219 lines
8.8 KiB
Python
Executable file
219 lines
8.8 KiB
Python
Executable file
#!/usr/bin/env python3
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# mCSM Wrapper
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import os,sys
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import subprocess
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import argparse
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import pandas as pd
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from mcsm import *
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#%% command line args
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help='drug name' , required=True)
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arg_parser.add_argument('-g', '--gene', help='gene name (case sensitive)', required=True) # case sensitive
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arg_parser.add_argument('-s', '--stage', help='mCSM Pipeline Stage', default = 'get', choices=['submit', 'get', 'format'], required=True)
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arg_parser.add_argument('-H', '--host', help='mCSM Server', default = 'http://biosig.unimelb.edu.au')
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arg_parser.add_argument('-U', '--url', help='mCSM Server URL', default = 'http://biosig.unimelb.edu.au/mcsm_lig/prediction')
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arg_parser.add_argument('-c', '--chain', help='Chain ID as per PDB, Case sensitive', default = 'A')
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arg_parser.add_argument('-l','--ligand', help='Ligand ID as per PDB, Case sensitive. REQUIRED only in "submit" stage', default = None)
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arg_parser.add_argument('-a','--affinity', help='Affinity in nM. REQUIRED only in "submit" stage', default = 10) #0.99 for pnca, gid, embb. For SP targets (alr,katg, rpob), use 10.
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arg_parser.add_argument('-pdb','--pdb_file', help = 'PDB File')
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arg_parser.add_argument('-m','--mutation_file', help = 'Mutation File, mcsm style')
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arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
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arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
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arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
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# stage: submit, output url file
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arg_parser.add_argument('--url_file', help = 'Output results url file. The result of stage "submit". By default, it creates a output result url file in the output dir: "output_dir + gene.lower() + _result_urls.txt" ')
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# stage: get, intermediate mcsm output file
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arg_parser.add_argument('--outfile_scraped', help = 'Output mcsm results scraped. The result of stage "get". By default, it creates an interim output file in the output dir: "output_dir + gene.lower() +_mcsm_output.csv" ')
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# stage: format, formatted output with scaled values, etc
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# FIXME: Don't call this stage until you have ALL the interim results for your snps as the normalisation will be affected!
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arg_parser.add_argument('--outfile_formatted', help = 'Output mcsm results formatted. The result of stage "format". By default, it creates a formatted output file in the output dir: "output_dir + gene.lower() + _complex_mcsm_norm.csv" ')
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arg_parser.add_argument('--debug', action='store_true', help = 'Debug Mode')
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args = arg_parser.parse_args()
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#=======================================================================
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#%% variables
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#host = "http://biosig.unimelb.edu.au"
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#prediction_url = f"{host}/mcsm_lig/prediction"
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#drug = ''
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#gene = ''
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#%%=====================================================================
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# Command line options
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gene = args.gene
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drug = args.drug
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stage = args.stage
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chain = args.chain
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ligand = args.ligand
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affinity = args.affinity
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pdb_filename = args.pdb_file
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mutation_filename = args.mutation_file
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result_urls = args.url_file
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mcsm_output = args.outfile_scraped
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outfile_format = args.outfile_formatted
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datadir = args.datadir
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indir = args.input_dir
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outdir = args.output_dir
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DEBUG = args.debug
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# Actual Globals :-)
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host = args.host
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prediction_url = args.url
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# submit_mcsm globals
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homedir = os.path.expanduser('~')
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#os.chdir(homedir + '/git/LSHTM_analysis/mcsm')
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gene_match = gene + '_p.'
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#============
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# directories
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#============
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if not datadir:
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datadir = homedir + '/git/Data/'
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if not indir:
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indir = datadir + drug + 'input/'
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if not outdir:
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outdir = datadir + drug + 'output/'
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#=======
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# input
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#=======
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if pdb_filename:
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in_filename_pdb = pdb_filename
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else:
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in_filename_pdb = gene.lower() + '_complex.pdb'
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infile_pdb = indir + in_filename_pdb
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#in_filename_snps = gene.lower() + '_mcsm_snps.csv' #(outfile_mcsm_snps, from data_extraction.py)
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#infile_snps = outdir + '/' + in_filename_snps
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if mutation_filename:
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in_filename_snps = mutation_filename
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else:
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in_filename_snps = gene.lower() + '_mcsm_formatted_snps.csv'
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infile_snps = outdir + in_filename_snps
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#=======
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# output
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#=======
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# mcsm_results globals
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if not result_urls:
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result_urls_filename = gene.lower() + '_result_urls.txt'
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result_urls = outdir + result_urls_filename
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if DEBUG:
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print('DEBUG: Result URLs:', result_urls)
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if not mcsm_output:
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mcsm_output_filename = gene.lower() + '_mcsm_output.csv'
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mcsm_output = outdir + mcsm_output_filename
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if DEBUG:
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print('DEBUG: mCSM output CSV file:', mcsm_output)
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# format_results globals
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#out_filename_format = gene.lower() + '_mcsm_processed.csv'
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if not outfile_format:
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out_filename_format = gene.lower() + '_complex_mcsm_norm.csv'
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outfile_format = outdir + out_filename_format
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if DEBUG:
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print('DEBUG: formatted CSV output:', outfile_format)
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#%%=====================================================================
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def submit_mcsm():
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# Example:
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# chain = 'A'
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# ligand_id = 'RMP'
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# affinity = 10
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print('Result urls and error file (if any) will be written in: ', outdir)
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# call function to format data to remove duplicate snps before submitting job
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mcsm_muts = format_data(infile_snps)
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mut_count = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
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infile_snps_len = os.popen('wc -l < %s' % infile_snps).read() # quicker than using Python :-)
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print('Total SNPs for', gene, ':', infile_snps_len)
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for mcsm_mut in mcsm_muts:
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print('Processing mutation: %s of %s' % (mut_count, infile_snps_len), mcsm_mut)
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if DEBUG:
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print('DEBUG: Parameters for mcsm_lig:', in_filename_pdb, mcsm_mut, chain, ligand, affinity, prediction_url, outdir, gene)
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# function call: to request mcsm prediction
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# which writes file containing url for valid submissions and invalid muts to respective files
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holding_page = request_calculation(infile_pdb, mcsm_mut, chain, ligand, affinity, prediction_url, outdir, gene, host)
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time.sleep(1)
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mut_count += 1
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# result_url = write_result_url(holding_page, result_urls, host)
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print('Request submitted'
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, '\nCAUTION: Processing will take at least ten'
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, 'minutes, but will be longer for more mutations.')
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#%%=====================================================================
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def get_results():
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output_df = pd.DataFrame()
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url_counter = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
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success_counter = 1
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infile_len = os.popen('wc -l < %s' % result_urls).read() # quicker than using Python :-)
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print('Total URLs:', infile_len)
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with open(result_urls, 'r') as urlfile:
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for line in urlfile:
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url_line = line.strip()
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# call functions
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results_interim = scrape_results(url_line)
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if results_interim is not None:
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print('Processing URL: %s of %s' % (url_counter, infile_len))
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result_dict = build_result_dict(results_interim)
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df = pd.DataFrame(result_dict, index=[url_counter])
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output_df = output_df.append(df)
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success_counter += 1
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url_counter += 1
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print('Total URLs: %s Successful: %s Failed: %s' % (url_counter-1, success_counter-1, (url_counter - success_counter)))
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#print('\nOutput file created:', output_dir + gene.lower() + '_mcsm_output.csv')
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output_df.to_csv(mcsm_output, index = None, header = True)
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#%%=====================================================================
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def format_results():
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print('Input file:', mcsm_output
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, '\n============================================================='
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, '\nOutput file:', outfile_format
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, '\n=============================================================')
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# call function
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mcsm_df_formatted = format_mcsm_output(mcsm_output)
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# writing file
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print('Writing formatted df to csv')
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mcsm_df_formatted.to_csv(outfile_format, index = False)
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print('Finished writing file:'
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, '\nFile:', outfile_format
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, '\nExpected no. of rows:', len(mcsm_df_formatted)
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, '\nExpected no. of cols:', len(mcsm_df_formatted.columns)
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, '\n=============================================================')
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#%%=====================================================================
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def main():
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if stage == 'submit':
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print('mCSM stage: submit mutations for mcsm analysis')
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submit_mcsm()
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elif stage == 'get':
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print('mCSM stage: get results')
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get_results()
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elif stage == 'format':
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print('mCSM stage: format results')
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format_results()
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else:
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print('ERROR: invalid stage')
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main()
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