5fcddbe40be61db85506092f6d7fb206f4fa90b1
jnavarr5
  Tue Nov 5 15:29:13 2024 -0800
Adding steps to create symlinks, refs #34097

diff --git src/hg/makeDb/doc/hg38/decipher.txt src/hg/makeDb/doc/hg38/decipher.txt
index 02a635b..0190c43 100644
--- src/hg/makeDb/doc/hg38/decipher.txt
+++ src/hg/makeDb/doc/hg38/decipher.txt
@@ -1,102 +1,110 @@
 ########################################################################################
-# DECIPHER Developmental Disorders panel in the Gene2Phenotype database (DDG2P)
+# DECIPHER Developmental Disorders panel in the Gene2Phenotype database (DDG2P), hg38/hg19
 # November 5, 2024 - Yesenia Puga, Jairo Navarro, Gerardo Perez
 
 # Download required files
 wget https://www.deciphergenomics.org/files/downloads/population_cnv_grch38.txt.gz  # Downloads the CNV data file for hg38
 wget http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes  # Downloads chromosome size data for hg38
 wget https://genome.ucsc.edu/goldenPath/help/examples/bedExample2.as  # Downloads the .as file defining custom track fields
 wget http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedToBigBed  # Downloads the bedToBigBed utility for converting BED to bigBed format
 chmod 700 bedToBigBed  # Changes permissions to make bedToBigBed executable
 
 # Prepare the BED file:
 # Decompresses and trims the CNV data to the first 15 fields
 zcat population_cnv_grch38.txt.gz | cut -f1-15 > population_cnv_grch38.bed
 
 # reorders columns to fit BED format
 awk 'BEGIN {OFS="\t"} {print $2, $3, $4, $1, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14, $15}' population_cnv_grch38.bed > population_cnv_grch38_reordered.bed
 
 # removes the header line
 tail -n +2 population_cnv_grch38_reordered.bed > population_cnv_grch38_final.bed
 
 # Adjust the BED file for bigBed conversion:
 # prepends 'chr' to chromosome numbers and adjusts fields for bigBed
 awk 'BEGIN{OFS="\t"} {print "chr"$1, $2, $3, $4, 0, ".", $2, $3, 0, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14, $15}' population_cnv_grch38_final.bed > population_cnv_grch38_final_chr.bed
 
 # Sort the BED file:
 # sorts lexicographically by chromosome and numerically by start position
 LC_ALL=C sort -k1,1 -k2,2n population_cnv_grch38_final_chr.bed > population_cnv_grch38_final_sorted.bed
 
 
 # Add RGB colors to the BED file using a Python script:
 python3 ../assign_rgb_to_bed.py population_cnv_grch38_final_sorted.bed output_population_cnv_grch38.bed
 
 # Convert the BED file to bigBed format, indexing by gene name for faster lookups:
 ./bedToBigBed -type=bed9+ -as=bedExample2.as -tab -extraIndex=name output_population_cnv_grch38.bed hg38.chrom.sizes population_cnv_grch38.bb
 
+# Add files to hive
+cp hg19/population_cnv_grch37.bb /hive/data/genomes/hg19/bed/ddg2p/ddg2pSyndromes.bb
+cp hg38/population_cnv_grch38.bb /hive/data/genomes/hg38/bed/ddg2p/ddg2pSyndromes.bb
+
+# Create symlinks from hive to /gbdb
+ln -s /hive/data/genomes/hg19/bed/ddg2p/ddg2pSyndromes.bb /gbdb/hg19/decipher/ddg2pSyndromes.bb
+ln -s /hive/data/genomes/hg38/bed/ddg2p/ddg2pSyndromes.bb /gbdb/hg38/decipher/ddg2pSyndromes.bb
+
 #######################################################
 Author: Yesenia Puga
 Program: assign_rgb_to_bed.py 
 #######################################################
  # The Python script 'assign_rgb_to_bed.py' is crucial in the data processing pipeline.
  # It assigns RGB color values based on CNV type ('loss', 'gain', 'del/dup')
  # to each entry, enhancing visual differentiation on the Genome Browser. It also updates the
  # 'type' column from numeric identifiers to descriptive text,
  # which is used in the browser for informative mouseover tooltips, aiding in quick and clear
  # variant identification.
 import csv
 import sys
 
 # Define the color mappings for the RGB column based on the CNV type
 def get_rgb_color(cnv_type):
     if cnv_type == "loss":
         return '255,0,0'  # Red
     elif cnv_type == "gain":
         return '0,0,255'  # Blue
     elif cnv_type == "del/dup":
         return '128,128,128'  # Grey
     else:
         raise ValueError(f"Unexpected CNV type: {cnv_type}")
 
 # Convert numeric CNV values to descriptive text
 cnv_descriptions = {
     '-1': 'loss',
     '0': 'del/dup',
     '1': 'gain'
 }
 
 def process_file(input_bed_file, output_bed_file):
     with open(input_bed_file, 'r') as infile, open(output_bed_file, 'w', newline='') as outfile:
         reader = csv.reader(infile, delimiter='\t')
         writer = csv.writer(outfile, delimiter='\t')
 
         for row in reader:
             if len(row) < 19:
                 raise ValueError("Row does not contain enough columns to include the CNV type.")
 
             cnv_type_value = row[18]
             if cnv_type_value not in cnv_descriptions:
                 raise ValueError(f"Invalid CNV type value found: {cnv_type_value}")
 
             cnv_type_description = cnv_descriptions[cnv_type_value]
             row[18] = cnv_type_description  # Update the type description in the 19th column
             rgb_value = get_rgb_color(cnv_type_description)
             
             if len(row) < 9:
                 raise ValueError("Row does not contain enough columns to include the RGB value.")
 
             row[8] = rgb_value  # Update RGB value in the 9th column
 
             writer.writerow(row)
 
         print(f"Updated file successfully. Output saved to {output_bed_file}.")
 
 if __name__ == "__main__":
     if len(sys.argv) != 3:
         print("Usage: python update_rgb.py <input_bed_file> <output_bed_file>")
     else:
         input_bed_file = sys.argv[1]
         output_bed_file = sys.argv[2]
         process_file(input_bed_file, output_bed_file)
 
 ########################################################################################