263 lines
12 KiB
R
263 lines
12 KiB
R
library(shiny)
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library(shinyjs)
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library(shinydashboard)
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#library("wesanderson") # ayyyy lmao hipster af
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library(dplyr)
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library(DT)
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library(ggplot2)
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library(grid) # for the info box
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library(plotly)
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library(shinycssloaders)
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library(NGLVieweR)
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library(httr)
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library(readr)
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library(RCurl)
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shinyServer(function(input, output){
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output$distPlot <- renderPlot({
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# generate bins based on input$bins from ui.R
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x <- faithful[, 2]
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bins <- seq(min(x), max(x), length.out = input$bins + 1)
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# draw the histogram with the specified number of bins
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hist(x, breaks = bins, col = 'darkgray', border = 'white',
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xlab = 'Waiting time to next eruption (in mins)',
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main = 'Histogram of waiting times')
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})
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### NGLViewer ####
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# Structure Viewer WebGL/NGLViewR window
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output$structure <- renderNGLVieweR({
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selected_gene=input$gene
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ngl_gene=as.character(genes[genes$Gene==selected_gene,"PDB"])
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NGLVieweR(ngl_gene) %>%
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addRepresentation("cartoon",
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param = list(name = "cartoon",
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#color="tan",
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colorScheme = "chainid",
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opacity = 1
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)
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) %>%
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stageParameters(backgroundColor = "white") %>%
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setQuality("high") %>%
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setFocus(0) %>%
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setSpin(FALSE)
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})
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output$af_structure <- renderNGLVieweR({
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selected_af_gene=input$gene
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ngl_af_gene=as.character(genes[genes$Gene==selected_af_gene,"AF_PDB"])
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#af_pdb=cat(content(GET(paste0(alphafold_url,"AF-",ngl_af_gene,"-F1-model_v4.pdb")), as="text"), "\n")
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af_pdb=content(GET(paste0(alphafold_url,"AF-",ngl_af_gene,"-F1-model_v4.pdb")), as="text")
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#print(af_pdb)
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#ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb')
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NGLVieweR(af_pdb, format="pdb") %>%
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addRepresentation("cartoon",
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param = list(name = "cartoon",
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#color="tan"
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colorScheme = "bfactor",
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opacity = 1
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)
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) %>%
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stageParameters(backgroundColor = "white") %>%
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setQuality("high") %>%
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setFocus(0) %>%
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setSpin(FALSE)
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})
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# output$table <- DT::renderDataTable({
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# selected_gene=input$gene
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# gene=as.character(genes[genes$Gene==selected_gene,"Gene"])
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# #unique_missense_genes[unique_missense_genes$gene_name==gene,]
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# unique_missense_genes
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# },
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# selection = "single",
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# search = list(search="alr"))
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output$table <- DT::renderDataTable(
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unique_missense_genes,
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selection = "single",
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options=list(
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search = list(search=as.character(genes[genes$Gene==input$gene,"Gene"]))
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)
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)
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observeEvent(input$table_rows_selected,{
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#req(length(input$table_row_selected) > 0)
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mutation_pdb = as.character(unique_missense_genes[input$table_rows_selected,"position_pdb"])
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mutation_af = as.character(unique_missense_genes[input$table_rows_selected,"position_af"])
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mutation = as.character(unique_missense_genes[input$table_rows_selected,"hgvd_p"])
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chain = as.character(unique_missense_genes[input$table_rows_selected,"chain"])
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# what the absolute FUCK is this mess? I JUST WANT THE MATCH ASDLASJASDASHFKLJASDFK
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# coming down from the trees was a mistake, and abandoning Perl doubly so.
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#clicked_position = regmatches(mutation, regexpr("[0-9]+", mutation, perl=TRUE))
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# Now update the 3D structure to highlight the clicked thing and then zoom in.
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NGLVieweR_proxy("structure") %>%
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addSelection('spacefill'
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, param = list(
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name = "Pos"
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, sele = trimws(paste0(mutation_pdb,':', chain))
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, color = "red"
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#, colorValue="00ff00"
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#, colorScheme="element"
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)
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)
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NGLVieweR_proxy("af_structure") %>%
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addSelection('spacefill'
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, param = list(
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name = "Pos"
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, sele = mutation_af
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, color = "red"
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#, colorValue="00ff00"
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#, colorScheme="element"
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)
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)
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NGLVieweR_proxy("af_structure") %>% updateZoomMove(
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center = mutation_af,
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zoom = mutation_af,
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duration = 1000, # animation time in ms
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z_offSet = -1
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)
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NGLVieweR_proxy("structure") %>% updateZoomMove(
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center = trimws(paste0(mutation_pdb,':', chain)),
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zoom = trimws(paste0(mutation_pdb,':', chain)),
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duration = 1000, # animation time in ms
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z_offSet = -1
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)
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output$information <- renderUI({
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selected_gene=input$gene
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HTML(paste0("<strong>Mutation: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mutationinformation_pdb"]),"<br/>",
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"<strong>PDB ID: </strong>", as.character(genes[genes$Gene==selected_gene,"PDB"]),"<br/>",
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"<strong>AlphaFold ID: </strong>", as.character(genes[genes$Gene==selected_gene,"AF_PDB"]), "<br/>",
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"<strong>Ligand Distance: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ligand_distance"]), "<br/>",
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"<strong>mCSM Lig: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_lig"]), "<br/>",
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"<strong>mmCSM Lig: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mmcsm_lig"]), "<br/>",
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"<strong>mCSM DUET: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_duet"]), "<br/>",
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"<strong>FoldX: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_foldx"]), "<br/>",
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"<strong>DeepDDG: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_deepddg"]), "<br/>",
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"<strong>Dynamut2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_dynamut2"]), "<br/>",
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"<strong>mCSM PPI2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_ppi2_affinity"]), "<br/>",
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"<strong>Interface Distance: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"interface_dist"]), "<br/>",
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"<strong>mCSM NA: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_na_affinity"]), "<br/>",
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"<strong>Consurf: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"consurf_score"]), "<br/>",
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#"<strong>SNAP2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"snap2_score"]), "<br/>",
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"<strong>SNAP2 Outcome: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"snap2_outcome"]), "<br/>",
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#"<strong>PROVEAN: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"provean_score"]), "<br/>",
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"<strong>PROVEAN Outcome: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"provean_outcome"]), "<br/>",
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"<strong>RSA: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"rsa"]), "<br/>",
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"<strong>Residue Depth: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"rd_values"]), "<br/>",
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"<strong>Total Samples: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"total_sample_count"]), "<br/>",
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"<strong>Distinct Lineages: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"distinct_lineage_count"]), "<br/>"
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)
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)
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})
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# output$debug <- renderPrint({
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# print(c(mutation, clicked_position))
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# })
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})
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observeEvent(
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{
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input$clear_ngl
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}, {
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NGLVieweR_proxy("structure") %>%
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removeSelection("Pos") %>%
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removeSelection("all_mutations_surface") %>%
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updateVisibility("cartoon", TRUE) %>%
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removeSelection("all_mutations")
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NGLVieweR_proxy("af_structure") %>%
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removeSelection("Pos") %>%
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removeSelection("all_mutations_surface") %>%
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updateVisibility("cartoon", TRUE) %>%
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removeSelection("all_mutations")
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})
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# add a surface representation and highlight all mutations on it
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observeEvent(
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{
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input$all_mutations
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}, {
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gene = input$gene
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mutations = paste0(":",
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as.matrix(unique_missense_genes[unique_missense_genes$gene_name == gene,c("chain")])[1],
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" and (",
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paste0(
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apply(
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unique_missense_genes[unique_missense_genes$gene_name == gene,c("position_pdb","chain")],
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1,
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function(x){
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paste0(trimws(x[1])
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)}
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),
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collapse=", "
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), ")"
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)
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mutations_af = paste0(":A and (",
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paste0(
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apply(
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unique_missense_genes[unique_missense_genes$gene_name == gene,c("position_af","chain")],
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1,
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function(x){
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paste0(trimws(x[1])
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)}
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),
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collapse=", "
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), ")"
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)
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#print(mutations)
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NGLVieweR_proxy("structure") %>%
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updateVisibility("cartoon", FALSE) %>%
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addSelection(type="cartoon",
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param = list(name = "all_mutations_surface",
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sele = "all",
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#color="tan",
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colorScheme="chainid",
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opacity = 0.2
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)
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) %>%
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addSelection(type="spacefill", #spacefill
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param = list(name = "all_mutations",
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color="orange",
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sele = mutations
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#colorScheme = "bfactor",
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#opacity = 1
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)
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)
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NGLVieweR_proxy("af_structure") %>%
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updateVisibility("cartoon", FALSE) %>%
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addSelection(type="cartoon",
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param = list(name = "all_mutations_surface",
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sele = "all",
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#color="tan",
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colorScheme = "bfactor",
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opacity = 0.2
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)
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) %>%
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addSelection(type="spacefill", #spacefill
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param = list(name = "all_mutations",
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color="orange",
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sele = mutations_af
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#colorScheme = "bfactor",
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#opacity = 1
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)
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)
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output$information <- renderUI({
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selected_gene=input$gene
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HTML(paste0("<strong>PDB ID: </strong>", as.character(genes[genes$Gene==selected_gene,"PDB"]),"<br/>",
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"<strong>AlphaFold ID: </strong>", as.character(genes[genes$Gene==selected_gene,"AF_PDB"]), "<br/>",
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"<strong>Gene: </strong>", selected_gene, "<br/>",
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"<strong>Mutations Observed: </strong>",as.character(as.matrix(unique_missense_genes[unique_missense_genes[,'gene_name']==selected_gene,"total_muts_per_gene"])[1]), "<br/>"
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)
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)
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})
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})
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})
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