if 5 in _RunExercise:
print('\n---Exercise 5---')
id = "PAX-6.5"
format = "fasta"
record = SeqIO.read(open(id + "." + format), format)
print("Record:\n", record)
record.seq.alphabet = IUPAC.unambiguous_dna
print("\nAlphabet altered to IUPAC.unambiguous_dna !!!")
accNb = record.id.split("|")[3]
print("Access number: ", accNb)
record.name = accNb
record.id = accNb
print("record.name and record.id have been altered !!!")
feature = SeqFeature()
feature.type = "gene"
feature.location = FeatureLocation(18, 200)
feature.strand = -1
record.features.append(feature)
print("record.features: ", record.features)
print("\nRecord:\n", record)
count = SeqIO.write(record, open(id + ".gb", "w"), "genbank")
print("Converted %i records" % count)
if 6 in _RunExercise:
print('\n---ORF---')
mail = ''
id = "NC_009926"
def redigest_code():
argscheck()
### making outfile
# output file
if entry2_input.get() == "":
outfile = 'redigest.'+ TIME + '.out'
else:
outfile=entry2_input.get()
out_file = open(outfile, 'wt+')
### processing gene sequences
if entry7_input.get() == "Multifasta gene file":
genomeSeq = "N"
elif entry7_input.get() == "Single genome sequence":
genomeSeq = "Y"
if genomeSeq == 'N':
### making report file
verbosity = 'Y'
report_file = outfile + '.csv'
# open report file
RF = open(report_file, 'wt+')
# reverse complement the reverse primer
if entry6_input.get() == "":
reverse = ""
else:
reverse = entry6_input.get()
reverse=str(Seq(reverse).reverse_complement())
# counter for the NAME
count=1
### iterating sequences
input_file = entry1_input.get()
infile= open(input_file, 'r')
# input file format
if entry8_input.get() == "Fasta":
informat = "fasta"
elif entry8_input.get() == "Genbank":
informat = "genbank"
for record in SeqIO.parse(infile, informat):
header=record.id
array=str(record.seq)
NAME=str('RED' + TIME + str(count))
#
if informat == 'genbank':
desc=str(', '.join(list(record.annotations["taxonomy"])))
else:
desc = ''
## adding primer sequence if provided
if entry5_input.get() == "":
forward = ""
else:
forward = entry5_input.get()
if forward is not None:
Farray=''.join(forward + array)
else:
Farray=array
### adding primer sequence if provided
if entry6_input.get() == "":
reverse = ""
else:
reverse = entry6_input.get()
if reverse is not None:
FarrayR=''.join(Farray.strip('\n') + reverse)
else:
FarrayR=Farray
### orientation based on tagged
if entry4_input.get() == "Forward":
tagg = str("F")
elif entry4_input.get() == "Reverse":
tagg = str("R")
tagged=tagg.upper()
if tagged == 'R':
FarrayRseqFR=str(Seq(FarrayR).reverse_complement())
SubFeat="TRF_RevComp"
else:
FarrayRseqFR=str(FarrayR)
SubFeat="TRF"
### Restriction Enzyme check from list
enzyme = entry3_input.get()
enzyme_RE = RestrictionBatch([enzyme])
### search the restriction sites position in sequence
FarrayRseqFR_RE=enzyme_RE.search(Seq(FarrayRseqFR))
### convert the dict to the list and indexing
index=list(FarrayRseqFR_RE.values())[0]
### checking if restriction site is present or sequence will be uncut
if not index:
fragment=len(FarrayRseqFR)
else:
fragment=index[0]
### adding size to header and trimming sequence to terminal fragment length
if not index:
### non-cut fragment header
FastaHeader=NAME + "|" + str(len(FarrayRseqFR)) + "_bp" + "|" + header
### non-cut fragment sequence
FastaSeq=FarrayRseqFR[:len(FarrayRseqFR)]
Feat=SeqFeature(FeatureLocation(start=0, end=len(FarrayRseqFR)), type="REDigest", ref=SubFeat)
else:
### cut fragment header
FastaHeader=NAME + "|" + str(fragment) + "_bp" + "|" + header
### cut fragment sequence and slicing to the fragment length
FastaSeq=FarrayRseqFR[:fragment]
Feat=SeqFeature(FeatureLocation(start=0, end=fragment), type="REDigest", ref=SubFeat)
### terminal-screen output, info about sequence header and all the fragments
### based of verbosity
if verbosity == 'Y':
print(" ", FastaHeader, '\t', FarrayRseqFR_RE)
### counter for the locus name
count +=1
### seq object
FastaSequence = SeqRecord(Seq(FastaSeq, IUPAC.IUPACAmbiguousDNA()), FastaHeader, description=desc, name=NAME)
### append features to seqobject
FastaSequence.features.append(Feat)
### seq object
if entry9_input.get() == "Fasta":
outformat = "fasta"
elif entry9_input.get() == "Genbank":
outformat = "genbank"
if outformat == 'genbank':
SeqIO.write(FastaSequence, out_file, outformat)
else:
SeqIO.write(FastaSequence, out_file, "fasta-2line")
### writing progress to file too
print(FastaHeader, '\t', FarrayRseqFR_RE, file=RF)
#####################
############################################################### Genome
else:
### parsing genome sequence
### making report file
if entry9_input.get() == "Fasta":
outformat = "fasta"
elif entry9_input.get() == "Genbank":
outformat = "genbank"
if entry2_input.get() == "":
outfile = 'redigest.'+ TIME + '.out'
else:
outfile=entry2_input.get()
report_file = outfile + '_RF.csv'
# open report file
RF = open(report_file, 'wt+')
print("Individual restriction fragments", file=RF)
print("[WRITING:] Individual restriction fragments to file:", report_file)
input_file = entry1_input.get()
infile= open(input_file, 'r')
# input file format
if entry8_input.get() == "Fasta":
informat = "fasta"
elif entry8_input.get() == "Genbank":
informat = "genbank"
for record in SeqIO.parse(infile, informat):
Gen_header=record.id
Gen_array=str(record.seq)
#
if informat == 'genbank':
desc=str(', '.join(list(record.annotations["taxonomy"])))
else:
desc = ''
### Restriction Enzyme check from list
enzyme = entry3_input.get()
enzyme_RE = RestrictionBatch([enzyme])
### search the restriction sites position in sequence
Gen_array_RE=enzyme_RE.search(Seq(Gen_array))
Gen_array_RE_V = list(Gen_array_RE.values())[0]
#
ID0 = 0
ID_min = min(Gen_array_RE_V)
ID_max = max(Gen_array_RE_V)
ID1 = 0
ID2 = 1
# first fragment from first nt to first cut
GenFastaSeq=Gen_array[0:ID_min]
GenFastaSeqLen = len(GenFastaSeq)
GenFastaHeader=Gen_header + "|" + str(GenFastaSeqLen) + "_bp|" + Gen_header
# verbosity
verbosity = "Y"
if verbosity == 'Y':
print(" ", GenFastaHeader)
# seq object
GenSeqRec = SeqRecord(Seq(GenFastaSeq, IUPAC.IUPACAmbiguousDNA()), GenFastaHeader, description=desc)
### seq object to file
if outformat == 'genbank':
SeqIO.write(GenSeqRec, out_file, outformat)
elif outformat == 'fasta':
SeqIO.write(GenSeqRec, out_file, outformat)
# report to file
print(GenFastaHeader, file=RF)
#
for GenomeFragment in Gen_array_RE_V:
while ID2 < len(Gen_array_RE_V):
GenFastaSeq=Gen_array[Gen_array_RE_V[ID1]:Gen_array_RE_V[ID2]]
GenFastaSeqLen = len(GenFastaSeq)
GenFastaHeader=Gen_header + "|" + str(GenFastaSeqLen) + "_bp|" + Gen_header
# verbosity
if verbosity == 'Y':
print(" ", GenFastaHeader)
# increment value for index
ID1 += 1
ID2 += 1
# seq object
GenSeqRec = SeqRecord(Seq(GenFastaSeq, IUPAC.IUPACAmbiguousDNA()), GenFastaHeader, description=desc)
### seq object to file
if outformat == 'genbank':
SeqIO.write(GenSeqRec, out_file, outformat)
elif outformat == 'fasta':
SeqIO.write(GenSeqRec, out_file, outformat)
# report to file
print(GenFastaHeader, file=RF)
# last fragment from last cut to last nt
GenFastaSeq=Gen_array[ID_max:]
GenFastaSeqLen = len(GenFastaSeq)
GenFastaHeader=Gen_header + "|" + str(GenFastaSeqLen) + "_bp|" + Gen_header
# verbosity
if verbosity == 'Y':
print(" ", GenFastaHeader)
# seq object
GenSeqRec = SeqRecord(Seq(GenFastaSeq, IUPAC.IUPACAmbiguousDNA()), GenFastaHeader, description=desc)
### seq object to file
if outformat == 'genbank':
SeqIO.write(GenSeqRec, out_file, outformat)
else:
SeqIO.write(GenSeqRec, out_file, outformat)
# report to file
print(GenFastaHeader, file=RF)
# getting all sequences from first nt to respective cut
report_file2 = outfile + '_TRF.csv'
# open report file
TRF2 = open(report_file2, 'wt+')
print("Terminal restriction fragments: from nucleotide 1 to respective cuts", file=TRF2)
print("[WRITING:] Terminal restriction fragments, from nucleotide 1 to respective cuts to file:", report_file2)
for GenomeFragment in Gen_array_RE_V:
GenFastaHeader=Gen_header + "|" + str(GenomeFragment) + "_bp|" + Gen_header
GenFastaSeq=Gen_array[:GenomeFragment]
### seq object
GenFastaSequence = SeqRecord(Seq(GenFastaSeq, IUPAC.IUPACAmbiguousDNA()), GenFastaHeader,
description=desc)
### terminal-screen output, info about sequence header and all the fragments
### based of verbosity
if verbosity == 'Y':
print(" ", GenFastaHeader)
### writing progress to file too
print(GenFastaHeader, file=TRF2)
## close files
TRF2.close()
RF.close()
# final close
out_file.close()
infile.close()
def run(self, record):
logging.info('Detecting BGCs using %s model in %s', self.detector_label, record.id)
protein_features = util.get_protein_features(record)
proteins_by_id = util.get_proteins_by_id(protein_features)
pfam_features = util.get_pfam_features(record)
if not len(pfam_features):
logging.warning('Warning: No Pfam domains in record %s, skipping BGC detection', record.id)
return
# Filter out previous clusters detected with the same detector label
num_prev_features = len(record.features)
record.features = [f for f in record.features if
not(f.type == 'cluster' and f.qualifiers.get('detector_label') == [self.detector_label])]
num_removed_features = num_prev_features - len(record.features)
if num_removed_features:
logging.warning('Warning: Removed %s previously clusters detected clusters with same label "%s". '
'Use --label DeepBGCMyLabel to preserve original clusters and add second set of clusters detected '
'with same model but different parameters.', num_removed_features, self.detector_label)
# Create DataFrame with Pfam sequence
pfam_sequence = util.create_pfam_dataframe_from_features(pfam_features, proteins_by_id)
# Predict BGC score of each Pfam
pfam_sequence[self.score_column] = self.model.predict(pfam_sequence)
# Get average BGC score for each protein
protein_scores = pfam_sequence.groupby('protein_id', sort=False)[self.score_column].mean()
# Add score to all Pfam features
for i, feature in enumerate(pfam_features):
feature.qualifiers[self.score_column] = ['{:.5f}'.format(pfam_sequence[self.score_column].iloc[i])]
# Add score to all protein features
for protein_id, score in protein_scores.items():
proteins_by_id[protein_id].qualifiers[self.score_column] = ['{:.5f}'.format(score)]
clusters = []
active_proteins = []
gap_proteins = []
# Create a list of cluster features by merging consecutive proteins with score satisfying given threshold
# Neighboring clusters within given number of nucleotides/proteins are merged
for protein in protein_features:
if self.score_column not in protein.qualifiers:
# TODO: Should proteins with no Pfam domains also be considered?
# Current protein did not have any Pfam domains, therefore it has no BGC score, ignore it
continue
score = float(protein.qualifiers[self.score_column][0])
# Inactive protein, add to gap
if score < self.score_threshold:
gap_proteins.append(protein)
# We just changed from active to inactive, add current list of active proteins as a cluster
if active_proteins:
clusters.append(active_proteins)
active_proteins = []
# Active protein
else:
# If no cluster is open, check if we should merge with the previous cluster
if not active_proteins and clusters:
prev_cluster_proteins = clusters[-1]
prev_end = prev_cluster_proteins[-1].location.end
if len(gap_proteins) <= self.merge_max_protein_gap or \
(protein.location.start - prev_end) <= self.merge_max_nucl_gap:
# Remove previous candidate and continue where it left off
clusters = clusters[:-1]
active_proteins = prev_cluster_proteins + gap_proteins
# Add current protein to cluster
active_proteins.append(protein)
gap_proteins = []
# Last protein was active, add list of active proteins as a cluster
if active_proteins:
clusters.append(active_proteins)
# Add detected clusters as features
record_num_detected = 0
for cluster_proteins in clusters:
start = cluster_proteins[0].location.start
end = cluster_proteins[-1].location.end
candidate_id = '{}_{}-{}.1'.format(record.id, int(start), int(end))
if self.min_nucl > 1:
nucl_length = end - start
if nucl_length < self.min_nucl:
logging.debug('Skipping cluster %s with %s < %s nucleotides', candidate_id, nucl_length, self.min_nucl)
continue
if self.min_proteins > 1:
num_proteins = len(cluster_proteins)
if num_proteins < self.min_proteins:
logging.debug('Skipping cluster %s with %s < %s proteins', candidate_id, num_proteins, self.min_proteins)
continue
if self.min_domains > 1 or self.min_bio_domains > 0:
pfam_ids = util.get_pfam_feature_ids(record)
num_domains = len(pfam_features)
if num_domains < self.min_domains:
logging.debug('Skipping cluster %s with %s < %s protein domains', candidate_id, num_domains, self.min_domains)
continue
num_bio_domains = len(util.filter_biosynthetic_pfam_ids(pfam_ids))
if num_bio_domains < self.min_bio_domains:
logging.debug('Skipping cluster %s with %s < %s known biosynthetic protein domains', candidate_id, num_bio_domains, self.min_bio_domains)
continue
scores = [float(feature.qualifiers[self.score_column][0]) for feature in cluster_proteins]
location = FeatureLocation(start, end)
qualifiers = {
self.score_column: ['{:.5f}'.format(np.mean(scores))],
'detector': [self.detector_name],
'detector_label': [self.detector_label],
'detector_version': [self.model.version],
'detector_version_timestamp': [self.model.timestamp],
'product': ['{}_putative'.format(self.detector_name)],
'bgc_candidate_id': [candidate_id]
}
record.features.append(SeqFeature(
location=location,
type="cluster",
qualifiers=qualifiers
))
record_num_detected += 1
self.num_detected += 1
# Sort all features by location
util.sort_record_features(record)
# Add detector metadata to the record as a structured comment
if 'structured_comment' not in record.annotations:
record.annotations['structured_comment'] = {}
comment_key = util.format_detector_meta_key(self.detector_label)
record.annotations['structured_comment'][comment_key] = collections.OrderedDict(
name=self.detector_name,
label=self.detector_label,
version=self.model.version,
version_timestamp=self.model.timestamp,
detection_timestamp_utc=datetime.utcnow().isoformat(),
score_threshold=self.score_threshold,
merge_max_nucl_gap=self.merge_max_nucl_gap,
merge_max_protein_gap=self.merge_max_protein_gap,
min_proteins=self.min_proteins,
min_domains=self.min_domains,
min_bio_domains=self.min_bio_domains
)
logging.info('Detected %s BGCs using %s model in %s', record_num_detected, self.detector_label, record.id)
sys.stderr.write(repr(parts) + "\n")
raise
flip = False
if q_start > q_end:
flip = not flip
q_start, q_end = q_end, q_start
if s_start > s_end:
flip = not flip
s_start, s_end = s_end, s_start
if flip:
c = colors.Color(0, 0, 1, alpha=0.25)
b = False
else:
c = colors.Color(1, 0, 0, alpha=0.25)
b = False
q_feature = q_set.add_feature(SeqFeature(
FeatureLocation(q_start - 1, q_end)),
color=c,
border=b)
s_feature = s_set.add_feature(SeqFeature(
FeatureLocation(s_start - 1, s_end)),
color=c,
border=b)
gd_diagram.cross_track_links.append(
CrossLink(q_feature, s_feature, c, b))
# NOTE: We are using the same colour for all the matches,
# with transparency. This means overlayed matches will appear darker.
# It also means the drawing order not very important.
# Note ACT puts long hits at the back, and colours by hit score
print("Drawing CDS features...")
for f, format in genomes:
def convert_gbk(gb_dir,
gb_out_dir,
rodeo_output,
bg_domains,
max_intergenic_distance=100,
product_class='thiopeptide'):
"""Convert a common genbank file to the genbank that mimics antiSMASH output.
Adds a feature 'cluster' with information about the class of the product.
The coordinates of this feature are boundaries of the group of adjacent genes on the same strand that includes RODEO query.
Marks genes with given domains as biosynthetic.
Parameters
----------
gb_dir : str
Directory with input genbank files.
gb_out_dir : str
Directory to store the output.
rodeo_output: RodeoOutput
RODEO output to use as a reference.
bg_domains : list
List of Pfam or TIGRFAMs IDs for domains that are important for your product biosynthesis.
max_intergenic_distance : int, optional
Maximum distance (nt) between genes within the biosynthetic gene cluster (default: 100).
product_class : string, optional
A putative class of the final product (default: thiopeptide).
Returns
-------
bool
True if successful, False otherwise.
"""
rodeo_output.table_proccessing(bg_domains, max_intergenic_distance)
operon_border_accs = (rodeo_output.operon_accs[0],
rodeo_output.operon_accs[-1])
biosynthetic_genes = rodeo_output.biosynthetic_genes
contig_edge = False
prot_id = rodeo_output.query
try:
genbank = SeqIO.parse('%s%s.gbk' % (gb_dir, prot_id), 'genbank')
for record in genbank: # Every file is expected to contain only one record
cluster_coords = OrderedDict([('start', 1), ('end', len(record))])
for feature in record.features:
if feature.type == 'CDS':
border_check = check_if_border(feature, operon_border_accs)
if border_check is not None:
cluster_coords[border_check[0]] = border_check[1]
if 'protein_id' in feature.qualifiers:
if feature.qualifiers['protein_id'][
0] in biosynthetic_genes:
feature.qualifiers['sec_met'] = [
'Kind: biosynthetic'
]
start, end = cluster_coords.values()
cluster_location = FeatureLocation(start, end)
cluster_qualifiers = OrderedDict([('contig_edge',
str(contig_edge)),
('product', product_class)])
cluster = SeqFeature(location=cluster_location,
type='cluster',
qualifiers=cluster_qualifiers)
record.features = [cluster] + record.features
SeqIO.write(record, '%s%s.gbk' % (gb_out_dir, prot_id), 'genbank')
return True
except Exception as e:
print e
return False
def mockup(features_list,
write=False,
pagesize="A4",
scale_fontsize=3,
label_size=2,
greytrack_fontsize=7,
x=0.05,
y=0.01,
track_size=0.2,
track_names="",
scale_ticks=False,
format="linear",
total_len=3000):
colors_cycle = [
colors.orchid, colors.cornflower, colors.lightseagreen,
colors.cornflower, colors.salmon
]
colors_cycle = cycle(colors_cycle)
gdd = GenomeDiagram.Diagram('Construct Diagram',
x=x,
y=y,
track_size=track_size)
for ix, track_info in enumerate(features_list):
track_len = 0
for i in track_info:
track_len += i[1]
track, features = new_track(gdd,
" " + track_names[ix],
smalltick=10,
scale_fontsize=scale_fontsize,
greytrack_fontsize=greytrack_fontsize,
scale_ticks=scale_ticks,
end=track_len)
feature_start = 0
for feature_info in track_info:
if feature_info[0] == "skip":
feature_start += feature_info[1]
continue
feature = SeqFeature(FeatureLocation(
feature_start, feature_start + feature_info[1]),
strand=feature_info[2])
if feature_info[0] == "LoxP":
feature_color = colors.yellow
elif feature_info[0] == "STOP":
feature_color = colors.red
elif feature_info[0] == "Restriction":
feature_color = colors.chartreuse
else:
feature_color = colors_cycle.next()
features.add_feature(feature,
name=feature_info[0],
label=True,
color=feature_color,
label_size=label_size,
label_color=feature_color,
label_angle=30,
sigil=feature_info[3],
arrowshaft_height=1)
feature_start += feature_info[1]
gdd.draw(format=format,
pagesize=pagesize,
fragments=1,
start=0,
end=total_len)
if write:
gdd.write("/home/chymera/src/AutoTransGeno/output/test.pdf", "PDF")
return gdd
def test_reverse_complement_seq(self):
s = SeqRecord(
Seq("ACTG"),
id="TestID",
name="TestName",
description="TestDescription",
dbxrefs=["TestDbxrefs"],
features=[SeqFeature(FeatureLocation(0, 3), type="Site")],
annotations={"organism": "bombyx"},
letter_annotations={"test": "abcd"},
)
rc = s.reverse_complement(
id=True,
name=True,
description=True,
dbxrefs=True,
features=True,
annotations=True,
letter_annotations=True,
)
self.assertEqual("CAGT", str(rc.seq))
self.assertEqual("TestID", rc.id)
self.assertEqual("TestID", s.reverse_complement(id="TestID").id)
self.assertEqual("TestName", rc.name)
self.assertEqual("TestName", s.reverse_complement(name="TestName").name)
self.assertEqual("TestDescription", rc.description)
self.assertEqual(
"TestDescription",
s.reverse_complement(description="TestDescription").description,
)
self.assertEqual(["TestDbxrefs"], rc.dbxrefs)
self.assertEqual(
["TestDbxrefs"], s.reverse_complement(dbxrefs=["TestDbxrefs"]).dbxrefs
)
self.assertEqual(
"[SeqFeature(FeatureLocation(ExactPosition(1), ExactPosition(4)), type='Site')]",
repr(rc.features),
)
rc2 = s.reverse_complement(
features=[SeqFeature(FeatureLocation(1, 4), type="Site")]
)
self.assertEqual(
"[SeqFeature(FeatureLocation(ExactPosition(1), ExactPosition(4)), type='Site')]",
repr(rc2.features),
)
self.assertEqual({"organism": "bombyx"}, rc.annotations)
self.assertEqual(
{"organism": "bombyx"},
s.reverse_complement(annotations={"organism": "bombyx"}).annotations,
)
self.assertEqual({"test": "dcba"}, rc.letter_annotations)
self.assertEqual(
{"test": "abcd"},
s.reverse_complement(
letter_annotations={"test": "abcd"}
).letter_annotations,
)
i += 1
genes_track = GenomeDiagram.Track('genes', greytrack=False, scale=False)
genes_track.add_set(feature_set1)
genes_track.add_set(feature_set2)
#%%
from Bio.SeqFeature import SeqFeature, FeatureLocation
snv_df = pd.read_csv(tab_dir + '/SNV_HUMAN_YFV_RESULTS.csv')
snv_series = snv_df.iloc[:, 2]
feature_set_SNV = GenomeDiagram.FeatureSet()
for position in snv_series:
snv = SeqFeature(FeatureLocation(position, position), strand=+1)
feature_set_SNV.add_feature(snv, color='red', strand=None)
SNV_track = GenomeDiagram.Track('SNV',
greytrack=False,
scale=True,
scale_format='SInt',
scale_fontsize=10,
scale_fontangle=90,
scale_largetick_interval=5000,
scale_smalltick_interval=1000,
scale_largeticks=0.5,
scale_smallticks=0.2)
SNV_track.add_set(feature_set_SNV)
def blastxml2gff3(blastxml, min_gap=3, trim=False, trim_end=False):
from Bio.Blast import NCBIXML
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.SeqFeature import SeqFeature, FeatureLocation
blast_records = NCBIXML.parse(blastxml)
records = []
for record in blast_records:
# http://www.sequenceontology.org/browser/release_2.4/term/SO:0000343
match_type = { # Currently we can only handle BLASTN, BLASTP
'BLASTN': 'nucleotide_match',
'BLASTP': 'protein_match',
}.get(record.application, 'match')
rec = SeqRecord(Seq("ACTG"), id=record.query)
for hit in record.alignments:
for hsp in hit.hsps:
qualifiers = {
"source": "blast",
"score": hsp.expect,
"accession": hit.accession,
"hit_id": hit.hit_id,
"length": hit.length,
"hit_titles": hit.title.split(' >')
}
desc = hit.title.split(' >')[0]
qualifiers['description'] = desc[desc.index(' '):]
# This required a fair bit of sketching out/match to figure out
# the first time.
#
# the match_start location must account for queries and
# subjecst that start at locations other than 1
parent_match_start = hsp.query_start - hsp.sbjct_start
# The end is the start + hit.length because the match itself
# may be longer than the parent feature, so we use the supplied
# subject/hit length to calculate the real ending of the target
# protein.
parent_match_end = hsp.query_start + hit.length + hsp.query.count(
'-')
# However, if the user requests that we trim the feature, then
# we need to cut the ``match`` start to 0 to match the parent feature.
# We'll also need to cut the end to match the query's end. It (maybe)
# should be the feature end? But we don't have access to that data, so
# We settle for this.
if trim:
if parent_match_start < 1:
parent_match_start = 0
if trim or trim_end:
if parent_match_end > hsp.query_end:
parent_match_end = hsp.query_end + 1
# The ``match`` feature will hold one or more ``match_part``s
top_feature = SeqFeature(FeatureLocation(
parent_match_start, parent_match_end),
type=match_type,
strand=0,
qualifiers=qualifiers)
# Unlike the parent feature, ``match_part``s have sources.
part_qualifiers = {
"source": "blast",
}
top_feature.sub_features = []
for start, end, cigar in generate_parts(hsp.query,
hsp.match,
hsp.sbjct,
ignore_under=min_gap):
part_qualifiers['Gap'] = cigar
part_qualifiers['ID'] = hit.hit_id
if trim:
# If trimming, then we start relative to the
# match's start
match_part_start = parent_match_start + start
else:
# Otherwise, we have to account for the subject start's location
match_part_start = parent_match_start + hsp.sbjct_start + start - 1
# We used to use hsp.align_length here, but that includes
# gaps in the parent sequence
#
# Furthermore align_length will give calculation errors in weird places
# So we just use (end-start) for simplicity
match_part_end = match_part_start + (end - start)
top_feature.sub_features.append(
SeqFeature(FeatureLocation(match_part_start,
match_part_end),
type="match_part",
strand=0,
qualifiers=copy.deepcopy(part_qualifiers)))
rec.features.append(top_feature)
rec.annotations = {}
records.append(rec)
return records
feature].location.start and strand != gene_list[
feature].strand:
break
if gene != True:
qualifiers['gene'] = gene
qualifiers['note'] = 'TransTerm HP conf=%s, tail_score=%s' % (
conf, tail_score)
else:
qualifiers['note'] = 'TransTerm HP conf=%s, tail_score=%s' % (
conf, tail_score)
qualifiers['regulatory_class'] = 'terminator'
qualifiers['TermGen_check'] = ['Checked!']
feature_location = FeatureLocation(start, end)
my_feature = SeqFeature(location=feature_location,
type='regulatory',
strand=strand,
qualifiers=qualifiers)
# adding terminators to genbank features list
for i in reversed(xrange(len(record.features))):
if record.features[i].location.start < start:
record.features.insert(i + 1, my_feature)
break
# editing features to add U-tail
new_features_list = []
for feature in record.features:
if feature.type == 'regulatory' and feature.qualifiers.has_key(
'TermGen_check'):
u_tail = 0
tail = ''
def prepare_visualization(options, seq_record):
# Check, whether (Sub)ClusterBlast data is encoded in source feature
sourceFeatures = utils.get_all_features_of_type(seq_record, 'source')
if len(sourceFeatures) == 0:
loc = FeatureLocation(0, len(seq_record.seq))
source_feature = SeqFeature(loc, type="source")
seq_record.features.append(source_feature)
sourceFeatures = utils.get_all_features_of_type(seq_record, 'source')
if 'extrarecord' in options:
if options.extrarecord.has_key(seq_record.id):
# As there is only one source feature per record we just can take the first one without cycling through all features
for key in options.extrarecord[seq_record.id].extradata.keys():
if key == 'ClusterBlastData':
logging.debug(
"prepare_visualization: Found ClusterBlastData storage object"
)
options.clusterblast = True
clusterBlastResults = options.extrarecord[
seq_record.id].extradata[key]
seq_record.internalhomologygroupsdict = clusterBlastResults.internalhomologygroupsdict
seq_record.known_compound_dict = clusterBlastResults.known_compound_dict
seq_record.nrhitgeneclusters = clusterBlastResults.nrhitgeneclusters
seq_record.qgeneclusterdata = clusterBlastResults.qgeneclusterdata
seq_record.queryclusterdata = clusterBlastResults.queryclusterdata
seq_record.pubchem_dict = clusterBlastResults.pubchem_dict
seq_record.pubmed_dict = clusterBlastResults.pubmed_dict
elif key == 'SubClusterBlastData':
logging.debug(
"prepare_visualization: Found SubClusterBlastData storage object"
)
options.subclusterblast = True
subclusterBlastResults = options.extrarecord[
seq_record.id].extradata[key]
seq_record.internalhomologygroupsdict = subclusterBlastResults.internalhomologygroupsdict
seq_record.sc_nrhitgeneclusters = subclusterBlastResults.sc_nrhitgeneclusters
# seq_record.sc_qgeneclusterdata = subclusterBlastResults.sc_qgeneclusterdata
seq_record.sc_queryclusterdata = subclusterBlastResults.sc_queryclusterdata
seq_record.pubchem_dict = subclusterBlastResults.pubchem_dict
seq_record.pubmed_dict = subclusterBlastResults.pubmed_dict
elif key == 'KnownClusterBlastData':
logging.debug(
"prepare_visualization: Found KnownClusterBlastData storage object"
)
options.knownclusterblast = True
knownclusterBlastResults = options.extrarecord[
seq_record.id].extradata[key]
seq_record.internalhomologygroupsdict = knownclusterBlastResults.internalhomologygroupsdict
seq_record.kc_nrhitgeneclusters = knownclusterBlastResults.kc_nrhitgeneclusters
# seq_record.kc_qgeneclusterdata = knownclusterBlastResults.sc_qgeneclusterdata
seq_record.kc_queryclusterdata = knownclusterBlastResults.kc_queryclusterdata
seq_record.pubchem_dict = knownclusterBlastResults.pubchem_dict
seq_record.pubmed_dict = knownclusterBlastResults.pubmed_dict
# elif key == 'MetabolicModelDataObj':
# pass
# else:
# logging.warn('Found key %s in options.clusterblastdata which does not match the hard coded choices!' % key)
# #
# load_pubmed_pubchem_links(seq_record)
# if options.clusterblast:
# load_clusterblast_outputdata(seq_record, options)
# if options.subclusterblast:
# load_subclusterblast_outputdata(seq_record, options)
# if options.knownclusterblast:
# load_knownclusterblast_outputdata(seq_record, options)
# load_genecluster_info(seq_record, options)
load_genecluster_info(seq_record, options)
if label in f.qualifiers.get("label", []))
def get_features_from_note(note):
return (f for f in features
if note in f.qualifiers.get("note", []))
# Add direct BsaI site
features.append(
SeqFeature(
type="protein_bind",
qualifiers={
# "label": ["BsaI"],
"bound_moiety": ["BsaI"],
"note": ["color: #ff0000; direction: RIGHT"],
# "note": ["This forward directional feature has 2 segments:\n"
# "1: 3 .. 8 / #ff0000\n"
# "2: 10 .. 13 / #ff0000\n"]
},
location=CompoundLocation([
FeatureLocation(2, 8, strand=1),
FeatureLocation(9, 13, strand=1)
]),
))
# Add reversed BsaI site
BsaI_site_r = Seq(BsaI.site).reverse_complement()
pos = (gba.seq + gba.seq).find(BsaI_site_r)
features.append(
SeqFeature(
type="protein_bind",
qualifiers={
for record in SeqIO.parse(fh, "fasta"):
sdf = pd.DataFrame({
'length': len(record),
'ID': record.id
},
index=[record.id])
df = df.append(sdf)
df.length = df.length.astype(int)
max_len = df.length.max()
#scale用ラダー染色体を書く
lad_rec = SeqRecord(RandomSeq(scale_max),
"ladder_" + str(scale_max) + " bp")
lad_feature = SeqFeature()
lad_feature_s = []
for i in range(1, scale_split, 2):
region_unit = int(scale_max / scale_split)
tmp_featur = SeqFeature(FeatureLocation(region_unit * i,
region_unit * (i + 1)),
type="gene",
strand=1)
tmp_start = region_unit * i
tmp_featur.qualifiers["locus_tag"] = str(tmp_start)
lad_feature_s.append(tmp_featur)
lad_rec.features = lad_feature_s
cur_chromosome = BasicChromosome.Chromosome(lad_rec.id)
# We add dummy features to the tracks for each cross-link BEFORE we add the
# arrow features for the genes. This ensures the genes appear on top:
for X, Y, X_vs_Y in [("NC_002703", "AF323668", A_vs_B),
("AF323668", "NC_003212", B_vs_C)]:
features_X = records[X].features
features_Y = records[Y].features
set_X = feature_sets[X]
set_Y = feature_sets[Y]
for score, x, y in X_vs_Y:
color = colors.linearlyInterpolatedColor(colors.white,
colors.firebrick, 0, 100,
score)
border = colors.lightgrey
f_x = get_feature(features_X, x)
F_x = set_X.add_feature(SeqFeature(
FeatureLocation(f_x.location.start, f_x.location.end, strand=0)),
color=color,
border=border)
f_y = get_feature(features_Y, y)
F_y = set_Y.add_feature(SeqFeature(
FeatureLocation(f_y.location.start, f_y.location.end, strand=0)),
color=color,
border=border)
gd_diagram.cross_track_links.append(CrossLink(F_x, F_y, color, border))
for record, gene_colors in zip([A_rec, B_rec, C_rec],
[A_colors, B_colors, C_colors]):
gd_feature_set = feature_sets[record.name]
i = 0
for feature in record.features:
def json2seqrecord(self, json_record):
uid = json_record["primaryAccession"]
if "recommendedName" in json_record["proteinDescription"]:
desc = json_record["proteinDescription"]["recommendedName"]["fullName"]["value"]
else:
desc = json_record["proteinDescription"]["submissionNames"][0]["fullName"]["value"]
ecs = ([x["value"] for x in json_record["proteinDescription"]["recommendedName"]["ecNumbers"]]
if ("recommendedName" in json_record["proteinDescription"] and
"ecNumbers" in json_record["proteinDescription"]["recommendedName"]) else [])
if "contains" in json_record["proteinDescription"]:
for pd in json_record["proteinDescription"]["contains"]:
if "ecNumbers" in pd["recommendedName"]:
ecs += [x["value"] for x in pd["recommendedName"]["ecNumbers"]]
r = SeqRecord(id=uid, name="", description=desc, seq=Seq(json_record["sequence"]["value"]))
if "genes" in json_record:
for gene in json_record["genes"]:
if "geneName" in gene:
val = gene["geneName"]["value"]
dbx = "UnipGene:" + val
r.dbxrefs.append(dbx)
if "synonyms" in json_record["genes"]:
for syn in json_record["genes"]["synonyms"]:
val = syn["value"]
dbx = "UnipGene:" + val
r.dbxrefs.append(dbx)
if "alternativeNames" in json_record["proteinDescription"]:
for an in json_record["proteinDescription"]["alternativeNames"]:
if "shortNames" in an:
for x in an["shortNames"]:
dbx = "UnipName:" + x["value"]
r.dbxrefs.append(dbx)
for ref in json_record["uniProtKBCrossReferences"]:
dbx = ref["database"] + ":" + ref["id"] if (ref["database"] + ":") not in ref["id"] else ref["id"]
r.dbxrefs.append(dbx)
self.dbx_dict[dbx] = {x["key"]: x["value"] for x in ref["properties"]}
if ref["database"] == "GO":
gt = self.dbx_dict[dbx]["GoTerm"]
self.dbx_dict[dbx]["GoTerm"] = ":".join(gt.split(":")[1:])
self.dbx_dict[dbx]["database"] = gt.split(":")[0]
if self.dbx_dict[dbx]["database"] == "b":
self.dbx_dict[dbx]["database"] = "biological_process"
if self.dbx_dict[dbx]["database"] == "c":
self.dbx_dict[dbx]["database"] = "molecular_function"
if self.dbx_dict[dbx]["database"] == "f":
self.dbx_dict[dbx]["database"] = "cellular_component"
for ref in (json_record["secondaryAccessions"] if "secondaryAccessions" in json_record else []
) + [json_record["uniProtkbId"], json_record["primaryAccession"]]:
dbx = "UnipAcc:" + ref.replace(" ", "_")
r.dbxrefs.append(dbx)
for ref in ecs:
dbx = "EC:" + ref
r.dbxrefs.append(dbx)
self.dbx_dict[dbx] = [["description", desc]]
r.dbxrefs = list(set(r.dbxrefs))
if "features" in json_record:
for f in json_record["features"]:
l = FeatureLocation(start=f["location"]["start"]["value"],
end=f["location"]["end"]["value"])
qual = {"description": f["description"]} if f["description"].replace("-", "").strip() else {}
if "featureId" in f:
qual["featureId"] = f["featureId"]
seqf = SeqFeature(type=f["type"], location=l, qualifiers=qual)
r.features.append(seqf)
return r
def check_simple_tRNA(self, filename, use_seqfeatures=False):
f1 = [(111889, 111961, -1, 'G01270'), (306383, 306456, 1, 'G01870'), (309274, 309347, -1, 'G01890'), (515493, 515566, 1, 'G02480'), (552639, 552711, 1, 'G02600'), (604401, 604474, 1, 'G02760'), (877648, 877720, 1, 'G03515'), (892513, 892585, 1, 'G03570'), (909809, 909882, -1, 'G03640'), (1159021, 1159092, 1, 'G04320'), (1324921, 1324959, 1, 'G04720'), (1583770, 1583844, -1, 'G05390'), (1817398, 1817470, 1, 'G05980'), (1978082, 1978156, 1, 'G06480'), (2025354, 2025427, 1, 'G06610'), (2107396, 2107467, -1, 'G06860'), (2111146, 2111217, -1, 'G06880'), (2177883, 2177957, 1, 'G07100'), (2334818, 2334891, 1, 'G07580'), (2406830, 2406902, -1, 'G07760'), (2588521, 2588593, 1, 'G08240'), (2846538, 2846611, -1, 'G08870'), (2879305, 2879377, 1, 'G08950'), (2939418, 2939490, 1, 'G09110'), (3431185, 3431257, -1, 'G10440'), (3676606, 3676644, 1, 'G11010'), (3678774, 3678848, -1, 'G11030'), (3881528, 3881608, 1, 'G11550'), (3914628, 3914700, -1, 'G11640'), (4266985, 4267059, -1, 'G12510'), (4285884, 4285956, -1, 'G12590'), (4440211, 4440284, 1, 'G13010'), (4522705, 4522779, -1, 'G13240'), (4709631, 4709703, 1, 'G13720'), (4741995, 4742068, 1, 'G13840'), (4743091, 4743164, 1, 'G13850'), (5189681, 5189755, -1, 'G15090'), (5309641, 5309713, -1, 'G15450'), (5380901, 5380983, 1, 'G15650'), (5518055, 5518128, -1, 'G16100'), (5619464, 5619537, -1, 'G16450'), (6038749, 6038831, 1, 'G17570'), (6075812, 6075884, 1, 'G17660'), (6075937, 6076011, -1, 'G17670'), (6345756, 6345828, 1, 'G18430'), (6488645, 6488726, 1, 'G18820'), (6948850, 6948934, -1, 'G20040'), (6995272, 6995344, -1, 'G20170'), (7004504, 7004576, 1, 'G20210'), (7016506, 7016579, 1, 'G20250'), (7082657, 7082729, 1, 'G20420'), (7242749, 7242821, -1, 'G20820'), (7499721, 7499793, -1, 'G21420'), (7656108, 7656180, -1, 'G21800'), (7884405, 7884443, -1, 'G22320'), (8520278, 8520352, -1, 'G24080'), (9143796, 9143870, 1, 'G26430'), (9158169, 9158242, 1, 'G26490'), (10089422, 10089494, 1, 'G28720'), (10089883, 10089955, 1, 'G28730'), (10090353, 10090425, 1, 'G28740'), (10090754, 10090826, 1, 'G28750'), (10092310, 10092382, 1, 'G28770'), (10092786, 10092858, 1, 'G28780'), (10093294, 10093366, 1, 'G28790'), (10093731, 10093803, 1, 'G28800'), (10094158, 10094230, 1, 'G28810'), (10096936, 10097008, 1, 'G28820'), (10097099, 10097171, 1, 'G28830'), (10097703, 10097775, 1, 'G28840'), (10098638, 10098710, 1, 'G28850'), (10099064, 10099136, 1, 'G28860'), (10099410, 10099482, 1, 'G28870'), (10099812, 10099884, 1, 'G28880'), (10100258, 10100330, 1, 'G28890'), (10101013, 10101085, 1, 'G28900'), (10101585, 10101657, 1, 'G28910'), (10101978, 10102050, 1, 'G28920'), (10106075, 10106147, 1, 'G28930'), (10106513, 10106585, 1, 'G28940'), (10106883, 10106955, 1, 'G28950'), (10107634, 10107706, 1, 'G28970'), (10108374, 10108446, 1, 'G28980'), (10108695, 10108767, 1, 'G28990'), (10207291, 10207364, -1, 'G29210'), (10756703, 10756776, 1, 'G30430'), (10963553, 10963627, -1, 'G30830'), (11104093, 11104167, 1, 'G31110'), (11797227, 11797265, -1, 'G32620'), (12097258, 12097327, -1, 'G33370'), (13687637, 13687710, 1, 'G36350'), (15733055, 15733127, -1, 'G42120'), (16588144, 16588216, -1, 'G43820'), (17159046, 17159118, 1, 'G45234'), (17159799, 17159871, 1, 'G45236'), (17160970, 17161042, 1, 'G45238'), (17161418, 17161490, 1, 'G45240'), (17162967, 17163039, 1, 'G45242'), (17163408, 17163480, 1, 'G45244'), (17164461, 17164533, 1, 'G45246'), (17735509, 17735582, 1, 'G48080'), (18139265, 18139337, -1, 'G49020'), (18234146, 18234220, -1, 'G49280'), (18312570, 18312607, 1, 'G49460'), (18391469, 18391542, 1, 'G49690'), (18556666, 18556746, 1, 'G50070'), (18561567, 18561647, 1, 'G50100'), (19428223, 19428297, 1, 'G52170'), (19502087, 19502161, -1, 'G52350'), (19688850, 19688887, -1, 'G52860'), (19851640, 19851714, 1, 'G53220'), (19929506, 19929578, -1, 'G53410'), (20416594, 20416667, -1, 'G54670'), (20794976, 20795058, 1, 'G55625'), (21272451, 21272533, 1, 'G56730'), (21272786, 21272823, 1, 'G56740'), (21273216, 21273253, 1, 'G56750'), (21273960, 21274042, 1, 'G56760'), (21274295, 21274332, 1, 'G56770'), (21274725, 21274762, 1, 'G56780'), (21275469, 21275551, 1, 'G56790'), (21275804, 21275841, 1, 'G56800'), (21276234, 21276271, 1, 'G56810'), (21276978, 21277060, 1, 'G56820'), (21277313, 21277350, 1, 'G56830'), (21277743, 21277780, 1, 'G56840'), (21278487, 21278569, 1, 'G56850'), (21278822, 21278859, 1, 'G56860'), (21279273, 21279310, 1, 'G56870'), (21280016, 21280098, 1, 'G56880'), (21280351, 21280388, 1, 'G56890'), (21280781, 21280818, 1, 'G56900'), (21281525, 21281607, 1, 'G56910'), (21281860, 21281897, 1, 'G56920'), (21282311, 21282348, 1, 'G56930'), (21283054, 21283136, 1, 'G56940'), (21283384, 21283421, 1, 'G56950'), (21283842, 21283879, 1, 'G56960'), (21284586, 21284668, 1, 'G56970'), (21284916, 21284953, 1, 'G56980'), (21285374, 21285411, 1, 'G56990'), (21286118, 21286200, 1, 'G57000'), (21286448, 21286485, 1, 'G57010'), (21286906, 21286943, 1, 'G57020'), (21287650, 21287732, 1, 'G57030'), (21287980, 21288017, 1, 'G57040'), (21288438, 21288475, 1, 'G57050'), (21289183, 21289265, 1, 'G57060'), (21289513, 21289550, 1, 'G57070'), (21289970, 21290007, 1, 'G57080'), (21290714, 21290796, 1, 'G57090'), (21291044, 21291081, 1, 'G57100'), (21291501, 21291538, 1, 'G57110'), (21292245, 21292327, 1, 'G57120'), (21292574, 21292611, 1, 'G57130'), (21293032, 21293069, 1, 'G57140'), (21293776, 21293858, 1, 'G57150'), (21294109, 21294146, 1, 'G57160'), (21294567, 21294604, 1, 'G57170'), (21295125, 21295207, 1, 'G57180'), (21295455, 21295492, 1, 'G57190'), (21295912, 21295949, 1, 'G57200'), (21296656, 21296738, 1, 'G57210'), (21296989, 21297026, 1, 'G57220'), (21297447, 21297484, 1, 'G57230'), (21298005, 21298087, 1, 'G57240'), (21298335, 21298372, 1, 'G57250'), (21298792, 21298829, 1, 'G57260'), (21299536, 21299618, 1, 'G57270'), (21299869, 21299906, 1, 'G57280'), (21300327, 21300364, 1, 'G57290'), (21300885, 21300967, 1, 'G57300'), (21301215, 21301252, 1, 'G57310'), (21301673, 21301710, 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f3 = [(259640, 259712, 1, 'G01705'), (469666, 469740, 1, 'G02315'), (476808, 476880, 1, 'G02335'), (586092, 586174, 1, 'G02715'), (981975, 982047, 1, 'G03845'), (984105, 984177, 1, 'G03852'), (1220234, 1220307, 1, 'G04525'), (1601343, 1601415, -1, 'G05525'), (1707743, 1707815, -1, 'G05755'), (1738796, 1738870, 1, 'G05835'), (1843329, 1843400, -1, 'G06105'), (1920038, 1920110, -1, 'G06335'), (2104961, 2105033, -1, 'G06665'), (2222251, 2222324, 1, 'G07025'), (2232470, 2232506, -1, 'G07055'), (2253680, 2253762, -1, 'G07115'), (2285607, 2285679, 1, 'G07185'), (2918418, 2918492, -1, 'G09505'), (2944616, 2944698, 1, 'G09585'), (2945700, 2945782, -1, 'G09595'), (3090548, 3090631, 1, 'G10015'), (3096220, 3096293, 1, 'G10035'), (3238371, 3238407, -1, 'G10415'), (3535151, 3535224, 1, 'G11285'), (3575849, 3575923, 1, 'G11395'), (3622697, 3622769, -1, 'G11505'), (3942012, 3942084, 1, 'G12385'), (3995103, 3995176, -1, 'G12585'), (4254534, 4254615, 1, 'G13223'), (4330778, 4330850, 1, 'G13335'), (4998147, 4998219, 1, 'G14855'), (5068300, 5068374, -1, 'G15055'), (5275155, 5275228, 1, 'G15585'), (5632857, 5632930, 1, 'G16552'), (6483945, 6484019, -1, 'G18815'), (6540636, 6540673, 1, 'G18952'), (6663713, 6663786, 1, 'G19235'), (7104314, 7104398, 1, 'G20365'), (7224223, 7224296, -1, 'G20655'), (7319582, 7319664, -1, 'G20885'), (7567399, 7567471, -1, 'G21475'), (9373610, 9373684, -1, 'G25715'), (9840420, 9840494, 1, 'G26747'), (10211564, 10211636, 1, 'G27555'), (10319498, 10319570, 1, 'G27825'), (10325875, 10325947, 1, 'G27845'), (10753667, 10753740, 1, 'G28685'), (10760629, 10760702, -1, 'G28695'), (11076814, 11076886, 1, 'G29095'), (11961645, 11961718, 1, 'G30345'), (16438025, 16438097, -1, 'G44955'), (16896875, 16896949, 1, 'G45935'), (16902623, 16902697, 1, 'G45955'), (16905147, 16905221, 1, 'G45965'), (17160736, 17160808, 1, 'G46585'), (17275564, 17275646, 1, 'G46875'), (17905395, 17905467, 1, 'G48275'), (17985575, 17985611, -1, 'G48515'), (18080062, 18080134, 1, 'G48745'), (18518796, 18518870, 1, 'G49925'), (18755788, 18755860, -1, 'G50505'), (18837020, 18837092, 1, 'G50665'), (18907851, 18907924, 1, 'G50835'), (18928413, 18928487, 1, 'G50895'), (19008621, 19008694, -1, 'G51135'), (19044371, 19044443, -1, 'G51265'), (19403651, 19403723, -1, 'G52285'), (19420345, 19420417, -1, 'G52345'), (19511965, 19512045, 1, 'G52565'), (19566013, 19566085, 1, 'G52765'), (19648105, 19648188, 1, 'G52955'), (19935354, 19935426, 1, 'G53775'), (19995918, 19995989, 1, 'G53965'), (20704664, 20704736, 1, 'G55735'), (20720151, 20720223, 1, 'G55795'), (20824495, 20824568, -1, 'G56085'), (21498293, 21498375, 1, 'G58035'), (21553258, 21553329, 1, 'G58165'), (21970486, 21970557, 1, 'G59415'), (22149699, 22149773, 1, 'G59923'), (22149823, 22149895, -1, 'G59926'), (22197810, 22197892, -1, 'G60075'), (22481215, 22481288, -1, 'G60805'), (22622384, 22622465, 1, 'G61105'), (22786896, 22786969, 1, 'G61545'), (22853496, 22853567, 1, 'G61715'), (22871101, 22871174, 1, 'G61755'), (22892781, 22892853, 1, 'G61825'), (23047854, 23047927, 1, 'G62245'), (23062444, 23062517, -1, 'G62285'), (23221682, 23221753, 1, 'G62735'), (23296567, 23296640, -1, 'G63003'), (23296728, 23296801, -1, 'G63006')]
f4 = [(33799, 33872, 1, 'G00085'), (424716, 424788, -1, 'G00985'), (562560, 562634, -1, 'G01355'), (611865, 611932, -1, 'G01455'), (808269, 808342, -1, 'G01865'), (901175, 901247, 1, 'G02055'), (1390894, 1390966, 1, 'G03135'), (1442004, 1442076, 1, 'G03285'), (1501605, 1501677, 1, 'G03405'), (1520781, 1520854, -1, 'G03435'), (5268124, 5268210, -1, 'G08345'), (6646425, 6646496, 1, 'G10815'), (6819287, 6819324, 1, 'G11177'), (6837555, 6837639, -1, 'G11213'), (6837769, 6837853, -1, 'G11216'), (6905479, 6905552, -1, 'G11355'), (6944721, 6944793, 1, 'G11405'), (7185697, 7185771, 1, 'G11985'), (7232792, 7232865, -1, 'G12065'), (7256408, 7256481, 1, 'G12115'), (7341420, 7341494, -1, 'G12405'), (7730956, 7731037, 1, 'G13265'), (7814197, 7814270, 1, 'G13445'), (8255695, 8255767, 1, 'G14345'), (8301720, 8301794, -1, 'G14415'), (8979656, 8979729, 1, 'G15775'), (9108317, 9108391, 1, 'G16105'), (9191590, 9191663, 1, 'G16235'), (9287230, 9287304, 1, 'G16465'), (9289706, 9289787, 1, 'G16475'), (9815215, 9815287, -1, 'G17612'), (9873524, 9873596, -1, 'G17765'), (9978117, 9978189, -1, 'G17975'), (10093077, 10093157, -1, 'G18255'), (10302011, 10302084, 1, 'G18725'), (10325975, 10326047, -1, 'G18815'), (10878733, 10878807, -1, 'G20115'), (11774472, 11774508, -1, 'G22265'), (11910299, 11910373, 1, 'G22635'), (11954751, 11954824, -1, 'G22754'), (11974951, 11975032, 1, 'G22785'), (12320119, 12320203, 1, 'G23635'), (12429608, 12429681, 1, 'G23915'), (12486211, 12486282, -1, 'G24025'), (12686148, 12686230, 1, 'G24565'), (13006243, 13006316, -1, 'G25435'), (13058840, 13058922, -1, 'G25585'), (13076582, 13076666, -1, 'G25635'), (13285431, 13285503, -1, 'G26225'), (13336345, 13336419, -1, 'G26375'), (13341501, 13341575, -1, 'G26385'), (13454562, 13454635, 1, 'G26675'), (13704787, 13704860, 1, 'G27395'), (13882922, 13882994, -1, 'G27875'), (13885196, 13885269, -1, 'G27885'), (14032495, 14032567, 1, 'G28362'), (14267286, 14267368, 1, 'G28915'), (14470283, 14470355, 1, 'G29415'), (15120655, 15120728, 1, 'G31075'), (15183089, 15183162, 1, 'G31265'), (15345717, 15345753, -1, 'G31695'), (15430229, 15430303, -1, 'G31895'), (15576655, 15576728, 1, 'G32265'), (15671398, 15671469, 1, 'G32475'), (15804553, 15804635, 1, 'G32765'), (16304128, 16304201, 1, 'G34035'), (16454700, 16454773, -1, 'G34415'), (16556627, 16556700, 1, 'G34695'), (16655290, 16655364, 1, 'G34975'), (17130054, 17130127, 1, 'G36197'), (17149473, 17149545, 1, 'G36245'), (17276705, 17276779, -1, 'G36635'), (17500800, 17500872, -1, 'G37175'), (18254982, 18255018, -1, 'G39195'), (18293773, 18293845, 1, 'G39345'), (18395021, 18395093, 1, 'G39615'), (18411258, 18411332, 1, 'G39672'), (18501705, 18501778, -1, 'G39865'), (18542164, 18542238, 1, 'G39985')]
f5 = [(150353, 150426, -1, 'G01365'), (389889, 389960, -1, 'G02025'), (508427, 508500, -1, 'G02385'), (530819, 530893, 1, 'G02435'), (559327, 559399, -1, 'G02505'), (588890, 588964, -1, 'G02615'), (614641, 614723, 1, 'G02725'), (642397, 642479, -1, 'G02815'), (858534, 858571, 1, 'G03445'), (862395, 862468, -1, 'G03452'), (970797, 970878, -1, 'G03705'), (984365, 984448, 1, 'G03745'), (998940, 999013, 1, 'G03775'), (1742692, 1742765, 1, 'G05795'), (1788651, 1788723, 1, 'G05945'), (1804616, 1804690, 1, 'G05985'), (1853302, 1853382, -1, 'G06125'), (2060153, 2060235, -1, 'G06685'), (2212678, 2212749, -1, 'G07135'), (2309512, 2309549, -1, 'G07315'), (2411148, 2411232, 1, 'G07625'), (2432263, 2432336, -1, 'G07675'), (2587826, 2587899, -1, 'G08075'), (2898867, 2898951, -1, 'G09345'), (2993327, 2993401, 1, 'G09655'), (3030817, 3030890, -1, 'G09755'), (3118377, 3118458, 1, 'G09975'), (3212351, 3212424, -1, 'G10235'), (3287553, 3287635, -1, 'G10455'), (3324702, 3324775, 1, 'G10525'), (3578295, 3578367, -1, 'G11225'), (3617058, 3617130, 1, 'G11325'), (3669000, 3669073, -1, 'G11475'), (4471050, 4471122, 1, 'G13845'), (4530475, 4530548, 1, 'G14035'), (4673902, 4673974, 1, 'G14495'), (4929562, 4929636, 1, 'G15175'), (5157641, 5157715, 1, 'G15805'), (5161514, 5161586, 1, 'G15815'), (5358918, 5359000, 1, 'G16375'), (5962699, 5962771, -1, 'G18005'), (5965972, 5966044, -1, 'G18015'), (5984378, 5984450, 1, 'G18085'), (6258146, 6258218, 1, 'G18755'), (6401240, 6401311, 1, 'G19095'), (7073531, 7073603, -1, 'G20852'), (7073944, 7074016, -1, 'G20854'), (7074357, 7074429, -1, 'G20856'), (7074773, 7074845, -1, 'G20858'), (7222059, 7222131, -1, 'G21378'), (7387890, 7387962, 1, 'G22315'), (7981400, 7981472, 1, 'G23665'), (8906418, 8906502, 1, 'G25585'), (8946826, 8946899, -1, 'G25625'), (9815405, 9815477, -1, 'G27715'), (11802284, 11802356, 1, 'G32017'), (13823211, 13823284, -1, 'G35605'), (15049737, 15049811, -1, 'G37795'), (15242547, 15242621, 1, 'G38155'), (15593086, 15593160, 1, 'G38905'), (15844253, 15844325, -1, 'G39535'), (15993514, 15993587, 1, 'G39895'), (16256865, 16256937, -1, 'G40545'), (16427812, 16427893, 1, 'G40945'), (16524760, 16524832, -1, 'G41265'), (16655393, 16655477, 1, 'G41605'), (16684663, 16684735, -1, 'G41675'), (17476402, 17476475, -1, 'G43455'), (17512768, 17512839, -1, 'G43535'), (17856811, 17856883, -1, 'G44283'), (17894906, 17894979, -1, 'G44375'), (18058014, 18058088, 1, 'G44705'), (18560206, 18560278, -1, 'G45715'), (18576071, 18576143, 1, 'G45745'), (18715888, 18715960, -1, 'G46105'), (18807534, 18807614, 1, 'G46325'), (18924749, 18924821, 1, 'G46595'), (19658828, 19658900, 1, 'G48465'), (19761400, 19761472, -1, 'G48675'), (19820360, 19820398, 1, 'G48835'), (20064048, 20064120, 1, 'G49435'), (20692447, 20692519, 1, 'G50805'), (20758903, 20758940, -1, 'G50995'), (20773555, 20773637, 1, 'G51055'), (21275059, 21275141, -1, 'G52355'), (21318105, 21318189, -1, 'G52495'), (21418369, 21418441, 1, 'G52815'), (21740339, 21740410, -1, 'G53487'), (22091631, 22091704, 1, 'G54365'), (22094087, 22094160, 1, 'G54375'), (22304851, 22304923, -1, 'G54865'), (22355897, 22355970, -1, 'G55045'), (22357726, 22357799, -1, 'G55055'), (22501995, 22502068, -1, 'G55505'), (22845356, 22845430, 1, 'G56365'), (22973066, 22973138, 1, 'G56745'), (23071996, 23072070, -1, 'G56975'), (23463219, 23463291, 1, 'G57885'), (23661936, 23662018, 1, 'G58495'), (23861431, 23861503, 1, 'G59055'), (23971167, 23971239, 1, 'G59385'), (23974655, 23974727, 1, 'G59395'), (24157171, 24157245, -1, 'G59945'), (24279805, 24279886, 1, 'G60285'), (24547401, 24547474, 1, 'G60963'), (24548892, 24548964, 1, 'G60966'), (24684507, 24684579, 1, 'G61345'), (24726891, 24726964, 1, 'G61445'), (24856205, 24856242, 1, 'G61835'), (25347261, 25347333, 1, 'G63145'), (25801340, 25801414, 1, 'G64505'), (25892619, 25892691, -1, 'G64735'), (25942291, 25942372, 1, 'G64855'), (25989903, 25989976, 1, 'G65015'), (26114755, 26114793, -1, 'G65305'), (26174414, 26174496, -1, 'G65445'), (26212684, 26212757, 1, 'G65535'), (26238859, 26238933, -1, 'G65615'), (26573248, 26573322, -1, 'G66535'), (26585622, 26585696, 1, 'G66568'), (26670495, 26670567, -1, 'G66755'), (26699933, 26700004, -1, 'G66817'), (26938897, 26938969, 1, 'G67455')]
entries = [("Chr I", "NC_003070", 30432563, f1, colors.red),
("Chr II", "NC_003071", 19705359, f2, colors.green),
("Chr III", "NC_003074", 23470805, f3, colors.blue),
("Chr IV", "NC_003075", 18585042, f4, colors.orange),
("Chr V", "NC_003076", 26992728, f5, colors.purple)]
max_length = max([row[2] for row in entries])
chr_diagram = BasicChromosome.Organism()
for name, acc, length, features, color in entries:
if False:
# How I generated the values above... and tested passing in SeqFeatures
filename = "/Users/pjcock/Documents/comp_genomics/seed/%s.gbk" % acc
import os
if not os.path.isfile(filename):
continue
from Bio import SeqIO
record = SeqIO.read(filename, "gb")
assert length == len(record)
features = [f for f in record.features if f.type=="tRNA"]
print(name)
# Strip of the first three chars, AT# where # is the chr
print([(int(f.location.start), int(f.location.end),
f.strand, f.qualifiers['locus_tag'][0][3:])
for f in features])
# Output was copy and pasted to the script, see above.
# Continue test using SeqFeature objects!
# To test colours from the qualifiers,
for i, f in enumerate(features):
f.qualifiers['color'] = [str(i % 16)]
elif use_seqfeatures:
# Features as SeqFeatures
features = [SeqFeature(FeatureLocation(start, end, strand),
qualifiers={"name": [label],
"color": [color]})
for (start, end, strand, label) in features]
else:
# Features as 5-tuples
features = [(start, end, strand, label, color)
for (start, end, strand, label) in features]
# I haven't found a nice source of data for real Arabidopsis
# cytobands, so these three are made up at random!
cytobands = []
for color in [colors.gray, colors.darkgray, colors.slategray]:
start = (length - 1000000) * random.random()
end = min(length, start + 1000000)
# Draw these with black borders, and a grey fill
cytobands.append((start, end, 0, None, colors.black, color))
# Draw these with black borders, and a brown fill:
cytobands.append((0, 1000000, 0, "First 1 Mbp", colors.black, colors.brown))
cytobands.append((length-1000000, length, 0, "Last 1 Mbp", colors.black, colors.brown))
# Additional dummy entry to check fill colour on both strands,
if name == "Chr III":
cytobands.append((11000000, 13000000, -1, "Reverse", "red", "yellow"))
elif name == "Chr V":
cytobands.append((9500000, 11000000, +1, "Forward", colors.red, colors.yellow))
# Create the drawing object for the chromosome
cur_chromosome = BasicChromosome.Chromosome(name)
# Set the length, adding an extra 20 percent for the tolomeres etc:
cur_chromosome.scale_num = max_length * 1.2
cur_chromosome.label_sep_percent = 0.15
# Add a dummy segment for allocating vertical space
# which can be used for feature label placement
spacer = BasicChromosome.SpacerSegment()
spacer.scale = 0.03 * max_length
cur_chromosome.add(spacer)
# Add an opening telomere
start = BasicChromosome.TelomereSegment()
start.scale = 0.02 * max_length
start.fill_color = colors.lightgrey
cur_chromosome.add(start)
# Add a body - using bp as the scale length here.
# Note we put the cytobands a start of combined list,
# as want them drawn underneath the tRNA markers.
body = BasicChromosome.AnnotatedChromosomeSegment(length, cytobands + features)
body.scale = length
cur_chromosome.add(body)
# Add a closing telomere
end = BasicChromosome.TelomereSegment(inverted=True)
end.scale = 0.02 * max_length
end.fill_color = colors.lightgrey
cur_chromosome.add(end)
# Another spacer
spacer = BasicChromosome.SpacerSegment()
spacer.scale = 0.03 * max_length
cur_chromosome.add(spacer)
# This chromosome is done
chr_diagram.add(cur_chromosome)
with warnings.catch_warnings():
# BiopythonWarning: Too many labels to avoid overlap
warnings.simplefilter("ignore", BiopythonWarning)
chr_diagram.draw(filename, "Arabidopsis thaliana tRNA")
def prodigal_parser(seq_file, sco_file, prefix, output_folder):
bin_ffn_file = '%s.ffn' % prefix
bin_faa_file = '%s.faa' % prefix
bin_gbk_file = '%s.gbk' % prefix
pwd_bin_ffn_file = '%s/%s' % (output_folder, bin_ffn_file)
pwd_bin_faa_file = '%s/%s' % (output_folder, bin_faa_file)
pwd_bin_gbk_file = '%s/%s' % (output_folder, bin_gbk_file)
# get sequence id list
id_to_sequence_dict = {}
sequence_id_list = []
for each_seq in SeqIO.parse(seq_file, 'fasta'):
id_to_sequence_dict[each_seq.id] = str(each_seq.seq)
sequence_id_list.append(each_seq.id)
# get sequence to cds dict and sequence to transl_table dict
current_seq_id = ''
current_transl_table = ''
current_seq_csd_list = []
seq_to_cds_dict = {}
seq_to_transl_table_dict = {}
for each_cds in open(sco_file):
if each_cds.startswith('# Sequence Data'):
# add to dict
if current_seq_id != '':
seq_to_cds_dict[current_seq_id] = current_seq_csd_list
seq_to_transl_table_dict[current_seq_id] = current_transl_table
# reset value
current_seq_id = each_cds.strip().split('=')[-1][1:-1].split(
' ')[0]
current_transl_table = ''
current_seq_csd_list = []
elif each_cds.startswith('# Model Data'):
current_transl_table = each_cds.strip().split(';')[-2].split(
'=')[-1]
else:
current_seq_csd_list.append('_'.join(
each_cds.strip().split('_')[1:]))
seq_to_cds_dict[current_seq_id] = current_seq_csd_list
seq_to_transl_table_dict[current_seq_id] = current_transl_table
bin_gbk_file_handle = open(pwd_bin_gbk_file, 'w')
bin_ffn_file_handle = open(pwd_bin_ffn_file, 'w')
bin_faa_file_handle = open(pwd_bin_faa_file, 'w')
gene_index = 1
for seq_id in sequence_id_list:
# create SeqRecord
current_sequence = Seq(id_to_sequence_dict[seq_id])
current_SeqRecord = SeqRecord(current_sequence, id=seq_id)
current_SeqRecord.seq.alphabet = generic_dna
transl_table = seq_to_transl_table_dict[seq_id]
# add SeqFeature to SeqRecord
for cds in seq_to_cds_dict[seq_id]:
# define locus_tag id
locus_tag_id = '%s_%s' % (prefix, "{:0>5}".format(gene_index))
# define FeatureLocation
cds_split = cds.split('_')
cds_start = SF.ExactPosition(int(cds_split[0]))
cds_end = SF.ExactPosition(int(cds_split[1]))
cds_strand = cds_split[2]
current_strand = None
if cds_strand == '+':
current_strand = 1
if cds_strand == '-':
current_strand = -1
current_feature_location = FeatureLocation(cds_start,
cds_end,
strand=current_strand)
# get nc sequence
sequence_nc = ''
if cds_strand == '+':
sequence_nc = id_to_sequence_dict[seq_id][cds_start -
1:cds_end]
if cds_strand == '-':
sequence_nc = str(
Seq(id_to_sequence_dict[seq_id][cds_start - 1:cds_end],
generic_dna).reverse_complement())
# translate to aa sequence
sequence_aa = str(
SeqRecord(Seq(sequence_nc)).seq.translate(table=transl_table))
# remove * at the end
sequence_aa = sequence_aa[:-1]
# export nc and aa sequences
export_dna_record(sequence_nc, locus_tag_id, '',
bin_ffn_file_handle)
export_aa_record(sequence_aa, locus_tag_id, '',
bin_faa_file_handle)
# Define feature type
current_feature_type = 'CDS'
# Define feature qualifiers
current_qualifiers_dict = {}
current_qualifiers_dict['locus_tag'] = locus_tag_id
current_qualifiers_dict['transl_table'] = transl_table
current_qualifiers_dict['translation'] = sequence_aa
# Create a SeqFeature
current_feature = SeqFeature(current_feature_location,
type=current_feature_type,
qualifiers=current_qualifiers_dict)
# Append Feature to SeqRecord
current_SeqRecord.features.append(current_feature)
gene_index += 1
# export to gbk file
SeqIO.write(current_SeqRecord, bin_gbk_file_handle, 'genbank')
bin_gbk_file_handle.close()
bin_ffn_file_handle.close()
bin_faa_file_handle.close()
def merge_overlapping_feature_in_simple_format(
self,
input_file_file_list,
scaffold_id_column,
feature_start_column,
feature_end_column,
output_file=None,
output_separator="\t",
comments_prefix="#",
input_separator="\t",
coordinates_type="1-based",
return_seqfeature_dict=False,
feature_type=None):
file_list = [input_file_file_list] if isinstance(
input_file_file_list, str) else input_file_file_list
record_dict_list = []
for filename in file_list:
record_dict_list.append(OrderedDict())
for line_list in self.file_line_as_list_generator(
filename,
comments_prefix=comments_prefix,
separator=input_separator):
if line_list[scaffold_id_column] not in record_dict_list[-1]:
record_dict_list[-1][line_list[scaffold_id_column]] = []
record_dict_list[-1][line_list[scaffold_id_column]].append([
(int(line_list[feature_start_column]) -
1 if coordinates_type == "1-based" else int(
line_list[feature_start_column])),
int(line_list[feature_end_column])
])
unified_dict = OrderedDict()
merged_dict = OrderedDict()
#print record_dict_list[0]
scaffold_set = set()
for record_dict in record_dict_list:
scaffold_set |= set(record_dict.keys())
for scaffold in scaffold_set:
unified_dict[scaffold] = []
merged_dict[scaffold] = []
for record_dict in record_dict_list:
for scaffold in record_dict:
unified_dict[scaffold] += record_dict[scaffold]
#print "AAAAAAAAAA"
#print scaffold, unified_dict[scaffold], record_dict[scaffold]
for scaffold in unified_dict:
if unified_dict[scaffold]:
unified_dict[scaffold].sort()
if unified_dict[scaffold] is None:
print(scaffold)
#print unified_dict
for scaffold in unified_dict:
number_of_records = len(unified_dict[scaffold])
if number_of_records == 0:
continue
# [a, b) [c, d), a < b, c < d
# after sorting c >= a
i = 1
prev_coordinates = deepcopy(unified_dict[scaffold][0])
#print scaffold, number_of_records, prev_coordinates
#print "\t", unified_dict[scaffold]
while i < number_of_records:
if unified_dict[scaffold][i][0] > prev_coordinates[1]: # c > b
#print "AAAAAA", "\t", prev_coordinates, unified_dict[scaffold][i]
merged_dict[scaffold].append(deepcopy(prev_coordinates))
prev_coordinates = deepcopy(unified_dict[scaffold][i])
elif unified_dict[scaffold][i][1] > prev_coordinates[
1]: # d > b; c<=b
#print "BBBBBB", "\t",prev_coordinates, unified_dict[scaffold][i]
prev_coordinates[1] = deepcopy(
unified_dict[scaffold][i][1])
else: # d <= b
#print "CCCCCC", "\t",prev_coordinates, unified_dict[scaffold][i]
pass
i += 1
if merged_dict[scaffold]:
if prev_coordinates != merged_dict[scaffold][-1]:
merged_dict[scaffold].append(prev_coordinates)
else:
merged_dict[scaffold].append(prev_coordinates)
#print "\t", unified_dict[scaffold]
#print "\t", merged_dict[scaffold]
#print unified_dict
#print merged_dict
if output_file:
with self.metaopen(output_file, "w") as out_fd:
for scaffold in merged_dict:
for feature in merged_dict[scaffold]:
out_fd.write(
output_separator.join(
map(str, [
scaffold, feature[0] +
1 if coordinates_type ==
"1-based" else feature[0], feature[1]
])) + "\n")
if return_seqfeature_dict and feature_type:
feature_dict = OrderedDict()
for region in merged_dict:
feature_dict[region] = []
for (start, stop) in merged_dict[region]:
feature_dict[region].append(
SeqFeature(FeatureLocation(start, stop),
type=feature_type,
strand=None))
return feature_dict
elif return_seqfeature_dict and (not feature_type):
raise ValueError(
"ERROR!!! Feature type for seqfeature records was not set!")
else:
return merged_dict
from Bio.Seq import Seq
from Bio.SeqFeature import SeqFeature, FeatureLocation
at5g40780 = Seq(
"MVAQAPHDDHQDDEKLAAARQKEIEDWLPITSSRNAKWWYSAFHNVTAMVGAGVLGLPYAMSQLGWGPGIAVLVLSWVITLYTLWQMVEMHEMVPGKRFDRYHELGQHAFGEKLGLYIVVPQQLIVEIGVCIVYMVTGGKSLKKFHELVCDDCKPIKLTYFIMIFASVHFVLSHLPNFNSISGVSLAAAVMSLSYSTIAWASSASKGVQEDVQYGYKAKTTAGTVFNFFSGLGDVAFAYAGHNVVLEIQATIPSTPEKPSKGPMWRGVIVAYIVVALCYFPVALVGYYIFGNGVEDNILMSLKKPAWLIATANIFVVIHVIGSYQIYAMPVFDMMETLLVKKLNFRPTTTLRFFVRNFYVAATMFVGMTFPFFGGLLAFFGGFAFAPTTYFLPCVIWLAIYKPKKYSLSWWANWVCIVFGLFLMVLSPIGGLRTIVIQAKGYKFYS"
)
# 단백질 시퀀스 왜 이렇게 길어 이거
feature = SeqFeature(FeatureLocation(0, 30), type="protein", strand=1)
feature_seq = at5g40780[feature.location.start:feature.location.end]
print(feature_seq)
# 아래나 위나 방식은 똑같은 듯 하다.
feature_seq2 = feature.extract(at5g40780)
print(feature_seq2)
if int(rw[ti['Amplicon_length']]) > 0:
start = int(rw[ti['Primer1_target_start']])
end = int(rw[ti['Primer2_target_end']])
if end > start:
strd = 1
truestart = start
trueend = end
else:
strd = -1
truestart = end
trueend = start
#print '{} Start:{}, End:{}'.format(indexa,start,end)
seq_feature=SeqFeature(FeatureLocation(truestart,trueend, strand=strd), type="Amplicon", \
id=indexa,\
qualifiers={'Plate':plate,'Well':well,'Gene':gene_name,\
'Amplicon':amplicon,\
'Amplicons':amplicons,\
'Strand':strd,'colour':col,\
#'note':'{} {}'.format(indexa,data[key]['Description']),\
'Strand_annotation':data[key]['Strand_annotation']})
genome.features.append(seq_feature)
faf = open(amplicons_features, 'w')
SeqIO.write(genome, faf, "gb")
faf.close()
#Map TF binding sites
TF_file = "../Transcription_Factors/TF_binding_clean.csv"
TF_data = readcsv(TF_file, delim=',')
def annotate_geneclusters(seq_record, options):
"""Re-annotate gene clusters in the seq_record"""
pfam_features = utils.get_pfam_features(seq_record)
cf_clusters = find_cf_clusters(pfam_features, seq_record, options)
# Integrate ClusterFinder clusters with existing cluster features
newclusters = []
cluster_features = utils.get_cluster_features(seq_record)
secmet_cds_features = utils.get_secmet_cds_features(seq_record)
for cf_cluster in cf_clusters:
overlaps = False
cf_type = "cf_putative"
for cluster in cluster_features:
if not utils.features_overlap(cf_cluster, cluster):
continue
overlaps = True
# Get signature genes from antiSMASH-predicted cluster
features_in_cluster = utils.get_cluster_cds_features(
cluster, seq_record)
cluster_sig_genes = [
gene for gene in secmet_cds_features
if gene in features_in_cluster
]
# Predict gene cluster borders using ClusterFinder
if options.borderpredict:
if ((cluster.location.end + cluster.location.start) /
2) in cf_cluster.location:
# Make sure that antiSMASH signature genes are still included in the cluster
for sig_gene in cluster_sig_genes:
startpoint = min(
[sig_gene.location.start, sig_gene.location.end])
endpoint = max(
[sig_gene.location.start, sig_gene.location.end])
if cf_cluster.location.start > startpoint:
cf_cluster.location = FeatureLocation(
startpoint, cf_cluster.location.end)
if cf_cluster.location.end < endpoint:
cf_cluster.location = FeatureLocation(
cf_cluster.location.start, endpoint)
cluster_border = SeqFeature(cf_cluster.location,
type="cluster_border")
cluster_border.qualifiers = {
"tool": ["clusterfinder"],
"probability": [cf_cluster.probability],
"note": ["best prediction"],
}
seq_record.features.append(cluster_border)
elif cf_cluster.location.start < cluster.location.start and cf_cluster.location.end > cluster.location.end:
cluster.location = cf_cluster.location
elif cf_cluster.location.start < cluster.location.start:
cluster.location = FeatureLocation(cf_cluster.location.start,
cluster.location.end)
elif cf_cluster.location.end > cluster.location.end:
cluster.location = FeatureLocation(cluster.location.start,
cf_cluster.location.end)
cluster.qualifiers['probability'] = [
"%01.4f" % cf_cluster.probability
]
if not overlaps and not ('borderpredict_only' in options
and options.borderpredict_only):
cf_cluster_CDSs = utils.get_cluster_cds_features(
cf_cluster, seq_record)
for CDS in cf_cluster_CDSs:
if 'sec_met' in CDS.qualifiers:
type_sec_met_qualifiers = [
feat for feat in CDS.qualifiers['sec_met']
if "Type: " in feat
]
for qualifier in type_sec_met_qualifiers:
if "cf_fatty_acid" in qualifier:
if cf_type == "cf_putative":
cf_type = "cf_fatty_acid"
elif cf_type == "cf_saccharide":
cf_type = "cf_fatty_acid-saccharide"
if "cf_saccharide" in qualifier:
if cf_type == "cf_putative":
cf_type = "cf_saccharide"
elif cf_type == "cf_fatty_acid":
cf_type = "cf_fatty_acid-saccharide"
new_cluster = SeqFeature(cf_cluster.location, type="cluster")
new_cluster.qualifiers['product'] = [cf_type]
new_cluster.qualifiers['probability'] = [
"%01.4f" % cf_cluster.probability
]
newclusters.append(new_cluster)
if len(newclusters):
seq_record.features.extend(newclusters)
renumber_clusters(seq_record, options)
],
"EC_number":
"3.5.2.6",
}
ampr_prom = next(get_features("AmpR promoter"), None)
ampr_prom = ampr_prom or next(get_features("AmpR Promoter"), None)
if ampr_prom is not None:
ampr_prom.qualifiers["label"] = ["AmpR Promoter"]
ampr_prom.qualifiers["note"] = ["color: #ff6666"]
ampr_term_start = gb.seq.find(AMPR_TERM)
if ampr is not None and ampr_term_start >= 0:
ampr_term = SeqFeature(
location=FeatureLocation(ampr_term_start, ampr_term_start + 94,
-1),
type="terminator",
qualifiers={
"label": "AmpR Terminator",
"note": ["color: #ff6666"]
},
)
gb.features.append(ampr_term)
# KanR recolor and annotations
kanr = next(get_features("KanR"), None)
if kanr is not None:
kanr.qualifiers.update({
"gene":
"aphA1",
"product":
"aminoglycoside phosphotransferase",
"EC_number":
defline = protein.description
pattern = re.compile('.+#\s(\d+)\s#\s(\d+)\s#\s(\S*1)\s#\sID.+')
match = pattern.match(defline)
start_pos = int(match.group(1))
end_pos = int(match.group(2))
strand_pos = int(match.group(3))
feat_loc = FeatureLocation(start_pos - 1,
end_pos) # adjust for 0-index
l_tag = protein.id
# consolidate feature annotations
quals = {
'note': defline,
'locus_tag': l_tag,
'translation': protein.seq
}
feature = SeqFeature(location=feat_loc,
strand=strand_pos,
id=protein.id,
type='CDS',
qualifiers=quals)
record.features.append(feature)
# save record with annotations
record.description = rec_name + "_with_ORFs"
record.name = rec_name
record.dbxrefs = ["Project: " + argv[1] + "/" + rec_name]
record.seq.alphabet = generic_dna
write_genbank(annot_gbk, record)
print "OK"
seq = Dseqrecord("")
colors = []
for hue in range(0, 360, 36):
for s in [0.2, 0.4, 0.6]:
r, g, b = colorsys.hsv_to_rgb(hue / 360, s, 255)
r, g, b = int(r), int(g), int(b)
hex = "#{0:02x}{1:02x}{2:02x}".format(r, g, b)
colors.append(f"{hex}")
colors = colors[::3] + colors[1::3] + colors[2::3]
colors = colors[::-1]
for hex in colors:
sf = SeqFeature(FeatureLocation(1, 9, strand=1), type="misc_feature")
sf.qualifiers["label"] = [
hex,
]
sf.qualifiers["ApEinfo_fwdcolor"] = [
hex,
]
se = Dseqrecord("agtagtcgta")
se.features.append(sf)
seq += se
from pydna.editor import ape
ape(seq)
print(colors)
def printClusterPics(cluNet, data_box, annot_def_dict, output_dir, ncutoff=8):
"""This abomination takes in a network of gene clusters, a dict of cluster_member_ID:pfam domains,
and a place to dump output. It goes thru and prints pictures of each cluster and its best buddies.
Possible defects:
Proteins can be related, but won't be colored if there is no pfam domain annotation.
Handling of multidomain proteins is very crude.
Also, much much too long."""
for node in cluNet.nodes():
#record the species for file printing poirposes
parentSpecies, parentAcession, parentWpID, parentAnnotation, parentStartstop = node.split(
"|")
#Make a new diagram. TODO add the path for the output and whatnot.
gd_diagram = GenomeDiagram.Diagram()
#Return the top most related clusters.
sortedClusters = getTopClusters(cluNet, node)[0]
#the largest nucleotide span in all the clusters under consideration.
maxSpan = getMaxSpan(sortedClusters, data_box)
#Get info about pfam domains in all the clusters under consideration
id_list = makeIDList(sortedClusters, data_box)
domainCounts = getPfamCounts(annot_def_dict, id_list)
colorDict = getColorDict(domainCounts)
# for each of the best clusters:
for e in sortedClusters:
#Make the track and add a feature set to the track. scale = 0 turns off the scale, greytrack adds a background. What is the 1 for? Who knows.
gd_track_for_features = gd_diagram.new_track(1,
name="",
scale=0,
greytrack=True)
gd_feature_set = gd_track_for_features.new_set()
#get the cluster_member_IDs from data_box using the cluster_ID
#TODO this will be replaced with a clusterID:clusterMemberID dict based on the BLAST2 db? maybe.
clusterMembers = data_box[e].keys()
#start stop span
sss = getClustSS(clusterMembers)
offset = int((maxSpan - sss[2]) / 2 - sss[0])
for cm in clusterMembers:
organism, acession, wpID, annotation, startstop = cm.split("|")
startstop = re.sub('[<>$%^&*#|\[\]]', "", startstop)
q_start = int(startstop.split("-")[0])
q_stop = int(startstop.split("-")[1])
#encode the directional info.
if q_start < q_stop:
q_strand = 1
label_angle = 15
elif q_start > q_stop:
q_strand = -1
label_angle = 179.9
#else:
# strand = None
#Uh, I think this should make the whole shebang roughly centered?
feature = SeqFeature(FeatureLocation(q_start + offset,
q_stop + offset),
ref=wpID,
strand=q_strand)
#What color should it be?
pfam = domainHash(annot_def_dict[cm])
if len(pfam) > 0 and pfam in colorDict:
color = colorDict[
pfam] #pfam is a tuple of domain names, usually just one
else:
color = "0x000000" #paint it black
gd_feature_set.add_feature(feature,
sigil="ARROW",
color=color,
label=True,
label_position="start",
name=feature.ref,
label_size=7,
label_angle=label_angle)
#TODO Add a legend at the bottom, too. each pfam dom is a colored square. ref = name of pfam domain.
gd_track_for_features = gd_diagram.new_track(0,
name="",
scale=0,
greytrack=False)
gd_feature_set = gd_track_for_features.new_set()
counter = 0
increment = maxSpan / len(colorDict.keys())
for domain_tup in colorDict:
q_start = round(counter * increment)
q_stop = round((counter + .33) * increment)
counter += 1
feature = SeqFeature(FeatureLocation(q_start, q_stop),
ref=domain_tup[0],
strand=None)
gd_feature_set.add_feature(feature,
sigil="BOX",
color=colorDict[domain_tup],
label=True,
name=feature.ref,
label_size=7,
label_angle=15)
#Done reading in all the info! Now write the plot
name = abspath(output_dir + "/" + parentSpecies + "-" + parentWpID +
".pdf")
drawMap(gd_diagram, name, plotWidth=maxSpan)
def __rshift__(self, index):
"""Rotate the sequence clockwise, preserving annotations.
"""
index %= len(self.seq) # avoid unnecessary cycles
if index == 0:
return self
elif index < 0:
return self << -index
newseq = self.seq[-index:] + self.seq[:-index]
newfeats = []
newletan = {
k: v[index:] + v[:index]
for k, v in six.iteritems(self.letter_annotations)
}
for feature in self.features:
loc = feature.location
if loc is None:
newloc = None
elif feature.type == "source" and loc.start == 0 and loc.end == len(
self):
newloc = loc
else:
_newloc = []
for part in (loc + index).parts:
if part.end >= len(newseq) and part.start >= len(newseq):
r = part.start // len(newseq) # remainder is used to
_newloc.append(
FeatureLocation( # make sure that part.end
start=part.start -
r * len(newseq), # is always after part.start
end=part.end -
r * len(newseq), # even on additional end
strand=part.strand, # overlap
ref=part.ref,
ref_db=part.ref_db,
))
else:
_newloc.append(part)
newloc = _newloc[0] if len(_newloc) == 1 else CompoundLocation(
_newloc)
newfeats.append(
SeqFeature(
location=newloc,
type=feature.type,
id=feature.id,
qualifiers=feature.qualifiers,
))
return type(self)(
seq=newseq,
id=self.id,
name=self.name,
description=self.description,
dbxrefs=self.dbxrefs,
features=newfeats,
annotations=self.annotations,
letter_annotations=newletan,
)
def merge_gbk(gbk_records, filter_size=0, gi=False):
'''
merge multiple contigs into a single DNA molecule with 200*N between contigs
keep source description from the first record
remove contigs smaller than <filter_size>
:param gbk_records:
:param filter_size:
:param gi:
:return:
'''
from Bio.SeqFeature import SeqFeature, FeatureLocation, ExactPosition
from Bio.SeqRecord import SeqRecord
n=0
if len(gbk_records) == 1:
merged_rec = gbk_records[0]
else:
for i, rec in enumerate(gbk_records):
# remove source feature of all records except the first one
if rec.features[0].type == 'source' and i != 0:
rec.features.pop(0)
# filter small contigs
if len(rec) > filter_size:
if n == 0:
n+=1
merged_rec = rec
else:
merged_rec+=rec
# you could insert a spacer if needed
# do not add spacer after the last contig
if i != len(gbk_records)-1:
merged_rec += "N" * 200
my_start_pos = ExactPosition(len(merged_rec)-200)
my_end_pos = ExactPosition(len(merged_rec))
my_feature_location = FeatureLocation(my_start_pos, my_end_pos)
my_feature = SeqFeature(my_feature_location, type="assembly_gap")
merged_rec.features.append(my_feature)
try:
merged_rec.id = gbk_records[0].annotations["accessions"][-1]
except KeyError:
merged_rec.id = gbk_records[0].id
if gi:
merged_rec.annotations["gi"] = gi
merged_rec.description = "%s" % gbk_records[0].annotations["organism"]
merged_rec.annotations = gbk_records[0].annotations
try:
merged_rec.name = gbk_records[0].annotations["accessions"][-1]
except KeyError:
merged_rec.name = gbk_records[0].id
my_start_pos = ExactPosition(0)
my_end_pos = ExactPosition(len(merged_rec))
merged_rec.features[0].location = FeatureLocation(my_start_pos, my_end_pos)
return merged_rec
