Updated scripts for conversion from PC to AF maps using CasQ Minerva and...

Updated scripts for conversion from PC to AF maps using CasQ Minerva and Hipathia producing sif and att files

Resources/Hipathia/sif_att_converter/README.md

+# Converter of COVID-19 CellDesigner diagrams "raw SIF" files obtained using CaSQ and MINERVA to HiPathia "SIF" and "ATT" file format.
+
+The present directory contains the code and files necessary to convert *Simple Interaction Files (SIF)*, generated with CaSQ python package, from curated COVID-19 related CellDesigner diagrams to HiPathia's *SIF* and *ATT (attribute)* files. The *ATT* files are generated using Minerva, as a proxy between CaSQ and CellDesigner, to retrieve details of each node. These *SIF* and *ATT* files are needed by the HiPathia mechanistic modelling metagraphinfo database in order to run the algorithm over the diagrams. After running the conversor, each diagram will count with a *"diagram.sif"* and a *"diagram.att"* file,moreover a *"names.pathways.txt"* file listing the names of the diagrams converted will be also generated.
+
+**The directory is structured into 2 folders and 2 scripts.**
+
+### Scripts
+
+** - functions_retrieve_sif_att_hipathia_from_CellDesigner.R**:
+
+The present script contains the functions needed for converting *raw SIF *files from CellDesigner to HiPathia *SIF* and *ATT* files. The script is autodescriptive and counts with 5 functions for the retrieval, preprocessing and formatting of the raw SIF files. The functions enable to handle API querys, retrieve detailed information of each node from a CellDesigner diagram and format the information into HiPathia's SIF and ATT files. Each function is fully describe in the script. DO NOT EDIT (only edit for coding purposes).
+
+
+
+
+** - example_sif_Hipathia.R**
+
+The present script contains the example code to run over the existing diagrams on the [https://git-r3lab.uni.lu/covid/models/-/tree/master/](https://git-r3lab.uni.lu/covid/models/-/tree/master/) repository, please in order to run the code correctly clone the repository on a local directory and recode the "base_dir" variable to that directory. The script will read the *Simple Interaction Files (SIF)* from the CellDesigner diagrams located in */models-master/Executable Modules/SBML_qual_build/sif* in the repository, generate HiPathia formatted *SIF* and *ATT* files for each diagram and a *"names.pathways.txt"* file listing the names of the diagrams converted. The files are by default stored in the **test folder**, this parameter can be change in the *outputfolder* parameter of the *hipathia.att.sif* function.
+
+<u>**CAUTION**:</u> Please when running the script make sure you recode the variable "base_dir" to the local directory where the repo *models-master* was cloned.
+
+
+
+### Resources folder
+Contains the *"exceptions.txt"* file, which contains for the nodes we desire to avoid, nodes that are not allowed in the metagraphinfo database such as viral gene nodes.
+
+### Test folder
+Empty folder that will store the results of running the *"example_sif_Hipathia.R"*
+
+
+### Contributors
+
+[Marek Ostaszewski](https://fairdomhub.org/people/665)
+
+[Marina Esteban-Medina](https://fairdomhub.org/people/1873)
+
+
+

Resources/Hipathia/sif_att_converter/example_sif_Hipathia.R

+      ###############################################################################
+##########   EXAMPLE CODE: From CellDseigner CaSQ "Raw sif" files  to         ############
+##############             Hipathia "sif" and "att" format using Minerva   #############
+     #############################################################################
+     
+###############################################################################################################     
+## IMPORTANT: CHANGE THE "base_dir" parameter to the local directory where the repository was cloned !!!!  ####
+      
+      base_dir <- "~/Desktop/models-master/"
+      
+#####################################################################################################      
+      
+#### 1. LOAD LIBRARIES  AND FUNCTIONS  #####
+
+             ## Load functions
+      source(file.path(paste0(base_dir, "/Resources/Hipathia/sif_att_converter/functions_retrieve_sif_att_hipathia_from_CellDesigner.R")), echo=TRUE) 
+
+      
+#### 2. LOAD FOLDER, CREATE LIST OF RAW_SIF_FILES; LOAD MINERVA MAPS AND INFO NEEDED ####
+      
+      ### Read the raw sif files from the corresponding folder in the repository
+  
+      sif_files_path <- file.path(paste0(base_dir,"/Executable Modules/SBML_qual_build/sif"))
+      
+      raw_sif_list <- list.files(path = sif_files_path , all.files = F) %>% .[grep("*_raw*", .)] 
+      
+      diagrams <- gsub("_stable_raw.sif", "", raw_sif_list) 
+      
+      ### Get MINERVA elements
+      
+      ### The address of the COVID-19 Disease Map in MINERVA
+      map <- "https://covid19map.elixir-luxembourg.org/minerva/api/"
+      
+      ### Get configuration of the COVID-19 Disease Map, to obtain the latest (default) version
+      cfg <- fromJSON(ask_GET(map, "configuration/"))
+      project_id <- cfg$options[cfg$options$type == "DEFAULT_MAP","value"]
+      
+      
+      ### The address of the latest (default) build 
+      mnv_base <- paste0(map,"projects/",project_id,"/")
+      
+      message(paste0("Asking for diagrams in: ", mnv_base, "models/"))
+      
+      ### Get diagrams
+      models <- ask_GET(mnv_base, "models/")
+      models <- fromJSON(models, flatten = F)
+      
+      ### Read the list of resources to be integrated, from the MINERVA build scripts
+      res <- read.csv(url("https://git-r3lab.uni.lu/covid/models/raw/master/Integration/MINERVA_build/resources.csv"),
+                      header = T, stringsAsFactors = F)
+      
+      
+#### 3. RETRIEVE TTRANSALTED SIF FILES WITH DETAILED INFORMATION OF THE NODES AND INTERACTIONS OF EACH GRAPH USING MINERVA #####
+      
+  ### The obtained translated_sif_files counts with: X-Y coords info, genesList in entrez Identifier and type of 
+  ###  node ( simple, complex or protein family). The info will be retrieved and stored as a data.frame for each diagram.                                                    
+      
+      translated_sif_files <- lapply(raw_sif_list[-c(6)],
+                                     function(x){extract_translatedSIF_Minerva(x)}) 
+      
+      
+#### 4. FORMAT TRANSALTED SIF FILES OBTAINED FOR HIPATHIA METAGRAPHINFO DATABASE ##### 
+      
+      
+      att_sif_Hformat <- mapply(hipathia.att.sif, minerva_translated_sif = translated_sif_files, diagram_name = diagrams[-6], 
+                                outputfolder = "~/Desktop/models-master/Resources/Hipathia/sif_att_converter/test")
+      
+      
\ No newline at end of file

Resources/Hipathia/sif_att_converter/functions_retrieve_sif_att_hipathia_from_CellDesigner.R

+##########################################################################################
+###############   FUNCTIONS CREATED TO RETRIEVE, EDIT AND EXPORT CaSQ      ###############
+###############   Simple Interaction Files (SIF) FROM CellDesigner TO      ###############
+###############   HiPathia "ATT" and "SIF" FORMAT USING Minerva AS PROXY   ###############
+##########################################################################################
+
+
+if (!require(pacman)) {
+  install.packages("pacman")
+}
+
+if (!require(BiocManager)) {
+  install.packages("BiocManager")
+}
+
+if (!requireNamespace("AnnotationDbi", quietly = TRUE)){
+  BiocManager::install("AnnotationDbi")
+}
+  
+if (!requireNamespace("hipathia", quietly = TRUE)){
+  BiocManager::install("hipathia", version = "3.10")
+}
+
+if (!requireNamespace("SummarizedExperiment", quietly = TRUE)){
+  BiocManager::install("SummarizedExperiment", version = "3.10")
+}
+
+if (!requireNamespace("org.Hs.eg.db", quietly = TRUE)){
+  BiocManager::install("org.Hs.eg.db", version = "3.10")
+}
+
+
+pacman::p_load("here","igraph","rentrez","reutils","hipathia", "biomaRt", "utils", "stringr", "SummarizedExperiment", "magrittr",
+               "AnnotationDbi","org.Hs.eg.db","dplyr","tidyr", "openxlsx", "data.table", "gdata", "httr", "jsonlite")
+
+
+##################################################
+## Project: COVID-19 Disease Map
+## Script purpose: Translate raw CellDesigner SIF obtained from CaSQ to Entrez identifiers using MINERVA 
+## Date: 11.06.2020
+## Author: Marek Ostaszewski
+##################################################
+
+## A convenience function to handle API queries
+ask_GET <- function(furl, fask) {
+  print(URLencode(paste0(furl, fask)))
+  resp <- httr::GET(url = URLencode(paste0(furl, fask)), write_memory(),
+                    httr::add_headers('Content-Type' = "application/x-www-form-urlencoded"),
+                    ### Currently ignoring SSL!
+                    httr::set_config(config(ssl_verifypeer = 0L)))
+  if(httr::status_code(resp) == 200) {
+    ### when the content is sent as a zip file, it needs to be handled differently,
+    ### i.e. saved to a tmp file and unzipped
+    if(headers(resp)$`content-type` == "application/zip") {
+      tmp <- tempfile()
+      tmpc <- file(tmp, "wb")
+      writeBin(httr::content(resp, as = "raw"), con = tmpc)
+      close(tmpc)
+      unzipped <- unzip(tmp)
+      file.remove(tmp)
+      return(unzipped)
+    }
+    return(httr::content(resp, as = "text"))
+  }
+  return(NULL)
+}
+
+### An utility function to retrieve Entrez based on the species id
+### if the id is a complex, the function goes recursively and fetches the ids of elements in this complex
+group_elements <- function(feid, felements, fentrez) {
+  pos <- which(felements$elementId == feid)
+  ### Any elements that may be nested in the 'feid' (CellDesigner alias)
+  incs <- felements$elementId[felements$complexId %in% felements$id[pos]]
+  if(length(incs) > 0) {
+    ### If nested elements found, run the function recursively for the contained elements
+    return(paste(unlist(sapply(incs, group_elements, felements, fentrez)), collapse = ";"))
+  } else {
+    ### If no nested elements, return Entrez
+    rid <- fentrez[[feid]]
+    if(is.na(rid)) {
+      ### If Entrez not available, return name
+      rid <- felements$name[pos]
+    }
+    return(rid)
+  }
+}
+
+### A workaround function to get information about hypothetical complexes; 
+### currently MINERVA API does not support this, we need to get the entire CD file and parse it
+get_groups <- function(fname) {
+  message(paste0("Getting groups for ", fname, "..."))
+  library(xml2)
+  ## Currently comment out the MINERVA download, some content dlded as binary!
+  cd_map <- read_xml(ask_GET(mnv_base,
+                             paste0("models/",
+                                    models$idObject[models$name == fname],
+                                    ":downloadModel?handlerClass=lcsb.mapviewer.converter.model.celldesigner.CellDesignerXmlParser")))
+  ### CellDesigner namespace
+  ns_cd <- xml_ns_rename(xml_ns(read_xml("<root>
+                                            <sbml xmlns = \"http://www.sbml.org/sbml/level2/version4\"/>
+                                            <cd xmlns = \"http://www.sbml.org/2001/ns/celldesigner\"/>
+                                          </root>")), 
+                         d1 = "sbml", d2 = "cd")
+  ### Get complex ids
+  cids <- xml_attr(xml_find_all(cd_map, "//cd:complexSpeciesAlias", ns_cd), "species")
+  ### For each check, which is hypothetical
+  hypocs <- sapply(cids, 
+                   function(x) {
+                     tcid <- xml_find_first(cd_map, paste0("//sbml:species[@id='", x, "']"), ns_cd)
+                     ifelse(length(tcid) > 0, xml_text(xml_find_first(tcid, ".//cd:hypothetical", ns_cd)), NA)
+                   })
+  names(hypocs) <- gsub("s_id_", "",names(hypocs))
+  return(hypocs)
+}
+
+### A convenience function that makes use of the previous ones to retrieve the translated SIF file from CellDesigner
+### and CaSQ using Minerva. The function allows to apply the operation to all the diagrams listed in "sif_raw_gitLab" 
+#' @param sif_raw_gitLab list of raw sif files obtained from CaSQ.
+
+extract_translatedSIF_Minerva <- function(sif_raw_gitLab, 
+                                          base_url = "https://git-r3lab.uni.lu/covid/models/-/raw/master/Executable%20Modules/SBML_qual_build/sif/"){
+  
+  message(paste0("Translating..", sif_raw_gitLab))
+  
+  ### Read in the raw SIF version (here straight from the gitLab Covid19)
+  
+  raw_sif <- read.table(url(paste0(base_url, sif_raw_gitLab)),
+                        sep = " ", header = F, stringsAsFactors = F)
+  
+  diagram <- gsub(pattern = "_raw.sif", ".xml", sif_raw_gitLab)
+  
+  
+  diag_name <- res[str_detect(res$Resource, diagram), "Name"]
+  
+  ### Get elements of the chosen diagram
+  model_elements <- fromJSON(ask_GET(paste0(mnv_base,"models/",models$idObject[models$name == diag_name],"/"), 
+                                     "bioEntities/elements/?columns=id,name,type,references,elementId,complexId,bounds"),
+                             flatten = F)
+  
+  message("Fetching entrez ids...")
+  
+  
+  ### Get information about Entrez identifiers from MINERVA elements
+  entrez <- sapply(model_elements$references, function(x) ifelse(length(x) == 0, NA, x[x$type == "ENTREZ", "resource"]))
+  names(entrez) <- model_elements$elementId
+  
+  cgroups <- get_groups(diag_name)
+  
+  message("Translating...")
+  
+  
+  ### Create a copy
+  translated_sif <- raw_sif
+  
+  
+  ### Retrieve Entrez and type for the entire columns of sources and targets
+  s.entrez <- sapply(raw_sif[,1], group_elements, model_elements, entrez)
+  s.type <- sapply(raw_sif[,1], function(x) { ifelse(x %in% names(cgroups),
+                                                     ifelse(is.na(cgroups[x]), "complex", "group"),
+                                                     "node") })
+  t.entrez <- sapply(raw_sif[,3], group_elements, model_elements, entrez)
+  t.type <- sapply(raw_sif[,3], function(x) { ifelse(x %in% names(cgroups),
+                                                     ifelse(is.na(cgroups[x]), "complex", "group"),
+                                                     "node") })
+  ### Collect x.y information
+  s.xy <- t(sapply(raw_sif[,1], function(x) unlist(model_elements$bounds[model_elements$elementId == x, c(3,4)])))
+  colnames(s.xy) <- c("source.x", "source.y")
+  t.xy <- t(sapply(raw_sif[,3], function(x) unlist(model_elements[model_elements$elementId == x,1][,c(3,4)])))
+  colnames(t.xy) <- c("targets.x", "targets.y")
+  
+  ### Combine into a single data frame
+  translated_sif <- data.frame(source = s.entrez, source.type = s.type,
+                               sign = raw_sif[,2], 
+                               target = t.entrez, target.type = t.type,
+                               s.xy, t.xy, stringsAsFactors = F)
+  
+  ## In case export is desired
+  # write.table(translated_sif, file = paste0(diag_name,"_translated_sif.txt"),
+  #             sep = "\t", quote = F, col.names = T, row.names = F)
+  
+  return(translated_sif)
+  message("Done.")  
+  
+}
+
+
+#################################################
+## Project: COVID-19 Disease Map
+## Script purpose: Translate Entrez identifiers SIF  files from MINERVA to HIPATHIA SIF and ATT files for metagraphinfo compilation.
+## Date: 21.10.2020 
+## Author: Marina Esteban Medina
+##################################################
+
+
+###################################################################################################
+### Function to pass translatedSIF_Minerva sif files information to Hipathia att and sif files ####
+#' @param minerva_translated_sif the sif file from CaSQ with entrez identifiers from MINERVA
+#' @param diagram_name name of the diagram we are adding
+#' @param outputfolder the output file path, it will return the sif an att file in Hipathia format
+#' @param exceptions file of the nodes exceptions, such as viral genes with entrez ID, that Hipathia does not processes
+########################################################################################################################
+
+hipathia.att.sif <- function (minerva_translated_sif, diagram_name, outputfolder, 
+                              exceptions = file.path(paste0(base_dir, "/Resources/Hipathia/sif_att_converter/resources/exceptions.txt"))) {
+  
+  message(paste0("Working on...", diagram_name))
+
+  exceptions_2avoid <- read.table(exceptions, stringsAsFactors = F) %>% mutate_all(as.character) %>% .$V1
+    
+  ## Clean auto interaction of nodes
+  minerva_translated_sif <- mutate_all(minerva_translated_sif, as.character)
+  minerva_translated_sif <- minerva_translated_sif[!minerva_translated_sif$source == minerva_translated_sif$target, ]
+  
+  ## Clean nodes that are not genes with and entrez ID nor a Function node.
+  ## We clean the source and target columns to avoid non gene-gene interactions.
+  minerva_translated_sif  <- minerva_translated_sif[which(!grepl("[a-z];[0-9]+|[A-Z];[0-9]+|[0-9]+;[a-z]|[0-9]+;[A-Z]|[A-Z]+[a-z]+[0-9]+[[:punct:]];[0-9]+|Unfolded protein", minerva_translated_sif$source)),] %>% 
+    .[which(!grepl("[a-z];[0-9]+|[A-Z];[0-9]+|[0-9]+;[a-z]|[0-9]+;[A-Z]|[A-Z]+[a-z]+[0-9]+[[:punct:]];[0-9]+|Unfolded protein", .$target)),] 
+  
+  ## Replace space with "_" in the diagrams name
+  
+  diagram_name <- gsub(" ", "_", diagram_name)
+  
+  ## Start bulding the att data frame, cols = ID, LABEL, X, Y, COLOR, SHAPE, TYPE, LABEL.CEX, LABEL.COLOR, WIDTH, HEIGHT, genesList
+  nodes <- unique(c(minerva_translated_sif$source, minerva_translated_sif$target))
+  
+  ## Create a table with the type of nodes, complex or protein family
+  
+  nodes_gene_type <- data.frame(nodes = c(minerva_translated_sif$source, minerva_translated_sif$target),
+                                type = c(minerva_translated_sif$source.type, minerva_translated_sif$target.type))%>% 
+    .[!duplicated(.$nodes),] %>% mutate_all(as.character)
+  
+  
+  
+  id_num <- seq_along(nodes)
+  
+  ## Create empty vectors for the columns of the att data.frame that we will then export to ".att" file of each diagram
+  ID = sapply(id_num, function(x) {paste("N", paste0("hsaCovid19", diagram_name ), x , sep = "-")})
+  
+  sif <- data.frame(ID, nodes)
+  
+  label <- c()
+  shape <- c()
+  type <- c()
+  genesList <- c()
+  
+  for (i in seq_along(nodes)){
+    
+    ## In the case of human gene nodes with entrez identifiers
+    if (grepl ("[A-Z]|[a-z]", nodes[i]) == F && nodes[i] != "8673700" && !(nodes[i] %in% exceptions_2avoid) ){ ## those are new entrez ID of SARS-CoV2 which we want to avoid
+      
+      message(paste0("Translating...", nodes[i]))
+      
+      label[i] <- str_split(nodes[i], pattern = ";")  %>% sapply ( . , function(x) { 
+        mapIds(org.Hs.eg.db, keys = x, column = "SYMBOL", keytype = "ENTREZID")
+      }) %>% paste(., collapse = ";")
+      
+      shape[i] <- "ellipse"
+      
+      type[i] <- "gene"
+      
+      genesList[i] <- nodes[i]
+      
+      if(nodes_gene_type$type[nodes_gene_type$nodes %in% nodes[i]] == "complex"){
+        genesList[i] <- gsub( ";", ",/,", nodes [i]) ##  substitute group sign (;) by what Hipathia takes as complex sign (,/,)
+      }
+      
+      ## In the case of function nodes 
+      
+    }else if(grepl ("^[A-Z]+[aeiou]+[[:punct:]]*|^[a-z]+[aeiou]+[[:punct:]]*", nodes[i]) == T){ 
+      
+      label[i] <- nodes[i]
+      
+      shape[i] <- "rectangle" ## Hipathia symbol for "function" element in the graph
+      
+      type[i] <- "node"
+      
+      genesList[i] <- "NA"
+      
+      ID[i] <- paste0(ID[i],"_func")
+      
+      
+      
+    }else{
+      
+      message(paste0("Translating...", nodes[i]))
+      
+      ## We only use this if we want to keep the non gene nodes visually  
+      label[i] <- nodes[i]
+      
+      shape[i] <- "circle"
+      
+      type[i] <- "other"
+      
+      genesList[i] <- "NA"
+    }
+  }
+  
+  
+  
+  X <- c()
+  
+  Y <- c()
+  
+  for (i in seq_along(nodes)){
+    
+    if(nodes[i] %in% minerva_translated_sif$source){
+      
+      X[i] <- minerva_translated_sif$source.x[match(nodes[i], minerva_translated_sif$source)] 
+      
+      Y[i] <- minerva_translated_sif$source.y[match(nodes[i], minerva_translated_sif$source)] 
+      
+    }else{
+      
+      X[i] <- minerva_translated_sif$targets.x[match(nodes[i], minerva_translated_sif$target)] 
+      
+      Y[i] <- minerva_translated_sif$targets.y[match(nodes[i], minerva_translated_sif$target)] 
+      
+    }
+    
+  }
+  
+  if(length(id_num) == 0 ){
+    
+    message(paste0("No human genes founded in ", diagram_name, "...DELETED "))
+    
+  }else{
+    
+    ## Color, label.cex, label.color, width and height are constant for all the nodes
+    att_hipathia = data.frame ( ID, label, X, Y, color = "white", shape, type, label.cex = "0.62", label.color = "black", 
+                                width = "46", height = "17", genesList )
+    
+    
+    ## Edit the .sif file in Hipathia format, subsitute interactions for "activation" or "inhibition".
+    
+    interaction = gsub("POSITIVE", "activation", minerva_translated_sif$sign) %>% gsub("NEGATIVE", "inhibition", .)
+    
+    sif_hipathia <- data.frame(source = sif$ID [match(minerva_translated_sif$source, sif$nodes)], interaction,
+                               target = sif$ID [match(minerva_translated_sif$target, sif$nodes)])
+    
+    sif_hipathia <- sif_hipathia[sif_hipathia$source %in% att_hipathia$ID, ]
+    
+    ## Create the "name.pathways_hsa.txt" file. This file serves as list of the pathways included in the metaginfo (metagraph-database) Hipathia takes as pathways database.
+    
+    name.pathways_hsa_file <- data.frame(code = paste0("Covid19", diagram_name), name = diagram_name)
+    
+    
+    ## Export the "att", "sif" and  "name.pathways_hsa.txt"  to the output folder selected
+    
+    write.table(att_hipathia, file = paste(outputfolder, paste0(paste0("hsaCovid19", diagram_name ) ,".att"), sep = "/"), sep = "\t", quote = F, col.names = T, row.names = F )
+    
+    write.table(sif_hipathia, file = paste(outputfolder, paste0(paste0("hsaCovid19", diagram_name ) ,".sif"), sep = "/"), sep = "\t", quote = F, col.names = F, row.names = F )
+    
+    write.table(name.pathways_hsa_file, file = paste(outputfolder, "name.pathways_hsa.txt", sep = "/"),append = T, sep = "\t", col.names = F, row.names = F, quote = F )
+    
+    message(paste0(diagram_name, "... Done"))
+    
+    ## The function also returns the  "sif and "att" data.frames created in a list for downstream actions.
+    
+    return(list("att" = att_hipathia, "sif" = sif_hipathia))
+    
+    
+  }
+  
+  
+}
+

Resources/Hipathia/sif_att_converter/resources/exceptions.txt

+8673700
+43740575
+443740570
+43740570
+43740571
+4348

Resources/Hipathia/sif_att_converter/test/hsaCovid19Apoptosis.att

+ID	label	X	Y	color	shape	type	label.cex	label.color	width	height	genesList
+N-hsaCovid19Apoptosis-1	FAS;FASLG	360	79.75	white	ellipse	gene	0.62	black	46	17	355,/,356
+N-hsaCovid19Apoptosis-2	BCL2L11;BBC3;BAD	1149	615	white	ellipse	gene	0.62	black	46	17	10018,/,27113,/,572
+N-hsaCovid19Apoptosis-3	BCL2;MCL1;BCL2L1	1013	470.5	white	ellipse	gene	0.62	black	46	17	596,/,4170,/,598
+N-hsaCovid19Apoptosis-4	CYCS;APAF1;CASP9	486	693	white	ellipse	gene	0.62	black	46	17	54205,/,317,/,842
+N-hsaCovid19Apoptosis-5	CYCS	998	743	white	ellipse	gene	0.62	black	46	17	54205
+N-hsaCovid19Apoptosis-6	APAF1	975	682	white	ellipse	gene	0.62	black	46	17	317
+N-hsaCovid19Apoptosis-7	CASP9	880	683	white	ellipse	gene	0.62	black	46	17	842
+N-hsaCovid19Apoptosis-8	AKT1	775	595	white	ellipse	gene	0.62	black	46	17	207
+N-hsaCovid19Apoptosis-9	TNFRSF1A;TNF	710	83	white	ellipse	gene	0.62	black	46	17	7132,/,7124
+N-hsaCovid19Apoptosis-10	TRADD;FADD	822.125	234.375	white	ellipse	gene	0.62	black	46	17	8717,/,8772
+N-hsaCovid19Apoptosis-11	FADD	532	273	white	ellipse	gene	0.62	black	46	17	8772
+N-hsaCovid19Apoptosis-12	TRADD	649.125	226.375	white	ellipse	gene	0.62	black	46	17	8717
+N-hsaCovid19Apoptosis-13	Orf7a	904	358.5	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-14	E	904	409.5	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-15	BAD	904	464	white	ellipse	gene	0.62	black	46	17	572
+N-hsaCovid19Apoptosis-16	CASP8	321.125	421.375	white	ellipse	gene	0.62	black	46	17	841
+N-hsaCovid19Apoptosis-17	CASP3	321.125	631.375	white	ellipse	gene	0.62	black	46	17	836
+N-hsaCovid19Apoptosis-18	BID	809.125	409.5	white	ellipse	gene	0.62	black	46	17	637
+N-hsaCovid19Apoptosis-19	MAPK14	896.125	820.375	white	ellipse	gene	0.62	black	46	17	1432
+N-hsaCovid19Apoptosis-20	M	672	661.5	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-21	CASP7	367.125	751.375	white	ellipse	gene	0.62	black	46	17	840
+N-hsaCovid19Apoptosis-22	Orf3b	250	895	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-23	Orf8a	250	940	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-24	N	250	985	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-25	S	250	805	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-26	Orf9b	250	1030	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-27	Orf6	250	850	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-28	Orf3a	784	878.5	white	circle	other	0.62	black	46	17	NA
+N-hsaCovid19Apoptosis-29	BAX	1140	958.75	white	ellipse	gene	0.62	black	46	17	581
+N-hsaCovid19Apoptosis-30	Apoptosis	600	1014.5	white	circle	other	0.62	black	46	17	NA

Resources/Hipathia/sif_att_converter/test/hsaCovid19Apoptosis.sif

+N-hsaCovid19Apoptosis-1	activation	N-hsaCovid19Apoptosis-11
+N-hsaCovid19Apoptosis-2	activation	N-hsaCovid19Apoptosis-29
+N-hsaCovid19Apoptosis-3	activation	N-hsaCovid19Apoptosis-2
+N-hsaCovid19Apoptosis-3	inhibition	N-hsaCovid19Apoptosis-29
+N-hsaCovid19Apoptosis-4	activation	N-hsaCovid19Apoptosis-7
+N-hsaCovid19Apoptosis-5	activation	N-hsaCovid19Apoptosis-4
+N-hsaCovid19Apoptosis-6	activation	N-hsaCovid19Apoptosis-4
+N-hsaCovid19Apoptosis-7	activation	N-hsaCovid19Apoptosis-4
+N-hsaCovid19Apoptosis-8	inhibition	N-hsaCovid19Apoptosis-4
+N-hsaCovid19Apoptosis-8	inhibition	N-hsaCovid19Apoptosis-7
+N-hsaCovid19Apoptosis-8	inhibition	N-hsaCovid19Apoptosis-15
+N-hsaCovid19Apoptosis-9	activation	N-hsaCovid19Apoptosis-10
+N-hsaCovid19Apoptosis-10	activation	N-hsaCovid19Apoptosis-16
+N-hsaCovid19Apoptosis-11	activation	N-hsaCovid19Apoptosis-10
+N-hsaCovid19Apoptosis-11	activation	N-hsaCovid19Apoptosis-16
+N-hsaCovid19Apoptosis-12	activation	N-hsaCovid19Apoptosis-10
+N-hsaCovid19Apoptosis-13	activation	N-hsaCovid19Apoptosis-3
+N-hsaCovid19Apoptosis-14	activation	N-hsaCovid19Apoptosis-3
+N-hsaCovid19Apoptosis-15	inhibition	N-hsaCovid19Apoptosis-3
+N-hsaCovid19Apoptosis-16	activation	N-hsaCovid19Apoptosis-17
+N-hsaCovid19Apoptosis-16	activation	N-hsaCovid19Apoptosis-18
+N-hsaCovid19Apoptosis-16	activation	N-hsaCovid19Apoptosis-21
+N-hsaCovid19Apoptosis-17	activation	N-hsaCovid19Apoptosis-30
+N-hsaCovid19Apoptosis-7	activation	N-hsaCovid19Apoptosis-17
+N-hsaCovid19Apoptosis-7	activation	N-hsaCovid19Apoptosis-21
+N-hsaCovid19Apoptosis-18	activation	N-hsaCovid19Apoptosis-29
+N-hsaCovid19Apoptosis-19	activation	N-hsaCovid19Apoptosis-5