def chgAlpha(self, newAlpha):
"""Accepts 'DNA' 'RNA' or 'protein' or an
alphabet object"""
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
alpha = None
if newAlpha == "DNA":
alpha = IUPAC.IUPACUnambiguousDNA()
self.typ = alpha
elif newAlpha == "RNA":
alpha = IUPAC.IUPACUnambiguousDNA()
self.typ = alpha
elif newAlpha == "protein":
alpha = IUPAC.IUPACProtein()
self.typ = alpha
else:
raise NameError, "type not 'DNA', 'RNA', or 'protein'"
if not alpha:
alpha = newAlpha
self.seq = Seq(self.seq.tostring(), alpha)
self.checkAlpha()
def cds_to_seqrecord(cds, parent_genome, gene_domains=[]):
"""Creates a SeqRecord object from a Cds and its parent Genome.
:param cds: A populated Cds object.
:type cds: Cds
:param phage_genome: Populated parent Genome object of the Cds object.
:param domains: List of domain objects populated with column attributes
:type domains: list
:returns: Filled Biopython SeqRecord object.
:rtype: SeqRecord
"""
record = SeqRecord(cds.translation)
record.seq.alphabet = IUPAC.IUPACProtein()
record.name = cds.id
if cds.locus_tag == "" or cds.locus_tag is None:
record.id = "".join(["DRAFT ", cds.id])
else:
record.id = cds.locus_tag
cds.set_seqfeature()
source = f"{parent_genome.host_genus} phage {cds.genome_id}"
source_feature = cds.create_seqfeature("source", 0, cds.translation_length,
1)
source_feature.qualifiers["organism"] = [source]
record.features = [source_feature]
record.features.append(
cds.create_seqfeature("Protein", 0, cds.translation_length, 1))
cds_feature = cds.create_seqfeature("CDS", 0, cds.translation_length, 1)
format_cds_seqrecord_CDS_feature(cds_feature, cds, parent_genome)
record.features.append(cds_feature)
region_features = get_cds_seqrecord_regions(gene_domains, cds)
for region_feature in region_features:
record.features.append(region_feature)
record.description = (f"{cds.seqfeature.qualifiers['product'][0]} "
f"[{source}]")
record.annotations = get_cds_seqrecord_annotations(cds, parent_genome)
return record
for chain in model:
print("Working on chain %s." % (chain.get_id()))
seq = list()
position = list()
for residue in chain:
res_id = residue.get_id()
if res_id[0] == ' ':
seq.append(three_to_one(residue.get_resname()))
position.append(res_id[1])
#print("%s -- %d" % (three_to_one(residue.get_resname()), res_id[1]))
my_prot = Seq(str(''.join(seq)), IUPAC.protein)
tmp_seq1 = SeqIO.read(conserved_fasta_file, "fasta", IUPAC.IUPACProtein())
my_x_seq = Seq(sub('[a-z]', 'X', str(tmp_seq1.seq)))
seq1 = SeqRecord(my_x_seq, id=tmp_seq1.id, name="", description="")
#print("%s" % seq1)
seq2 = SeqRecord(my_prot, id="Chain_" + chain.get_id(), name="", description="")
myseqs = [seq1, seq2]
fasta_filename = prefix + "_" + chain.get_id() + ".fasta"
align_filename = prefix + "_" + chain.get_id() + ".align"
SeqIO.write(myseqs, fasta_filename, "fasta")
input_file = args.input_file
dataset_id = input_file.split("_")[0]
contig_length_filter = "/hps/nobackup2/production/metagenomics/aalmeida/scripts/EMBL-EBI/filter_contigs_len.py"
subprocess.call("%s -f %s -l 1" % (contig_length_filter, input_file), stdout = subprocess.DEVNULL, stderr = subprocess.DEVNULL, shell = True)
new_name_search = re.search(r"(%s\w+)\.fna(\w+\.fasta)" % dataset_id, ",".join(os.listdir()))
new_name = new_name_search.group(1) + new_name_search.group(2)
os.rename(new_name_search.group(0), new_name)
input_file = new_name
predicted_viruses = virus_pred(input_file)
SeqIO.write(predicted_viruses, "%s_viral_sequences.fna" % dataset_id, "fasta")
input_file = "%s_viral_sequences.fna" % dataset_id
hmmer_result = hmmer_domtbl(input_file)
informative_df = ratio_evalue(hmmer_result)
os.mkdir("%s_annotated_viral_sequences" % dataset_id)
for contig in SeqIO.parse(input_file, "fasta", IUPAC.IUPACUnambiguousDNA()):
for protein in SeqIO.parse("%s_viral_CDS.faa" % dataset_id, "fasta", IUPAC.IUPACProtein()):
if contig.id in protein.id:
protein_fields = protein.description.split(" # ")
CDS_annotation = SeqFeature(FeatureLocation(int(protein_fields[1]), int(protein_fields[2])), type = "CDS", strand = int(protein_fields[3]))
CDS_annotation.qualifiers["locus_tag"] = [protein.id]
CDS_annotation.qualifiers["transl_table"] = [11]
CDS_annotation.qualifiers["translation"] = [str(protein.seq)]
if protein.id in list(informative_df["query"].values):
if list(informative_df["query"].values).count(protein.id) > 1:
best_hit = max(informative_df[informative_df["query"] == protein.id]["Abs_Evalue_exp"].items(), key = operator.itemgetter(1))
if best_hit[1] >= 10:
CDS_annotation.qualifiers["result"] = ["high confidence"]
else:
CDS_annotation.qualifiers["result"] = ["low confidence"]
CDS_annotation.qualifiers["taxon"] = [informative_df.loc[best_hit[0], "Taxon"], informative_df.loc[best_hit[0], "Abs_Evalue_exp"]]
else:
