/
RunPaml_SiteModels.py
140 lines (95 loc) · 5.16 KB
/
RunPaml_SiteModels.py
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def single_cluster_analysis(cluster_id, cluster_folder, output_folder, temp_folder, outfile_notfound):
"""
This function take the group, alignment, tree and folder information and runs a paml analysis using the M1a, M2a,
M7 and M8 models
The working dir is important (different from the output dir), because different PAML runs at the same time may
override each other.
This is particularly important if running this script in more than one processor
"""
from SelectionAnalysis import paml_stats
from SelectionAnalysis import paml_prepare
from SelectionAnalysis import paml_run
cluster_file = cluster_folder + "/" + cluster_id + ".fna" # Add fna extension
#Check that the cluster file exists, if not continue
if not os.path.exists(cluster_file):
outfile_notfound.write(cluster_id + "\n")
#Make a new tree, no confidence values in the branches
new_tree = paml_prepare.run_fasttree(cluster_file, temp_folder)
#Make the new alignment, and get information about the alignment
new_alignment_file, number_sequences, alignment_length = \
paml_prepare.adjust_alignment(cluster_file, temp_folder)
#Run PAML for each branch in the cluster with both models
paml_sites_results = paml_run.paml_sites(new_alignment_file, new_tree, output_folder, temp_folder)
#Calculate pvalue
pvalue_m1_m2 = paml_stats.lrt(paml_sites_results[1].get("lnL"), paml_sites_results[2].get("lnL"), 2)
pvalue_m7_m8 = paml_stats.lrt(paml_sites_results[7].get("lnL"), paml_sites_results[8].get("lnL"), 2)
#Store the omega and proportion of sites,based on the M8 model
try:
proportion_sites = float(paml_sites_results[8]["site_classes"][10]["proportion"])
omega_value = float(paml_sites_results[8]["site_classes"][10]["omega"])
except TypeError:
proportion_sites = 0
omega_value = 0
#Store final results
summary_results = [cluster_id, number_sequences, alignment_length, round(pvalue_m1_m2, 3), round(pvalue_m7_m8, 3),
proportion_sites, omega_value]
print summary_results
return summary_results
if __name__ == '__main__':
import os
import argparse
import multiprocessing
from SelectionAnalysis import paml_stats
program_description = "Script that takes a list of clusters and runs PAML (codeml), using a site approach." \
"The models used are M1a vs M2a and M7 vs M8. "
parser = argparse.ArgumentParser(description=program_description)
parser.add_argument("-c", "--cluster_list", type=str, help="Cluster file", required=True)
parser.add_argument("-n", "--cluster_folder", type=str, help="Output folder", required=True)
parser.add_argument("-o", "--output_directory", type=str, help="Output folder", required=True)
parser.add_argument("-p", "--num_processors", type=int, help="Number of processors to use", required=True)
parser.add_argument("-f", "--fdr", help="Perform false discovery rate")
args = parser.parse_args()
#Check for the output folder and also create the temporal folder
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
#Output files
output_file = open(args.output_directory + "/paml_results.txt", 'w')
no_results_file = open(args.output_directory + "/no_results.txt", 'w')
#Read the cluster file and group file
clusters_to_analyze = [line.rsplit()[0] for line in open(args.cluster_list) if line.strip()]
#Added on 3/29/14, to fix problems running processes in parallel
pool = multiprocessing.Pool(args.num_processors)
cluster_paml_results = []
groups_no_data = []
i = 1
def store_results(result):
output_file.write("\t".join(str(x) for x in result) + "\n")
cluster_paml_results.append(result)
run_results = []
for cluster in clusters_to_analyze:
temporal_folder = args.output_directory + "/temp_" + str(i)
if not os.path.exists(temporal_folder):
os.makedirs(temporal_folder)
i += 1
r = pool.apply_async(single_cluster_analysis, args=(cluster, args.cluster_folder,
args.output_directory, temporal_folder, no_results_file),
callback=store_results)
run_results.append(r)
pool.close()
pool.join()
#for result in cluster_paml_results:
# output_file.write("\t".join(str(x) for x in result) + "\n")
#for entry in groups_no_data:
# no_results_file.write(entry + "\n")
output_file.close()
no_results_file.close()
#Run False discovery rate analysis, if chosen. I need to correct both, the M1a vs M2 pvalues, and then
#M7 vs M8
if args.fdr:
#Correct M1-M2
corrected_pvalue_results = paml_stats.fdr(cluster_paml_results, 3)
#Correct M7-M8
corrected_pvalue_results = paml_stats.fdr(corrected_pvalue_results, 5)
corrected_results_file = open(args.output_directory + "/paml_results_corrected.txt", "w")
for result in corrected_pvalue_results:
corrected_results_file.write("\t".join(str(x) for x in result) + "\n")