def parse_file(file_name, type = 'DNA'):
"""Parse the given file into a FastaAlignment object.
Arguments:
o file_name - The location of the file to parse.
o type - The type of information contained in the file.
"""
if type.upper() == 'DNA':
alphabet = IUPAC.ambiguous_dna
elif type.upper() == 'RNA':
alphabet = IUPAC.ambiguous_rna
elif type.upper() == 'PROTEIN':
alphabet = IUPAC.protein
else:
raise ValueError("Invalid type %s passed. Need DNA, RNA or PROTEIN"
% type)
# create a new alignment object
fasta_align = FastaAlignment(Alphabet.Gapped(alphabet))
# now parse the file and fill up the alignment object
align_file = open(file_name, 'r')
parser = Fasta.RecordParser()
iterator = Fasta.Iterator(align_file, parser)
cur_align = iterator.next()
while cur_align:
fasta_align.add_sequence(cur_align.title, cur_align.sequence)
cur_align = iterator.next()
return fasta_align
def test_schema_representation(self):
"""Convert sequences into schema representations.
"""
# get a set of schemas we want to code the sequence in
schema_bank = self._load_schema_repository()
top_schemas = schema_bank.get_top(25)
schema_coder = Schema.SchemaCoder(top_schemas, self.schema)
# get the sequences one at a time, and encode them
fasta_handle = open(self.test_file, 'r')
seq_parser = Fasta.SequenceParser(alphabet=IUPAC.unambiguous_dna)
iterator = Fasta.Iterator(fasta_handle, seq_parser)
while 1:
seq_record = iterator.next()
if seq_record is None:
break
schema_values = schema_coder.representation(seq_record.seq)
if VERBOSE:
print "Schema values:", schema_values
fasta_handle.close()
def setUp(self):
test_file = os.path.join('NeuralNetwork', 'enolase.fasta')
diff_file = os.path.join('NeuralNetwork', 'repeat.fasta')
self.test_records = []
self.diff_records = []
# load the records
for file, records in ((test_file, self.test_records),
(diff_file, self.diff_records)):
handle = open(file, 'r')
seq_parser = Fasta.SequenceParser(alphabet=IUPAC.unambiguous_dna)
iterator = Fasta.Iterator(handle, seq_parser)
while 1:
seq_record = iterator.next()
if seq_record is None:
break
records.append(seq_record)
handle.close()
self.motif_finder = Motif.MotifFinder()
def setUp(self):
test_file = os.path.join('NeuralNetwork', 'enolase.fasta')
diff_file = os.path.join('NeuralNetwork', 'repeat.fasta')
self.test_records = []
self.diff_records = []
# load the records
for file, records in ((test_file, self.test_records),
(diff_file, self.diff_records)):
handle = open(file, 'r')
seq_parser = Fasta.SequenceParser(alphabet=IUPAC.unambiguous_dna)
iterator = Fasta.Iterator(handle, seq_parser)
while 1:
seq_record = iterator.next()
if seq_record is None:
break
records.append(seq_record)
handle.close()
self.num_schemas = 2
schema_ga = Schema.GeneticAlgorithmFinder()
schema_ga.min_generations = 1
self.finder = Schema.SchemaFinder(num_schemas=self.num_schemas,
schema_finder=schema_ga)
def test_record_iterator(self):
"""Test the iterator with a Record Parser.
"""
parser = Fasta.RecordParser()
iterator = Fasta.Iterator(self.test_handle, parser)
for rec in iter(iterator):
assert isinstance(rec, Fasta.Record)
def test_sequence_iterator(self):
"""Test the iterator with a Sequence Parser.
"""
parser = Fasta.SequenceParser()
iterator = Fasta.Iterator(self.test_handle, parser)
for rec in iter(iterator):
assert isinstance(rec, SeqRecord.SeqRecord)
def ReadFile(self):
self.parser = Fasta.RecordParser()
self.iter = Fasta.Iterator(handle=open(self.file), parser=self.parser)
while 1:
rec = self.iter.next()
if not rec: break
self.header = rec.title.split()[0].split(',')[0]
self.HandleRecord(rec)
def read_fasta_file(self, file):
genes = []
iter = Fasta.Iterator(handle = open(file), parser = Fasta.RecordParser())
while 1:
rec = iter.next()
if not rec: break
genes.append((rec.sequence, rec.title))
return genes
def test_parsing_comments(self):
"""Parse FASTA files with # style comment lines in them.
"""
handle = open(os.path.join("Fasta", "f003"))
iterator = Fasta.Iterator(handle, Fasta.RecordParser())
num_recs = 0
for rec in iter(iterator):
num_recs += 1
assert num_recs == 2
def runDisEMBLpipeline():
try:
smooth_frame = 8
peak_frame = 8
join_frame = 4
fold_coils = 1.2
fold_hotloops = 1.4
fold_rem465 = 1.2
mode = 'scores'
try:
file = open(sys.argv[1], 'r')
except:
mode = 'default'
except:
print '\nDisEMBL.py sequence_file \n'
print 'A default run would be: ./DisEMBL.py fasta_file'
raise SystemExit
#db = sys.stdin
parser = Fasta.RecordParser()
iterator = Fasta.Iterator(file, parser)
while 1:
try:
cur_record = iterator.next()
sequence = upper(cur_record.sequence)
# Run NN
COILS_raw, HOTLOOPS_raw, REM465_raw = JensenNet(sequence)
# Run Savitzky-Golay
REM465_smooth = SavitzkyGolay(smooth_frame, 0, REM465_raw)
COILS_smooth = SavitzkyGolay(smooth_frame, 0, COILS_raw)
HOTLOOPS_smooth = SavitzkyGolay(smooth_frame, 0, HOTLOOPS_raw)
sys.stdout.write('> ' + cur_record.title + '\n')
sys.stdout.write('# COILS ')
reportSlicesTXT(
getSlices(COILS_smooth, fold_coils, join_frame, peak_frame,
0.43), sequence)
sys.stdout.write('# REM465 ')
reportSlicesTXT(
getSlices(REM465_smooth, fold_rem465, join_frame, peak_frame,
0.50), sequence)
sys.stdout.write('# HOTLOOPS ')
reportSlicesTXT(
getSlices(HOTLOOPS_smooth, fold_hotloops, join_frame,
peak_frame, 0.086), sequence)
sys.stdout.write('# RESIDUE COILS REM465 HOTLOOPS\n')
for i in range(len(REM465_smooth)):
sys.stdout.write(sequence[i] + '\t' +
fpformat.fix(COILS_smooth[i], 5) + '\t' +
fpformat.fix(REM465_smooth[i], 5) + '\t' +
fpformat.fix(HOTLOOPS_smooth[i], 5) + '\n')
except AttributeError:
break
file.close()
return
def test_sequence_alphabet(self):
"""Setting the alphabet for the Sequence Parser.
"""
parser = Fasta.SequenceParser(alphabet =
IUPAC.unambiguous_dna)
rec = parser.parse(self.handles[0])
assert rec.seq.alphabet == IUPAC.unambiguous_dna
def test_new_iterator(self):
"""Ensure the Fasta iterator works like a Python 2.2 iterator.
"""
n = 0
iterator = Fasta.Iterator(self.test_handle)
for rec in iter(iterator):
n += 1
assert n == 3
def test_record_parser(self):
"""Basic operation of the Record Parser.
"""
parser = Fasta.RecordParser()
for index in range(len(self.handles)):
handle = self.handles[index]
rec = parser.parse(handle)
assert isinstance(rec, Fasta.Record)
assert len(rec.title) == self.lengths[index][0]
assert len(rec.sequence) == self.lengths[index][1]
def test_sequence_title_convert(self):
"""Test title conversion for the Sequence Parser.
"""
def test_title2ids(title):
return "id", "name", "description"
parser = Fasta.SequenceParser(title2ids = test_title2ids)
rec = parser.parse(self.handles[0])
assert rec.id == "id"
assert rec.name == "name"
assert rec.description == "description"
def runDisEMBLpipeline():
try:
smooth_frame = int(sys.argv[1])
peak_frame = int(sys.argv[2])
join_frame = int(sys.argv[3])
fold_coils = float(sys.argv[4])
fold_hotloops = float(sys.argv[5])
fold_rem465 = float(sys.argv[6])
file = str(sys.argv[7])
except:
print '\nDisEMBL.py smooth_frame peak_frame join_frame fold_coils fold_hotloops fold_rem465 sequence_file\n'
print 'A default run would be: ./DisEMBL.py 8 8 4 1.2 1.4 1.2 fasta_file'
raise SystemExit
db = open(file, 'r')
parser = Fasta.RecordParser()
iterator = Fasta.Iterator(db, parser)
while 1:
try:
cur_record = iterator.next()
sequence = upper(cur_record.sequence)
# Run NN
COILS_raw, HOTLOOPS_raw, REM465_raw = JensenNet(sequence)
# Run Savitzky-Golay
REM465_smooth = SavitzkyGolay(smooth_frame, 0, REM465_raw)
COILS_smooth = SavitzkyGolay(smooth_frame, 0, COILS_raw)
HOTLOOPS_smooth = SavitzkyGolay(smooth_frame, 0, HOTLOOPS_raw)
sys.stdout.write('> ' + cur_record.title + '_COILS ')
reportSlicesTXT(
getSlices(COILS_smooth, fold_coils, join_frame, peak_frame,
0.43), sequence)
sys.stdout.write('> ' + cur_record.title + '_REM465 ')
reportSlicesTXT(
getSlices(REM465_smooth, fold_rem465, join_frame, peak_frame,
0.50), sequence)
sys.stdout.write('> ' + cur_record.title + '_HOTLOOPS ')
reportSlicesTXT(
getSlices(HOTLOOPS_smooth, fold_hotloops, join_frame,
peak_frame, 0.086), sequence)
sys.stdout.write('\n')
except AttributeError:
break
return
def test_record_basic(self):
"""Basic test on Record
"""
def pbool(b):
if b:
return 1
return 0
r = Fasta.Record()
assert pbool(type(r.title) is StringType) # StringType
assert pbool(type(r.sequence) is StringType) # StringType
def _load_schema_repository(self):
"""Helper function to load a schema repository from a file.
This also caches a schema bank, to prevent having to do this
time consuming operation multiple times.
"""
# if we already have a cached repository, return it
if self.schema_bank is not None:
return self.schema_bank
# otherwise, we'll read in a new schema bank
# read in the all of the motif records
motif_handle = open(self.test_file, 'r')
seq_parser = Fasta.SequenceParser(alphabet=IUPAC.unambiguous_dna)
iterator = Fasta.Iterator(motif_handle, seq_parser)
seq_records = []
while 1:
seq_record = iterator.next()
if seq_record is None:
break
seq_records.append(seq_record)
motif_handle.close()
# find motifs from the file
motif_finder = Motif.MotifFinder()
motif_size = 9
motif_bank = motif_finder.find(seq_records, motif_size)
schema_bank = self.factory.from_motifs(motif_bank, .1, 2)
# cache the repository
self.schema_bank = schema_bank
return schema_bank
def setUp(self):
test_file = os.path.join('NeuralNetwork', 'enolase.fasta')
self.test_records = []
# load the records
handle = open(test_file, 'r')
seq_parser = Fasta.SequenceParser(alphabet=IUPAC.unambiguous_dna)
iterator = Fasta.Iterator(handle, seq_parser)
while 1:
seq_record = iterator.next()
if seq_record is None:
break
self.test_records.append(seq_record)
handle.close()
self.sig_finder = Signature.SignatureFinder()
def main():
# create a substitution matrix
sub_matrix = SubstitutionMatrix('blosum50')
# set up for alignment
aligner = NWAlign(sub_matrix)
print "Testing a simple alignment..."
seq1 = "HEAGAWGHEE"
seq2 = "PAWHEAE"
aligner.align(seq1, seq2)
align1, align2 = aligner.get_optimal_alignment()
score = aligner.get_optimal_score()
print "Alignment Score:", score
print align1.data
print align2.data
print "Testing a more complex alignment..."
test_file = "PEPCarboxylase.fasta"
print "Getting sequences from the file PEPCarboxylase.fasta..."
seq_list = []
scanner = Fasta._Scanner()
handler = FASTAHandler(seq_list)
file = open(test_file, 'r')
scanner.feed(file, handler)
scanner.feed(file, handler)
#print seq_list
print "Aligning sequences..."
aligner = NWAlign(sub_matrix)
aligner.align(seq_list[0][0:150], seq_list[1][0:150])
align1, align2 = aligner.get_optimal_alignment()
score = aligner.get_optimal_score()
print "Alignment Score:", score
line_width = 25
current_position = 0
current_position = current_position + line_width
# pretty print the alignment
while current_position < len(align1):
print ""
print align1.data[current_position - line_width:current_position]
print align2.data[current_position - line_width:current_position]
current_position = current_position + line_width
# print whatever is left
print ""
print align1.data[current_position - line_width:len(align1) - 1]
print align2.data[current_position - line_width:len(align2) - 1]
def __str__(self):
"""Print out a fasta version of the alignment info."""
return_string = ''
for item in self._records:
new_f_record = Fasta.Record()
new_f_record.title = item.description
new_f_record.sequence = item.seq.data
return_string = return_string + str(new_f_record) + os.linesep + os.linesep
# have a extra newline, so strip two off and add one before returning
return return_string.rstrip() + os.linesep
def extract_organisms(file, num_records):
scanner = Fasta._Scanner()
consumer = SpeciesExtractor()
file_to_parse = UndoHandle(open(file, "r"))
for fasta_record in range(num_records):
scanner.feed(file_to_parse, consumer)
file_to_parse.close()
return consumer.species_list
def test_sequence_parser(self):
"""Basic operation of the Sequence Parser.
"""
parser = Fasta.SequenceParser()
for index in range(len(self.handles)):
handle = self.handles[index]
rec = parser.parse(handle)
assert isinstance(rec, SeqRecord.SeqRecord)
assert isinstance(rec.seq, Seq.Seq)
assert rec.seq.alphabet == Alphabet.generic_alphabet
assert len(rec.seq) == self.lengths[index][1]
assert len(rec.description) == self.lengths[index][0]
def extract_organisms(file_to_parse):
# set up the parser and iterator
parser = Fasta.RecordParser()
file = open(file_to_parse, 'r')
iterator = Fasta.Iterator(file, parser)
all_species = []
while 1:
cur_record = iterator.next()
if cur_record is None:
break
# extract the info from the title
new_species = cur_record.title.split()[1]
# append the new species to the list if it isn't there
if new_species not in all_species:
all_species.append(new_species)
return all_species
def get_seqs(blastRootDirectory):
if len(sys.argv) >= 2:
numSeqs = int(sys.argv[1])
if numSeqs < 0 or numSeqs > 100000:
print 'requested number of sequences is outside allowable range (1-100000). Using default (1000)'
numSeqs = 10
else:
numSeqs = 10
print 'requesting', numSeqs, 'query sequences from the server'
seqs = phamServer.request_seqs(server, numSeqs, client)
'''Builds the file to be blasted from the sequences given'''
f = open(os.path.join(blastRootDirectory, 'filetoblast.txt'), 'w')
print seqs
'''takes the new set of sequences and checks if they exist in the local database
and, if so, writes the sequence id and translation to a separate FASTA formated input
file to be passed to the BLASTALL executable'''
for GeneID in seqs:
parser = Fasta.RecordParser()
infile = open(os.path.join(blastRootDirectory, 'blastDB.fasta'))
iterator = Fasta.Iterator(infile, parser)
while 1:
record = iterator.next()
if not record:
break
record_id = record.title
if GeneID == record_id:
f.write('>' + record.title + '\n' + record.sequence + '\n')
f.close()
return (len(seqs))
def test_basic_iterator(self):
"""Ensure the Fasta iterator works returning text.
"""
i = Fasta.Iterator(self.test_handle)
rec_info = {0 : ">gi|1348912|gb|G26680|G26680",
1 : ">gi|1348917|gb|G26685|G26685",
2 : ">gi|1592936|gb|G29385|G29385"}
for rec_num in range(3):
rec = i.next()
lines = rec.split("\n")
title_part = lines[0].split()
assert title_part[0] == rec_info[rec_num]
# make sure we keep getting None when the iterator is done
assert i.next() is None
assert i.next() is None
def __init__(self,**kwargs):
self.db_dir = DEFAULT_DB_DIR
self.index_filename = DEFAULT_INDEX_FILENAME
self.seqres_filename = DEFAULT_SEQRES_FILENAME
for key,value in kwargs:
if key in ['db_dir']:
self.db_path = arg
elif key in ['index_filename']:
self.index_filename = arg
elif key in ['seqres_filename']:
self.seqres_filename = arg
self.full_index_filename = os.path.join(self.db_dir,self.index_filename)
self.full_seqres_filename = os.path.join(self.db_dir,self.seqres_filename)
self.offsets = {}
self.namesByPdbid = {}
self.seqres_file = file(self.full_seqres_filename)
self.fasta_parser = Fasta.RecordParser()
self.load_index_file()
def main(blast_file):
db_dir = os.path.join(os.getcwd(), "db")
cur_dbs = get_available_dbs(db_dir)
length_cutoff = 0.2
blast_clusters, all_lengths = get_blast_clusters(blast_file, length_cutoff)
filter_clusters = filter_by_organism(blast_clusters, org_includes, cur_dbs)
length_plot(all_lengths, blast_file)
cluster_grouper = SimilarityClusterGrouper(2, 200, [(0.9, 10)])
all_groups = cluster_grouper.get_final_groups(filter_clusters)
base, ext = os.path.splitext(blast_file)
cluster_file = base + "-bcluster%s.txt"
for gindex, group in enumerate(all_groups):
print '-----------'
with open(cluster_file % gindex, "w") as out_handle:
for gitem in group:
db_rec = get_db_rec(gitem, cur_dbs)
print gitem, db_rec["org_scientific_name"]
rec = Fasta.Record()
rec.title = gitem
rec.sequence = db_rec["seq"]
out_handle.write(str(rec) + "\n")
# and builds an index as a set of files on disc in the sub-directory
# my_orchid_dict.idx
# Note that the alphabet is explicitly defined for the sequences.
import os
from Bio import Fasta
from Bio.Alphabet import IUPAC
def get_accession_num(fasta_record):
title_atoms = fasta_record.title.split()
accession_atoms = title_atoms[0].split('|')
gb_name = accession_atoms[3]
# strip the version info before returning
return gb_name[:-2]
if not os.path.isdir("my_orchid_dict.idx") :
#Build a new index
Fasta.index_file("ls_orchid.fasta", "my_orchid_dict.idx",
get_accession_num)
else :
print "Reusing existing index"
dna_parser = Fasta.SequenceParser(IUPAC.ambiguous_dna)
orchid_dict = Fasta.Dictionary("my_orchid_dict.idx", dna_parser)
for id_num in orchid_dict.keys():
print 'id number:', id_num
print 'description:', orchid_dict[id_num].description
print 'sequence:', orchid_dict[id_num].seq
else:
return open(file_name, 'r')
if __name__ == "__main__":
import getopt
opts, args = getopt.getopt(sys.argv[1:], 'hs:t:')
if not opts or len(args) != 1:
usage()
sys.exit('Error usage')
fasta_file = open(args[0])
parser = Fasta.RecordParser()
for o, a in opts:
if o == '-h':
usage()
sys.exit(0)
elif o == '-s':
sieve = get_sieve(get_input_handle(a))
iterator = FastaSelectiveIterator(sieve, fasta_file, parser)
for record in iterator:
print record
elif o == '-t':
translator = FastaTranslator(get_input_handle(a), reverse=True)
iterator = Fasta.Iterator(fasta_file, parser)
for record in iterator:
print translator(record)
#!/usr/bin/env python
"""Example showing how to deal with internet BLAST from Biopython.
This code is described in great detail in the BLAST section of the Biopython
documentation.
"""
# standard library
import cStringIO
# biopython
from Bio.Blast import NCBIWWW
from Bio import Fasta
# first get the sequence we want to parse from a FASTA file
file_for_blast = open('m_cold.fasta', 'r')
f_iterator = Fasta.Iterator(file_for_blast)
f_record = f_iterator.next()
print 'Doing the BLAST and retrieving the results...'
result_handle = NCBIWWW.qblast('blastn', 'nr', f_record)
# save the results for later, in case we want to look at it
save_file = open('m_cold_blast.out', 'w')
blast_results = result_handle.read()
save_file.write(blast_results)
save_file.close()
print 'Parsing the results and extracting info...'
b_parser = NCBIWWW.BlastParser()
def main(ipr_number, num_clusters, out_dir):
charge_window = 75
db_dir = os.path.join(os.getcwd(), "db")
cur_db = shelve.open(os.path.join(db_dir, ipr_number))
tax_graph = build_tax_graph(cur_db)
uniprot_ids = []
info_array = []
for db_domain in cur_db.keys():
if not cur_db[db_domain].get("is_uniref_child", ""):
uniprot_ids.append(db_domain)
db_item = cur_db[db_domain]
cur_cluster_info = [
float(db_item["charge"]),
float(db_item["charge_region"]) * 10.0,
len(db_item.get("db_refs", [])) * 5.0,
calc_domain_distance(db_item) * 50.0,
#max(len(db_item.get("string_interactors", [])) - 1, 0),
]
info_array.append(cur_cluster_info)
info_array = numpy.array(info_array)
print 'Num genes', len(info_array), num_clusters
cluster_ids, error, nfound = Cluster.kcluster(
info_array, nclusters=num_clusters, npass=50) #, method='a', dist='c')
#tree = Cluster.treecluster(info_array, method='a', dist='c')
#cluster_ids = tree.cut(num_clusters)
cluster_dict = collections.defaultdict(lambda: [])
for i, cluster_id in enumerate(cluster_ids):
cluster_dict[cluster_id].append(uniprot_ids[i])
out_seq_file = os.path.join(out_dir, "%s-seqs.fa" % (ipr_number))
out_seq_handle = open(out_seq_file, "w")
for index, cluster_group in enumerate(cluster_dict.values()):
print '***********', index
org_dists = []
for uniprot_id in cluster_group:
org = cur_db[uniprot_id]["org_scientific_name"]
distance = networkx.dijkstra_path_length(tax_graph, 'Mus musculus',
org)
org_dists.append((distance, org, uniprot_id))
org_dists.sort()
members = []
for d, o, u in org_dists:
charge_plot_img = calc_charge_plot(u, cur_db[u], charge_window,
out_dir)
base, ext = os.path.splitext(charge_plot_img)
disorder_plot_img = "%s-idr%s" % (base, ext)
rec = Fasta.Record()
rec.title = u
rec.sequence = cur_db[u]["seq"]
out_seq_handle.write(str(rec) + "\n")
members.append(
dict(
organism=o,
uniprot_id=get_uniprot_links([u]),
alt_names=get_alt_names(cur_db[u]),
alt_ids=get_uniprot_links(cur_db[u].get(
"uniref_children", [])),
charge=cur_db[u]["charge"],
charge_region="%0.2f" % cur_db[u]["charge_region"],
charge_plot_img=charge_plot_img,
disorder_plot_img=disorder_plot_img,
domains=len(cur_db[u].get("db_refs", [])),
interactions=get_string_link(
u,
max(
len(cur_db[u].get("string_interactors", [])) - 1,
0)),
description=cur_db[u].get("function_descr", " "),
c_distance="%0.2f" % calc_domain_distance(cur_db[u]),
))
with open(
os.path.join(out_dir,
"%s-cluster%s.html" % (ipr_number, index)),
"w") as out_handle:
tmpl = Template(cluster_template)
out_handle.write(tmpl.render(cluster_members=members))
def initialize():
"""
Parse command line options, and read input Fasta file.
Construct a dictionary contains the following fields:
sequences a list of dictionary objects having 'title',
'sequence', and 'motif_position' attributes (see
also the docstring of gibbs.Gibbs.__init__)
width width of motif to find
weight weight to use for pseudocounts
iterations number of non-improving iterations before stopping
shifts maximum phase shifts to detect
ps_freq frequency of detecting phase shifts
init_occurrences number of base occurrences to use for initial motif
positions heuristic
init_width width of patterns to use for initial motif positions
heuristic
Return the constructed dictionary.
"""
parser = OptionParser(usage = "usage: %prog -i FILE -w WIDTH [-h] "
"[options]",
version = "PyMotif %s (%s)" % (VERSION, DATE),
description = "PyMotif is an implementation of the "
"Gibbs sampling algorithm for finding local "
"alignments of DNA sequences. "
"See the accompanied README file for usage "
"instructions and the documentation directory for "
"implementation details.")
parser.add_option("-i", "--input", dest="input", metavar="FILE",
help="read FILE in Fasta format")
parser.add_option("-w", "--width", dest="width", metavar="WIDTH",
type="int", help="find motif of width WIDTH")
parser.add_option("-t", "--iterations", dest="iterations",
metavar="ITERATIONS", default=ITERATIONS_DEFAULT,
type="int", help="number of non-improving iterations "
"(default " + str(ITERATIONS_DEFAULT) + ")")
parser.add_option("-p", "--pseudo", dest="pseudo", metavar="WEIGHT",
default=PSEUDOCOUNTS_WEIGHT_DEFAULT, type="float",
help="use WEIGHT for weight of pseudocounts (default " +
str(PSEUDOCOUNTS_WEIGHT_DEFAULT) + ")")
parser.add_option("-s", "--phase-shifts", dest="shifts", metavar="SHIFTS",
default=PHASE_SHIFTS_DEFAULT, type="int",
help="detect phase shifts of width SHIFTS (default " +
str(PHASE_SHIFTS_DEFAULT) + ")")
parser.add_option("-f", "--ps-frequency", dest="frequency",
metavar="FREQ", default=PS_FREQUENCY_DEFAULT,
type="int", help="if SHIFTS>0, detect phase shifts "
"every FREQ iterations (default " +
str(PS_FREQUENCY_DEFAULT) + ")")
parser.add_option("-n", "--init-num-occurrences", dest="initoccurrences",
metavar="OCCURRENCES",
default=INIT_NUM_OCCURRENCES_DEFAULT, type="int",
help="number of base occurrences to use for initial "
"positions heuristic (default " +
str(INIT_NUM_OCCURRENCES_DEFAULT) + ")")
parser.add_option("-v", "--init-pattern-width", dest="initwidth",
metavar="WIDTH", default=INIT_PATTERN_WIDTH_DEFAULT,
type="int", help="if OCCURRENCES>0, width of pattern "
"to use for initial positions heuristic (defaults to "
"value of --width)")
parser.add_option("-c", "--cow", action="store_true", dest="cow",
default=False, help="display cow (not recommended)")
(options, args) = parser.parse_args()
if options.cow:
s = ""
for _ in range(10):
s += choice("ATCG")
# Created with the cowsay program
print """ ____________
< %s >
------------
\ ^__^
\ (oo)\_______
(__)\ )\/\\
||----w |
|| ||""" % s
sys.exit(0)
if not options.input:
parser.error("input file required")
if not options.width:
parser.error("width argument required")
if options.width < 2:
parser.error("please use a sane motif width")
# Read contents of Fasta file
try:
file = open(options.input)
except IOError:
parser.error("could not read file %s" % options.input)
fasta_parser = Fasta.RecordParser()
# Iterator for sample data
fasta_iterator = Fasta.Iterator(file, fasta_parser)
# A list containing a dictionary object for each sequence
sequences = [{'title': record.title,
'sequence': record.sequence,
'motif_position': 0}
for record in fasta_iterator]
# We could do some more error checking on the input file here, like
# checking there's only ATCG and at least a few of them, but for now
# this is enough
if len(sequences) < 2:
parser.error("found %i sequences in input file %s" % (len(sequences),
options.input))
return {'sequences': sequences,
'width': options.width,
'weight': options.pseudo,
'iterations': options.iterations,
'shifts': options.shifts,
'ps_freq': options.frequency,
'init_occurrences': options.initoccurrences,
'init_width': options.initwidth}
#! /usr/bin/env python
import sys, os
import time
from Bio import Fasta
DEFAULT_DICT_FILE = '/project1/structure/mliang/pdb/derived_data/pdb_seqres.idx'
DEFAULT_OUTFH = sys.stdout
dict_file = DEFAULT_DICT_FILE
outfh = DEFAULT_OUTFH
start_time = time.time()
fdict = Fasta.Dictionary(dict_file)
elapse_time = time.time() - start_time
print >> sys.stderr, "Time to load dictionary:", elapse_time
start_time = time.time()
chainmap = {}
for key in fdict.keys():
chainmap.setdefault(key[:4], []).append(key)
elapse_time = time.time() - start_time
print >> sys.stderr, "Time to build chain map:", elapse_time
start_time = time.time()
args = sys.argv[1:]
if not args:
args = sys.stdin
for field in args:
fields = field.strip().split()
