def DDI_annotation (extension, pfam_path, filter_flag, spe_level, interact, pfams, human_pfam, suffix):
filelist = utilities.find_files(pfam_path, extension, None)
for myfile in filelist:
#myfile = pfam_path + "/" + samplelist + "/" + samplelist + ".interpro.PfamDomain.tsv"
if not os.path.isfile(myfile):
config.logger.info ("ERROR! File not exist: " + myfile)
else:
myout = re.sub(extension, suffix, myfile)
myout_detail = re.sub(".tsv", ".detail.tsv", myout)
peptide = collect_pfam_info (myfile)
assign_interaction (filter_flag, spe_level, pfams, human_pfam, interact, peptide, myout, myout_detail)
def collect_pfam_info(cluster_mem, extension, ann_path, outfile): # list.txt
pfams = {}
filelist = utilities.find_files(ann_path, extension, None)
for myfile in filelist:
#myfile = ann_path + "/" + samplelist + "/" + samplelist + ".interpro.PfamDomain.tsv"
if not os.path.isfile(myfile):
config.logger.info("ERROR! File not exist: " + myfile)
continue
open_file = open(myfile, "r")
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^" + utilities.PROTEIN_ID, line):
continue
info = line.split("\t")
myid = info[0]
if not myid in cluster_mem:
continue
pfam = info[1]
if not myid in pfams:
pfams[myid] = {}
pfams[myid][pfam] = info[2]
# foreach line
open_file.close()
# foreach samplelist
# output details
outfile1 = re.sub("_proteinfamilies.", "_proteinfamilies.ORF.", outfile)
open_out = open(outfile1, "w")
open_out.write(utilities.PROTEIN_ID + "\ttype\tdetail\tdescription\n")
for myid in sorted(pfams.keys()):
myacc = ""
myann = ""
for item in sorted(pfams[myid].keys()):
if myacc == "":
myacc = item
else:
myacc = myacc + ";" + item
if myann == "":
myann = pfams[myid][item]
else:
myann = myann + ";" + pfams[myid][item]
open_out.write(myid + "\tPfam_PfamDomain\t" + myacc + "\t" + myann +
"\n")
# foreach seqID
open_out.close()
return pfams
def collect_counts(map_path, extension, gene_cluster):
counts = {}
mysample = {}
'''
samples = {}
open_file = open(sample_file, "r")
for line in open_file:
line = line.strip()
if not len(line):
continue
samples[line.split("\t")[0]] = ""
# foreach sample
open_file.close()
'''
filelist = utilities.find_files(map_path, extension, None)
for myfile in filelist:
mym = re.search("([^\/]+)$", myfile)
sample = mym.group(1)
sample = re.sub("." + extension, "", sample)
if not os.path.isfile(myfile):
config.logger.info("ERROR! File not exist: " + myfile)
continue
mysample[sample] = ""
open_file = open(myfile, "r")
for line in open_file:
line = line.strip()
if not len(line):
continue
if re.search("^#", line):
continue
if re.search("^Geneid", line):
continue
info = line.split("\t")
myid = info[0]
if not myid in gene_cluster: # not specified genes
continue
mycount = info[-1]
if mycount == str(0): # no counts
continue
if not myid in counts:
counts[myid] = {}
counts[myid][sample] = mycount
# foreach line
open_file.close()
# foreach samplelist
return counts, mysample
def format_contig_info(contig_path, extension, outfile):
filelist = utilities.find_files(contig_path, extension, None)
open_out = open(outfile, "w")
for myfile in filelist:
myfile = myfile.strip()
if not len(myfile):
continue
sample = myfile
mym = re.search("([^\/]+)$", sample)
sample = mym.group(1)
sample = re.sub("." + extension, "", sample)
# collect seq info
if not os.path.isfile(myfile):
config.logger.info("WARNING! Contig file doesn't exist!\t" +
myfile)
continue
open_contig = open(myfile, "r")
contigs = {}
contig_order = []
myid = ""
for line in open_contig:
line = line.strip()
if not len(line):
continue
if re.search("^>", line):
mym = re.search(">([\S]+)", line)
myid_old = mym.group(1)
#myid_new = sample + "_contig_" + mym.group(1) + "|" + sample + "|"
#myid = re.sub(myid_old + "[\s]+", myid_new, line)
myid = ">" + sample + "_contig_" + mym.group(1)
if not myid in contigs:
contig_order.append(myid)
contigs[myid] = ""
continue
contigs[myid] = contigs[myid] + line
# foreach line
open_contig.close()
# output contig sequence
for myid in contig_order:
if myid in contigs:
open_out.write(myid + "\n" + contigs[myid] + "\n")
# foreach contig
# foreach sample
open_out.close()
def collect_transmembrane_info (cluster_mem, extension, ann_path, outfile): # list.txt
transmem = {}
details = {}
filelist = utilities.find_files(ann_path, extension, None)
for myfile in filelist:
if not os.path.isfile(myfile):
config.logger.info ("ERROR! File not exist: " + myfile)
else:
open_file = open(myfile, "r")
titles = {}
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
info = line.split("\t")
if re.search("^" + utilities.PROTEIN_ID, line):
for item in info:
titles[item] = info.index(item)
continue
myid = info[titles[utilities.PROTEIN_ID]]
if not myid in cluster_mem:
continue
details[info[titles[utilities.PROTEIN_ID]]] = info[titles["Prediction"]]
sample = re.sub("_[\d]+$", "", myid)
if not sample in transmem:
transmem[sample] = {}
transmem[sample][info[titles[utilities.PROTEIN_ID]]] = info[titles["Prediction"]]
# foreach line
open_file.close()
# foreach samplelist
# output details
outfile1 = re.sub("_proteinfamilies.", "_proteinfamilies.ORF.", outfile)
open_out = open(outfile1, "w")
open_out.write(utilities.PROTEIN_ID + "\ttype\tdetail\tdescription\n")
for myid in sorted(details.keys()):
myinfo = details[myid]
open_out.write(myid + "\tTMHMM_transmembrane\tTMHMM_transmembrane\t" + myinfo + "\n")
open_out.close()
return transmem
def gene_calling(workflow, assembly_dir, assembly_extentsion, input_dir,
extension, extension_paired, gene_call_type, prokka_dir,
prodigal_dir, threads, gene_file, gene_PC_file, protein_file,
protein_sort, gene_info, complete_gene, complete_protein):
"""
This set of tasks will run gene-calling workflow.
Args:
workflow (anadama2.workflow): An instance of the workflow class.
assembly_dir: The direcory path of assembly results.
sample_file: The sample list file.
prokka_dir: The direcory path of prokka results.
prodigal_dir: The direcory path of prodigal results.
gene_file: The fasta file of gene nucleotide sequences.
gene_PC_file: The fasta file of protein coding gene nucleotide sequences.
protein_file: The fasta file of protein sequences.
protein_sort: The sorted fasta file of protein sequences.
gene_info: The summaized gene calling file.
complete_gene: The fasta file of gene nucleotide sequences for complete ORFs.
complete_protein: The fasta file of protein sequences for complete ORFs.
Requires:
prokka 1.14-dev: rapid prokaryotic genome annotation (recommend to close '-c' parameter in prodigal)
prodigal v2.6: gene prediction
usearch (tested with usearch v9.0.2132_i86linux64)
assembled contig files
Returns:
string: name of gene files
Example:
from anadama2 import Workflow
from MetaWIBELE.characterize import characterization
# create an anadama2 workflow instance
workflow=Workflow()
# add gene calling tasks
mygene, myprotein = preprocessing_tasks.gene_calling (workflow, assembly_dir, args.sample_file,
prokka_dir, prodigal_dir,
gene_file, gene_PC_file, protein_file, protein_sort,
gene_info, complete_gene, complete_protein)
# run the workflow
workflow.go()
"""
config.logger.info("###### Start gene_calling module ######")
time_equation = config.time # xxx hours defined in global config
mem_equation = config.memory # xxx GB defined in global config
# ================================================
# collect sequences
# ================================================
if extension_paired:
extension_paireds = extension_paired.split(",")
sample_files = utilities.find_files(input_dir, extension_paireds[0],
None)
samples = utilities.sample_names(sample_files, extension_paireds[0],
None)
else:
sample_files = utilities.find_files(input_dir, extension, None)
samples = utilities.sample_names(sample_files, extension, None)
sequence_files = []
for mysample in samples:
myfile = os.path.join(assembly_dir, mysample,
mysample + "%s" % assembly_extentsion)
sequence_files.append(myfile)
# foreach sample
filtered_contigs = sequence_files
# ================================================
# Gene calling
# ================================================
fna_file = []
faa_file = []
gff_files = []
fna_file_tmp = []
faa_file_tmp = []
gff_files_tmp = []
## Using Prodigal
if gene_call_type == "prodigal" or gene_call_type == "both":
os.system("mkdir -p " + prodigal_dir)
for contig in filtered_contigs:
contig_base = os.path.basename(contig).split(os.extsep)[0]
annotation_dir = os.path.join(prodigal_dir, contig_base)
os.system("mkdir -p " + annotation_dir)
gff_file = os.path.join(annotation_dir, '%s.gff' % contig_base)
cds_file = os.path.join(annotation_dir, '%s.fna' % contig_base)
cds_aa = os.path.join(annotation_dir, '%s.faa' % contig_base)
score = os.path.join(annotation_dir,
'%s.gene_score.txt' % contig_base)
stdout_log = os.path.join(annotation_dir,
'%s.stdout.log' % contig_base)
faa_file_tmp.append(cds_aa)
workflow.add_task_gridable(
'prodigal -m -p meta -i [depends[0]] '
'-f gff -o [targets[0]] -d [targets[1]] -s [targets[3]] '
'-a [targets[2]] '
'>[args[0]] 2>&1',
depends=[contig, TrackedExecutable("prodigal")],
targets=[gff_file, cds_file, cds_aa, score],
args=[stdout_log],
cores=threads,
mem=mem_equation,
time=time_equation,
name=contig_base + "__prodigal")
for myfile in faa_file_tmp:
myname = os.path.basename(myfile)
myfile_new = os.path.join(prodigal_dir, myname)
faa_file.append(myfile_new)
workflow.add_task("ln -fs [depends[0]] [targets[0]]",
depends=[myfile],
targets=[myfile_new],
cores=1,
name="ln__" + myname)
myfna = re.sub(".faa", ".fna", myfile)
myfna_new = re.sub(".faa", ".fna", myfile_new)
if gene_call_type == "prodigal":
fna_file.append(myfna_new)
mygff_new = re.sub(".faa", ".gff", myfile_new)
gff_files.append(mygff_new)
prokka_dir = prodigal_dir
workflow.add_task("ln -fs [depends[0]] [targets[0]]",
depends=[myfna],
targets=[myfna_new],
cores=1,
name="ln__" + os.path.basename(myfna))
mygff = re.sub(".faa", ".gff", myfile)
mygff_new = re.sub(".faa", ".gff", myfile_new)
workflow.add_task("ln -fs [depends[0]] [targets[0]]",
depends=[mygff],
targets=[mygff_new],
cores=1,
name="ln__" + os.path.basename(mygff))
if gene_call_type == "prokka" or gene_call_type == "both":
## Calling genes with Prokka
os.system("mkdir -p " + prokka_dir)
for contig in filtered_contigs:
contig_base = os.path.basename(contig).split(os.extsep)[0]
sample = os.path.basename(contig_base)
annotation_dir = os.path.join(prokka_dir, sample)
os.system("mkdir -p " + annotation_dir)
stdout_log = os.path.join(
annotation_dir, '%s.prokka.bacteria.stdout.log' % contig_base)
score = os.path.join(annotation_dir,
'%s.gene_score.txt' % contig_base)
gene_nuc = os.path.join(annotation_dir, '%s.ffn' % contig_base)
gene_aa = os.path.join(annotation_dir, '%s.faa' % contig_base)
gff_file = os.path.join(annotation_dir, '%s.gff' % contig_base)
fna_file_tmp.append(gene_nuc)
gff_files_tmp.append(gff_file)
workflow.add_task_gridable(
'prokka --prefix [args[0]] --addgenes --addmrna --force --metagenome '
'--cpus [args[2]] '
'--outdir [args[1]] [depends[0]] '
'>[args[3]] 2>&1 ',
depends=[contig, TrackedExecutable("prokka")],
targets=[gene_nuc, gene_aa, gff_file],
args=[sample, annotation_dir, threads, stdout_log],
cores=threads,
mem=mem_equation,
time=time_equation,
name=contig_base + "__prokka")
for myfile in gff_files_tmp:
myname = os.path.basename(myfile)
myfile_new = os.path.join(prokka_dir, myname)
gff_files.append(myfile_new)
for myfile in fna_file_tmp:
myname = os.path.basename(myfile)
myfile_new = os.path.join(prokka_dir, myname)
fna_file.append(myfile_new)
workflow.add_task("ln -fs [depends[0]] [targets[0]]",
depends=[myfile],
targets=[myfile_new],
cores=1,
name="ln__" + myname)
myfaa = re.sub(".ffn", ".faa", myfile)
myfaa_new = re.sub(".ffn", ".faa", myfile_new)
if gene_call_type == "prokka":
faa_file.append(myfaa_new)
prodigal_dir = prokka_dir
workflow.add_task("ln -fs [depends[0]] [targets[0]]",
depends=[myfaa],
targets=[myfaa_new],
cores=1,
name="ln__" + os.path.basename(myfaa))
mygff = re.sub(".ffn", ".gff", myfile)
mygff_new = re.sub(".ffn", ".gff", myfile_new)
workflow.add_task("ln -fs [depends[0]] [targets[0]]",
depends=[mygff],
targets=[mygff_new],
cores=1,
name="ln__" + os.path.basename(mygff))
# ================================================
# Summarize sequences
# ================================================
#mem_equation = "50000"
### combine gene sequences ###
nuc_type = "ffn"
if gene_call_type == "prodigal":
nuc_type = "fna"
mylog = re.sub(".fna", ".log", gene_file)
workflow.add_task(
'metawibele_combine_gene_sequences -p [args[0]] -e [args[1]] -o [targets[0]] > [args[2]] 2>&1 ',
depends=utilities.add_to_list(
fna_file, TrackedExecutable("metawibele_combine_gene_sequences")) +
fna_file_tmp + gff_files + gff_files_tmp,
targets=[gene_file],
args=[prokka_dir, nuc_type, mylog],
cores=1,
name="combine_gene_sequences")
### combine protein sequences ###
## collect sequences
mylog = re.sub(".faa", ".log", protein_file)
workflow.add_task(
'metawibele_format_protein_sequences -p [args[0]] -q [args[1]] -e faa -o [targets[0]] '
'-m [targets[1]] >[args[2]] 2>&1 ',
depends=utilities.add_to_list(
faa_file, TrackedExecutable("metawibele_format_protein_sequences"))
+ faa_file_tmp + gff_files + gff_files_tmp,
targets=[protein_file, gene_info],
args=[prokka_dir, prodigal_dir, mylog],
cores=1,
name="format_protein_sequences")
## sort by length and filter out short-length sequence
mylog = re.sub(".faa", ".log", protein_sort)
workflow.add_task(
'usearch -sortbylength [depends[0]] '
'-fastaout [targets[0]] -minseqlength 0 >[args[0]] 2>&1 ',
depends=[protein_file, TrackedExecutable("usearch")],
targets=[protein_sort],
args=[mylog],
cores=1,
name="usearch__sorting")
## extract nucleotide sequence for protein coding genes
mylog = re.sub(".fna", ".log", gene_PC_file)
workflow.add_task(
'metawibele_extract_protein_coding_genes -g [depends[0]] -p [depends[1]] -o [targets[0]] > [args[0]] 2>&1 ',
depends=[
gene_file, protein_sort,
TrackedExecutable("metawibele_extract_protein_coding_genes")
],
targets=[gene_PC_file],
args=[mylog],
cores=1,
name="extract_protein_coding_genes")
## extract sequences
mylog = re.sub(".fna", ".log", complete_gene)
workflow.add_task(
'metawibele_extract_complete_ORF_seq -t complete -m [depends[0]] -i [depends[1]] -o [targets[0]] >[args[0]] 2>&1',
depends=[
gene_info, gene_PC_file,
TrackedExecutable("metawibele_extract_complete_ORF_seq")
],
targets=[complete_gene],
args=[mylog],
cores=1,
name='extract_complete_ORF_seq')
mylog = re.sub(".faa", ".log", complete_protein)
workflow.add_task(
'metawibele_extract_complete_ORF_seq -t complete -m [depends[0]] -i [depends[1]] -o [targets[0]] >[args[0]] 2>&1',
depends=[
gene_info, protein_sort,
TrackedExecutable("metawibele_extract_complete_ORF_seq")
],
targets=[complete_protein],
args=[mylog],
cores=1,
name='extract_complete_ORF_seq')
return complete_gene, complete_protein
def collect_sequence(ann_path, extension, partial_path, outfile):
filelist = utilities.find_files(ann_path, extension, None)
open_out = open(outfile, "w")
outfile2 = re.sub(".faa", ".abnormal_seq.faa", outfile)
outfile2 = re.sub(".fasta", ".abnormal_seq.fasta", outfile)
open_out2 = open(outfile2, "w")
gff = {}
types = {}
partial = {}
for myfile in filelist:
sample = myfile
mym = re.search("([^\/]+)$", sample)
sample = mym.group(1)
sample = re.sub("." + extension, "", sample)
# collect gff info that is corresponded the sequences file
contigs = {}
mapping = {}
mygff = re.sub("." + extension, ".gff", myfile)
if not os.path.isfile(mygff):
config.logger.info("ERROR! Gff file doesn't exist!\t" + mygff)
continue
config.logger.info("Read gff file: " + mygff)
open_gff = open(mygff, "r")
for line in open_gff:
line = line.strip()
if not len(line):
continue
if re.search("^#", line):
if re.search("^##sequence-region", line):
mym = re.search(
"##sequence-region\s+([\S]+)\s+([\d]+)\s+([\d]+)",
line)
tmp_contig = mym.group(1)
contigs[tmp_contig] = str(mym.group(2)) + "\t" + str(
mym.group(3))
if re.search("^# Sequence Data", line):
mytmp = line.split(";")
mym = re.sub("\"", "", mytmp[-1])
mym = re.search("^seqhdr\=([\S]+)[\s]+[\s\S]+len\=([\d]+)",
mym)
tmp_contig = mym.group(1)
contigs[tmp_contig] = str(1) + "\t" + str(mym.group(2))
continue
if re.search("^>", line):
break
info = line.split("\t")
feature = info[2]
start = info[3]
stop = info[4]
strand = info[6]
desc = info[8]
myinfo = desc.split(";")
if re.search("ID\=([^\;]+)", desc):
myid = re.search("ID\=([^\;]+)", desc)
myid = myid.group(1)
else:
continue
gene_name = "NA"
gene_id = "NA"
gene_num = "NA"
sample_id = "NA"
contig_id = info[0]
contig_len = "NA\tNA"
if contig_id in contigs:
contig_len = str(contigs[contig_id])
for item in myinfo:
if re.search("locus_tag=", item):
mym = re.search("locus_tag=([\S]+)", item)
gene_id = mym.group(1)
if re.search("Name=", item):
mym = re.search("Name=([\S]+)", item)
gene_name = mym.group(1)
# foreach item
if not re.search("locus_tag=", desc):
gene_id = sample + "_" + re.sub("_", "-", myid)
if not re.search("Name=", desc):
gene_name = sample + "_" + re.sub("_", "-", myid)
if re.search("\_", gene_id):
mym = re.search("^([^\_]+)\_([\S]+)", gene_id)
sample_id = mym.group(1)
gene_num = mym.group(2)
if not re.search("locus_tag=", desc):
gene = gene_id
sample_id = sample
else:
gene = sample + "_" + gene_num
contig = sample + "_contig_" + contig_id
if feature == "gene":
if not sample in gff:
gff[sample] = {}
if not gene_id in gff[sample]:
gff[sample][
gene_id] = gene + "\t" + gene_id + "\t" + gene_name + "\t" + start + "\t" + stop + "\t" + strand + "\n" + contig + "\t" + contig_id + "\t" + contig_len + "\n" + sample + "\t" + sample_id
if feature != "gene" and feature != "mRNA":
if not sample in types:
types[sample] = {}
if not gene_id in types[sample]:
types[sample][gene_id] = feature
if feature == "CDS":
new_id = contig_id + "\t" + start + "\t" + stop + "\t" + strand
mapping[new_id] = gene + "\t" + gene_id
if not re.search("locus_tag=", desc):
if not sample in gff:
gff[sample] = {}
if not gene_id in gff[sample]:
gff[sample][
gene_id] = gene + "\t" + gene_id + "\t" + gene_name + "\t" + start + "\t" + stop + "\t" + strand + "\n" + contig + "\t" + contig_id + "\t" + contig_len + "\n" + sample + "\t" + sample_id
# foreach line
open_gff.close()
# collect sequences from prodigal results including pratial info
myfile1 = re.sub(ann_path, partial_path, myfile)
open_file = open(myfile1, "r")
AA_seq = {}
myname = ""
flag = 0
hit_num = 0
total_num = 0
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^>", line): # sequence id
total_num = total_num + 1
line = re.sub("^>", "", line)
info = line.split(" # ")
if len(info) < 4:
# debug
config.logger.info("WARNING! No info!\t" + myfile1 + "\t" +
line)
continue
myref = re.sub("_[\d]+$", "", info[0])
mystart = info[1]
mystop = info[2]
mystrand = "+"
if info[3] == "-1":
mystrand = "-"
myid = myref + "\t" + mystart + "\t" + mystop + "\t" + mystrand
myname = myid
flag = 0
if myid in mapping:
hit_num = hit_num + 1
# debug
#print("Mapping:" + myid + "\t" + mapping[myid])
gene, gene_id = mapping[myid].split("\t")
myname = ">" + gene
if not myname in AA_seq:
AA_seq[myname] = ""
mym = re.search("partial=([\d]+)", info[-1])
mypartial = mym.group(1)
if not sample in partial:
partial[sample] = {}
partial[sample][gene_id] = mypartial
flag = 1
else:
# debug
config.logger.info("No mapping info!\t" + line)
continue
else:
if flag == 1:
if myname in AA_seq:
AA_seq[myname] = AA_seq[myname] + line
# foreach line
open_file.close()
if hit_num != total_num:
open_file = open(myfile, "r")
myname = ""
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^>", line): # sequence id
mym = re.search("^>([^\_]+)\_([\S]+)", line)
sample_id = mym.group(1)
gene_num = mym.group(2)
gene = sample + "_" + gene_num
mym = re.search("^>([\S]+)", line)
gene_id = mym.group(1)
myname = ">" + gene
if not myname in AA_seq:
AA_seq[myname] = ""
else:
myname = ""
continue
if not sample in partial:
partial[sample] = {}
partial[sample][gene_id] = "00"
else:
if myname in AA_seq:
AA_seq[myname] = AA_seq[myname] + line
# foreach line
open_file.close()
for myname in sorted(AA_seq.keys()):
myseq = AA_seq[myname]
myseq = re.sub("\*$", "", myseq)
AA_seq[myname] = myseq
if re.search("\*", myseq): # terminal codon in CDS
#print("Abnormal CDS\t" + sample + "\t" + myname)
open_out2.write(myname + "\n" + AA_seq[myname] + "\n")
continue
else:
open_out.write(myname + "\n" + AA_seq[myname] + "\n")
# foreach sample
open_out.close()
return gff, types, partial
def collect_DDI_info(cluster_mem, extension, ann_path, level, label,
outfile): # list.txt
DDIs = {}
anns = {}
titles = {}
filelist = utilities.find_files(ann_path, extension, None)
for myfile in filelist:
#myfile = ann_path + "/" + samplelist + "/" + samplelist + "." + suffix
if not os.path.isfile(myfile):
config.logger.info("ERROR! File not exist: " + myfile)
continue
open_file = open(myfile, "r")
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
info = line.split("\t")
if re.search("^" + utilities.PROTEIN_ID, line):
for item in info:
titles[item] = info.index(item)
continue
myid = info[titles[utilities.PROTEIN_ID]]
if not myid in cluster_mem:
continue
mytype = info[titles["Type"]]
mylevel = info[titles["Interaction"]]
mypfam = info[titles["Pfam1_ID"]] + ":" + info[titles["Pfam2_ID"]]
myann = info[titles["Pfam1_ann"]] + ":" + info[titles["Pfam2_ann"]]
if not myid in DDIs:
DDIs[myid] = {}
DDIs[myid][mylevel + "\t" + mypfam] = ""
anns[mypfam] = myann
# foreach line
open_file.close()
# foreach samplelist
# output details
outfile1 = re.sub("_proteinfamilies.", "_proteinfamilies.ORF.", outfile)
outfile2 = re.sub(".tsv", ".detail.tsv", outfile1)
open_out1 = open(outfile1, "w")
open_out2 = open(outfile2, "w")
open_out2.write(utilities.PROTEIN_ID +
"\ttype\tdetail\tannotation\tinteraction\n")
open_out1.write(
utilities.PROTEIN_ID +
"\tType\tInteraction\tPfam1_ID\tPfam2_ID\tPfam1_ann\tPfam2_ann\n")
for myid in sorted(DDIs.keys()):
mypfam = ""
myann = ""
mylevel = ""
for item in sorted(DDIs[myid].keys()):
tmp = item.split("\t")
#myt = "DOMINE_interaction"
myt = label
myl = tmp[0]
pfam1, pfam2 = tmp[1].split(":")
ann1 = "NA:NA"
if tmp[1] in anns:
ann1 = anns[tmp[1]]
ann1 = re.sub(":", "\t", ann1)
open_out1.write(myid + "\t" + myt + "\t" + myl + "\t" + pfam1 +
"\t" + pfam2 + "\t" + ann1 + "\n")
if level != "no":
if tmp[0] != "NA":
if tmp[0] != level:
continue
mypfam = mypfam + tmp[1] + ";"
mylevel = mylevel + tmp[0] + ";"
if tmp[1] in anns:
myann = myann + anns[tmp[1]] + ";"
else:
myann = myann + "NA;"
# foreach DDI
mypfam = re.sub(";$", "", mypfam)
myann = re.sub(";$", "", myann)
mylevel = re.sub(";$", "", mylevel)
if mypfam == "":
continue
#open_out2.write(myid + "\tDOMINE_interaction\t" + mypfam + "\t" + myann + "\t" + mylevel + "\n")
open_out2.write(myid + "\t" + label + "\t" + mypfam + "\t" + myann +
"\t" + mylevel + "\n")
# foreach seqID
open_out1.close()
open_out2.close()
return DDIs, anns
def collect_ann_info(cluster_mem, extension, ann_path, types,
outfile): # list.txt
anns = {}
anns_info = {}
filelist = utilities.find_files(ann_path, extension, None)
for myfile in filelist:
for suffix in types:
myfile1 = re.sub(extension, suffix, myfile)
if not os.path.isfile(myfile1):
#print ("File not exist!\t" + myfile1)
continue
open_file = open(myfile1, "r")
mym = re.search("interpro.([\S]+).tsv", suffix)
mytype = "InterProScan_" + mym.group(1)
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^" + utilities.PROTEIN_ID, line):
continue
info = line.split("\t")
myid = info[0]
if not myid in cluster_mem:
continue
acc = info[1]
if not myid in anns:
anns[myid] = {}
if not mytype in anns[myid]:
anns[myid][mytype] = {}
if not mytype in anns_info:
anns_info[mytype] = {}
if info[2] == "":
info[2] = "NA"
if info[3] == "":
info[3] = "NA"
anns[myid][mytype][acc] = info[2] + "\t" + info[3]
anns_info[mytype][acc] = info[2] + "\t" + info[3]
# foreach line
open_file.close()
# foreach type of annotation
#foreach samplelist
# output details
outfile1 = re.sub("_proteinfamilies.", "_proteinfamilies.ORF.", outfile)
open_out = open(outfile1, "w")
open_out.write(utilities.PROTEIN_ID +
"\ttype\tdetail\tdescription\tInterPro_accession\n")
for myid in sorted(anns.keys()):
for mytype in sorted(anns[myid].keys()):
myinfo1 = ""
myinfo2 = ""
myinfo3 = ""
for myacc in sorted(anns[myid][mytype].keys()):
myinfo1 = myinfo1 + myacc + ";"
tmp = anns[myid][mytype][myacc].split("\t")
myinfo2 = myinfo2 + tmp[0] + ";"
myinfo3 = myinfo3 + tmp[1] + ";"
# foreach annotated accession
myinfo1 = re.sub(";$", "", myinfo1)
myinfo2 = re.sub(";$", "", myinfo2)
myinfo3 = re.sub(";$", "", myinfo3)
if myinfo1 == "":
continue
open_out.write(myid + "\t" + mytype + "\t" + myinfo1 + "\t" +
myinfo2 + "\t" + myinfo3 + "\n")
# foreach type
# foreach seqID
open_out.close()
return anns, anns_info
def collect_localizing_info(cluster_mem, extension, ann_path,
outfile): # list.txt
gram_p = {}
gram_n = {}
archaea = {}
location = {}
score = {}
location_n = {}
location_p = {}
location_a = {}
filelist = utilities.find_files(ann_path, extension, None)
for myfile in filelist:
#myfile = ann_path + "/" + samplelist + "/" + samplelist + ".psortb.gram_positive.out.location.tsv"
if not os.path.isfile(myfile):
config.logger.info("ERROR! File not exist: " + myfile)
else:
open_file = open(myfile, "r")
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^name", line):
continue
info = line.split("\t")
myid = info[0]
if not myid in cluster_mem:
continue
sample = re.sub("_[\d]+$", "", myid)
if not sample in gram_p:
gram_p[sample] = {}
if not info[0] in location:
location[info[0]] = info[1]
score[info[0]] = info[2]
else:
if float(info[2]) > float(score[info[0]]):
location[info[0]] = info[1]
score[info[0]] = info[2]
location_p[info[0]] = info[1]
gram_p[sample][info[0]] = info[1]
# foreach line
open_file.close()
#myfile = ann_path + "/" + samplelist + "/" + samplelist + ".psortb.gram_negative.out.location.tsv"
myfile1 = re.sub("psortb.gram_positive.out.location.tsv",
"psortb.gram_negative.out.location.tsv", myfile)
if not os.path.isfile(myfile1):
config.logger.info("ERROR! File not exist: " + myfile1)
else:
open_file = open(myfile1, "r")
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^name", line):
continue
info = line.split("\t")
myid = info[0]
if not myid in cluster_mem:
continue
sample = re.sub("_[\d]+$", "", myid)
if not sample in gram_n:
gram_n[sample] = {}
if not info[0] in location:
location[info[0]] = info[1]
score[info[0]] = info[2]
else:
if float(info[2]) > float(score[info[0]]):
location[info[0]] = info[1]
score[info[0]] = info[2]
location_n[info[0]] = info[1]
gram_n[sample][info[0]] = info[1]
# foreach line
open_file.close()
#myfile = ann_path + "/" + samplelist + "/" + samplelist + ".psortb.archaea.out.location.tsv"
myfile1 = re.sub("psortb.gram_positive.out.location.tsv",
"psortb.archaea.out.location.tsv", myfile)
if not os.path.isfile(myfile1):
config.logger.info("ERROR! File not exist: " + myfile1)
else:
open_file = open(myfile1, "r")
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^name", line):
continue
info = line.split("\t")
myid = info[0]
if not myid in cluster_mem:
continue
sample = re.sub("_[\d]+$", "", myid)
if not sample in archaea:
archaea[sample] = {}
if not info[0] in location:
location[info[0]] = info[1]
score[info[0]] = info[2]
else:
if float(info[2]) > float(score[info[0]]):
location[info[0]] = info[1]
score[info[0]] = info[2]
location_a[info[0]] = info[1]
archaea[sample][info[0]] = info[1]
# foreach line
open_file.close()
# foreach samplelist
# output details
outfile1 = re.sub("_proteinfamilies.", "_proteinfamilies.ORF.", outfile)
open_out = open(outfile1, "w")
open_out.write(utilities.PROTEIN_ID + "\ttype\tdetail\tscore\n")
for myid in sorted(location.keys()):
mytype = location[myid]
if mytype == "Unknown":
mytype = "PSORTb_unknown"
if mytype == "Cytoplasmic":
mytype = "PSORTb_cytoplasmic"
if mytype == "CytoplasmicMembrane":
mytype = "PSORTb_cytoplasmicMembrane"
if mytype == "Extracellular":
mytype = "PSORTb_extracellular"
if mytype == "Cellwall":
mytype = "PSORTb_cellWall"
if mytype == "OuterMembrane":
mytype = "PSORTb_outerMembrane"
if mytype == "Periplasmic":
mytype = "PSORTb_periplasmic"
myscore = "NA"
if myid in score:
myscore = score[myid]
mydetail = re.sub("PSORTb_", "", mytype)
open_out.write(myid + "\t" + mytype + "\t" + mydetail + "\t" +
str(myscore) + "\n")
# foreach seqID
open_out.close()
return gram_p, gram_n, archaea, location, score, location_p, location_n, location_a
def extract_psortb_info (extension, psortb_path):
filelist = utilities.find_files(psortb_path, extension, None)
for myfile in filelist:
# gram+
#myfile = psortb_path + "/" + samplelist + "/" + samplelist + ".psortb.gram_positive.out.txt"
if not os.path.isfile(myfile):
config.logger.info ("ERROR! File not exist: " + myfile)
else:
config.logger.info ("OK!\t" + myfile)
open_file = open(myfile, "r")
myid = ""
out_p = []
flag = 0
for line in open_file:
line = line.strip()
if not len(line):
continue
if re.search("SeqID:", line):
mym = re.search("SeqID:\s+([\S]+)", line)
myid = mym.group(1)
continue
# id
if re.search("Final", line):
flag = 1
continue
if flag == 1:
if not len(line):
continue
line = re.sub("\s+", "\t", line)
info = line.split("\t")
mypredict = "NA"
myscore = 0
if not re.search("[\S]+", info[0]):
mypredict = info[1]
myscore = info[-1]
else:
mypredict = info[0]
myscore = info[-1]
if mypredict == myscore:
myscore = 0
flag = 0
if re.search("Unknown", mypredict):
mypredic = "Unknown"
myscore = re.sub("\s+", "", str(myscore))
if re.search("[a-zA-Z]+", myscore):
myscore = 0
#open_out.write(myid + "\t" + mypredict + "\t" + str(myscore) + "\n")
out_p.append(myid + "\t" + mypredict + "\t" + str(myscore))
# foreach line
myout = re.sub(".txt", ".location.tsv", myfile)
open_out = open(myout, "w")
open_out.write("name\ttype\tscore\n")
for item in out_p:
open_out.write(item + "\n")
open_file.close()
# if file exist
# gram-
#myfile = psortb_path + "/" + samplelist + "/" + samplelist + ".psortb.gram_negtive.out.txt"
myfile1 = re.sub("psortb.gram_positive.out.txt", "psortb.gram_negative.out.txt", myfile)
if not os.path.isfile(myfile1):
config.logger.info ("ERROR! File not exist: " + myfile1)
else:
config.logger.info ("OK!\t" + myfile1)
open_file = open(myfile1, "r")
out_n = []
myid = ""
flag = 0
for line in open_file:
line = line.strip()
if not len(line):
continue
if re.search("SeqID:", line):
mym = re.search("SeqID:\s+([\S]+)", line)
myid = mym.group(1)
continue
# id
if re.search("Final", line):
flag = 1
continue
if flag == 1:
if not len(line):
continue
line = re.sub("\s+", "\t", line)
info = line.split("\t")
mypredict = "NA"
myscore = 0
if not re.search("[\S]+", info[0]):
mypredict = info[1]
myscore = info[-1]
else:
mypredict = info[0]
myscore = info[-1]
if mypredict == myscore:
myscore = 0
flag = 0
if re.search("Unknown", mypredict):
mypredic = "Unknown"
myscore = re.sub("\s+", "", str(myscore))
if re.search("[a-zA-Z]+", myscore):
myscore = 0
#open_out.write(myid + "\t" + mypredict + "\t" + str(myscore) + "\n")
out_n.append(myid + "\t" + mypredict + "\t" + str(myscore))
# foreach line
#myout = re.sub(".txt", ".location.tsv", myfile)
myout = re.sub("gram_negative.out.txt", "gram_negative.out.location.tsv", myfile1)
open_out = open(myout, "w")
open_out.write("name\ttype\tscore\n")
for item in out_n:
open_out.write(item + "\n")
open_out.close()
# if file exist
# archaea
#myfile = psortb_path + "/" + samplelist + "/" + samplelist + ".psortb.archaea.out.txt"
myfile1 = re.sub("psortb.gram_positive.out.txt", "psortb.archaea.out.txt", myfile)
if not os.path.isfile(myfile1):
config.logger.info ("ERROR! File not exist: " + myfile1)
continue
config.logger.info("OK!\t" + myfile1)
open_file = open(myfile1, "r")
myid = ""
out_a = []
flag = 0
for line in open_file:
line = line.strip()
if not len(line):
continue
if re.search("SeqID:", line):
mym = re.search("SeqID:\s+([\S]+)", line)
myid = mym.group(1)
continue
# id
if re.search("Final", line):
flag = 1
continue
if flag == 1:
line = re.sub("\s+", "\t", line)
info = line.split("\t")
mypredict = "NA"
myscore = 0
if not re.search("[\S]+", info[0]):
mypredict = info[1]
myscore = info[-1]
else:
mypredict = info[0]
myscore = info[-1]
if mypredict == myscore:
myscore = 0
flag = 0
if re.search("Unknown", mypredict):
mypredic = "Unknown"
myscore = re.sub("\s+", "", str(myscore))
if re.search("[a-zA-Z]+", myscore):
myscore = 0
#open_out.write(myid + "\t" + mypredict + "\t" + str(myscore) + "\n")
out_a.append(myid + "\t" + mypredict + "\t" + str(myscore))
# foreach line
myout = re.sub(".txt", ".location.tsv", myfile1)
open_out = open(myout, "w")
open_out.write("name\ttype\tscore\n")
for item in out_a:
open_out.write(item + "\n")
open_file.close()
def collect_sequence(gene_path, extension, outfile):
sampleid = {}
filelist = utilities.find_files(gene_path, extension, None)
open_out = open(outfile, "w")
outfile1 = re.sub(".fna", "_protein_coding.fna", outfile)
#open_out1 = open(outfile1, "w")
for myfile in filelist:
sample = myfile
mym = re.search("([^\/]+)$", sample)
sample = mym.group(1)
sample = re.sub("." + extension, "", sample)
mygff = re.sub("." + extension, ".gff", myfile)
# collect protein-coding IDs
gffs = {}
open_gff = open(mygff, "r")
for line in open_gff:
line = line.strip()
if not len(line):
continue
if re.search("^\#", line):
continue
if re.search("^\>", line):
break
info = line.split("\t")
if info[2] == "CDS": # protein-coding genes
mym = re.search("^ID=([^\;]+)", info[-1])
gffs[mym.group(1)] = ""
# debug
#print("Protein-coding gene:\t" + mym.group(1))
else:
# debug
config.logger.info("Skip non-CDS\t" + info[2])
# foreach line
open_gff.close()
# output sequences
open_file = open(myfile, "r")
flag = 0
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^\>", line): # sequence id
if re.search("ID\=", line):
mym = re.search("ID\=([^\;]+)", line)
mygene = mym.group(1)
mym = re.search("\>([\S]+)", line)
myid = mym.group(1)
myid_new = sample + "_" + re.sub("_", "-", mygene)
sampleid[sample] = sample
line = re.sub(myid, myid_new, line)
else:
mym = re.search("\>([\S]+)", line)
mygene = mym.group(1)
mym = re.search("\>([^\_]+)", line)
myid = mym.group(1)
sampleid[sample] = myid
line = re.sub(myid, sample, line)
open_out.write(line + "\n")
if mygene in gffs:
flag = 1
#open_out1.write(line + "\n")
else:
# debug
config.logger.info("Skip non protein coding sequences: " +
mygene + "\t" + line)
flag = 0
continue
else:
open_out.write(line + "\n")
#if flag == 1:
# open_out1.write(line + "\n")
# foreach line
open_file.close()
# foeach sample
open_out.close()
#open_out1.close()
return sampleid
def extract_interproscan_info (extension, interproscan_path):
filelist = utilities.find_files(interproscan_path, extension, None)
for myfile in filelist:
#myfile = interproscan_path + "/" + samplelist + "/" + samplelist + ".interproscan.txt"
if not os.path.isfile(myfile):
config.logger.info ("ERROR! File not exist: " + myfile)
else:
config.logger.info ("OK!\t" + myfile)
myout1 = re.sub(".interproscan.txt", ".signalp.signaling.tsv", myfile)
myout2 = re.sub(".interproscan.txt", ".tmhmm.transmembrane.tsv", myfile)
myout3 = re.sub(".interproscan.txt", ".phobius.signaling.tsv", myfile)
myout4 = re.sub(".interproscan.txt", ".phobius.transmembrane.tsv", myfile)
myout5 = re.sub(".interproscan.txt", ".interpro.PfamDomain.tsv", myfile)
myout6 = re.sub(".interproscan.txt", ".interpro.SUPERFAMILY.tsv", myfile)
myout7 = re.sub(".interproscan.txt", ".interpro.PROSITEPROFILES.tsv", myfile)
myout8 = re.sub(".interproscan.txt", ".interpro.Gene3D.tsv", myfile)
myout9 = re.sub(".interproscan.txt", ".interpro.PANTHER.tsv", myfile)
myout10 = re.sub(".interproscan.txt", ".interpro.TIGRFAM.tsv", myfile)
myout11 = re.sub(".interproscan.txt", ".interpro.SFLD.tsv", myfile)
myout12 = re.sub(".interproscan.txt", ".interpro.ProDom.tsv", myfile)
myout13 = re.sub(".interproscan.txt", ".interpro.Hamap.tsv", myfile)
myout14 = re.sub(".interproscan.txt", ".interpro.SMART.tsv", myfile)
myout15 = re.sub(".interproscan.txt", ".interpro.CDD.tsv", myfile)
myout16 = re.sub(".interproscan.txt", ".interpro.PROSITEPATTERNS.tsv", myfile)
myout17 = re.sub(".interproscan.txt", ".interpro.PRINTS.tsv", myfile)
myout18 = re.sub(".interproscan.txt", ".interpro.PIRSF.tsv", myfile)
myout19 = re.sub(".interproscan.txt", ".interpro.MobiDBLite.tsv", myfile)
myout20 = re.sub(".interproscan.txt", ".interpro.Coils.tsv", myfile)
open_out1 = open(myout1, "w")
open_out2 = open(myout2, "w")
open_out3 = open(myout3, "w")
open_out4 = open(myout4, "w")
open_out5 = open(myout5, "w")
open_out6 = open(myout6, "w")
open_out7 = open(myout7, "w")
open_out8 = open(myout8, "w")
open_out9 = open(myout9, "w")
open_out10 = open(myout10, "w")
open_out11 = open(myout11, "w")
open_out12 = open(myout12, "w")
open_out13 = open(myout13, "w")
open_out14 = open(myout14, "w")
open_out15 = open(myout15, "w")
open_out16 = open(myout16, "w")
open_out17 = open(myout17, "w")
open_out18 = open(myout18, "w")
open_out19 = open(myout19, "w")
open_out20 = open(myout20, "w")
open_file = open(myfile, "r")
open_out1.write(utilities.PROTEIN_ID + "\tSP\tPrediction\tStart\tEnd\n")
open_out2.write(utilities.PROTEIN_ID + "\tTM\tPrediction\tStart\tEnd\n")
open_out3.write(utilities.PROTEIN_ID + "\tSP\tPrediction\tStart\tEnd\n")
open_out4.write(utilities.PROTEIN_ID + "\tTM\tPrediction\tStart\tEnd\n")
open_out5.write(utilities.PROTEIN_ID + "\tPfam\tDescription\tInterPro\tEvalue\n")
open_out6.write(utilities.PROTEIN_ID + "\tSUPERFAMILY\tDescription\tInterPro\tEvalue\n")
open_out7.write(utilities.PROTEIN_ID + "\tProSiteProfiles\tDescription\tInterPro\tEvalue\n")
open_out8.write(utilities.PROTEIN_ID + "\tGene3D\tDescription\tInterPro\tEvalue\n")
open_out9.write(utilities.PROTEIN_ID + "\tPANTHER\tDescription\tInterPro\tEvalue\n")
open_out10.write(utilities.PROTEIN_ID + "\tTIGRFAM\tDescription\tInterPro\tEvalue\n")
open_out11.write(utilities.PROTEIN_ID + "\tSFLD\tDescription\tInterPro\tEvalue\n")
open_out12.write(utilities.PROTEIN_ID + "\tProDom\tDescription\tInterPro\tEvalue\n")
open_out13.write(utilities.PROTEIN_ID + "\tHamap\tDescription\tInterPro\tEvalue\n")
open_out14.write(utilities.PROTEIN_ID + "\tSMART\tDescription\tInterPro\tEvalue\n")
open_out15.write(utilities.PROTEIN_ID + "\tCDD\tDescription\tInterPro\tEvalue\n")
open_out16.write(utilities.PROTEIN_ID + "\tProSitePatterns\tDescription\tInterPro\tEvalue\n")
open_out17.write(utilities.PROTEIN_ID + "\tPRINTS\tDescription\tInterPro\tEvalue\n")
open_out18.write(utilities.PROTEIN_ID + "\tPIRSF\tDescription\tInterPro\tEvalue\n")
open_out19.write(utilities.PROTEIN_ID + "\tMobiDBLite\tDescription\tInterPro\tEvalue\n")
open_out20.write(utilities.PROTEIN_ID + "\tCoils\tDescription\tInterPro\tEvalue\n")
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^#", line):
continue
info = line.split("\t")
myid = info[0]
mytype = info[3]
myacc = info[4]
mydec = info[5]
start = info[6]
end = info[7]
myscore = info[8]
mystatus = info[9]
if len(info) < 12:
interproacc = "NA"
interprodec = "NA"
else:
interproacc = info[11]
interprodec = info[12]
if start == "":
start = "NA"
if end == "":
end = "NA"
if interproacc == "":
interproacc = "NA"
if interprodec == "":
interprodec = "NA"
if mydec == "":
mydec = "NA"
if interprodec != "NA":
mydec = interprodec
if mystatus != "T": # not reliable prediction
continue
# SignalP
if mytype == "SignalP_GRAM_NEGATIVE" or mytype == "SignalP_GRAM_POSITIVE":
open_out1.write(myid + "\t" + mytype + "\t" + myacc + "\t" + start + "\t" + end + "\n")
# TMHMM
if mytype == "TMHMM":
open_out2.write(myid + "\t" + mytype + "\t" + myacc + "\t" + start + "\t" + end + "\n")
# Phobius
if mytype == "Phobius":
if re.search("SIGNAL_PEPTIDE", myacc): # signal peptide
open_out3.write(myid + "\t" + myacc + "\t" + mydec + "\t" + start + "\t" + end + "\n")
if re.search("TRANSMEMBRANE", myacc): # transmembrane
open_out4.write(myid + "\t" + myacc + "\t" + mydec + "\t" + start + "\t" + end + "\n")
# Pfam
if mytype == "Pfam":
open_out5.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# SUPERFAMILY
if mytype == "SUPERFAMILY":
open_out6.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# ProSiteProfiles
if mytype == "ProSiteProfiles":
open_out7.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# Gene3D
if mytype == "Gene3D":
open_out8.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# PANTHER
if mytype == "PANTHER":
open_out9.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# TIGRFAM
if mytype == "TIGRFAM":
open_out10.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# SFLD
if mytype == "SFLD":
open_out11.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# ProDom
if mytype == "ProDom":
open_out12.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# Hamap
if mytype == "Hamap":
open_out13.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# SMART
if mytype == "SMART":
open_out14.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# CDD
if mytype == "CDD":
open_out15.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# ProSitePatterns
if mytype == "ProSitePatterns":
open_out16.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# PRINTS
if mytype == "PRINTS":
open_out17.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# PIRSF
if mytype == "PIRSF":
open_out18.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# MobiDBLite
if mytype == "MobiDBLite":
open_out19.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# Coils
if mytype == "Coils":
open_out20.write(myid + "\t" + myacc + "\t" + mydec + "\t" + interproacc + "\t" + myscore + "\n")
# foreach line
open_out1.close()
open_out2.close()
open_out3.close()
open_out4.close()
open_out5.close()
open_out6.close()
open_out7.close()
open_out8.close()
open_out9.close()
open_out10.close()
open_out11.close()
open_out12.close()
open_out13.close()
open_out14.close()
open_out15.close()
open_out16.close()
open_out17.close()
open_out18.close()
open_out19.close()
open_out20.close()
open_file.close()
def assembly(workflow, input_dir, extension, extension_paired, threads,
output_folder, contigs):
"""
This set of tasks will run assembly on the input files provided.
Args:
workflow (anadama2.workflow): An instance of the workflow class.
input_dir: The direcory path of fastq files.
extension: The extension for all reads files, e.g. .fastq.gz
extension_paired: The extension for paired reads, e.g. _R1.fastq.gz,_R2.fastq.gz
threads (int): The number of threads/cores for clustering to use.
output_folder (string): The path of the output folder.
contigs: The summarized contig file.
Requires:
metahit v1.1.3: A program for assembling metagenomic sequencing reads
fastq files
Returns:
string: the name of contigs file.
Example:
from anadama2 import Workflow
from MetaWIBELE.characterize import characterization
# create an anadama2 workflow instance
workflow=Workflow()
# add assembly tasks
mycontigs = preprocessing_tasks.assembly (workflow, input_dir, args.sample_file,
args.extension_paired, args.extension_orphan,
args.threads,
assembly_dir, contigs)
# run the workflow
workflow.go()
"""
config.logger.info("###### Start assembly module ######")
time_equation = config.time # xxx hours defined in global config
mem_equation = config.memory # xxx GB defined in global config
# ================================================
# collect sequences
# ================================================
pair_identifier = None
pair_identifier2 = None
if extension_paired:
extension_paireds = extension_paired.split(",")
pair_identifier = re.sub(extension, "", extension_paireds[0])
pair_identifier2 = re.sub("1", "2", pair_identifier)
sample_files = utilities.find_files(input_dir, extension_paireds[0],
None)
samples = utilities.sample_names(sample_files, extension_paireds[0],
None)
else:
extension_paireds = [extension]
sample_files = utilities.find_files(input_dir, extension, None)
samples = utilities.sample_names(sample_files, extension, None)
split_dir = input_dir
assembly_dir = output_folder
split_files = []
contigs_list = []
for sample in samples:
mypair = "none"
myorphan = "none"
mypair_tmp = []
for item in extension_paireds:
if item == "none":
continue
myfile = os.path.join(split_dir, sample + item)
if os.path.isfile(myfile):
mypair_tmp.append(myfile)
else:
sys.exit("File not exist! " + myfile)
if len(mypair_tmp) == 1:
# split into paired reads files
mypair_tmp = utilities.split_paired_reads(mypair_tmp[0], extension,
pair_identifier)
if len(mypair_tmp) == 1:
myorphan = mypair_tmp[0]
if len(mypair_tmp) == 2:
mypair = ",".join(mypair_tmp)
if len(mypair_tmp) == 3:
mypair = ",".join(mypair_tmp[0:2])
myorphan = mypair_tmp[2]
else:
if len(mypair_tmp) == 2:
mypair = ",".join(mypair_tmp)
if len(mypair_tmp) == 3:
tmp1 = []
tmp2 = []
for i in mypair_tmp:
if re.search(pair_identifier, i):
tmp1.append(i)
elif re.search(pair_identifier2, i):
tmp1.append(i)
else:
tmp2.append(i)
if len(tmp1) > 0:
mypair = ",".join(tmp1)
if len(tmp2) > 0:
myorphan = ",".join(tmp2)
split_files.append((sample, mypair, myorphan))
seq_base = sample
megahit_contig_dir = os.path.join(assembly_dir, seq_base)
megahit_contig = os.path.join(megahit_contig_dir,
'%s.contigs.fa' % seq_base)
contigs_list.append(megahit_contig)
## run MEGAHIT
os.system("mkdir -p " + assembly_dir)
for (sample, mypair, myorphan) in split_files:
seq_base = sample
megahit_contig_dir = os.path.join(assembly_dir, seq_base)
megahit_contig = os.path.join(megahit_contig_dir,
'%s.contigs.fa' % seq_base)
## MEGAHIT needs memory in a byte format so let's take care of data
#time_equation = "24*60 if file_size('[depends[0]]') < 25 else 6*24*60" # 24 hours or more depending on file size
#mem_equation = "32*1024 if file_size('[depends[0]]') < 25 else 3*32*1024" # 32 GB or more depending on file size
mylog = os.path.join(assembly_dir, '%s.log' % seq_base)
if mypair != "none":
tmp = mypair.split(",")
if len(tmp) == 2: # paired reads:
tmp = mypair.split(",")
f_seq = tmp[0]
r_seq = tmp[1]
if myorphan != "none":
workflow.add_task_gridable(
"rm -rf " + megahit_contig_dir + " && " +
"megahit -1 [depends[0]] -2 [depends[1]] -r [args[2]] -t [args[0]] -o [args[3]] --out-prefix [args[1]] >[args[4]] 2>&1",
depends=[f_seq, r_seq,
TrackedExecutable("megahit")],
targets=[megahit_contig],
args=[
threads, seq_base, myorphan, megahit_contig_dir,
mylog
],
cores=threads,
mem=mem_equation,
time=time_equation,
name=sample + "__megahit")
else:
workflow.add_task_gridable(
"rm -rf " + megahit_contig_dir + " && " +
"megahit -1 [depends[0]] -2 [depends[1]] -t [args[0]] -o [args[2]] --out-prefix [args[1]] >[args[3]] 2>&1",
depends=[f_seq, r_seq,
TrackedExecutable("megahit")],
targets=[megahit_contig],
args=[threads, seq_base, megahit_contig_dir, mylog],
cores=threads,
mem=mem_equation,
time=time_equation,
name=sample + "__megahit")
else:
workflow.add_task_gridable(
"rm -rf " + megahit_contig_dir + " && " +
"megahit -r [depends[0]] -t [args[0]] -o [args[2]] --out-prefix [args[1]] >[args[3]] 2>&1",
depends=[mypair, TrackedExecutable("megahit")],
targets=[megahit_contig],
args=[threads, seq_base, megahit_contig_dir, mylog],
cores=threads,
mem=mem_equation,
time=time_equation,
name=sample + "__megahit")
else:
if myorphan != "none":
workflow.add_task_gridable(
"rm -rf " + megahit_contig_dir + " && " +
"megahit -r [depends[0]] -t [args[0]] -o [args[2]] --out-prefix [args[1]] >[args[3]] 2>&1",
depends=[myorphan, TrackedExecutable("megahit")],
targets=[megahit_contig],
args=[threads, seq_base, megahit_contig_dir, mylog],
cores=threads,
mem=mem_equation,
time=time_equation,
name=sample + "__megahit")
for myfile in contigs_list:
myname = os.path.basename(myfile)
myfile_new = os.path.join(assembly_dir, myname)
workflow.add_task("ln -fs [depends[0]] [targets[0]]",
depends=[myfile],
targets=[myfile_new],
cores=1,
name="ln__" + myname)
## combine contigs sequences
mylog = contigs + ".log"
workflow.add_task(
"metawibele_format_contig_sequences -p [args[0]] -e contigs.fa -o [targets[0]] > [args[1]] 2>&1",
depends=utilities.add_to_list(
contigs_list,
TrackedExecutable("metawibele_format_contig_sequences")),
targets=[contigs],
args=[assembly_dir, mylog],
cores=1,
name="format_contig_table")
return contigs_list
def collect_signaling_info(cluster_mem, extension, ann_path,
outfile): # split.list
gram_p = {}
gram_n = {}
signals = {}
signals_n = {}
signals_p = {}
#open_list = open(listfile, "r")
#for samplelist in open_list.readlines():
filelist = utilities.find_files(ann_path, extension, None)
for myfile in filelist:
if not os.path.isfile(myfile):
config.logger.info("ERROR! File not exist: " + myfile)
continue
open_file = open(myfile, "r")
titles = {}
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
info = line.split("\t")
if re.search("^" + utilities.PROTEIN_ID, line):
for item in info:
titles[item] = info.index(item)
continue
if not info[titles[utilities.PROTEIN_ID]] in cluster_mem:
continue
if not info[titles[utilities.PROTEIN_ID]] in signals:
signals[info[titles[utilities.PROTEIN_ID]]] = {}
signals[info[titles[utilities.PROTEIN_ID]]][info[
titles["Prediction"]]] = ""
mytype = info[titles["SP"]]
sample = info[titles[utilities.PROTEIN_ID]]
sample = re.sub("_[\d]+$", "", sample)
if mytype == "SignalP_GRAM_POSITIVE":
signals_p[info[titles[utilities.PROTEIN_ID]]] = info[
titles["Prediction"]]
if not sample in gram_p:
gram_p[sample] = 0
gram_p[sample] = gram_p[sample] + 1
if mytype == "SignalP_GRAM_NEGATIVE":
signals_n[info[titles[utilities.PROTEIN_ID]]] = info[
titles["Prediction"]]
if not sample in gram_n:
gram_n[sample] = 0
gram_n[sample] = gram_n[sample] + 1
# foreach line
open_file.close()
# foreach sample
# output details for each ORF
outfile1 = re.sub("_proteinfamilies.", "_proteinfamilies.ORF.", outfile)
open_out = open(outfile1, "w")
open_out.write(utilities.PROTEIN_ID + "\ttype\tdetail\tdescription\n")
for myid in sorted(signals.keys()):
myinfo = ";".join(signals[myid].keys())
open_out.write(myid + "\tSignalP_signaling\tSignalP_signaling\t" +
myinfo + "\n")
# foreach seqID
open_out.close()
return gram_p, gram_n, signals, signals_p, signals_n
def gene_catalog(workflow, complete_gene, complete_protein, input_dir,
extension, extension_paired, threads, prefix_gene_catalog,
gene_catalog, gene_catalog_nuc, gene_catalog_prot,
mapping_dir, gene_catalog_saf, gene_catalog_count):
"""
This set of tasks will build gene catalogs.
Args:
workflow (anadama2.workflow): An instance of the workflow class.
complete_gene: The fasta file of gene nucleotide sequences for complete ORFs.
complete_protein: The fasta file of protein sequences for complete ORFs.
mapping_dir: The direcory path of mapping results.
prefix_gene_catalog: The prefix of gene catalog file.
gene_catalog: The gene catalog file.
gene_catalog_nuc: The fastq file of nucleotide sequences for gene catalogs.
gene_catalog_prot: The fastq file of protein sequences for gene catalogs.
gene_catalog_saf: The SAF gtf file for gene catalogs.
gene_catalog_count: The count file for gene catalogs.
Requires:
bowtie2 (tested with 2.3.2)
samtools (tested with 1.5)
featureCounts (tested with Version 1.6.2)
the nucleotide and amino acid sequences for gene catalogs
fastq files for each sample
Returns:
string: file names of gene catalogs
Example:
from anadama2 import Workflow
from MetaWIBELE.characterize import characterization
# create an anadama2 workflow instance
workflow=Workflow()
# add quality control tasks for the fastq files
mygene_catalog, mycounts = preprocessing_tasks.gene_catalogs (workflow, complete_gene, complete_protein,
mapping_dir,
prefix_gene_catalog, gene_catalog, gene_catalog_nuc, gene_catalog_prot,
gene_catalog_saf, gene_catalog_count)
# run the workflow
workflow.go()
"""
config.logger.info("###### Start gene_catalog module ######")
time_equation = config.time # xxx hours defined in global config
mem_equation = config.memory # xxx GB defined in global config
### run gene-catalog workflow
mylog = gene_catalog_nuc + ".log"
myclust = gene_catalog_nuc + ".clstr"
workflow.add_task(
'cd-hit-est -i [depends[0]] [args[0]] -o [targets[0]] >[args[1]] 2>&1 ',
depends=[complete_gene, TrackedExecutable("cd-hit-est")],
targets=[gene_catalog_nuc, myclust],
args=[config.cd_hit_gene_opts, mylog],
cores=threads,
name="cd-hit-est")
mylog = gene_catalog + ".log"
workflow.add_task(
'metawibele_extract_cluster -c [depends[0]] -o [targets[0]] >[args[0]] 2>&1 ',
depends=[myclust,
TrackedExecutable("metawibele_extract_cluster")],
targets=[gene_catalog],
args=[mylog],
cores=1,
name="extract_cluster_CD-hit")
mylog = gene_catalog_prot + ".log"
workflow.add_task(
'metawibele_extract_non_redundance_seq -r [depends[0]] -i [depends[1]] -o [targets[0]] >[args[0]] 2>&1 ',
depends=[
gene_catalog_nuc, complete_protein,
TrackedExecutable("metawibele_extract_non_redundance_seq")
],
targets=[gene_catalog_prot],
args=[mylog],
cores=1,
name="extract_non_redundance_seq")
### get the abundance of gene catalog
# run gene-abundance workflow
mylog = gene_catalog_saf + ".log"
workflow.add_task(
'metawibele_gene_abundance_indexRef -r [depends[0]] -t gene -b [args[0]] -o [targets[0]] >[args[1]] 2>&1 ',
depends=[
gene_catalog_nuc,
TrackedExecutable("metawibele_gene_abundance_indexRef")
],
targets=[gene_catalog_saf],
args=[prefix_gene_catalog, mylog],
cores=1,
name="gene_abundance_indexRef")
## collect sequences
if extension_paired:
extension_paireds = extension_paired.split(",")
sample_files = utilities.find_files(input_dir, extension_paireds[0],
None)
samples = utilities.sample_names(sample_files, extension_paireds[0],
None)
else:
sample_files = utilities.find_files(input_dir, extension, None)
samples = utilities.sample_names(sample_files, extension, None)
## bowtie2 will map reads to gene categories
flt_seqs = []
for sample in samples:
seq_file = "NA"
if extension_paired:
tmp = extension_paired.split(",")
else:
if extension != "none":
tmp = extension.split(",")
for item in tmp:
if seq_file == "NA":
seq_file = os.path.join(input_dir, sample + '%s' % item)
else:
seq_file = seq_file + "," + os.path.join(
input_dir, sample + '%s' % item)
flt_seqs.append((sample, seq_file))
# foreah sample
## Now run bowtie2 to map reads to gene categories
mappings = []
mappings_tmp = []
#mem_equation = "2*12*1024 if file_size('[depends[0]]') < 10 else 4*12*1024"
#time_equation = "2*60 if file_size('[depends[0]]') < 10 else 2*2*60"
for (sample, seq_file) in flt_seqs:
seq_base = sample
mydir = os.path.join(mapping_dir, sample)
os.system("mkdir -p " + mydir)
sample_counts = os.path.join(mydir, seq_base + ".sort.bed")
stdout_log = os.path.join(mydir, '%s.mapping.stdout.log' % seq_base)
mappings_tmp.append(sample_counts)
workflow.add_task(
'metawibele_gene_abundance -r [depends[0]] -u [args[0]] -t [args[1]] -s [args[2]] -w [args[3]] '
'> [args[4]] 2>&1 ',
depends=[
gene_catalog_nuc, gene_catalog_saf,
TrackedExecutable("metawibele_gene_abundance")
],
targets=[sample_counts],
args=[seq_file, threads, seq_base, mydir, stdout_log],
cores=1,
name=sample + "__gene_abundance")
for myfile in mappings_tmp:
myname = os.path.basename(myfile)
myfile_new = os.path.join(mapping_dir, myname)
mappings.append(myfile_new)
workflow.add_task("ln -fs [depends[0]] [targets[0]]",
depends=[myfile],
targets=[myfile_new],
cores=1,
name="ln__" + myname)
# collect abundance
mylog = gene_catalog_count + ".log"
workflow.add_task(
'metawibele_gene_catalog_abundance -p [args[0]] -s sort.bed -c [args[1]] -o [targets[0]] >[args[2]] 2>&1 ',
depends=utilities.add_to_list(
mappings, TrackedExecutable("metawibele_gene_catalog_abundance")),
targets=[gene_catalog_count],
args=[mapping_dir, gene_catalog, mylog],
cores=1,
name="gene_catalog_abundance")
return gene_catalog, gene_catalog_count
def collect_phobius_info(cluster_mem, extension, ann_path,
outfile): # list.txt
transmem = {}
signal = {}
detail_signal = {}
detail_trans = {}
filelist = utilities.find_files(ann_path, extension, None)
for myfile in filelist:
if not os.path.isfile(myfile):
config.logger.info("ERROR! File not exist: " + myfile)
else:
open_file = open(myfile, "r")
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^" + utilities.PROTEIN_ID, line):
continue
info = line.split("\t")
myid = info[0]
if not myid in cluster_mem:
continue
sample = re.sub("_[\d]+$", "", myid)
if not sample in signal:
signal[sample] = {}
signal[sample][info[0]] = info[2]
detail_signal[info[0]] = info[2]
# foreach line
open_file.close()
myfile = re.sub(extension, "phobius.transmembrane.tsv", myfile)
if not os.path.isfile(myfile):
config.logger.info("ERROR! File not exist: " + myfile)
else:
open_file = open(myfile, "r")
for line in open_file.readlines():
line = line.strip()
if not len(line):
continue
if re.search("^" + utilities.PROTEIN_ID, line):
continue
info = line.split("\t")
myid = info[0]
if not myid in cluster_mem:
continue
sample = re.sub("_[\d]+$", "", myid)
if not sample in transmem:
transmem[sample] = {}
transmem[sample][info[0]] = info[2]
detail_trans[info[0]] = info[2]
# foreach line
# foreach samplelist
# output details
outfile1 = re.sub("_proteinfamilies.", "_proteinfamilies.ORF.signaling.",
outfile)
open_out = open(outfile1, "w")
open_out.write(utilities.PROTEIN_ID + "\ttype\tdetail\tdescription\n")
for myid in sorted(detail_signal.keys()):
myinfo = detail_signal[myid]
open_out.write(myid + "\tPhobius_signaling\tPhobius_signaling\t" +
myinfo + "\n")
# foreach seqID
open_out.close()
outfile1 = re.sub("_proteinfamilies.",
"_proteinfamilies.ORF.transmembrane.", outfile)
open_out = open(outfile1, "w")
open_out.write(utilities.PROTEIN_ID + "\ttype\tdetail\tdescription\n")
for myid in sorted(detail_trans.keys()):
myinfo = detail_trans[myid]
open_out.write(myid +
"\tPhobius_transmembrane\tPhobius_transmembrane\t" +
myinfo + "\n")
# foreach seqID
open_out.close()
return signal, transmem
