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
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
def cluster_analysis(cluster_list, cluster_folder, group_branches,
output_folder, temporal_folder, results, no_data,
not_found):
"""
Function used to run the analysis on the cluster list. It will run PAML for each group, and then it will
calculate the stats
"""
from SelectionAnalysis import paml_stats
from SelectionAnalysis import paml_prepare
for cluster in cluster_list:
cluster_file = cluster_folder + "/" + cluster + ".fna" # Add fna extension
#Check that the cluster file exists, if not continue
if not os.path.exists(cluster_file):
not_found.append(cluster)
continue
#Make a new tree, no confidence values in the branches
new_tree = paml_prepare.run_fasttree(cluster_file, temporal_folder)
#Make the new alignment, and get information about the alignment
new_alignment_file, number_sequences, alignment_length = paml_prepare.adjust_alignment(
cluster_file, temporal_folder)
#Run PAML for each branch in the cluster with both models
paml_site_branch_results = run_paml_per_group(group_branches,
new_alignment_file,
new_tree, output_folder,
temporal_folder)
for group in paml_site_branch_results:
#Store those clusters and groups that were not analyzed
if paml_site_branch_results[group] is None:
no_data.append([cluster, group])
else:
pvalue = paml_stats.lrt(
paml_site_branch_results[group]["Ma"].get("lnL"),
paml_site_branch_results[group]["M1a"].get("lnL"), 1)
proportion_sites = float(paml_site_branch_results[group]["Ma"]["site_classes"][2]["proportion"]) + \
float(paml_site_branch_results[group]["Ma"]["site_classes"][3]["proportion"])
average_omega = (
float(paml_site_branch_results[group]["Ma"]["site_classes"]
[2]["branch types"]["foreground"]) +
float(paml_site_branch_results[group]["Ma"]["site_classes"]
[3]["branch types"]["foreground"])) / 2
#Store the final results
#Group, Nseqs, Length, p-value, P1 in Ma, Omega in W
results.append([
cluster, group, number_sequences, alignment_length,
round(pvalue, 3), proportion_sites, average_omega
])
def cluster_analysis(cluster_list, cluster_folder, group_branches, output_folder, temporal_folder, results, no_data, not_found):
"""
Function used to run the analysis on the cluster list. It will run PAML for each group, and then it will
calculate the stats
"""
from SelectionAnalysis import paml_stats
from SelectionAnalysis import paml_prepare
for cluster in cluster_list:
cluster_file = cluster_folder + "/" + cluster + ".fna" # Add fna extension
#Check that the cluster file exists, if not continue
if not os.path.exists(cluster_file):
not_found.append(cluster)
continue
#Make a new tree, no confidence values in the branches
new_tree = paml_prepare.run_fasttree(cluster_file, temporal_folder)
#Make the new alignment, and get information about the alignment
new_alignment_file, number_sequences, alignment_length = paml_prepare.adjust_alignment(cluster_file, temporal_folder)
#Run PAML for each branch in the cluster with both models
paml_site_branch_results = run_paml_per_group(group_branches, new_alignment_file, new_tree,
output_folder, temporal_folder)
for group in paml_site_branch_results:
#Store those clusters and groups that were not analyzed
if paml_site_branch_results[group] is None:
no_data.append([cluster, group])
else:
pvalue = paml_stats.lrt(paml_site_branch_results[group]["Ma"].get("lnL"),
paml_site_branch_results[group]["M1a"].get("lnL"), 1)
proportion_sites = float(paml_site_branch_results[group]["Ma"]["site_classes"][2]["proportion"]) + \
float(paml_site_branch_results[group]["Ma"]["site_classes"][3]["proportion"])
average_omega = (float(paml_site_branch_results[group]["Ma"]["site_classes"][2]["branch types"]["foreground"]) +
float(paml_site_branch_results[group]["Ma"]["site_classes"][3]["branch types"]["foreground"])) / 2
#Store the final results
#Group, Nseqs, Length, p-value, P1 in Ma, Omega in W
results.append([cluster, group, number_sequences, alignment_length,
round(pvalue, 3), proportion_sites, average_omega])
