The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.
ViSEAGO is like a workflow with input lists of genes and associated genomic resources in order to perform enrichment tests, followed by a clustering of GO terms based on theirs semantic similarity. Most of outputs (tables and graphs) could be visualized in static or interactive mode.
NB: This vignette is not runnable and provide a pseudo-code to illustrate ViSEAGO package capabilities.
Firstly, you need to provide the gene background (= expressed genes) and selection files.
Or a table with gene identifiers and statistical value to use for ranking.
The Gene Ontology (GO) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge .
Currently, the 3 predominant genomic ressources are EntrezGene , Ensembl , and Uniprot-GOA . Furthermore, an easy access with the R langage  to some genomic GO annotations from EntrezGene is available using Bioconductor  org.xx.eg.db databases. However, there are important differences between supported species, and corresponding genes knowledge according to database used. This has necessary an impact on GO annotations and enrichment and downstream analyses.
More precisely, since December 2006, the GOA and Ensembl Compara teams have used the gene orthology data obtained from Ensembl Compara to project GO terms from a source species onto one or more target species. For this pipeline, only one to one and apparent one to one orthologies are used, and only manually annotated GO terms with an experimental evidence type of either EXP, IDA, IEP, IGI, IMP or IPI are projected. GO annotations created using this technique receive the evidence code ‘IEA’ (Inferred from Electronic Annotation).
The easy access to GO annotations for all the availables species from these databases are provided by:
# connect to Bioconductor Bioconductor<-ViSEAGO::Bioconductor2GO() # connect to EntrezGene EntrezGene<-ViSEAGO::EntrezGene2GO() # connect to Ensembl Ensembl<-ViSEAGO::Ensembl2GO() # connect to Uniprot-GOA Uniprot<-ViSEAGO::Uniprot2GO() # connect to Custom file Custom<-ViSEAGO::Custom2GO(system.file("extdata/customfile.txt",package = "ViSEAGO"))
In order to know if your target species is supported by the database, we can display the available organisms in an interactive table using
ViSEAGO::available_organisms method. The organism key identifiant is always displayed in the first column of the table.
# Display table of available organisms with Bioconductor ViSEAGO::available_organisms(Bioconductor) # Display table of available organisms with EntrezGene ViSEAGO::available_organisms(EntrezGene) # Display table of available organisms with Ensembl ViSEAGO::available_organisms(Ensembl) # Display table of available organisms with Uniprot ViSEAGO::available_organisms(Uniprot) # Display table of available organisms with Custom ViSEAGO::available_organisms(Custom)
At this step, we need to provide the organism key identifiant based on the genomic ressource used (Bioconductor, EntrezGene, Ensembl, or Uniprot), in order to extract and store the species GO annotations using
# load GO annotations from Bioconductor myGENE2GO<-ViSEAGO::annotate( "bioconductor_id", Bioconductor ) # load GO annotations from EntrezGene myGENE2GO<-ViSEAGO::annotate( "EntrezGene_id", EntrezGene ) # load GO annotations from EntrezGene # with the add of GO annotations from orthologs genes (see above) myGENE2GO<-ViSEAGO::annotate( "EntrezGene_id", EntrezGene, ortholog = TRUE ) # load GO annotations from Ensembl myGENE2GO<-ViSEAGO::annotate( "Ensembl_id", Ensembl ) # load GO annotations from Uniprot myGENE2GO<-ViSEAGO::annotate( "Uniprot_id", Uniprot ) # load GO annotations from Custom myGENE2GO<-ViSEAGO::annotate( "Custom_id", Custom )
The GO terms enrichment tests can be performed with the topGO package , which support several algorithms that improve GO group scoring using the underlying GO graph topology.
First, we create a
topGOdata object, using
ViSEAGO::create_topGOdata method, with inputs as: genes selection, genes background, GO terms category used (MF, BP, or CC), and minimum of annotated genes by GO terms (nodeSize).
Now, we perform the GO enrichment tests for BP category with Fisher’s exact test with one of the four available algorithms (classic, elim, weight, and weight01) using
The GO terms enrichment tests can be performed with the fgsea package , with inputs as: preranked gene identifiers with statistical values , GO terms category used (MF, BP, or CC), fgsea algorithm, and fgsea parameters to use.
We combine results of enrichment tests into an object using
ViSEAGO::merge_enrich_terms method. A table of enriched GO terms in at least one comparison is displayed in interactive mode, or printed in a file using
ViSEAGO::show_table method. The printed table contains for each enriched GO terms, additional columns including the list of significant genes and frequency (ratio of number of significant genes to number of background genes) evaluated by comparison.
Several graphs summarize important features. An interactive barchart showing the number of GO terms enriched or not in each comparison, using
ViSEAGO::GOcount method. Intersections of lists of enriched GO terms between comparisons are displayed in an Upset plot using
GO annotations of genes created at a previous step and enriched GO terms are combined using
ViSEAGO::build_GO_SS method. The Semantic Similarity (SS) between enriched GO terms are calculated using
ViSEAGO::compute_SS_distances method which is a wrapper of functions implemented in the GOSemSim package . Among the SS algorithms between GO terms, 4 of them use information content (IC) of a GO term which is computed by the negative log probability of the term occurring in GO corpus. A rarely used term contains a greater amount of information. These IC-based mehods are Resnik, Lin, Rel, and Jiang. A Graph-based method, using the topology of GO graph structure to compute semantic similarity is also available with the Wang method. The built object
myGOs contains all informations of enriched GO terms and computed SS distances between them.
A Multi Dimensional Scale (MDS) plot with
ViSEAGO::MDSplot method provides a representation of distances among a set of enriched GO terms on the two first dimensions. Some patterns could appear at this time and could be investigated in an interactive mode. The plot could be also printed in a png file.
To fully explore the results of this functional analysis, a hierarchical clustering method using
ViSEAGO::GOterms_heatmap is performed based on one of SS distances (i.e
Wang) between the enriched GO terms and a chosen aggregation criteria (i.e
Clusters of enriched GO terms are obtained by cutting branches off the dendrogram. Here, we choose a dynamic branch cutting method based on the shape of clusters using dynamicTreeCut [11,12]. It builds clusters in a bottom top manner in two steps. In step 1, branches that satisfy specific criteria for being clusters, based on the merging information of the dendrogram, are detected. A simple control of the relative sensitivity to cluster splitting is provided in the parameter deepSplit (The higher the value, the more and smaller clusters will be produced), and the minimum cluster size with minClusterSize. In step 2, all previously unassigned objects are tested for sufficient proximity to clusters detected in the first step using a modified Partitioning Around Medoids (PAM). If the closest cluster is close enough in a precise sense that we define below, the object is assigned to that cluster. The PAM stage will be performed using pamStage=TRUE parameter, and respect the dendrogram using pamRespectsDendro=TRUE parameter, in the sense an object can be PAM-assigned only to clusters that lie below it on the branch that the object is merged into.
Enriched GO terms are ranked in a dendrogram as a result of a hierarchical clustering and colored depending on their cluster assignation. Additional illustrations are displayed: the GO description of GO terms (trimmed if more than 50 characters), a heatmap of -log10(pvalue) of enrichment test for each comparison, and optionally the Information Content (IC). The illustration is displayed with
# GOterms heatmap with the default parameters Wang_clusters_wardD2<-ViSEAGO::GOterms_heatmap( myGOs, showIC=TRUE, showGOlabels=TRUE, GO.tree=list( tree=list( distance="Wang", aggreg.method="ward.D2" ), cut=list( dynamic=list( pamStage=TRUE, pamRespectsDendro=TRUE, deepSplit=2, minClusterSize =2 ) ) ), samples.tree=NULL )
A table of enriched GO terms in at least one of the comparison is displayed in interactive mode, or printed in a file using
ViSEAGO::show_table method. The columns GO cluster number and IC value are added to the previous table of enriched terms table. The printed table contains for each enriched GO terms, additional columns including the list of significant genes and frequency (ratio between the number of significant genes and number of background genes in a specific GO tag) evaluated by comparison.
We also display a Multi Dimensional Scale (MDS) plot with the overlay of GO terms clusters from
Wang_clusters_wardD2 object with
ViSEAGO::MDSplot method. It is a way to check the coherence of GO terms clusters on the MDS plot.
This part is dedicated to explore relationships between clusters of GO terms defined by the last hierarchical clustering with
ViSEAGO::GOterms_heatmap method. This analysis should be conducted if the number of clusters of GO terms is large enough and therefore difficult to investigate.
A Semantic Similarity (SS) between clusters of GO terms, previously defined, are calculated using
The availables SS algorithms between GO terms clusters, implemented in the GOSemSim package , could be max, average, rcmax, or BMA.
A colored Multi Dimensional Scale (MDS) plot with
ViSEAGO::MDSplot method provides a representation of distances between the clusters of GO terms. Each circle represents a cluster of GO terms and its size depends on the number of GO terms that it contains. Clusters of GO terms that are close should share a functional coherence.
A new hierarchical clustering using
ViSEAGO::GOclusters_heatmap is performed based on one of the SS distance (i.e.
BMA) between the clusters of GO terms, previously computed, and a chosen aggregation criteria (i.e
Clusters of GO terms are ranked in a dendrogram and branches are colored depending on the cluster assignation. Additional illustrations are displayed: definition of the cluster of GO terms corresponds to the first common GO term ancestor followed by the cluster label in brackets, and heatmap of GO terms count from the corresponding cluster. The illustration is displayed with
A complete functional Gene Ontology (GO) enrichment analysis can be performed using ViSEAGO package. It facilitates the interpretation of biological processes, functions, or cellular component involved in the study. It provides both a synthetic and detailed view using interactive functionalities respecting GO graph structure and ensuring functional coherence.
Also, comparisons of enrichment can be made on large datasets and for complex experimental designs, such as explained in mouse mammary gland status comparison example (see
Moreover, we can explore the effect of the GO semantic similarity algorithms on the tree structure, such as illustrate in the clustering comparions example (see
_ platform x86_64-pc-linux-gnu arch x86_64 os linux-gnu system x86_64, linux-gnu status RC major 4 minor 2.0 year 2022 month 04 day 21 svn rev 82226 language R version.string R version 4.2.0 RC (2022-04-21 r82226) nickname Vigorous Calisthenics
1. Blake JA, Christie KR, Dolan ME, Drabkin HJ, Hill DP, Ni L, et al. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015;43:D1049–1056.
2. Brown GR, Hem V, Katz KS, Ovetsky M, Wallin C, Ermolaeva O, et al. Gene: a gene-centered information resource at NCBI. Nucleic Acids Res. 2015;43:36–42.
3. Aken BL, Achuthan P, Akanni W, Amode MR, Bernsdorff F, Bhai J, et al. Ensembl 2017. Nucleic Acids Res. 2017;45:D635–42.
4. Huntley RP, Sawford T, Mutowo-Meullenet P, Shypitsyna A, Bonilla C, Martin MJ, et al. The GOA database: gene Ontology annotation updates for 2015. Nucleic Acids Res. 2015;43:D1057–1063.
5. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2017. Available from: https://www.R-project.org/
6. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–21.
7. Fong JH, Murphy TD, Pruitt KD. Comparison of RefSeq protein-coding regions in human and vertebrate genomes. BMC Genomics. 2013;14:654.
8. Alexa A, Rahnenfuhrer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22:1600–7.
9. Korotkevich G, Sukhov V, Sergushichev A. Fast gene set enrichment analysis. bioRxiv [Internet]. Cold Spring Harbor Labs Journals; 2019; Available from: http://biorxiv.org/content/early/2016/06/20/060012
10. Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics. 2010;26:976–8.
11. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics. 2008;24:719–20.
12. Langfelder P, Zhang B, Steve Horvath. DynamicTreeCut: Methods for detection of clusters in hierarchical clustering dendrograms [Internet]. 2016. Available from: https://CRAN.R-project.org/package=dynamicTreeCut