PhyloProfile
2020-03-14
Abstract
PhyloProfile: dynamic visualization and exploration of multi-layered phylogenetic profilesPackage
PhyloProfile 1.0.7
Introduction
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.
How to install PhyloProfile
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/.
Input
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:
| geneID | ncbiID | orthoID | FAS_F | FAS_B |
|---|---|---|---|---|
| 100136at6656 | ncbi36329 | 100136at6656|PLAF7@36329@1|Q8ILT8|1 | 0.9875289 | 0.8427314 |
| 100136at6656 | ncbi319348 | 100136at6656|POLVAN@319348@0|319348_0:004132|1 | 1.0000000 | 1.0000000 |
| 100136at6656 | ncbi208964 | 100136at6656|PSEAE@208964@1|Q9I5U5|1 | 0.9971027 | 0.9971027 |
| 100136at6656 | ncbi418459 | 100136at6656|PUCGT@418459@1|E3KFA2|1 | 0.9895679 | 0.8232540 |
| 100136at6656 | ncbi10116 | 100136at6656|RAT@10116@1|G3V7R8|1 | 0.9996617 | 0.8541265 |
| 100136at6656 | ncbi284812 | 100136at6656|SCHPO@284812@1|Q9USU2|1 | 0.9994874 | 0.9994874 |
| 100136at6656 | ncbi35128 | 100136at6656|THAPS@35128@1|B8C2N6|1 | 0.9852370 | 0.7002961 |
| 100136at6656 | ncbi7070 | 100136at6656|TRICA@7070@1|D6X457|1 | 1.0000000 | 1.0000000 |
| 100136at6656 | ncbi237631 | 100136at6656|USTMA@237631@1|A0A0D1C927|1 | 0.9912998 | 0.6172244 |
| 100136at6656 | ncbi559292 | 100136at6656|YEAST@559292@1|P41819|1 | 0.9978912 | 0.9978912 |
The WIKI accompanying PhyloProfile gives a comprehensive guide of how to format input data.
Features and capabilities
Interactive visualization and dynamic exploration of phylogenetic profiles
Together with several functions for exploring phylogenetic profiles, we provide an interactive visualization application implemented with Shiny (https://CRAN.R-project.org/package=shiny).
Figure 1: PhyloProfileโs GUI
For both command-based and visualization analyses, users can:
- dynamically change the resolution of the analysis from invidual species to phyla or entire kingdoms by collapsing the input taxa into higher systematic rank. Species are automatically linked to the NCBI taxonomy, and are ordered in increasing taxonomic distance from a user-specified reference taxon. PhyloProfile is able to represent co-orthologs (in-paralogs), if the working taxonomic rank is the deepest one (e.g.ย strain or species) that can be found in the input taxa.
- dynamically filter data by applying different thresholds to the integrated information (e.g.ย increasing the fraction of species in a systematic group that must harbor an ortholog before the gene is considered present in this group reduces the impact of spurious ortholog identification on evolutionary interpretations).
- dynamically modify the apperance of profile with diverse plot configuration options.
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, PhyloProfileโs interface will be automatically varied according to userโs input files, such as the names of two additional information layers or list of input taxa.
Analysis functions
PhyloProfile provides several functions for dynamically analyzing phylogenetic profiles.
Profile clustering
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)
#> $type
#> [1] "phylogram"
#>
#> $use.edge.length
#> [1] TRUE
#>
#> $node.pos
#> [1] 1
#>
#> $node.depth
#> [1] 1
#>
#> $show.tip.label
#> [1] TRUE
#>
#> $show.node.label
#> [1] FALSE
#>
#> $font
#> [1] 3
#>
#> $cex
#> [1] 1
#>
#> $adj
#> [1] 0
#>
#> $srt
#> [1] 0
#>
#> $no.margin
#> [1] FALSE
#>
#> $label.offset
#> [1] 0
#>
#> $x.lim
#> [1] 0.0000000 0.1136892
#>
#> $y.lim
#> [1] 1 4
#>
#> $direction
#> [1] "rightwards"
#>
#> $tip.color
#> [1] "black"
#>
#> $Ntip
#> [1] 4
#>
#> $Nnode
#> [1] 3
#>
#> $root.time
#> NULL
#>
#> $align.tip.label
#> [1] FALSE
Gene age estimation
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"
#' 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, rankName, refTaxon,
var1Cutoff, var2Cutoff, percentCutoff
)
#> geneID cat age
#> 1: 100136at6656 0000001 07_LUCA
#> 2: 100265at6656 0000011 06_Eukaryota
#> 3: 101621at6656 0000001 07_LUCA
#> 4: 103479at6656 0000011 06_EukaryotaCore gene identification
Phylogenomic reconstructions are typically based on a collection of core genes (Daubin et al., 2002), i.e.ย 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")
processedProfileData <- fullProcessedProfile
#' Choose the working rank and a set of taxa of interest
rankName <- "class"
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
#' Identify core genes
getCoreGene(
rankName,
taxaCore,
processedProfileData,
var1Cutoff, var2Cutoff,
percentCutoff, coreCoverage
)
#> [1] "100136at6656" "101621at6656" "103479at6656"Group comparison
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)[4]
#' compare the selected variable between the in-group and out-group taxa
compareTaxonGroups(data, inGroup, TRUE, variable, 0.05)
#> 103479at6656 100136at6656 101621at6656 100265at6656
#> 0.1399542 0.4889198 0.5620258 0.5850421Distribution analysis
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)
#> orthoID var1 var2
#> 4596 100136at6656|PLAF7@36329@1|Q8ILT8|1 0.9875289 0.8427314
#> 4597 100136at6656|POLVAN@319348@0|319348_0:004132|1 1.0000000 1.0000000
#> 4598 100136at6656|PSEAE@208964@1|Q9I5U5|1 0.9971027 0.9971027
#> 4599 100136at6656|PUCGT@418459@1|E3KFA2|1 0.9895679 0.8232540
#> 4600 100136at6656|RAT@10116@1|G3V7R8|1 0.9996617 0.8541265
#> 4602 100136at6656|SCHPO@284812@1|Q9USU2|1 0.9994874 0.9994874
#' 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
)
Examples
Process raw input
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)
#> geneID supertaxon supertaxonID var1 presSpec category
#> 1 100136at6656 1001_Mammalia 40674 0.9996617 1 cat
#> 2 100136at6656 1002_Aves 8782 0.9994875 1 cat
#> 3 100136at6656 1003_Actinopteri 186623 1.0000000 1 cat
#> 4 100136at6656 1005_Leptocardii 2682552 1.0000000 1 cat
#> 5 100136at6656 1006_Insecta 50557 1.0000000 1 cat
#> 6 100136at6656 1007_Branchiopoda 6658 0.9887614 1 cat
#> orthoID var2 paralog taxonMod
#> 1 100136at6656|RAT@10116@1|G3V7R8|1 0.8674499 1 Mammalia
#> 2 100136at6656|CHICK@9031@1|E1C0U9|1 0.9994875 1 Aves
#> 3 100136at6656|DANRE@7955@1|B0S688|1 0.8277451 1 Actinopteri
#> 4 100136at6656|BRAFL@7739@1|C3YM50|1 1.0000000 1 Leptocardii
#> 5 100136at6656|HELME@34740@0|34740_0:00109a|1 0.9167176 1 Insecta
#> 6 100136at6656|DAPMA@35525@0|35525_0:001fd7|1 0.7286372 1 BranchiopodaCreate profile plot
Generate phylogenetic profile heatmap after processing the raw input file.
#' Load built-in processed data
data("fullProcessedProfile", package="PhyloProfile")
#' Create data for plotting
plotDf <- dataMainPlot(fullProcessedProfile)
#' You can also choose a subset of genes and/or taxa for plotting with:
#' selectedTaxa <- c("Mammalia", "Echinoidea", "Gunneridae")
#' selectedSeq <- "all"
#' plotDf <- dataCustomizedPlot(
#' fullProcessedProfile, 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
# )
Create protein domain architecture plot
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)
#> Warning in RColorBrewer::brewer.pal(n = n, name = "Set1"): minimal value for n is 3, returning requested palette with 3 different levels
grid::grid.draw(plot)
Other use cases
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)
#> ncbiID fullName rank parentID
#> 1 1117 Cyanobacteria phylum 1798711
#> 2 1224 Proteobacteria phylum 2
#> 3 1224 Proteobacteria phylum 2
#> 4 1224 Proteobacteria phylum 2
#> 5 1297 Deinococcus-Thermus phylum 1783272
#> 6 2836 Bacillariophyta phylum 2696291Get 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")
]
)
#> ncbiID fullName supertaxon supertaxonID rank
#> 1 ncbi36329 Plasmodium falciparum 3D7 1016_Apicomplexa 5794 phylum
#> 2 ncbi224324 Aquifex aeolicus VF5 1006_Aquificae 200783 phylum
#> 3 ncbi7165 Anopheles gambiae 1010_Arthropoda 6656 phylum
#> 4 ncbi319348 Polypedilum vanderplanki 1010_Arthropoda 6656 phylum
#> 5 ncbi7260 Drosophila willistoni 1010_Arthropoda 6656 phylum
#> 6 ncbi7227 Drosophila melanogaster 1010_Arthropoda 6656 phylumMore examples? Please tell us what you want to see ;-)
How to cite
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 3041โ3043, https://doi.org/10.1093/bioinformatics/bty225
Or use the citation function in R CMD to have the citation in BibTex or LaTeX format
citation("PhyloProfile")
#>
#> To cite PhyloProfile in publications use:
#>
#> 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 3041โ3043
#> https://doi.org/10.1093/bioinformatics/bty225
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {{PhyloProfile}: Dynamic visualization and exploration of multi-layered phylogenetic profiles},
#> author = {Ngoc Vinh Tran and Bastian Greshake Tzovaras and Ingo Ebersberger},
#> journal = {Bioinformatics},
#> volume = {34(17)},
#> pages = {3041โ3043},
#> year = {2018},
#> url = {https://doi.org/10.1093/bioinformatics/bty225},
#> }How to contribute
Thanks so much for your interest in contributing to PhyloProfile ๐๐๐พ
Contributions to PhyloProfile can take many forms. If you are
biologist, you can
- report bugs for both online & standalone version,
- tell us about what features you would love to see,
- improve our documentation, both in the Wiki and in our R ADME,
- discuss about non-coding issues
biologist and love coding, you can
- fix existing bugs,
- or add new features. Some things weโd love to see are: add scripts to add out-of-the-box support for further orthology prediction tools, increasing the test coverage from 0% to something above that, or basically whatever great idea you have!
- and all points for non-coding contributors as well :)
not biologist but can code, it would be great if you can
- test the tool in different environments (Windows, Linux, Mac - Firefox, Chrome, IE, Safari,โฆ),
- suggest a better user interface,
- improve the code quality
Donโt hesitate to get in touch with us if you have any questions. You can contact us at tran@bio.uni-frankfurt.de
Contributors
SessionInfo()
Here is the output of sessionInfo() on the system on which this document was
compiles:
#> R version 3.6.3 (2020-02-29)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
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#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> character(0)
#>
#> other attached packages:
#> [1] PhyloProfile_1.0.7
#>
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#> [91] vctrs_0.2.4 dbplyr_1.4.2
#> [93] tidyselect_1.0.0 xfun_0.12References
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