New tree plot

bin/gene_overlaps.R

@@ -2,6 +2,7 @@
 
 # Code written by Chris Wyatt with some editing by ChatGPT
 
+library(dplyr)
 library(GenomicRanges)
 
 # Parse command-line arguments
@@ -19,6 +20,9 @@ read_gff_to_granges <- function(gff_file) {
     gff_data <- read.delim(gff_file, header = FALSE, comment.char = "#")
     colnames(gff_data) <- c("seqname", "source", "feature", "start", "end",
                             "score", "strand", "frame", "attribute")
+    gff_data <- gff_data %>%
+      filter(strand %in% c("-","+")) 
+
     gr <- GRanges(
         seqnames = gff_data$seqname,
         ranges = IRanges(start = gff_data$start, end = gff_data$end),
@@ -67,7 +71,6 @@ for (i in seq_len(length(overlap_results))) {
     if (query_idx != subject_idx) {
         query_gene <- genes[query_idx]
         subject_gene <- genes[subject_idx]
-
         overlap_range <- intersect(ranges(query_gene), ranges(subject_gene))
         overlap_length <- width(overlap_range)
 

bin/gene_overlaps_table.py

@@ -0,0 +1,63 @@
+#!/usr/bin/python3
+
+# Written using ChatGPT
+
+import pandas as pd
+import argparse
+
+# Set up the argument parser
+parser = argparse.ArgumentParser(description='Extract gene statistics from files.')
+parser.add_argument('input_files', nargs='+', help='List of input files.')
+parser.add_argument('output_file', help='Path to save the output TSV file.')
+parser.add_argument('--include-sense', action='store_true', help='Include sense genes count.')
+parser.add_argument('--include-antisense', action='store_true', help='Include antisense genes count.')
+
+# Parse the arguments
+args = parser.parse_args()
+
+# Initialize an empty list to store the results
+results = []
+
+# Process each input file
+for file in args.input_files:
+    try:
+        # Load the file into a DataFrame
+        df = pd.read_csv(file, sep='\t', header=None, names=['Statistic', 'Count'])
+
+        # Extract required statistics
+        total_genes = df.loc[df['Statistic'] == 'Total number of genes', 'Count'].values[0]
+        overlapping_genes = df.loc[df['Statistic'] == 'Total number of overlapping genes', 'Count'].values[0]
+
+        # Optional statistics
+        sense_genes = df.loc[df['Statistic'] == 'Number of genes fully contained in sense direction', 'Count'].values[0] if args.include_sense else "NA"
+        antisense_genes = df.loc[df['Statistic'] == 'Number of genes fully contained in antisense direction', 'Count'].values[0] if args.include_antisense else "NA"
+        print(sense_genes)
+
+        # Collect results in a dictionary
+        entry = {
+            'File': file,
+            'Total_genes': total_genes,
+            'Overlapping_genes': overlapping_genes,
+        }
+        if args.include_sense:
+            entry['Fully_contained_sense_genes'] = sense_genes
+        if args.include_antisense:
+            entry['Fully_contained_antisense_genes'] = antisense_genes
+
+        results.append(entry)
+    except Exception as e:
+        print(f"Error processing {file}: {e}")
+        continue
+
+# Convert the results to a DataFrame
+columns = ['File', 'Total_genes', 'Overlapping_genes']
+if args.include_sense:
+    columns.append('Fully_contained_sense_genes')
+if args.include_antisense:
+    columns.append('Fully_contained_antisense_genes')
+
+result_df = pd.DataFrame(results, columns=columns)
+
+# Write the result to the output file
+result_df.to_csv(args.output_file, sep='\t', index=False)
+print(f"Extraction completed successfully. Output saved to {args.output_file}.")