diff --git a/modules/nf-core/gprofiler2/gost/templates/gprofiler2_gost.R b/modules/nf-core/gprofiler2/gost/templates/gprofiler2_gost.R
index e172ab35ae5c..3e982ee3e946 100644
--- a/modules/nf-core/gprofiler2/gost/templates/gprofiler2_gost.R
+++ b/modules/nf-core/gprofiler2/gost/templates/gprofiler2_gost.R
@@ -39,14 +39,14 @@
 #' @return named list of options and values similar to optparse
 
 parse_args <- function(x) {
-    args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1]
-    args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE))
-
-    # Ensure the option vectors are length 2 (key/ value) to catch empty ones
-    args_vals <- lapply(args_vals, function(z) { length(z) <- 2; z})
-
-    parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1]))
-    parsed_args[! is.na(parsed_args)]
+  args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1]
+  args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE))
+  
+  # Ensure the option vectors are length 2 (key/ value) to catch empty ones
+  args_vals <- lapply(args_vals, function(z) { length(z) <- 2; z})
+  
+  parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1]))
+  parsed_args[! is.na(parsed_args)]
 }
 
 #' Flexibly read CSV or TSV files
@@ -58,24 +58,24 @@ parse_args <- function(x) {
 #' @return output Data frame
 
 read_delim_flexible <- function(file, header = TRUE, row.names = NULL, check.names = F) {
-
-    ext <- tolower(tail(strsplit(basename(file), split = "\\\\.")[[1]], 1))
-
-    if (ext == "tsv" || ext == "txt") {
-        separator <- "\\t"
-    } else if (ext == "csv") {
-        separator <- ","
-    } else {
-        stop(paste("Unknown separator for", ext))
-    }
-
-    read.delim(
-        file,
-        sep = separator,
-        header = header,
-        row.names = row.names,
-        check.names = check.names
-    )
+  
+  ext <- tolower(tail(strsplit(basename(file), split = "\\\\.")[[1]], 1))
+  
+  if (ext == "tsv" || ext == "txt") {
+    separator <- "\\t"
+  } else if (ext == "csv") {
+    separator <- ","
+  } else {
+    stop(paste("Unknown separator for", ext))
+  }
+  
+  read.delim(
+    file,
+    sep = separator,
+    header = header,
+    row.names = row.names,
+    check.names = check.names
+  )
 }
 
 #' Round numeric dataframe columns to fixed decimal places by applying
@@ -87,27 +87,27 @@ read_delim_flexible <- function(file, header = TRUE, row.names = NULL, check.nam
 #'
 #' @return output Data frame
 round_dataframe_columns <- function(df, columns = NULL, digits = -1) {
-    if (digits == -1) {
-        return(df)                              # if -1, return df without rounding
-    }
-
-    df <- data.frame(df, check.names = FALSE)   # make data.frame from vector as otherwise, the format will get messed up
-    if (is.null(columns)) {
-        columns <- colnames(df)[(unlist(lapply(df, is.numeric), use.names=F))]    # extract only numeric columns for rounding
-    }
-
-    df[,columns] <- round(
-        data.frame(df[, columns], check.names = FALSE),
-        digits = digits
-    )
-
-    # Convert columns back to numeric
-
-    for (c in columns) {
-        df[[c]][grep("^ *NA\$", df[[c]])] <- NA
-        df[[c]] <- as.numeric(df[[c]])
-    }
-    df
+  if (digits == -1) {
+    return(df)                              # if -1, return df without rounding
+  }
+  
+  df <- data.frame(df, check.names = FALSE)   # make data.frame from vector as otherwise, the format will get messed up
+  if (is.null(columns)) {
+    columns <- colnames(df)[(unlist(lapply(df, is.numeric), use.names=F))]    # extract only numeric columns for rounding
+  }
+  
+  df[,columns] <- round(
+    data.frame(df[, columns], check.names = FALSE),
+    digits = digits
+  )
+  
+  # Convert columns back to numeric
+  
+  for (c in columns) {
+    df[[c]][grep("^ *NA\$", df[[c]])] <- NA
+    df[[c]] <- as.numeric(df[[c]])
+  }
+  df
 }
 
 ################################################
@@ -122,25 +122,24 @@ round_dataframe_columns <- function(df, columns = NULL, digits = -1) {
 
 # Set defaults and classes
 opt <- list(
-    de_file = '$de_file',
-    de_id_column = 'gene_id',
-    organism = NULL,
-    sources = NULL,
-    output_prefix = ifelse('$task.ext.prefix' == 'null', '$meta.id', '$task.ext.prefix'),
-    significant = T,
-    measure_underrepresentation = F,
-    correction_method = 'gSCS',
-    evcodes = F,
-    pval_threshold = 0.05,
-    gmt_file = '$gmt_file',
-    token = NULL,
-    background_file = '$background_file',
-    background_column = NULL,
-    domain_scope = 'annotated',
-    min_diff = 1,
-    round_digits = -1,
-    palette_name = 'Blues',
-    archive = 'gprofiler'
+  de_file = '$de_file',
+  de_id_column = 'gene_id',
+  organism = NULL,
+  sources = NULL,
+  output_prefix = ifelse('$task.ext.prefix' == 'null', '$meta.id', '$task.ext.prefix'),
+  significant = T,
+  measure_underrepresentation = F,
+  correction_method = 'gSCS',
+  evcodes = T,
+  pval_threshold = 0.05,
+  gmt_file = '$gmt_file',
+  token = NULL,
+  background_file = '$background_file',
+  background_column = NULL,
+  domain_scope = 'annotated',
+  min_diff = 1,
+  round_digits = -1,
+  palette_name = 'Blues'
 )
 
 opt_types <- lapply(opt, class)
@@ -149,38 +148,38 @@ opt_types <- lapply(opt, class)
 
 args_opt <- parse_args('$task.ext.args')
 for ( ao in names(args_opt)) {
-    if (! ao %in% names(opt)) {
-        stop(paste("Invalid option:", ao))
-    } else {
-
-        # Preserve classes from defaults where possible
-        if (! is.null(opt[[ao]])) {
-            args_opt[[ao]] <- as(args_opt[[ao]], opt_types[[ao]])
-        }
-        opt[[ao]] <- args_opt[[ao]]
+  if (! ao %in% names(opt)) {
+    stop(paste("Invalid option:", ao))
+  } else {
+    
+    # Preserve classes from defaults where possible
+    if (! is.null(opt[[ao]])) {
+      args_opt[[ao]] <- as(args_opt[[ao]], opt_types[[ao]])
     }
+    opt[[ao]] <- args_opt[[ao]]
+  }
 }
 # Check if required parameters have been provided
 required_opts <- c('output_prefix')
 missing <- required_opts[unlist(lapply(opt[required_opts], is.null)) | ! required_opts %in% names(opt)]
 
 if (length(missing) > 0) {
-    stop(paste("Missing required options:", paste(missing, collapse=', ')))
+  stop(paste("Missing required options:", paste(missing, collapse=', ')))
 }
 if (is.null(opt\$organism) && opt\$gmt_file == "" && is.null(opt\$token)) {
-    stop('Please provide organism, gmt_file or token.')
+  stop('Please provide organism, gmt_file or token.')
 }
 
 # Check file inputs are valid
 
 for (file_input in c('de_file')) {
-    if (is.null(opt[[file_input]])) {
-        stop(paste("Please provide", file_input), call. = FALSE)
-    }
-
-    if (! file.exists(opt[[file_input]])) {
-        stop(paste0('Value of ', file_input, ': ', opt[[file_input]], ' is not a valid file'))
-    }
+  if (is.null(opt[[file_input]])) {
+    stop(paste("Please provide", file_input), call. = FALSE)
+  }
+  
+  if (! file.exists(opt[[file_input]])) {
+    stop(paste0('Value of ', file_input, ': ', opt[[file_input]], ' is not a valid file'))
+  }
 }
 
 ################################################
@@ -191,7 +190,6 @@ for (file_input in c('de_file')) {
 
 library(gprofiler2)
 library(ggplot2)
-library(yaml)
 
 ################################################
 ################################################
@@ -200,9 +198,9 @@ library(yaml)
 ################################################
 
 de.genes <-
-    read_delim_flexible(
-        file = opt\$de_file
-    )
+  read_delim_flexible(
+    file = opt\$de_file
+  )
 
 output_prefix <- paste0(opt\$output_prefix, ".gprofiler2")
 
@@ -210,223 +208,280 @@ output_prefix <- paste0(opt\$output_prefix, ".gprofiler2")
 file.create(paste(output_prefix, 'all_enriched_pathways', 'tsv', sep = '.'))
 
 if (nrow(de.genes) > 0) {
-
-    query <- de.genes[[opt\$de_id_column]]
-
-    ################################################
-    ################################################
-    # Run gprofiler processes and generate outputs #
-    ################################################
-    ################################################
-
-    set.seed(1) # This will ensure that reruns have the same plot colors
-
-    sources <- opt\$sources
-    if (!is.null(sources)) {
-        sources <-  strsplit(opt\$sources, split = ",")[[1]]
-    }
-
-    if (!is.null(opt\$token)) {
-
-        # First check if a token was provided
-        token <- opt\$token
-
-    } else if (!is.null(opt\$organism)) {
-
-        # Next, check if organism was provided. Get the GMT file from gprofiler and save both the full file as well as the filtered one to metadata
-        base_url <- paste0("https://biit.cs.ut.ee/", opt\$archive)
-        gmt_url <- paste0(base_url, "//static/gprofiler_full_", opt\$organism, ".ENSG.gmt")
-        set_base_url(base_url)
-        tryCatch(
-            {
-                gmt_path <- paste0("gprofiler_full_", opt\$organism, ".ENSG.gmt")
-                if (!is.null(sources)) {
-                    gmt_path <- paste0("gprofiler_full_", opt\$organism, ".", paste(sources, collapse="_"), ".ENSG_filtered.gmt")
-                }
-                download <- download.file(gmt_url, gmt_path)
-                if (download != 0) {
-                    print("Failed to fetch the GMT file from gprofiler with this URL:")
-                    print(gmt_url)
-                    print("For reproducibility reasons, try to download the GMT file manually by visiting https://biit.cs.ut.ee/gprofiler/gost, then selecting the correct organism and, in datasources, clicking 'combined ENSG.gmt'.")
-                } else {
-                    if (!is.null(sources)) {
-                        gmt <- Filter(function(line) any(startsWith(line, sources)), readLines(gmt_path))
-                        print(paste0("GMT file successfully downloaded and filtered. Please note that for some sources, the GMT file may not contain any entries as these cannot be retrieved from gprofiler; in this case, the GMT file may be completely empty."))
-                        writeLines(gmt, gmt_path)
-                    }
-                }
-            },
-            error=function(gost_error) {
-                print("Failed to fetch the GMT file from gprofiler with this URL:")
-                print(gmt_url)
-                print("Got error:")
-                print(gost_error)
-                print("For reproducibility reasons, please try to download the GMT file manually by visiting https://biit.cs.ut.ee/gprofiler/gost, then selecting the correct organism and, in datasources, clicking 'combined ENSG.gmt'. Then provide it to the pipeline with the parameter `--gmt_file`")
-            }
-        )
-        token <- opt\$organism
-
-    } else {
-
-        # Last option: Use custom GMT file
-        gmt_path <- opt\$gmt_file
-
-        # If sources are set, extract only requested entries (gprofiler will NOT filter automatically!)
+  
+  query <- de.genes[[opt\$de_id_column]]
+  
+  # Optional map: Ensembl ID -> gene symbol (used only for display)
+  symbol_map <- NULL
+  # Try to build from columns in the DE table (offline, preferred)
+  id_candidates    <- c("gene_id","feature_id","feature","Ensembl_ID","ensembl_gene_id")
+  name_candidates  <- c("gene_name","feature_name","symbol","external_gene_name","mgi_symbol","hgnc_symbol")
+  id_col   <- id_candidates[id_candidates %in% colnames(de.genes)][1]
+  name_col <- name_candidates[name_candidates %in% colnames(de.genes)][1]
+  if (!is.na(id_col) && !is.na(name_col)) {
+    symbol_map <- setNames(as.character(de.genes[[name_col]]), as.character(de.genes[[id_col]]))
+  }
+  
+  ################################################
+  ################################################
+  # Run gprofiler processes and generate outputs #
+  ################################################
+  ################################################
+  
+  set.seed(1) # This will ensure that reruns have the same plot colors
+  
+  sources <- opt\$sources
+  if (!is.null(sources)) {
+    sources <-  strsplit(opt\$sources, split = ",")[[1]]
+  }
+  if (!is.null(sources)) {
+    sources <-  strsplit(opt\$sources, split = ",")[[1]]
+  }
+  
+  if (!is.null(opt\$token)) {
+    
+    # First check if a token was provided
+    token <- opt\$token
+    
+  } else if (!is.null(opt\$organism)) {
+    
+    # Next, check if organism was provided. Get the GMT file from gprofiler and save both the full file as well as the filtered one to metadata
+    gmt_url <- paste0("https://biit.cs.ut.ee/gprofiler//static/gprofiler_full_", opt\$organism, ".ENSG.gmt")
+    tryCatch(
+      {
+        gmt_path <- paste0("gprofiler_full_", opt\$organism, ".ENSG.gmt")
         if (!is.null(sources)) {
-            gmt <- Filter(function(line) any(startsWith(line, sources)), readLines(opt\$gmt))
-            gmt_path <- paste0(strsplit(basename(opt\$gmt_file), split = "\\\\.")[[1]][[1]], ".", paste(sources, collapse="_"), "_filtered.gmt")
+          gmt_path <- paste0("gprofiler_full_", opt\$organism, ".", paste(sources, collapse="_"), ".ENSG_filtered.gmt")
+        }
+        download <- download.file(gmt_url, gmt_path)
+        if (download != 0) {
+          print("Failed to fetch the GMT file from gprofiler with this URL:")
+          print(gmt_url)
+          print("For reproducibility reasons, try to download the GMT file manually by visiting https://biit.cs.ut.ee/gprofiler/gost, then selecting the correct organism and, in datasources, clicking 'combined ENSG.gmt'.")
+        } else {
+          if (!is.null(sources)) {
+            gmt <- Filter(function(line) any(startsWith(line, sources)), readLines(gmt_path))
+            print(paste0("GMT file successfully downloaded and filtered. Please note that for some sources, the GMT file may not contain any entries as these cannot be retrieved from gprofiler; in this case, the GMT file may be completely empty."))
             writeLines(gmt, gmt_path)
+          }
         }
-        token <- upload_GMT_file(gmt_path)
-
-        # Add gost ID to output GMT name so that it can be reused in future runs
-        file.rename(gmt_path, paste0(strsplit(basename(opt\$gmt_file), split = "\\\\.")[[1]][[1]], ".", paste(sources, collapse="_"), "_gostID_", token, "_filtered.gmt"))
-
+      },
+      error=function(gost_error) {
+        print("Failed to fetch the GMT file from gprofiler with this URL:")
+        print(gmt_url)
+        print("Got error:")
+        print(gost_error)
+        print("For reproducibility reasons, please try to download the GMT file manually by visiting https://biit.cs.ut.ee/gprofiler/gost, then selecting the correct organism and, in datasources, clicking 'combined ENSG.gmt'. Then provide it to the pipeline with the parameter `--gmt_file`")
+      }
+    )
+    token <- opt\$organism
+    
+  } else {
+    
+    # Last option: Use custom GMT file
+    gmt_path <- opt\$gmt_file
+    
+    # If sources are set, extract only requested entries (gprofiler will NOT filter automatically!)
+    if (!is.null(sources)) {
+      gmt <- Filter(function(line) any(startsWith(line, sources)), readLines(opt\$gmt_file))
+      gmt_path <- paste0(strsplit(basename(opt\$gmt_file), split = "\\\\.")[[1]][[1]], ".", paste(sources, collapse="_"), "_filtered.gmt")
+      writeLines(gmt, gmt_path)
     }
-
-
-    # If custom background_file was provided, read it
-    if (opt\$background_file != "") {
-        intensities_table <- read_delim_flexible(
-            file = opt\$background_file
-        )
-        # If only 1 col, it is a list, not a matrix
-        if (ncol(intensities_table) == 1) {
-            background <- intensities_table[,1]                                 # Extract first column from df
-            background <- append(background, colnames(intensities_table)[1])    # First entry was put into header, add it to vector
+    token <- upload_GMT_file(gmt_path)
+    
+    # Add gost ID to output GMT name so that it can be reused in future runs
+    file.rename(gmt_path, paste0(strsplit(basename(opt\$gmt_file), split = "\\\\.")[[1]][[1]], ".", paste(sources, collapse="_"), "_gostID_", token, "_filtered.gmt"))
+    
+  }
+  
+  
+  # If custom background_file was provided, read it
+  if (opt\$background_file != "") {
+    intensities_table <- read_delim_flexible(
+      file = opt\$background_file
+    )
+    # If only 1 col, it is a list, not a matrix
+    if (ncol(intensities_table) == 1) {
+      background <- intensities_table[,1]                                 # Extract first column from df
+      background <- append(background, colnames(intensities_table)[1])    # First entry was put into header, add it to vector
+    } else {
+      # Otherwise it's a matrix
+      # Set rownames to background_column if param was set
+      if (!is.null(opt\$background_column)) {
+        if (opt\$background_column %in% colnames(intensities_table)) {
+          rownames(intensities_table) <- intensities_table[[opt\$background_column]]
+          intensities_table[[opt\$background_column]] <- NULL
         } else {
-            # Otherwise it's a matrix
-            # Set rownames to background_column if param was set
-            if (!is.null(opt\$background_column)) {
-                if (opt\$background_column %in% colnames(intensities_table)) {
-                    rownames(intensities_table) <- intensities_table[[opt\$background_column]]
-                    intensities_table[[opt\$background_column]] <- NULL
-                } else {
-                    stop(paste0("Invalid background_column argument: ", opt\$background_column,
-                                ". Valid columns are: ", paste(colnames(intensities_table), collapse=", "), "."))
-                }
-            } else {
-
-            # Otherwise set rownames to first column
-                rownames(intensities_table) <- intensities_table[,1]
-                intensities_table <- intensities_table[,-1]
-            }
-
-            # Rownames are set, now remove non-numeric columns
-            nums <- unlist(lapply(intensities_table, is.numeric), use.names = FALSE)
-            intensities_table <- intensities_table[, nums]
-            # Keep only rownames which have abundance
-            background <- rownames(subset(intensities_table, rowSums(intensities_table, na.rm = TRUE)>0))
+          stop(paste0("Invalid background_column argument: ", opt\$background_column,
+                      ". Valid columns are: ", paste(colnames(intensities_table), collapse=", "), "."))
         }
-    } else {
-        background <- NULL
+      } else {
+        
+        # Otherwise set rownames to first column
+        rownames(intensities_table) <- intensities_table[,1]
+        intensities_table <- intensities_table[,-1]
+      }
+      
+      # Rownames are set, now remove non-numeric columns
+      nums <- unlist(lapply(intensities_table, is.numeric), use.names = FALSE)
+      intensities_table <- intensities_table[, nums]
+      # Keep only rownames which have abundance
+      background <- rownames(subset(intensities_table, rowSums(intensities_table, na.rm = TRUE)>0))
     }
-
-    # Name the query as it will otherwise be called 'query_1' which will also determine the gostplot title
-    q <- list(query)
-    names(q) <- c(output_prefix)
-    gost_results <- gost(
-        query=q,
-        organism=token,
-        significant=opt\$significant,
-        measure_underrepresentation=opt\$measure_underrepresentation,
-        correction_method=opt\$correction_method,
-        sources=sources,
-        evcodes=opt\$evcodes,
-        user_threshold=opt\$pval_threshold,
-        custom_bg=background,
-        domain_scope=opt\$domain_scope
+  } else {
+    background <- NULL
+  }
+  
+  # Name the query as it will otherwise be called 'query_1' which will also determine the gostplot title
+  q <- list(query)
+  names(q) <- c(output_prefix)
+  gost_results <- gost(
+    query=q,
+    organism=token,
+    significant=opt\$significant,
+    measure_underrepresentation=opt\$measure_underrepresentation,
+    correction_method=opt\$correction_method,
+    sources=sources,
+    evcodes=opt\$evcodes,
+    user_threshold=opt\$pval_threshold,
+    custom_bg=background,
+    domain_scope=opt\$domain_scope
+  )
+  
+  if (!is.null(gost_results)) {
+    # Create interactive plot and save to HTML
+    interactive_plot <- gostplot(gost_results, capped=T, interactive=T)
+    
+    # Save interactive plot as HTML
+    htmlwidgets::saveWidget(
+      widget = interactive_plot,
+      file = paste(output_prefix, 'gostplot', 'html', sep = '.')
     )
-
-    if (!is.null(gost_results)) {
-        # Create interactive plot and save to HTML
-        interactive_plot <- gostplot(gost_results, capped=T, interactive=T)
-
-        # Save interactive plot as HTML
-        htmlwidgets::saveWidget(
-                    widget = interactive_plot,
-                    file = paste(output_prefix, 'gostplot', 'html', sep = '.')
-                    )
-
-        # Create a static plot and save to PNG
-        static_plot <- gostplot(gost_results, capped=T, interactive=F)
-        ggsave(plot = static_plot, filename = paste(output_prefix, 'gostplot', 'png', sep = '.'), width = 10, height = 7)
-
-        # Subset gost results to those pathways with a min. number of differential features
-        gost_results\$result <- gost_results\$result[which(gost_results\$result\$intersection_size>=opt\$min_diff),]
-
-        # annotate query size (number of differential features in contrast)
-        gost_results\$result\$original_query_size <- rep(length(as.character(de.genes\$Ensembl_ID)), nrow(gost_results\$result))
-
-        # R object for other processes to use
-
-        saveRDS(gost_results, file = paste(output_prefix, 'gost_results.rds', sep = '.'))
-
-        # Write full enrichment table (except parents column as that one throws an error)
-
-        gost_results\$results <- data.frame(
-                round_dataframe_columns(gost_results\$result[,-which(names(gost_results\$result) == "parents")], digits=opt\$round_digits),
-                check.names = FALSE
-            )
-
-        write.table(
-            gost_results\$results,
-            file = paste(output_prefix, 'all_enriched_pathways', 'tsv', sep = '.'),
-            col.names = TRUE,
-            row.names = FALSE,
-            sep = '\t',
-            quote = FALSE
+    
+    # Create a static plot and save to PNG
+    static_plot <- gostplot(gost_results, capped=T, interactive=F)
+    ggsave(plot = static_plot, filename = paste(output_prefix, 'gostplot', 'png', sep = '.'), width = 10, height = 7)
+    
+    # Subset gost results to those pathways with a min. number of differential features
+    gost_results\$result <- gost_results\$result[which(gost_results\$result\$intersection_size>=opt\$min_diff),]
+    
+    # If we don't have symbols from the DE table, build a map via g:Profiler now (requires organism)
+    conv_map <- NULL
+    if (is.null(symbol_map) && !is.null(opt\$organism)) {
+      ids_all <- unique(trimws(unlist(strsplit(paste(gost_results\$result\$intersection, collapse=","), ","))))
+      if (length(ids_all) > 0) {
+        conv <- tryCatch(
+          # let g:Profiler pick the right namespace; we'll use the 'name' column as the symbol
+          gprofiler2::gconvert(ids_all, organism = opt\$organism),
+          error = function(e) NULL
         )
-
-        # Iterate over the enrichment results by source and save separate tables
-        for (df in split(gost_results\$result, gost_results\$result\$source)){
-
-            db_source <- df\$source[1]
-            df_subset <- data.frame(
-                Pathway_name = df\$term_name,
-                Pathway_code = df\$term_id,
-                DE_genes = df\$intersection_size,
-                Pathway_size = df\$term_size,
-                Fraction_DE = df\$recall,
-                Padj = df\$p_value,
-                DE_genes_names = df\$intersection
-            )
-            df_subset <- data.frame(
-                round_dataframe_columns(df_subset, digits=opt\$round_digits),
-                check.names = FALSE
-            )
-            write.table(
-                df_subset,
-                file = paste(output_prefix, db_source, 'sub_enriched_pathways', 'tsv', sep = '.'),
-                col.names = TRUE,
-                row.names = FALSE,
-                sep = '\t',
-                quote = FALSE
-            )
-
-            # For plot, shorten pathway names as they can get quite long (full name can be looked up in the table)
-            df_subset\$Pathway_name <- sapply(df_subset\$Pathway_name, substr, start=1, stop=50)
-
-            # Extract 3 colors from the chosen palette (2 are sufficient, but brewer.pal has a minimum of 3); first and last will be used for plot
-            colors <- RColorBrewer::brewer.pal(3, opt\$palette_name)
-
-            # Enriched pathways horizontal barplots of padj values
-            p <- ggplot(df_subset, aes(x=reorder(Pathway_name, Fraction_DE), y=Fraction_DE)) +
-                geom_bar(aes(fill=Padj), stat="identity", width = 0.7) +
-                geom_text(aes(label=paste0(df_subset\$DE_genes, "/", df_subset\$Pathway_size)), vjust=0.4, hjust=-0.2, size=3) +
-                theme(plot.title.position = "plot") +
-                coord_flip() +
-                scale_y_continuous(limits = c(0.00, 1.24), breaks = seq(0, 1.24, by = 0.25)) +
-                ggtitle(paste("Enriched", db_source, "pathways")) +
-                xlab("") + ylab("Enriched fraction (DE features / Pathway size)") +
-                scale_fill_continuous(high = colors[1], low = colors[3])
-
-            # Save plot with set width to ensure there is enough space for the labels; adapt height to nrow but limit it to 100 as there will be an error for too high values
-            ggsave(p, filename = paste(output_prefix, db_source, 'sub_enriched_pathways', 'png', sep = '.'), device = "png", width=10, height=min(100, 1.5+nrow(df_subset)*0.15), limitsize=F)
+        if (!is.null(conv) && nrow(conv) > 0) {
+          conv_map <- setNames(as.character(conv\$name), as.character(conv\$input))
         }
+      }
     }
+    
+    # annotate query size (number of differential features in contrast)
+    gost_results\$result\$original_query_size <- rep(length(unique(as.character(query))), nrow(gost_results\$result))
+    
+    # R object for other processes to use
+    saveRDS(gost_results, file = paste(output_prefix, 'gost_results.rds', sep = '.'))
+    
+    # Write full enrichment table (except parents column as that one throws an error)
+    gost_results\$results <- data.frame(
+      round_dataframe_columns(gost_results\$result[,-which(names(gost_results\$result) == "parents")], digits=opt\$round_digits),
+      check.names = FALSE
+    )
+    
+    write.table(
+      gost_results\$results,
+      file = paste(output_prefix, 'all_enriched_pathways', 'tsv', sep = '.'),
+      col.names = TRUE,
+      row.names = FALSE,
+      sep = '\t',
+      quote = FALSE
+    )
+    
+    # Iterate over the enrichment results by source and save separate tables
+    for (df in split(gost_results\$result, gost_results\$result\$source)){
+      
+      db_source <- df\$source[1]
+      
+      # 1) Keep original Ensembl IDs from g:Profiler intersection
+      intersection_ids <- df\$intersection
+      
+      # 2) Build symbols vector using DE-derived map first, then g:Profiler fallback
+      intersection_symbols <- vapply(strsplit(df\$intersection, ","), function(vec){
+        vec <- trimws(vec)
+        
+        # start empty
+        sym <- rep(NA_character_, length(vec))
+        
+        # fill from DE symbol_map (if present)
+        if (!is.null(symbol_map)) {
+          tmp <- symbol_map[vec]
+          sym[!is.na(tmp) & tmp != ""] <- tmp[!is.na(tmp) & tmp != ""]
+        }
+        
+        # fill remaining from gconvert map (if present)
+        if (!is.null(conv_map)) {
+          tmp2 <- conv_map[vec]
+          need <- is.na(sym) | sym == ""
+          sym[need & !is.na(tmp2) & tmp2 != ""] <- tmp2[need & !is.na(tmp2) & tmp2 != ""]
+        }
+        
+        # fallback to original IDs
+        sym[is.na(sym) | sym == ""] <- vec[is.na(sym) | sym == ""]
+        paste(sym, collapse = ",")
+      }, FUN.VALUE = character(1))
+      
+      df_subset <- data.frame(
+        Pathway_name   = df\$term_name,
+        Pathway_code   = df\$term_id,
+        DE_genes       = df\$intersection_size,
+        Pathway_size   = df\$term_size,
+        Fraction_DE    = df\$recall,
+        Padj           = df\$p_value,
+        DE_genes_ids   = intersection_ids,
+        DE_genes_names = intersection_symbols
+      )
+      
+      df_subset <- data.frame(
+        round_dataframe_columns(df_subset, digits=opt\$round_digits),
+        check.names = FALSE
+      )
+      write.table(
+        df_subset,
+        file = paste(output_prefix, db_source, 'sub_enriched_pathways', 'tsv', sep = '.'),
+        col.names = TRUE,
+        row.names = FALSE,
+        sep = '\t',
+        quote = FALSE
+      )
+      
+      # For plot, shorten pathway names as they can get quite long (full name can be looked up in the table)
+      df_subset\$Pathway_name <- sapply(df_subset\$Pathway_name, substr, start=1, stop=50)
+      
+      # Extract 3 colors from the chosen palette (2 are sufficient, but brewer.pal has a minimum of 3); first and last will be used for plot
+      colors <- RColorBrewer::brewer.pal(3, opt\$palette_name)
+      
+      # Enriched pathways horizontal barplots of padj values
+      p <- ggplot(df_subset, aes(x=reorder(Pathway_name, Fraction_DE), y=Fraction_DE)) +
+        geom_bar(aes(fill=Padj), stat="identity", width = 0.7) +
+        geom_text(aes(label=paste0(df_subset\$DE_genes, "/", df_subset\$Pathway_size)), vjust=0.4, hjust=-0.2, size=3) +
+        theme(plot.title.position = "plot") +
+        coord_flip() +
+        scale_y_continuous(limits = c(0.00, 1.24), breaks = seq(0, 1.24, by = 0.25)) +
+        ggtitle(paste("Enriched", db_source, "pathways")) +
+        xlab("") + ylab("Enriched fraction (DE features / Pathway size)") +
+        scale_fill_continuous(high = colors[1], low = colors[3])
+      
+      # Save plot with set width to ensure there is enough space for the labels; adapt height to nrow but limit it to 100 as there will be an error for too high values
+      ggsave(p, filename = paste(output_prefix, db_source, 'sub_enriched_pathways', 'png', sep = '.'), device = "png", width=10, height=min(100, 1.5+nrow(df_subset)*0.15), limitsize=F)
+    }
+  }
 } else {
-    print("No differential features found, pathway enrichment analysis with gprofiler2 will be skipped.")
+  print("No differential features found, pathway enrichment analysis with gprofiler2 will be skipped.")
 }
 
 ################################################
@@ -448,25 +503,16 @@ sink()
 r.version <- strsplit(version[['version.string']], ' ')[[1]][3]
 gprofiler2.version <- as.character(packageVersion('gprofiler2'))
 ggplot2.version <- as.character(packageVersion('ggplot2'))
-if (is.null(opt\$token) & !is.null(opt\$organism)) {
-    gprofiler_data.version <- as.yaml(get_version_info(opt\$organism))
-} else {
-    gprofiler_data.version <- as.yaml(get_version_info())
-}
-# The YAML comes out a bit messy for the gprofiler data, not nested correctly
-# when we use writeLines and gprofiler_data, so I write it via yaml
-write_yaml(
-    list(
-    '"$task.process"'=list(
-        "r-ggplot2"=ggplot2.version,
-        "r-gprofiler2"=gprofiler2.version,
-        "gprofiler-data"=gprofiler_data.version
-        )
-    ),
-    file='versions.yml'
-)
+writeLines(
+  c(
+    '"\${task.process}":',
+    paste('    r-base:', r.version),
+    paste('    r-ggplot2:', ggplot2.version),
+    paste('    r-gprofiler2:', gprofiler2.version)
+  ),
+  'versions.yml')
 
 ################################################
 ################################################
 ################################################
-################################################
+################################################
\ No newline at end of file