def make_index(gff_file):
"""
Make a sqlite database for fast retrieval of features.
"""
import GFFutils
db_file = gff_file + ".db"
if need_update(gff_file, db_file):
if op.exists(db_file):
os.remove(db_file)
GFFutils.create_gffdb(gff_file, db_file)
return GFFutils.GFFDB(db_file)
def make_index(gff_file):
"""
Make a sqlite database for fast retrieval of features.
"""
import GFFutils
db_file = gff_file + ".db"
if need_update(gff_file, db_file):
if op.exists(db_file):
os.remove(db_file)
GFFutils.create_gffdb(gff_file, db_file)
return GFFutils.GFFDB(db_file)
def to_sam(self, fasta):
"""
Prints items as SAM lines
"""
s = []
genome = GFFutils.Genome(fasta)
for item in self.items:
s.append(item.to_sam(genome))
return ''.join(s)
def to_fastq(self, fasta):
"""
Creates sequences and fake quality scores. Sequence names are the same
as the GFFFeature.id.
"""
genome = GFFutils.Genome(fasta)
s = []
for item in self.items:
s.append(item.to_fastq(genome))
return ''.join(s)
def main(gff_file, fasta_file, parents, children):
db_file = gff_file + ".db"
if not op.exists(db_file):
GFFutils.create_gffdb(gff_file, db_file)
f = Fasta(fasta_file)
g = GFFutils.GFFDB(db_file)
parents = set(parents.split(','))
parents_iter = [g.features_of_type(x) for x in parents]
parents_list = itertools.chain(*parents_iter)
children_list = set(children.split(','))
for feat in parents_list:
children = []
for c in g.children(feat.id, 1):
if c.featuretype not in children_list: continue
child = f.sequence(dict(chr=c.chrom, start=c.start, stop=c.stop,
strand=c.strand))
children.append((child, c))
if not children:
print >>sys.stderr, "[warning] %s has no children with type %s" \
% (feat.id, ','.join(children_list))
continue
# sort children in incremental position
children.sort(key=lambda x: x[1].start)
# reverse children if negative strand
if feat.strand=='-': children.reverse()
feat_seq = ''.join(x[0] for x in children)
print ">%s" % feat.id
print feat_seq
def main(gff_file, fasta_file, parents, children):
db_file = gff_file + ".db"
if not op.exists(db_file):
GFFutils.create_gffdb(gff_file, db_file)
f = Fasta(fasta_file)
g = GFFutils.GFFDB(db_file)
parents = set(parents.split(','))
parents_iter = [g.features_of_type(x) for x in parents]
parents_list = itertools.chain(*parents_iter)
children_list = set(children.split(','))
for feat in parents_list:
children = []
for c in g.children(feat.id, 1):
if c.featuretype not in children_list: continue
child = f.sequence(
dict(chr=c.chrom, start=c.start, stop=c.stop, strand=c.strand))
children.append((child, c))
if not children:
print >>sys.stderr, "[warning] %s has no children with type %s" \
% (feat.id, ','.join(children_list))
continue
# sort children in incremental position
children.sort(key=lambda x: x[1].start)
# reverse children if negative strand
if feat.strand == '-': children.reverse()
feat_seq = ''.join(x[0] for x in children)
print ">%s" % feat.id
print feat_seq
#!/usr/bin/python
import sys
import numpy as np
np.set_printoptions(threshold=np.inf)
#from itertools import count, tee, izip, islice
import GFFutils
import pyBigWig
import matplotlib.pyplot as plt
G = GFFutils.GFFDB("/home/user/dm1.db")
C_bw = pyBigWig.open("/home/user/Symb_treatedVscontrol_50bin.bw")
Gene_final = []
def separate_exon_intron(EI_list, EI1, EI2, EI3):
for idx, ele in enumerate(EI_list):
ele = len(ele)
if int(idx) == 0:
EI1.append(ele)
elif int(idx) == 1:
EI2.append(ele)
elif int(idx) == 2:
EI3.append(ele)
exon_len = []
upstream_3000, exon1, intron1, exon2, intron2, exon3,intron3, after_1000, before_1000,intron_3,exon_3, intron_2, exon_2, intron_1, exon_1, downstream_3000 = [],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
for mRNA in G.features_of_type('mRNA'):
exons = list(G.children(mRNA, featuretype='exon'))
#!/usr/bin/python
usage = """
Create a GFF database from a GFF file, e.g., downloaded from FlyBase.
Ryan Dale 2010 ([email protected])
"""
import GFFutils
import optparse
import os
import sys
op = optparse.OptionParser(usage=usage)
op.add_option('--gff',dest='gff',help='Input GFF file')
op.add_option('--gffdb',dest='gffdb',help='Destination GFF database file')
options,args = op.parse_args()
if not options.gff or not options.gffdb:
op.print_help()
print '\nERROR: Please specify an input GFF file and an output GFF database file'
sys.exit()
if not os.path.exists(options.gff):
print 'GFF file %s does not exist!' % options.gff
sys.exit()
GFFutils.create_gffdb(options.gff, options.gffdb)
import GFFutils
G = GFFutils.GFFDB('dm3.db')
exon_len = []
exon1 = []
exon2 = []
exon3 = []
exon_3 = []
exon_2 = []
exon_1 = []
intron1 = []
intron2 = []
intron3 = []
intron_3 = []
intron_2 = []
intron_1 = []
gene_count = 0
for mRNA in G.features_of_type('mRNA'):
# print(mRNA)
exons = list(G.children(mRNA, featuretype='exon'))
introns = list(G.interfeatures(exons))
if mRNA.strand == "-":
first_3_exons = exons[-3:]
last_3_exons = exons[:3]
first_3_introns = introns[-3:]
last_3_introns = introns[:3]
else:
first_3_exons = exons[:3]
last_3_exons = exons[-3:]
first_3_introns = introns[:3]
Optionally adds a "chr" to the beginning of each chromosome.
Writes to stdout.
"""
op = optparse.OptionParser(usage=usage)
op.add_option('--addchr',
action='store_true',
help='Prefix each chromosome with "chr" in the output file.')
options, args = op.parse_args()
gfffn = args[0]
# this is a list of featuretypes that you want to remove.
culled_features = [
'orthologous_to', 'pcr_product', 'BAC_cloned_genomic_insert'
]
f = GFFutils.GFFFile(gfffn)
for feature in f:
if (feature.start is None) or (feature.stop is None):
continue
if (feature.start < 0) or (feature.stop < 0) or (feature.start >
feature.stop):
continue
if feature.featuretype in culled_features:
continue
if options.addchr:
feature.chr = 'chr' + feature.chr
sys.stdout.write(feature.tostring())
g10.parse(gene_models4)
gene_models5 = gene_models5.splitlines(True)
g11 = GenomeModel(chrom_start=1, scalar=5, read_length=3, debug=False)
g11.parse(gene_models5)
gene_models6 = gene_models6.splitlines(True)
g12 = GenomeModel(chrom='chr3R',
chrom_start=101,
scalar=5,
read_length=3,
debug=False)
g12.parse(gene_models6)
here = os.path.dirname(__file__)
genome = GFFutils.Genome(os.path.join(here, 'data/dm3.chr2L.oneline.fa'))
def feature_exists(genome_model_obj,
start,
stop,
featuretype,
chrom='chr2L',
strand='+'):
# Checks to see if a feature exists. Does not check names, only genomic
# coords and featuretype
for feature in genome_model_obj.features:
if (feature.start == start) and (feature.stop == stop) \
and (feature.chrom == chrom) and (feature.strand == strand) \
and (feature.featuretype == featuretype):
return True
