knitr::opts_chunk$set( library(PhyloProfile), collapse = TRUE, comment = "#>", dev = 'png', crop = NULL )
Phylogenetic profiles capture the presence - absence pattern of genes across species (Pellegrini et al., 1999). The presence of an ortholog in a given species is often taken as evidence that also the corresponding function is represented (Lee et al., 2007). Moreover, if two genes agree in their phylogenetic profile, it can suggest that they functionally interact (Pellegrini et al., 1999). Phylogenetic profiles are therefore commonly used for tracing functional protein clusters or metabolic networks across species and through time. However, orthology inference is not error-free (Altenhoff et al., 2016), and orthology does not guarantee functional equivalence for two genes (Studer and Robinson-Rechavi, 2009). Therefore, phylogenetic profiles are often integrated with accessory information layers, such as sequence similarity, domain architecture similarity, or semantic similarity of Gene Ontology-term descriptions.
Various approaches exist to visualize such profiles. However, there is still a shortage of tools that provide a comprehensive set of functions for the display, filtering and analysis of multi-layered phylogenetic profiles comprising hundreds of genes and taxa. To close this methodological gap, we present here PhyloProfile, an R-based tool to visualize, explore and analyze multi-layered phylogenetic profiles.
To install the PhyloProfile package via Bioconductor using BiocManager:
if (!requireNamespace("BiocManager")) install.packages("BiocManager") BiocManager::install("PhyloProfile")
To install the dev version from Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(version='devel') BiocManager::install("PhyloProfile")
To install the dev version from github:
if (!requireNamespace("devtools")) install.packages("devtools", repos = "http://cran.us.r-project.org") devtools::install_github( "BIONF/PhyloProfile", INSTALL_opts = c('--no-lock'), build_vignettes = TRUE )
Or use directly the online version at http://applbio.biologie.uni-frankfurt.de/phyloprofile/.
PhyloProfile expects as a main input the phylogenetic distribution of orthologs, or more generally of homologs. This information can be complemented with domain architecture annotation and data for up to two additional annotation layers.
Beside tab delimited text and sequences in FASTA format, the tool also accepts orthoXML (Schmitt et al., 2011), or a list of OMA IDs (Altenhoff et al., 2015) as input.
Here is an example of a tab delimited input with two additional annotation layers:
data("mainLongRaw", package="PhyloProfile") knitr::kable(head(mainLongRaw, 10), row.names = FALSE)
The WIKI accompanying PhyloProfile gives a comprehensive guide of how to format input data.
Together with several functions for exploring phylogenetic profiles, we provide an interactive visualization application implemented with Shiny (https://CRAN.R-project.org/package=shiny).
For both command-based and visualization analyses, users can:
PhyloProfile is able to represent the entire data matrix (
Main profile) or to
visualize only a subset of genes and taxa for a detailed inspection
Customized profile), without the need of modifying the input data.
Besides, PhyloProfile's interface will be automatically varied according to user's input files, such as the names of two additional information layers or list of input taxa.
PhyloProfile provides several functions for dynamically analyzing phylogenetic profiles.
The identification of proteins with similar phylogenetic profiles is a crucial step in the identification and characterization of novel functional protein interaction networks (Pellegrini, 2012). PhyloProfile offers the option to cluster genes according to the distance of their phylogenetic profiles.
### An example for plotting the clustered profiles tree. ### See ?getDendrogram for more details. #' Load built-in data data("finalProcessedProfile", package="PhyloProfile") data <- finalProcessedProfile #' Calculate distance matrix #' Check ?getDistanceMatrix profileType <- "binary" profiles <- getDataClustering( data, profileType, var1AggregateBy, var2AggregateBy) method <- "mutualInformation" distanceMatrix <- getDistanceMatrix(profiles, method) #' Create clustered profile tree clusterMethod <- "complete" dd <- clusterDataDend(distanceMatrix, clusterMethod) getDendrogram(dd)
PhyloProfile can estimate the evolutionary age of a gene from the phylogenetic profiles using an LCA algorithm (Capra et al., 2013). Specifically, the last common ancestor of the two most distantly related species displaying a given gene serves as the minimal gene age. Age estimates are dynamically updated upon filtering of the data.
### An example for calculating gene age for the built-in data set. ### See ?estimateGeneAge for more details. #' Load built-in data data("fullProcessedProfile", package="PhyloProfile") #' Choose the working rank and the reference taxon rankName <- "class" refTaxon <- "Mammalia" #' Count taxa within each supertaxon taxonIDs <- levels(as.factor(fullProcessedProfile$ncbiID)) sortedInputTaxa <- sortInputTaxa( taxonIDs, rankName, refTaxon, NULL ) taxaCount <- plyr::count(sortedInputTaxa, "supertaxon") #' Set cutoff for 2 additional variables and the percentage of present species #' in each supertaxon var1Cutoff <- c(0,1) var2Cutoff <- c(0,1) percentCutoff <- c(0,1) #' Estimate gene age estimateGeneAge( fullProcessedProfile, taxaCount, rankName, refTaxon, var1Cutoff, var2Cutoff, percentCutoff )
Phylogenomic reconstructions are typically based on a collection of core genes (Daubin et al., 2002), i.e. genes that are shared among all genomes in a taxon collection. PhyloProfile enables users to select a set of taxa and returns their core genes.
### An example for calculating core gene set for the built-in data set. ### See ?getCoreGene for more details. #' Load built-in data data("fullProcessedProfile", package="PhyloProfile") #' Choose the working rank and a set of taxa of interest rankName <- "class" refTaxon <- "Mammalia" taxaCore <- c("Mammalia", "Saccharomycetes", "Insecta") #' Set cutoff for 2 additional variables and the percentage of present species #' in each supertaxon var1Cutoff <- c(0.75, 1.0) var2Cutoff <- c(0.75, 1.0) percentCutoff <- c(0.0, 1.0) #' Set core coverage, the % of taxa that must be present in the selected set coreCoverage <- 1 #' Count taxa within each supertaxon taxonIDs <- levels(as.factor(fullProcessedProfile$ncbiID)) sortedInputTaxa <- sortInputTaxa( taxonIDs, rankName, refTaxon, NULL ) taxaCount <- plyr::count(sortedInputTaxa, "supertaxon") #' Identify core genes getCoreGene( rankName, taxaCore, fullProcessedProfile, taxaCount, var1Cutoff, var2Cutoff, percentCutoff, coreCoverage )
This function is used to compare the distribution of the additional variables between two taxon groups, an in- and an out-group. Users can define the in-group and all taxa not included in this are used as the out-group. The value distributions of the variables are then compared using statistical tests (Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney) using the specified significant level (0.05 by default). Genes that have a significantly different distribution will be shown in the candidate gene list for further analysis.
#' Load built-in data data("mainLongRaw", package="PhyloProfile") data <- mainLongRaw #' choose the in-group taxa inGroup <- c("ncbi9606", "ncbi10116") #' choose variable to be compared variable <- colnames(data) #' compare the selected variable between the in-group and out-group taxa compareTaxonGroups(data, inGroup, TRUE, variable, 0.05)
The interpretation of phylogenetic profiles, and the result of downstream analyses can change substantially upon filtering the data. To help users to decide on reasonable filtering thresholds, PhyloProfile provides a function to plot the distributions of the values incurred by the integrated information layers.
### An example for plotting the distribution of the 1st additional variable. ### See ?createVarDistPlot for more details. #' Load built-in data data("mainLongRaw", package="PhyloProfile") #' Process data for distribution analysis #' See ?createVariableDistributionData data <- createVariableDistributionData( mainLongRaw, c(0, 1), c(0.5, 1) ) head(data, 6) #' Choose a variable for plotting and set the variable name varType <- "var1" varName <- "Variable 1" #' Set cutoff for the percentage of present species in each supertaxon percentCutoff <- c(0,1) #' Set text size distTextSize <- 12 #' Create distribution plot createVarDistPlot( data, varName, varType, percentCutoff, distTextSize )
Process raw input (in different format) into a dataframe that contains all required information for the phylogenetic profile analysis.
#' Load built-in data #' If input data is in other format (e.g. fasta, OrthoXML, or wide matrix), #' see ?createLongMatrix rawInput <- system.file( "extdata", "test.main.long", package = "PhyloProfile", mustWork = TRUE ) #' Set working rank and the reference taxon rankName <- "class" refTaxon <- "Mammalia" #' Input a user-defined taxonomy tree to replace NCBI taxonomy tree (optional) taxaTree <- NULL #' Choose how to aggregate the additional variables when pocessing the data #' into supertaxon var1AggregateBy <- "max" var2AggregateBy <- "mean" #' Set cutoffs for for percentage of species present in a supertaxon, #' allowed number of co orthologs, and cutoffs for the additional variables percentCutoff <- c(0.0, 1.0) coorthologCutoffMax <- 10 var1Cutoff <- c(0.75, 1.0) var2Cutoff <- c(0.5, 1.0) #' Choose the relationship of the additional variables, if they are related to #' the orthologous proteins or to the species var1Relation <- "protein" var2Relation <- "species" #' Identify categories for input genes (by a mapping tab-delimited file) groupByCat <- FALSE catDt <- NULL #' Process the input file into a phylogenetic profile data that contains #' taxonomy information and the aggregated values for the 2 additional variables profileData <- fromInputToProfile( rawInput, rankName, refTaxon, taxaTree, var1AggregateBy, var2AggregateBy, percentCutoff, coorthologCutoffMax, var1Cutoff, var2Cutoff, var1Relation, var2Relation, groupByCat, catDt ) head(profileData)
Generate phylogenetic profile heatmap after processing the raw input file.
#' Load built-in processed data data("superTaxonProfile", package="PhyloProfile") #' Create data for plotting plotDf <- dataMainPlot(superTaxonProfile) #' You can also choose a subset of genes and/or taxa for plotting with: #' selectedTaxa <- c("Mammalia", "Echinoidea", "Gunneridae") #' selectedSeq <- "all" #' plotDf <- dataCustomizedPlot( #' superTaxonProfile, selectedTaxa, selectedSeq #' ) #' Identify plot's parameters plotParameter <- list( "xAxis" = "taxa", "var1ID" = "FAS_FW", "var2ID" = "FAS_BW", "lowColorVar1" = "#FF8C00", "highColorVar1" = "#4682B4", "lowColorVar2" = "#FFFFFF", "highColorVar2" = "#F0E68C", "paraColor" = "#07D000", "xSize" = 8, "ySize" = 8, "legendSize" = 8, "mainLegend" = "top", "dotZoom" = 0, "xAngle" = 60, "guideline" = 0, "colorByGroup" = FALSE ) #' Generate profile plot heatmapPlotting(plotDf, plotParameter) #' To highlight a gene and/or taxon of interest taxonHighlight <- "Mammalia" rankName <- "class" geneHighlight <- "none" #' Then use ?highlightProfilePlot function # highlightProfilePlot( # plotDf, plotParameter, taxonHighlight, rankName, geneHighlight # )
Generate the domain architecture plot for a gene of interest and its orthologs.
#' Load protein domain architecture file domainFile <- system.file( "extdata", "domainFiles/101621at6656.domains", package = "PhyloProfile", mustWork = TRUE ) #' Identify IDs of gene of interest and its ortholog partner seedID <- "101621at6656" orthoID <- "101621at6656|AGRPL@224129@0|224129_0:001955|1" info <- c(seedID, orthoID) #' Get data for 2 selected genes from input file domainDf <- parseDomainInput(seedID, domainFile, "file") #' Generate plot plot <- createArchiPlot(info, domainDf, 9, 9) grid::grid.draw(plot)
Get taxonomy IDs and names in a specified rank for taxa in the input file.
#' Load raw input data("mainLongRaw", package="PhyloProfile") inputDf <- mainLongRaw #' Set working rank and the reference taxon rankName <- "phylum" #' Get taxonomy IDs and names for the input data inputTaxonID <- getInputTaxaID(inputDf) inputTaxonName <- getInputTaxaName(rankName, inputTaxonID) head(inputTaxonName)
Get taxonomy info for list of input taxa and sort it based on the taxonomy distance to a reference taxon
#' Get list of taxon IDs and names for input profile data("mainLongRaw", package="PhyloProfile") inputDf <- mainLongRaw rankName <- "phylum" inputTaxonID <- getInputTaxaID(inputDf) #' Input a user-defined taxonomy tree to replace NCBI taxonomy tree (optional) inputTaxaTree <- NULL #' Sort taxonomy list based on a selected refTaxon refTaxon <- "Microsporidia" sortedTaxonomy <- sortInputTaxa( taxonIDs = inputTaxonID, rankName = rankName, refTaxon = refTaxon, taxaTree = inputTaxaTree ) head( sortedTaxonomy[ , c("ncbiID", "fullName", "supertaxon", "supertaxonID", "rank") ] )
More examples? Please tell us what you want to see ;-)
Ngoc-Vinh Tran, Bastian Greshake Tzovaras, Ingo Ebersberger, PhyloProfile: dynamic visualization and exploration of multi-layered phylogenetic profiles, Bioinformatics, Volume 34, Issue 17, 01 September 2018, Pages 3041–3043, https://doi.org/10.1093/bioinformatics/bty225
Or use the citation function in R CMD to have the citation in BibTex or LaTeX format
Thanks so much for your interest in contributing to PhyloProfile 🎉👍🍾
Contributions to PhyloProfile can take many forms. If you are
biologist, you can
biologist and love coding, you can
not biologist but can code, it would be great if you can
Don't hesitate to get in touch with us if you have any questions. You can contact us at firstname.lastname@example.org
Here is the output of
sessionInfo() on the system on which this document was
sessionInfo(package = "PhyloProfile")
Adebali, O. and Zhulin, I.B. (2017) Aquerium: A web application for comparative exploration of domain-based protein occurrences on the taxonomically clustered genome tree. Proteins, 85, 72-77.
Altenhoff, A.M. et al. (2016) Standardized benchmarking in the quest for orthologs. Nat Methods, 13, 425-430.
Altenhoff, A.M. et al. (2015) The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements. Nucleic Acids Res, 43, D240-249.
Capra, J.A. et al. (2013) How old is my gene? Trends Genet, 29, 659-668. Daubin, V., Gouy, M. and Perriere, G. (2002) A phylogenomic approach to bacterial phylogeny: evidence of a core of genes sharing a common history. Genome Res, 12, 1080-1090.
Huerta-Cepas, J., Serra, F. and Bork, P. (2016) ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data. Mol Biol Evol, 33, 1635-1638.
Koestler, T., Haeseler, A.v. and Ebersberger, I. (2010) FACT: Functional annotation transfer between proteins with similar feature architectures. BMC Bioinformatics, 11, 417.
Lee, D., Redfern, O. and Orengo, C. (2007) Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol, 8, 995-1005.
Moore, A.D. et al. (2014) DoMosaics: software for domain arrangement visualization and domain-centric analysis of proteins. Bioinformatics, 30, 282-283.
Pellegrini, M. (2012) Using phylogenetic profiles to predict functional relationships. Methods Mol Biol, 804, 167-177.
Pellegrini, M. et al. (1999) Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci U S A, 96, 4285-4288.
Schmitt, T. et al. (2011) Letter to the editor: SeqXML and OrthoXML: standards for sequence and orthology information. Brief. Bioinform., 12, 485-488.
Studer, R.A. and Robinson-Rechavi, M. (2009) How confident can we be that orthologs are similar, but paralogs differ? Trends Genet, 25, 210-216.
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