In emilyso-99/MicroMetaGraph: MicroMetaGraph: Construction Interaction Networks using Metatranscriptomic Data
library(knitr)
opts_chunk$set(fig.align = "center",
out.width = "90%",
fig.width = 6, fig.height = 5.5,
dev.args=list(pointsize=10),
par = TRUE, # needed for setting hook
collapse = TRUE, # collapse input & ouput code in chunks
warning = FALSE)
knit_hooks$set(par = function(before, options, envir)
{ if(before && options$fig.show != "none")
par(family = "sans", mar=c(4.1,4.1,1.1,1.1), mgp=c(3,1,0), tcl=-0.5)
})
set.seed(1) # for exact reproducibility
Introduction
MicroMetaGraphis an R package that, when given a set of input dataset of genes/proteins, constructs interaction networks of the present proteins or ortholog groups. This inclues performinngn queries on custom datasets, compiling evidence and graphing in a node-edge visualization. Any classification information pertaining to the dataset is printed onto the Console. The package is targeted for researchers who are interested in how proteins are grouped and interact based on functional similarity. This document gives a tour of MicroMetaGraph (version 0.1.0). It was written in R Markdown, using the knitr package for production. See help(package = "MicroMetaGraph") for further details.
To download MicroMetaGraph, use the following commands:
require("devtools")
install_github("emilyso-99/MicroMetaGraph", build_vignettes = TRUE)
library("MicroMetaGraph")
To list all sample functions available in the package:
ls("package:MicroMetaGraph")
To list all sample datasets available in the package:
data(package = "MicroMetaGraph")
Components
The components of the package are as follows:
GetOrthology, when given an input vector of protein IDs from the GenBank database, uses datasets from the COG database to correctly map the proteins to their respective orthologous groups
orthologyResults <- GetOrthology(c("339670.Bamb_6476","7739.JGI124825","203124.Tery_0122"))
FunctionalAnnotation, when given an input set of orthologs, uses data from the COG database to get further information about the ortholog's functional classification
annotationResults <- FunctionalAnnotation(orthologyResults)
CogLinker, when given an input sequence of orthologs and a certain benchmark (similar to a confidence score), will calculate the interactions between orthologs that meet this benchmark derived from the STRING database.
LinkerResults <- CogLinker(orthologyResults)
ProteinNetwork and OrthologNetwork are two functions that actually construct the plot of the interaction network. When given an input vector of protein IDs from the Eggnog database and a chosen benchmark (for interaction scores), these two functions use the functions mentioned above to retreive the pertinent information for the network. The differences between the two is that the nodes in ProteinNetwork correspond to one protein while in OrthologNetwork they correspond to one orthlog group. Therefore, if you have several protein Eggnog IDs as your input but they all belong to the same orthologous group, OrthologNetwork will only produce a graph with one node.
The output is provided under the code snippet:
input <-c("339670.Bamb_6476","7739.JGI124825","203124.Tery_0122")
ProteinNetwork(input,benchmark=0)
## Package References
[So, E (2020) MicroMetaGraph: Constructing Ortholog Based Interaction Networks.
Unpublished.](https://github.com/emilyso-99/MicroMetaGraph)
## Other References
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Wickham, H. and Bryan, J. (2019). *R Packages* (2nd edition). Newton, Massachusetts: O'Reilly Media. https://r-pkgs.org/
Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. *Nucleic Acids Res. 2000*;28(1):33-36. doi:10.1093/nar/28.1.33
Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. *Nucleic Acids Res.* 2019;47(D1):D607-D613. doi:10.1093/nar/gky1131
----
wzxhzdk:8
emilyso-99/MicroMetaGraph documentation built on Jan. 1, 2021, 12:18 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.
library(knitr) opts_chunk$set(fig.align = "center", out.width = "90%", fig.width = 6, fig.height = 5.5, dev.args=list(pointsize=10), par = TRUE, # needed for setting hook collapse = TRUE, # collapse input & ouput code in chunks warning = FALSE) knit_hooks$set(par = function(before, options, envir) { if(before && options$fig.show != "none") par(family = "sans", mar=c(4.1,4.1,1.1,1.1), mgp=c(3,1,0), tcl=-0.5) }) set.seed(1) # for exact reproducibility
Introduction
MicroMetaGraphis an R package that, when given a set of input dataset of genes/proteins, constructs interaction networks of the present proteins or ortholog groups. This inclues performinngn queries on custom datasets, compiling evidence and graphing in a node-edge visualization. Any classification information pertaining to the dataset is printed onto the Console. The package is targeted for researchers who are interested in how proteins are grouped and interact based on functional similarity. This document gives a tour of MicroMetaGraph (version 0.1.0). It was written in R Markdown, using the knitr package for production. See help(package = "MicroMetaGraph") for further details.
To download MicroMetaGraph, use the following commands:
require("devtools") install_github("emilyso-99/MicroMetaGraph", build_vignettes = TRUE) library("MicroMetaGraph")
To list all sample functions available in the package:
ls("package:MicroMetaGraph")
To list all sample datasets available in the package:
data(package = "MicroMetaGraph")
Components
The components of the package are as follows:
GetOrthology, when given an input vector of protein IDs from the GenBank database, uses datasets from the COG database to correctly map the proteins to their respective orthologous groups
orthologyResults <- GetOrthology(c("339670.Bamb_6476","7739.JGI124825","203124.Tery_0122"))
FunctionalAnnotation, when given an input set of orthologs, uses data from the COG database to get further information about the ortholog's functional classification
annotationResults <- FunctionalAnnotation(orthologyResults)
CogLinker, when given an input sequence of orthologs and a certain benchmark (similar to a confidence score), will calculate the interactions between orthologs that meet this benchmark derived from the STRING database.
LinkerResults <- CogLinker(orthologyResults)
ProteinNetwork and OrthologNetwork are two functions that actually construct the plot of the interaction network. When given an input vector of protein IDs from the Eggnog database and a chosen benchmark (for interaction scores), these two functions use the functions mentioned above to retreive the pertinent information for the network. The differences between the two is that the nodes in ProteinNetwork correspond to one protein while in OrthologNetwork they correspond to one orthlog group. Therefore, if you have several protein Eggnog IDs as your input but they all belong to the same orthologous group, OrthologNetwork will only produce a graph with one node.
The output is provided under the code snippet:
input <-c("339670.Bamb_6476","7739.JGI124825","203124.Tery_0122") ProteinNetwork(input,benchmark=0)
## Package References
[So, E (2020) MicroMetaGraph: Constructing Ortholog Based Interaction Networks.
Unpublished.](https://github.com/emilyso-99/MicroMetaGraph)
## Other References R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Wickham, H. and Bryan, J. (2019). *R Packages* (2nd edition). Newton, Massachusetts: O'Reilly Media. https://r-pkgs.org/ Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. *Nucleic Acids Res. 2000*;28(1):33-36. doi:10.1093/nar/28.1.33 Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. *Nucleic Acids Res.* 2019;47(D1):D607-D613. doi:10.1093/nar/gky1131 ---- wzxhzdk:8
emilyso-99/MicroMetaGraph documentation built on Jan. 1, 2021, 12:18 a.m.
## Other References R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Wickham, H. and Bryan, J. (2019). *R Packages* (2nd edition). Newton, Massachusetts: O'Reilly Media. https://r-pkgs.org/ Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. *Nucleic Acids Res. 2000*;28(1):33-36. doi:10.1093/nar/28.1.33 Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. *Nucleic Acids Res.* 2019;47(D1):D607-D613. doi:10.1093/nar/gky1131 ---- wzxhzdk:8
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.