parser.add_option('-o','--output',dest='output',default=None,
help='file to write fasta records to [default: stdout]')
if __name__ == '__main__' :
opts, args = parser.parse_args(sys.argv[1:])
if len(args) != 1 :
parser.error('Exactly one argument is required')
org_settings = get_org_settings(args[0])
refgene_fn = org_settings['refgene_anno_path']
refgene_f = RefGeneFile(refgene_fn)
nib_db = NibDB(nib_dirs=[org_settings['genome_dir']])
gene_list = None
if opts.gene_list :
gene_list = [x.strip() for x in open(opts.gene_list).readlines()]
id_index = 'bin'
if opts.gene_type != gene_type_choices[0] :
if opts.gene_type == 'refgene' :
id_index = 'name'
seq_recs = []
gene_map = defaultdict(list)
for rec in refgene_f :
if gene_list and rec[id_index] not in gene_list : continue # skip this one
st, end = max(0,int(rec['txStart'])-opts.upstream), min(int(rec['txStart'])+opts.downstream,nib_db.db_info[rec['chrom']]['nbases'])
from chipsequtil import get_org_settings, BEDFile
from chipsequtil.nib import NibDB
from pprint import pprint
genome_dir = get_org_settings('mm9')['genome_dir']
db = NibDB(nib_dirs=[genome_dir])
fasta_headers,seqs = db.get_fasta_from_bed('shuffled_peaks.bed')
pprint(seqs[:10])
def seq_msp(fafile,seqfile,genome='mm9',convert=True,bedFrag=False):
start=-3
hang='NNN'
match=[]
#find CCGG positions using Fasta file
fa=open(fafile)
for line in fa:
l=line.strip('\n')
if l[0]=='>':
ch=l[1:]
continue
if l=='NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN':
start+=len(l)
hang=l[-3:]
continue
else:
seq=hang+l
mers=[seq[x:(x+4)] for x in range(len(seq)-4)]
for i,m in enumerate(mers):
if m=='ccgg': match.append(start+i)
hang=seq[-3:]
start+=len(l)
print len(match)
fa.close()
FRAG=[]
#find cut sites 40-220bp and save as tuple
for x,y in zip(match[:-1],match[1:]):
d=y-x
if d>40 and d<250: FRAG.append((x,y))
print len(FRAG)
#nibDB the cut sites 40bp 5'-3' and
#save each as a pair of Fasta items with keys chr:position(strand)
seq_dict={}
ids,loci=[],[]
BF=[]
for x,y in FRAG:
if bedFrag: BF.append([ch,str(x+1),str(y+3)])
#for x
start=x+1
stop=x+41
key=ch+':'+str(start)+'+'
loc=(ch,start,stop,'+')
ids.append(key)
loci.append(loc)
#for y
start=y-37
stop=y+3
key=ch+':'+str(stop)+'-'
loc=(ch,start,stop,'-')
ids.append(key)
loci.append(loc)
if bedFrag: np.savetxt(seqfile.replace('.fa','_frag.bed'),BF,fmt='%s',delimiter='\t')
if genome=='hg18': DB=NibDB(nib_dirs='/nfs/genomes/human_gp_mar_06/')
else: DB=NibDB(nib_dirs=chipsequtil.get_org_settings('mm9')['genome_dir'])
fa_ids,seqs=DB.get_fasta_batch(loci)
for id,seq in zip(ids,seqs):
if convert: biseq=seq.replace('c','t')
else: biseq=seq
if id[-1]=='+':
seq_dict[id]=biseq
else:
#seq_dict[id]=seq[::-1]
seq_dict[id]=biseq
Fasta.write(seq_dict,seqfile)
# for pvalue vs motif score
pval_num_bins = 20
pval_bin_size = all_peaks[:]['pvalue'].size/pval_num_bins
# try to take at least 100 sequences, at most 10% of bin size
sample_percent = max(min(1.,100./pval_bin_size),0.1)
pval_bin_memo = {}
if opts.top_n is not None :
peaks = all_peaks[0:opts.top_n]
peak_pvals = peak_pvals[peak_pval_inds][0:opts.top_n]
else :
peaks = all_peaks
# extract fasta sequences for these peaks
nibDb = NibDB(nib_dirs=get_org_settings(org)['genome_dir'])
"""
# get the peak sequences
sys.stderr.write('Getting peak sequences\n')
fasta_batch = []
for i in range(peaks.size) :
fasta_batch.append((str(peaks[i]['chr']),int(peaks[i]['start']),int(peaks[i]['end']),'+'))
fg_fasta_headers, fg_fasta = nibDb.get_fasta_batch(fasta_batch)
# need a dict for background sampling
# headers have genome_dir and .nib in them, strip that out
sys.stderr.write('Converting nib output to dict\n')
fg_fasta_headers = list(fg_fasta_headers)
fg_fasta_dict = {}
for h,s in zip(fg_fasta_headers,fg_fasta) :
