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SSN_tools.py
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SSN_tools.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 6 11:14:09 2019
@author: Allison Walker
"""
from Bio import SeqIO
from Bio import Seq
from Bio import AlignIO
from Bio.Align.Applications import ClustalwCommandline
from Bio import Phylo
from Bio.Align.Applications import MuscleCommandline
from Bio import pairwise2
from xml.etree import ElementTree as ET
import numpy as np
import math
import subprocess
SSN_filenames = ["20799_thiolase_N_term_25_full_ssn.xgmml",
"21330_abc_35_full_ssn.xgmml",
"20870_acyl_transferase_pfam_100_full_ssn.xgmml",
"20923_aaa_30_full_ssn.xgmml",
"20928_abc2_30_full_ssn.xgmml",
"20929_acyl-coa_dehydrogenase_c_30_full_ssn.xgmml",
"20930_acyl-coa_dehydrogenase_n_20_full_ssn.xgmml",
"20931_alcohol_dehydrogenase_groes_20_full_ssn.xgmml",
"20932__alpha_beta_hydrolase_family_40_full_ssn.xgmml",
"20935_aminotransferase_40_full_ssn.xgmml",
"20944__beta-ketoacyl_synthase__c_55_full_ssn.xgmml",
"20941_beta-ketoacyl_synthase__n_100_full_ssn.xgmml",
"20942_cytochrome_p450_40_full_ssn.xgmml",
"20943_degt_dnrj_eryc1_strs_aminotransferase_60_full_ssn.xgmml",
"20946_enoyl-_acyl_carrier_protein_45_full_ssn.xgmml",
"20947_erythronolide_synthase_docking_9_full_ssn.xgmml",
"20948_fad_binding_60_full_ssn.xgmml",
"20949_glycosyl_transferase_family_2_20_full_ssn.xgmml",
"20953_glycosyltransferase_family_28_25_full_ssn.xgmml",
"20951_glycosyl_transferases_group_1_35_full_ssn.xgmml",
"20954_glycosyltransferase_like_family_2_25_full_ssn.xgmml",
"20955__glyoxalase_bleomycin_resistance_35_full_ssn.xgmml",
"20957_kr_domain_55_full_ssn.xgmml",
"20960_lanthionine_synthetase_c-like_30_full_ssn.xgmml",
"20963_major_facilitator_superfamily_45_full_ssn.xgmml",
"20977_methyltransferase_small_domain_20_full_ssn.xgmml",
"20976_methyltransferase_domain_45_full_ssn.xgmml",
"20979_nad_dependent_epimerase_45_full_ssn.xgmml",
"20982_ndp-hexose_2_3-dehydratase_70_full_ssn.xgmml",
"20986_o-methyltransferase_40_full_ssn.xgmml",
"20987_oxidoreductase_family__c_25_full_ssn.xgmml",
"20988_oxidoreductase_family_30_full_ssn.xgmml",
"20989__phosphopantetheine_attachment_25_full_ssn.xgmml",
"20991_polyketide_cyclase_50_full_ssn.xgmml",
"20992_polyketide_synthase_dehydratase_65_full_ssn.xgmml",
"20993_protein_of_unknown_function_20_full_ssn.xgmml",
"20998_short_chain_dehydrogenase_50_full_ssn.xgmml",
"21000_snoal-like_30_full_ssn.xgmml",
"21001_spab_c-terminal_25_full_ssn.xgmml",
"21003_sugar__and_other__transporter_20_full_ssn.xgmml",
"21005_transcriptional_regulatory_18_full_ssn.xgmml",
"21004_thioesterase_domain_40_full_ssn.xgmml",
"21006_ubie_coq5_methyltransferase_25_full_ssn.xgmml",
"21008_udp-glucoronosyl_25_full_ssn.xgmml",
"21009_ycao-like_40_full_ssn.xgmml",
"21010_zinc-binding_dehydrogenase_40_full_ssn.xgmml",
"23780_pyridine_nucleotide_oxidoreductase_full_ssn.xgmml"]
SSN_map_filenames = ["20800_20799_thiolase_N_term_25_full_ssn_coloredssn_mapping_table",
"21332_21330_abc_35_full_ssn_coloredssn_mapping_table",
"20878_20870_acyl_transferase_pfam_100_full_ssn_coloredssn_mapping_table",
"21013_20923_aaa_30_full_ssn_coloredssn_mapping_table",
"21015_20928_abc2_30_full_ssn_coloredssn_mapping_table",
"21016_20929_acyl-coa_dehydrogenase_c_30_full_ssn_coloredssn_mapping_table",
"21017_20930_acyl-coa_dehydrogenase_n_20_full_ssn_coloredssn_mapping_table",
"21018_20931_alcohol_dehydrogenase_groes_20_full_ssn_coloredssn_mapping_table",
"21019_20932__alpha_beta_hydrolase_family_40_full_ssn_coloredssn_mapping_table",
"21021_20935_aminotransferase_40_full_ssn_coloredssn_mapping_table",
"21028_20944__beta-ketoacyl_synthase__c_55_full_ssn_coloredssn_mapping_table",
"21025_20941_beta-ketoacyl_synthase__n_100_full_ssn_coloredssn_mapping_table",
"21026_20942_cytochrome_p450_40_full_ssn_coloredssn_mapping_table",
"21027_20943_degt_dnrj_eryc1_strs_aminotransferase_60_full_ssn_coloredssn_mapping_table",
"21029_20946_enoyl-_acyl_carrier_protein_45_full_ssn_coloredssn_mapping_table",
"21030_20947_erythronolide_synthase_docking_9_full_ssn_coloredssn_mapping_table",
"21031_20948_fad_binding_60_full_ssn_coloredssn_mapping_table",
"21032_20949_glycosyl_transferase_family_2_20_full_ssn_coloredssn_mapping_table",
"21035_20953_glycosyltransferase_family_28_25_full_ssn_coloredssn_mapping_table",
"21033_20951_glycosyl_transferases_group_1_35_full_ssn_coloredssn_mapping_table",
"21036_20954_glycosyltransferase_like_family_2_25_full_ssn_coloredssn_mapping_table",
"21037_20955__glyoxalase_bleomycin_resistance_35_full_ssn_coloredssn_mapping_table",
"21039_20957_kr_domain_55_full_ssn_coloredssn_mapping_table",
"21040_20960_lanthionine_synthetase_c-like_30_full_ssn_coloredssn_mapping_table",
"21043_20963_major_facilitator_superfamily_45_full_ssn_coloredssn_mapping_table",
"21045_20977_methyltransferase_small_domain_20_full_ssn_coloredssn_mapping_table",
"21044_20976_methyltransferase_domain_45_full_ssn_coloredssn_mapping_table",
"21047_20979_nad_dependent_epimerase_45_full_ssn_coloredssn_mapping_table",
"21049_20982_ndp-hexose_2_3-dehydratase_70_full_ssn_coloredssn_mapping_table",
"21052_20986_o-methyltransferase_40_full_ssn_coloredssn_mapping_table",
"21053_20987_oxidoreductase_family__c_25_full_ssn_coloredssn_mapping_table",
"21054_20988_oxidoreductase_family_30_full_ssn_coloredssn_mapping_table",
"21055_20989__phosphopantetheine_attachment_25_full_ssn_coloredssn_mapping_table",
"21056_20991_polyketide_cyclase_50_full_ssn_coloredssn_mapping_table",
"21057_20992_polyketide_synthase_dehydratase_65_full_ssn_coloredssn_mapping_table",
"21058_20993_protein_of_unknown_function_20_full_ssn_coloredssn_mapping_table",
"21063_20998_short_chain_dehydrogenase_50_full_ssn_coloredssn_mapping_table",
"21064_21000_snoal-like_30_full_ssn_coloredssn_mapping_table",
"21065_21001_spab_c-terminal_25_full_ssn_coloredssn_mapping_table",
"21067_21003_sugar__and_other__transporter_20_full_ssn_coloredssn_mapping_table",
"21069_21005_transcriptional_regulatory_18_full_ssn_coloredssn_mapping_table",
"21068_21004_thioesterase_domain_40_full_ssn_coloredssn_mapping_table",
"21070_21006_ubie_coq5_methyltransferase_25_full_ssn_coloredssn_mapping_table",
"21071_21008_udp-glucoronosyl_25_full_ssn_coloredssn_mapping_table",
"21072_21009_ycao-like_40_full_ssn_coloredssn_mapping_table",
"21073_21010_zinc-binding_dehydrogenase_40_full_ssn_coloredssn_mapping_table",
"23815_23780_pyridine_nucleotide_oxidoreductase_full_ssn_coloredssn_mapping_table"]
ssn_thresholds = [25,35,100,30,30,30,20,20,40,40,55,100,40,60,45,9,60,
20,25,35,25,35,55,30,45,20,45,45,70,40,25,30,
25,50,65,20,50,30,25,20,18,40,25,25,40,40,30]
def findAllPFAMSeqs(genbank_file, pfam_name):
cluster_domains = []
score_cutoff = 20
record = SeqIO.read(open(genbank_file, 'rU'),"genbank")
features = record.features
for feature in features:
if feature.type == "PFAM_domain":
score = float(feature.qualifiers["score"][0])
if score <score_cutoff:
continue
domain_description = feature.qualifiers["description"][0]
#domain_description = domain_description.lower().replace("\n", " ")
if pfam_name == domain_description:
cluster_domains.append(feature.qualifiers["translation"])
return cluster_domains
def makePFAMNameDictionary(SSN_pfam_names, SSN_feature_names):
prev_feature_name = ""
pfam_name_dictionary = {}
i = 0
for name in SSN_feature_names:
if name[0:name.rfind("_")] == prev_feature_name:
continue
pfam_name_dictionary[SSN_pfam_names[i]] = name[0:name.rfind("_")]
prev_feature_name = name[0:name.rfind("_")]
i+=1
return pfam_name_dictionary
def generateSSNFeatureMatrix(clusters, SSN_pfam_names, SSN_feature_names, included_clusters, blast_exe,genome_name, data_path):
pfam_name_dictionary =makePFAMNameDictionary(SSN_pfam_names, SSN_feature_names)
SSN_feature_matrix = np.zeros((len(clusters), len(SSN_feature_names)))
j = 0
for c in clusters:
print(c)
filename = c
i = 0
for pfam in SSN_pfam_names:
pfam_sequences = findAllPFAMSeqs(filename, pfam)
for sequence in pfam_sequences:
if len(sequence) == 0:
continue
#print pfam
#print sequence
ssn_seq_filename = data_path + "SSN/gene_sequences/" + pfam_name_dictionary[pfam] +"_domains.fasta"
ssn_membership =findSSNMembership(c, sequence, ssn_seq_filename, i, included_clusters[pfam_name_dictionary[pfam]], blast_exe,genome_name,data_path)
print(ssn_membership)
for cluster in ssn_membership:
#print pfam_name_dictionary[pfam] + "_" + cluster
if pfam_name_dictionary[pfam] + "_" + cluster not in SSN_feature_names:
print("no match")
continue
#print "match"
SSN_feature_matrix[j,SSN_feature_names.index(pfam_name_dictionary[pfam] + "_" + cluster)] +=1
#print c
#print cluster
#print ""
i+= 1
print(SSN_feature_matrix[j,:])
j += 1
#print SSN_feature_matrix
return SSN_feature_matrix
def getGeneName(network_fn):
tree = ET.parse(network_fn)
root = tree.getroot()
node_to_gene_ids = {}
for child in root:
if "node" in child.tag:
node_id = child.attrib['id']
#print node_id
gene_id = child[1][0].attrib['value']
node_to_gene_ids[node_id] = gene_id
return node_to_gene_ids
def parseBLAST(blast_out):
return
def findSSNMembership(cluster_name, sequence, ssn_seq_filename, ssn_index, included_clusters, blast_exe, genome_name,data_path):
#read all the sequences from the sequence file, discard any with the same name as current cluster
#print cluster_name
#print ssn_seq_filename
seq_file = open(ssn_seq_filename, 'r')
sequences = {}
for line in seq_file:
if ">" in line:
gene_name = line.replace("\n","").replace(">","").replace("\r","")
else:
sequences[gene_name] = line.replace("\n","")
#group sequences by SSN cluster
sequences_in_SSN = {}
gene_ids_in_SSN = {}
SSN_filename = SSN_filenames[ssn_index]
node_to_gene = getGeneName(data_path+"SSN/" +SSN_filename)
SSN_map_file =open(data_path+"SSN/" +SSN_map_filenames[ssn_index] + ".txt")
for line in SSN_map_file:
#print line
if "UniProt" in line:
continue
node_id = line.split()[0]
cluster_id = line.split()[1]
if cluster_id not in included_clusters:
continue
gene_id = node_to_gene[node_id]
if cluster_name in gene_id:
continue
if cluster_id not in sequences_in_SSN:
sequences_in_SSN[cluster_id] = []
gene_ids_in_SSN[cluster_id] = []
sequences_in_SSN[cluster_id].append(sequences[gene_id])
gene_ids_in_SSN[cluster_id].append(gene_id)
SSN_map_file.close()
#figure out which clusters sequences with e-value score lower than threshold
clusters_with_match = []
threshold = ssn_thresholds[ssn_index]
waiting = True
while waiting:
try:
temp_fasta = open(data_path+"temp_file/"+genome_name+"seq1.fasta", 'w')
waiting = False
except Exception as e:
print(e)
print("file error!!")
temp_fasta.write(">"+cluster_name+"\n")
temp_fasta.write(sequence[0])
temp_fasta.close()
for cluster in sequences_in_SSN:
#print cluster
for seq in sequences_in_SSN[cluster]:
waiting = True
while waiting:
try:
temp_fasta = open(data_path+"temp_file/"+genome_name+"seq2.fasta", 'w')
waiting = False
except:
print("file error!!")
temp_fasta.write(">"+cluster +"\n")
temp_fasta.write(seq)
temp_fasta.close()
gap_open = '11'
gap_extend = '1'
comp_based = 2
cline = '{0} -query {1}temp_file/{2}seq1.fasta -subject {1}temp_file/{2}seq2.fasta -gapopen {3} -gapextend {4} -comp_based_stats {5} -use_sw_tback -outfmt \"6\" -max_hsps 1 -evalue {6}'.format(blast_exe,data_path,genome_name,gap_open,gap_extend,comp_based,5)
#cline = 'blastp -query /home/asw23/antismash_on_full_genome/temp_file/seq1.fasta'
child = subprocess.Popen(cline,stdout=subprocess.PIPE, shell=True)
my_out,my_err = child.communicate()
score = 0
#print sequence[0]
#print ""
#print seq
#print my_out
if len(my_out.split()) >=12:
score = float(my_out.split()[11])
score = -1*math.log10((2**(-1*score))*len(sequence[0])*len(seq))
if score > threshold:
#print threshold
#print "score: " + str(score)
clusters_with_match.append(cluster)
break
cline = '{0} -query {1}temp_file/{2}seq2.fasta -subject {1}temp_file/{2}seq1.fasta -gapopen {3} -gapextend {4} -comp_based_stats {5} -use_sw_tback -outfmt \"6\" -max_hsps 1 -evalue {6}'.format(blast_exe,data_path,genome_name,gap_open,gap_extend,comp_based,5)
#child = subprocess.Popen(cline,shell=True,stdout=subprocess.PIPE,close_fds=True)
child = subprocess.Popen(cline,stdout=subprocess.PIPE,shell=True)
my_out,my_err = child.communicate()
if len(my_out.split()) >=12:
score = float(my_out.split()[11])
score = -1*math.log10((2**(-1*score))*len(sequence[0])*len(seq))
if score > threshold:
clusters_with_match.append(cluster)
break
#(cluster_counts, cluster_list) = readSSNFile(name)
#print clusters_with_match
return clusters_with_match