package offers functions to:
- Visualize all sugar degradation pathways that are present in one microbe of interest. (See function constructFullNetwork)
- Compare a microbe’s overall similarity of sugar degradation profile to other microbes. (See functions overallSimilarity and competeMicrobiota)
- Functions that help users to check their datasets and extract information. (See checkUserED and extractCarboGenes)
To install the package:
require("devtools")
devtools::install_github("MolyWang/microCompet", build_vignettes = TRUE)
library("microCompet")To run the Shiny app:
microCompet::runMicroCompet()To list the available 5 functions and 3 datasets in the package:
ls("package:microCompet")
data(package = "microCompet")As dataset names indicated, EnzymaticReactions describes enzymatic
reactions involved in simple sugar degradation pathways, and
EnzymeDistribution includes data regarding the distribution of
these enzymes in various gut microbiota members. For more information on
these genomes, you can see GenomesInfo and find the original
annotated genome from NCBI using provided Accession ID. For more
details, check the dataset description files by ?DatasetName
These two functions can be treated as exported helper functions for users. The most readily available genome files are in genbank(.gb or .gbk) format and extractCarboGenes would extract genes encoding sugar degradation enzymes for other functions to operate on. While checkUserED helps user to check some required features/columns in provided dataset EnzymeDistribution. Datasets pass this check will work properly with overallSimilarity and competeMicrobiota.
Function constructFullNetwork takes a vector of genes and plot its full sugar degradation pathways, the following image is a sample output using the provided Lactobacillus_johnsonii.gb genome. Function overallSimilarity count sugar degradation genes in common between the given genome and other microbial species, and creates an interactive radar graph. The final function competeMicrobiota visualize available microbes in terms of pathway completeness, suggesting their ability to fully degrade indicated sugar sources. overallSimilarity and competeMicrobiota aim to interpret microbial competition from two related angles.
Refer to package vignettes for more details.
browseVignettes("microCompet")The package tree structure is provided below.
- microCompet
|- microCompet.Rproj
|- DESCRIPTION
|- NAMESPACE
|- LICENSE
|- README
|- data
|- EnzymaticReactions.rda
|- EnzymeDistribution.rda
|- GenomesInfo.rda
|- inst
|- CITATION
|- extdata
|- competeMicrobiota.png
|- fullNet.PGN
|- Klebsiella_variicola.gb
|- Lactobacillus_johnsonii.gb
|- microbiome.jpg
|- overallSimilarity.png
|- overview.png
|- shiny-scripts
|- app.R
|- man
|- allSugarScoresForOneGenome.Rd
|- calculateStepsForOneSugar.Rd
|- calclateTotalStepsForAllSugars.Rd
|- checkUserED.Rd
|- compareTwoGenomes.Rd
|- competeMicrobiota.Rd
|- completenessForAllPathways.Rd
|- constructFullNetwork.Rd
|- createEdgeFrame.Rd
|- createNodeFrame.Rd
|- EnzymaticReactions.Rd
|- EnzymeDistribution.Rd
|- extractCarboGenes.Rd
|- GenomesInfo.Rd
|- overallSimilarity.Rd
|- transformToVector.Rd
|- TransformToVector.Rd
|- R
|- checkUserED.R
|- competeMicrobiota.R
|- constructFullNetwork.R
|- data.R
|- extractCarboGenes.R
|- overallSimilarity.R
|- runMicroCompet.R
|- tests
|- testthat.R
|- testthat
|- test-checkUserED.R
|- test-competMicrobiota.R
|- test-constructFullNetwork.R
|- test-extractCarboGenes.R
|- test-overallSimilarity.RThe author of this package is Zhuyi Wang. Information included in EnzymaticReactions are retrieved from MetaCyc (metacyc.org), while data included in EnzymeDistribution are annotated genome downloaded from NCBI (see details in GenomesInfo) and processed by Python scripts (also written by Zhuyi). Functions overallSimilarity and competeMicrobiota took advantage of one function chartJSRadar from R package radarchart, which decorates dataframes with interactive features and beautifully selected colors that agree with my aesthetics. Another function constructFullNetwork was dependency-heavy, it is built upon functions from igraph and ggraph, which then depend on network, sna, and ggplot2.
-
Butts, C. 2008. “network: a Package for Managing Relational Data in R.” Journal of Statistical Software. 24(2). <URL: https://www.jstatsoft.org/v24/i02/paper>.
-
Butts, C. 2020. network: Classes for Relational Data. The Statnet Project (<URL:http://www.statnet.org>). R package version 1.16.1, <URL: https://CRAN.R-project.org/package=network>.
-
Butts, C.T. 2020. sna: Tools for Social Network Analysis. R package version 2.6. https://CRAN.R-project.org/package=sna
-
Csardi, G., Nepusz, T. 2006. The igraph software package for complex network research. InterJournal. https://igraph.org
-
Grolemund, G. (2015). Learn Shiny - Video Tutorials. https://shiny.rstudio.com/tutorial/
-
Karp, P.D., Riley, M., Paley, S.M., and Pellegrini-Toole A. 2002. The MetaCyc Database. Nucleic Acids Re. 30(1):59-61. doi:10.1093/nar/30.1.59
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National Center for Biotechnology Information (NCBI)[Internet]. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information [1988]. Available from: https://www.ncbi.nlm.nih.gov/
-
Pedersen, T.L. 2020. ggraph: An Implementation of Grammar of Graphics for Graphs and Networks. R package version 2.0.3. https://CRAN.R-project.org/package=ggraph
-
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/.
-
Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
-
Wickham, H. 2020. R Packages. https://r-pkgs.org/
-
Xie, Y. 2020. bookdown: Authoring Books and Technical Documents with R Markdown. https://bookdown.org/yihui/bookdown/
This package was developed as part of an assessment for 2019-2020 BCB410H: Applied Bioinformatics, University of Toronto, Toronto, CANADA.
A quick example for constructFullNetwork, and this would reproduce
the network image you saw in the Overview part, making use of
extractCarboGenes and the carried genbank file
Lactobacillus_johnsonii.gb. This package also comes with another
genbank file Klebsiella_variicola.gb (find it in inst/extdata).
Klebsiella genus has a more complete sugar degradation system and
would produce a busier image. More function detail in function
description files with ?functionName
require("microCompet")
ER <- microCompet::EnzymaticReactions
ED <- microCompet::EnzymeDistribution
fullEnzymeGeneList <- ED$Gene
genomeFilePath <- system.file("extdata",
"Lactobacillus_johnsonii.gb", #You can also try "Klebsiella_variicola.gb"
package = "microCompet",
mustWork = TRUE)
carboGenes <- extractCarboGenes(genomeFilePath, fullEnzymeGeneList)
fullPathway <- constructFullNetwork("Lactobacillus johnsonii", carboGenes, ER)
fullPathway
The second example quickly shows you a sample output from overallSimilarity. You can comment out the indicated lines if you’ve run the previous example. extractCarboGenes is a slow function and you want to play the cute image as soon as possible!
require("microCompet")
require("radarchart")
genomeName <- "L. johnsonii"
# Uncomment the indicated lines if you haven't run the previous example.
ED <- microCompet::EnzymeDistribution #
fullEnzymeGeneVec <- ED$Gene #
genomeFilePath <- system.file("extdata", #
"Lactobacillus_johnsonii.gb", #
package = "microCompet", #
mustWork = TRUE) #
carboGenes <- extractCarboGenes(genomeFilePath, fullEnzymeGeneVec) #
overSimiFig <- overallSimilarity(genomeName, carboGenes, ED, 5, 13)
overSimiFig
The third example shows a sample image from competeMicrobiota. Same as before, you don’t have to rerun code for generating geneVec carboGenes. Run this in your R console, navigate through your favourite microbes, and enjoy its interactive features (unfortunately loss during knitting TAT).
require("microCompet")
require("radarchart")
genomeName <- "L. johnsonii"
ED <- microCompet::EnzymeDistribution
fullEnzymeGeneVec <- ED$Gene #
genomeFilePath <- system.file("extdata", #
"Lactobacillus_johnsonii.gb", #
package = "microCompet", #
mustWork = TRUE) #
carboGenes <- extractCarboGenes(genomeFilePath, fullEnzymeGeneVec) #
firstMicrobe <- 5
lastMicrobe <- 13
ER <- microCompet::EnzymaticReactions
compMicro <- competeMicrobiota(genomeName, carboGenes, ER,
ED, firstMicrobe, lastMicrobe)
compMicro
As on 2020-11-26, I, Zhuyi Wang, consent to leave this package public and share it with future BCB410 students.