def Main():
args=ArgParse()
# initiate genome object from UCSC genome browser
chromInfo=Genome(db=args.genome).chromInfo
chroms={}
sum_size=0
sum_size_list=[]
#store chromInfo
for i in range(chromInfo.count()):
try:
if "random" in chromInfo[i].chrom: continue
chroms[chromInfo[i].chrom]=chromInfo[i].size
sum_size+=chromInfo[i].size
sum_size_list.append((chromInfo[i].chrom,sum_size))
except:
break
print >> sys.stderr, sum_size_list[-1][1]
print >> sys.stderr, chroms
print >>sys.stderr, "Chromosome information readed, %d chromosomes"%(len(chroms))
i=0
while i < args.num:
# randomly select one chromosome and a region in this chromosome
chrom=random_chr(sum_size_list)
size=chroms[chrom]
length=int(gauss(args.mean,args.sd))
start=randrange(1,size-length-1)
end=start+length-1
strand=choice(['+','-'])
print "\t".join(str(f) for f in [chrom,start,end,i+1,0,strand])
i=i+1
def Main():
args = ArgParse()
# initiate genome object from UCSC genome browser
chromInfo = Genome(db=args.genome).chromInfo
chroms = {}
sum_size = 0
sum_size_list = []
#store chromInfo
for i in range(chromInfo.count()):
try:
if "random" in chromInfo[i].chrom: continue
chroms[chromInfo[i].chrom] = chromInfo[i].size
sum_size += chromInfo[i].size
sum_size_list.append((chromInfo[i].chrom, sum_size))
except:
break
print >> sys.stderr, sum_size_list[-1][1]
print >> sys.stderr, chroms
print >> sys.stderr, "Chromosome information readed, %d chromosomes" % (
len(chroms))
i = 0
while i < args.num:
# randomly select one chromosome and a region in this chromosome
chrom = random_chr(sum_size_list)
size = chroms[chrom]
length = int(gauss(args.mean, args.sd))
start = randrange(1, size - length - 1)
end = start + length - 1
strand = choice(['+', '-'])
print "\t".join(str(f) for f in [chrom, start, end, i + 1, 0, strand])
i = i + 1
def test_dataframe(self):
g = Genome('hg18')
kg = g.dataframe('cpgIslandExt')
self.assert_(kg.shape[0] == g.cpgIslandExt.count())
q = g.cpgIslandExt.filter(g.cpgIslandExt.chromStart < 300000).limit(10)
df = g.dataframe(q)
self.assert_(df.shape[0] == 10)
def test_dataframe(self):
g = Genome('hg18')
kg = g.dataframe('cpgIslandExt')
self.assert_(kg.shape[0] == g.cpgIslandExt.count())
q = g.cpgIslandExt.filter(g.cpgIslandExt.chromStart < 300000).limit(10)
df = g.dataframe(q)
self.assert_(df.shape[0] == 10)
def test_bed_gene_pred(self):
g = Genome('hg19')
from sqlalchemy import and_
from cStringIO import StringIO
query = g.knownGene.filter(and_(g.knownGene.txStart > 10000, g.knownGene.txEnd < 20000))
c = StringIO()
Genome.save_bed(query, c)
c.seek(0)
rows = c.readlines()
for toks in (row.split("\t") for row in rows):
self.assert_(len(toks) == 12)
self.assert_(int(toks[1]) > 10000)
self.assert_(int(toks[2]) < 20000)
def test_bed_gene_pred(self):
g = Genome('hg19', host="localhost", user="******")
from sqlalchemy import and_
from cStringIO import StringIO
query = g.knownGene.filter(and_(g.table('knownGene').c.txStart > 10000, g.table('knownGene').c.txEnd < 20000))
c = StringIO()
Genome.save_bed(query, c)
c.seek(0)
rows = c.readlines()
for toks in (row.split("\t") for row in rows):
self.assert_(len(toks) == 12)
self.assert_(int(toks[1]) > 10000)
self.assert_(int(toks[2]) < 20000)
def test_mirror(self):
try:
os.unlink('/tmp/__u.db')
except OSError:
pass
g = Genome('hg18')
g.mirror(['chromInfo'], 'sqlite:////tmp/__u.db')
a = str(g.chromInfo.filter().first())
gs = Genome('sqlite:////tmp/__u.db')
b = str(gs.chromInfo.filter().first())
self.assertEqual(a, b)
os.unlink('/tmp/__u.db')
def test_mirror(self):
try:
os.unlink('/tmp/__u.db')
except OSError:
pass
g = Genome('hg18')
g.mirror(['chromInfo'], 'sqlite:////tmp/__u.db')
a = str(g.chromInfo.filter().first())
gs = Genome('sqlite:////tmp/__u.db')
b = str(gs.chromInfo.filter().first())
self.assertEqual(a, b)
os.unlink('/tmp/__u.db')
def test_blat(self):
try:
import requests
except ImportError:
return
g = Genome('hg18')
f = g.refGene[19]
f.chrom = "chr6"
f.txStart = 135646802
f.txEnd = 135646832
r = list(f.blat())
self.assert_(str(f.txStart) in repr(r), r)
self.assert_(str(f.txEnd) in repr(r), r)
def mirror(genome, tables, connection_string):
destination, dengine = make_session(connection_string)
dmeta = MetaData(bind=dengine)
orig_counts = []
for table_name in tables:
# cause it ot be mapped
table = getattr(genome, table_name)._table
print(('Mirroring', table_name), file=sys.stderr)
table = set_table(genome, table, table_name, connection_string, dmeta)
try:
table.create(dengine)
except sqlalchemy.exc.OperationalError:
pass
destination.commit()
ins = table.insert()
columns = list(table.columns.keys())
records = []
table_obj = getattr(genome, table_name)._table
t = getattr(genome, table_name)
for ii, record in enumerate(page_query(table_obj.select(), t.session)):
data = dict(
(str(column), getattr(record, column)) for column in columns)
records.append(data)
if ii % 20000 == 0 and ii > 0:
destination.execute(ins, records)
print(("processing record %i" % ii), file=sys.stderr)
destination.commit()
records = []
destination.execute(ins, records)
destination.commit()
orig_counts.append(getattr(genome, table_name).count())
destination, dengine = make_session(connection_string)
from . import Genome
newg = Genome(connection_string)
new_counts = [getattr(newg, table_name).count() for table_name in tables]
for tbl, oc, nc in zip(tables, orig_counts, new_counts):
if oc != nc:
print(("ERROR: mirrored table '%s' has %i \
rows while the original had %i" % (tbl, nc, oc)),
file=sys.stderr)
return newg
def test_bins(self):
bins = Genome.bins(12345, 56779)
expected = set([1, 9, 73, 585])
self.assertEqual(bins, expected)
def setUp(self):
self.dba = Genome('hg18')
self.dbb = Genome('hg19')
def setUp(self):
self.db = Genome('hg18')
self.gene = self.db.refGene.filter_by(name2="MUC5B").first()
regions[item.chrom][0] = {}
regions[item.chrom][0][item.chromEnd] = "+"
#regions[item.chrom]["end"]=item.chromEnd
#regions[item.chrom]["chrom"]=item.chrom
#regions[item.chrom]["start"]=0
regions["chrM"] = {}
regions["chrM"][0] = {}
regions["chrM"][0][16569] = "+"
return (regions)
###MAIN PROGRAM###
file = '/Users/mok6/Desktop/Doug_NEW/dataNewII/SP04HU_PyPu'
print "start"
#get start and end of chromosomes, this is from dbcruz packages, local copy
regions = get_regions(
Genome(
"sqlite:////Users/mok6/Dropbox/LiClipse/MelArray/hg19_c.db").cytoband)
print "end load chr boundaries"
#load dimer data, second parameter is 0=filtered, 1=not filtered
DamagePos = loadDamagePosII(file, "1")
print "dimer data loaded"
#perform sliding window approach
slidingWindowII(regions, DamagePos, file)
print "sliding window done"
def pipeline(col_num, step, dist, acf_dist, prefix, threshold, seed,
bed_files, mlog=True, region_filter_p=1, region_filter_n=None,
genome_control=False, db=None, use_fdr=True):
sys.path.insert(0, op.join(op.dirname(__file__), ".."))
from cpv import acf, slk, fdr, peaks, region_p, stepsize, filter
from cpv._common import genome_control_adjust, genomic_control, bediter
import operator
if step is None:
step = min(acf_dist, stepsize.stepsize(bed_files, col_num))
print("calculated stepsize as: %i" % step, file=sys.stderr)
lags = list(range(1, acf_dist, step))
lags.append(lags[-1] + step)
prefix = prefix.rstrip(".")
putative_acf_vals = acf.acf(bed_files, lags, col_num, simple=False,
mlog=mlog)
acf_vals = []
# go out to max requested distance but stop once an autocorrelation
# < 0.05 is added.
for a in putative_acf_vals:
# a is ((lmin, lmax), (corr, N))
# this heuristic seems to work. stop just above the 0.08 correlation
# lag.
if a[1][0] < 0.04 and len(acf_vals) > 2: break
acf_vals.append(a)
if a[1][0] < 0.04 and len(acf_vals): break
# save the arguments that this was called with.
with open(prefix + ".args.txt", "w") as fh:
print(" ".join(sys.argv[1:]) + "\n", file=fh)
import datetime
print("date: %s" % datetime.datetime.today(), file=fh)
from .__init__ import __version__
print("version:", __version__, file=fh)
with open(prefix + ".acf.txt", "w") as fh:
acf_vals = acf.write_acf(acf_vals, fh)
print("wrote: %s" % fh.name, file=fh)
print("ACF:\n", open(prefix + ".acf.txt").read(), file=sys.stderr)
spvals, opvals = array.array('f'), array.array('f')
with ts.nopen(prefix + ".slk.bed.gz", "w") as fhslk:
fhslk.write('#chrom\tstart\tend\tp\tregion-p\n')
for chrom, results in slk.adjust_pvals(bed_files, col_num, acf_vals):
fmt = chrom + "\t%i\t%i\t%.4g\t%.4g\n"
for row in results:
row = tuple(row)
fhslk.write(fmt % row)
opvals.append(row[-2])
spvals.append(row[-1])
print("# original lambda: %.2f" % genomic_control(opvals), file=sys.stderr)
del opvals
gc_lambda = genomic_control(spvals)
print("wrote: %s with lambda: %.2f" % (fhslk.name, gc_lambda),
file=sys.stderr)
if genome_control:
fhslk = ts.nopen(prefix + ".slk.gc.bed.gz", "w")
adj = genome_control_adjust([d['p'] for d in bediter(prefix + ".slk.bed.gz", -1)])
for i, line in enumerate(ts.nopen(prefix + ".slk.bed.gz")):
print("%s\t%.5g" % (line.rstrip("\r\n"), adj[i]), file=fhslk)
fhslk.close()
print("wrote: %s" % fhslk.name, file=sys.stderr)
with ts.nopen(prefix + ".fdr.bed.gz", "w") as fh:
fh.write('#chrom\tstart\tend\tp\tregion-p\tregion-q\n')
for bh, l in fdr.fdr(fhslk.name, -1):
fh.write("%s\t%.4g\n" % (l.rstrip("\r\n"), bh))
print("wrote: %s" % fh.name, file=sys.stderr)
fregions = prefix + ".regions.bed.gz"
with ts.nopen(fregions, "w") as fh:
list(peaks.peaks(prefix + ".fdr.bed.gz", -1 if use_fdr else -2, threshold, seed,
dist, fh, operator.le))
n_regions = sum(1 for _ in ts.nopen(fregions))
print("wrote: %s (%i regions)" % (fregions, n_regions), file=sys.stderr)
if n_regions == 0:
sys.exit()
with ts.nopen(prefix + ".regions-p.bed.gz", "w") as fh:
N = 0
fh.write("#chrom\tstart\tend\tmin_p\tn_probes\tz_p\tz_sidak_p\n")
# use -2 for original, uncorrected p-values in slk.bed
for region_line, slk_p, slk_sidak_p, sim_p in region_p.region_p(
prefix + ".slk.bed.gz",
prefix + ".regions.bed.gz", -2,
step):
fh.write("%s\t%.4g\t%.4g\n" % (region_line, slk_p, slk_sidak_p))
fh.flush()
N += int(slk_sidak_p < 0.05)
print("wrote: %s, (regions with corrected-p < 0.05: %i)" \
% (fh.name, N), file=sys.stderr)
regions_bed = fh.name
#if all(h in header for h in ('t', 'start', 'end')):
if region_filter_n is None: region_filter_n = 0
with ts.nopen(prefix + ".regions-t.bed", "w") as fh:
N = 0
for i, toks in enumerate(filter.filter(bed_files[0],
regions_bed, p_col_name=col_num)):
if i == 0: toks[0] = "#" + toks[0]
else:
if float(toks[6]) > region_filter_p: continue
if int(toks[4]) < region_filter_n: continue
#if region_filter_t and "/" in toks[7]:
# # t-pos/t-neg. if the lower one is > region_filter_t?
# vals = map(int, toks[7].split("/"))
# if min(vals) > region_filter_t: continue
N += 1
print("\t".join(toks), file=sys.stderr)
print(("wrote: %s, (regions with region-p "
"< %.3f and n-probes >= %i: %i)") \
% (fh.name, region_filter_p, region_filter_n, N),
file=sys.stderr)
try:
from cpv import manhattan
regions = manhattan.read_regions(fh.name)
manhattan.manhattan(prefix + ".slk.bed.gz", 3, prefix.rstrip(".") + ".manhattan.png",
False, ['#959899', '#484B4C'], "", False, None,
regions=regions, bonferonni=False)
except ImportError:
pass # they dont have matplotlib
if db is not None:
from cruzdb import Genome
g = Genome(db)
lastf = fh.name
with open(prefix + ".anno.%s.bed" % db, "w") as fh:
fh.write('#')
g.annotate(lastf, ("refGene", "cpgIslandExt"), out=fh,
feature_strand=True, parallel=len(spvals) > 500)
print("wrote: %s annotated with %s" % (fh.name, db), file=sys.stderr)
DIR_PROJ = "/home/mokha/Documents/Krauthammer_Lab"
DIR_CURR = DIR_PROJ + "/PythonClasses/SVSv5"
DIR_DATA = DIR_CURR + "/TestData"
DIR_RESULTS = DIR_CURR + "/TestResults"
# DIR_RESULTS = DIR_CURR + "/Results/160729_Analyze_KF"
# DIR_RESULTS = DIR_CURR + "/Results/160731_Analyze_KF"
# DIR_RESULTS = DIR_CURR + "/Results/160909_Analyze_KF"
DIR_FUSION = DIR_PROJ + "/160510_GeneFusions"
print "------------ TDD: 161002_IsoformFusion_1.py ------------"
#set kinase gene annotation file
KinaseFusion.set_kinasefile( DIR_FUSION + "/Data/160910_KinaseAnnots_hg38_Final.txt" )
obj_cruzdb = Genome( 'sqlite:////tmp/hg38_v2.db' )
#set cruzdb Genome database instance
Isoform.set_cruzdb( obj_cruzdb )
#CASE: This returns "None" for the kinase domain for the kinase gene (TLK2 - NM_001284363)
#Fusion - ASIC2:TLK2
hash_multi_isoform = { "orientation": 'fr',
"chrom_start": 'chr17',
"chrom_end": 'chr17',
"pos_start": 34038904,
"pos_end": 62565136,
"read_span": 5,
"read_matepair": 5,
"read_matepair_break": 5 }
obj_mif = MultiIsoformFusion( hash_multi_isoform ) #MIF = MultiIsoform Fusion instance
import os.path as op
from toolshed import reader
from cruzdb import Genome
def lamina():
if not op.exists('lamina.bed'):
fh = open('lamina.bed', 'w')
fh.write("#chrom\tstart\tend\tvalue\n")
for gff in reader('http://www.nature.com/nature/journal/v453/n7197/extref/nature06947-s2.txt', header=False):
fh.write("\t".join([gff[0], gff[3], gff[4], gff[5]]) + "\n")
fh.close()
return 'lamina.bed'
fname = 'supplement/Additional-File-11_lamina.anno.bed'
hg18 = Genome('sqlite:///hg18.db')
if not op.exists(fname):
fhout = open(fname, 'w')
hg18.annotate(lamina(), ('refGene', ), feature_strand=True, in_memory=True, parallel=True, out=fhout)
fhout.close()
for cutoff in (0.90, 0.95):
fh = open('/tmp/genes-%.2f.txt' % cutoff, 'w')
for d in reader(fname):
if float(d['value']) < cutoff: continue
if d['refGene_distance'] == '0' or \
d['refGene_distance'].startswith("0;"):
print >>fh, "\n".join(d['refGene_name'].split(";"))
fh.close()
cutoff = 0.90
def test_bins(self):
bins = Genome.bins(12345, 56779)
expected = set([1, 9, 73, 585])
self.assertEqual(bins, expected)
submitters='1000GENOMES,ABI,BCM-HGSC-SUB,BCM_SSAHASNP,BGI,BL,BUSHMAN,COMPLETE_GENOMICS,DDI,ENSEMBL,EVA-GONL,EVA_DECODE,EVA_GENOME_DK,EVA_UK10K_ALSPAC,EVA_UK10K_TWINSUK,GMI,HAMMER_LAB,HGSV,HUMANGENOME_JCVI,ILLUMINA-UK,JMKIDD_LAB,PJP,SSAHASNP,SSMP,TISHKOFF,WEILL_CORNELL_DGM,',
alleleFreqCount=2,
alleles='C,T,',
alleleNs='2634.000000,2374.000000,',
alleleFreqs='0.525958,0.474042,',
bitfields=set(['maf-5-all-pops', 'maf-5-some-pop'])
)
'''
# snps
reader = TsvReader(snpfile, cnames=False)
snplist = list(set(r[snpcol] for r in reader))
reader.close()
from cruzdb import Genome
g = Genome(genome)
outfiletmp = outfile + '.tmp'
writer = TsvWriter(outfiletmp)
for i in range(0, len(snplist), 1000):
chunk = snplist[i:i + 1000]
sql = 'SELECT chrom, chromStart, chromEnd, name, score, strand, refUCSC, alleles, alleleFreqs FROM snp{dbsnpver} WHERE name in ({snps})'.format(
dbsnpver=dbsnpver, snps=', '.join("'{}'".format(s) for s in chunk))
result = g.sql(sql)
for r in result:
allfreqs = dict(zip(r.alleles.split(','),
r.alleleFreqs.split(',')))
reffreq = allfreqs.get(r.refUCSC, '0')
if r.refUCSC in allfreqs:
del allfreqs[r.refUCSC]
if '' in allfreqs:
del allfreqs['']
for ii, record in enumerate(page_query(table_obj.select(), t.session)):
data = dict(
(str(column), getattr(record, column)) for column in columns)
records.append(data)
if ii % 20000 == 0 and ii > 0:
destination.execute(ins, records)
print >> sys.stderr, "processing record %i" % ii
destination.commit()
records = []
destination.execute(ins, records)
destination.commit()
orig_counts.append(getattr(genome, table_name).count())
destination, dengine = make_session(connection_string)
from . import Genome
newg = Genome(connection_string)
new_counts = [getattr(newg, table_name).count() for table_name in tables]
for tbl, oc, nc in zip(tables, orig_counts, new_counts):
if oc != nc:
print >> sys.stderr, "ERROR: mirrored table '%s' has %i \
rows while the original had %i" % (tbl, nc, oc)
return newg
if __name__ == "__main__":
if True:
from cruzdb import Genome
g = Genome('hg18')
mirror(g, ['chromInfo'], 'sqlite:////tmp/u.db')
{% if args.header %}
skip = {{args.skip}} + 1
{% else %}
skip = {{args.skip}}
{% endif %}
# get the snps
delimit = {{args.delimit | quote}}
comment = {{args.comment | quote}}
col = {{args.col}}
with open({{i.snpfile | quote}}) as f:
snps = sorted(set([line.split(delimit)[col] for i, line in enumerate(f.read().splitlines()) if i >= skip and line.strip() and not line.startswith(comment)]))
genome = {{args.genome | quote}}
g = Genome (db=genome)
dbsnp = g.snp{{args.dbsnpver}}
fout = open ("{{o.outfile}}", "w")
for snp in snps:
s = dbsnp.filter_by(name=snp).first()
if not s:
sys.stderr.write('pyppl.log.warning: Cannot find coordinates for SNP: %s\n' % snp)
else:
# chr start end name score strand otherinfo
chrom = s.chrom
start = s.chromStart
end = s.chromEnd
name = snp
strand = s.strand
ref = s.refUCSC
print vcf_ex.head()
# Rename columns to fix the syntax in chromosome number
names = vcf_ex.columns.values
new_names = ['CHROM']
new_names.extend(names[1:])
print "\n", new_names
vcf_ex.columns = new_names
# If QUAL > 0.5, sample passes
vcf_ex_sub = vcf_ex.loc[vcf_ex.QUAL > 0.5, ['CHROM', 'POS']].copy()
print vcf_ex_sub.head()
# Get the Genome object from cruzdb
# connects to MySQL genome browser at UCSC
g = Genome('hg38')
# Convert table 'refGene' to pandas dataframe
# columns of interest 'chrom' (chrX, %s), 'txStart' (number, %s), 'txEnd' (number , %s)
print "Extracting reference genome table (HG38) from UCSC Genome Browser"
df = g.dataframe('refGene')
df[['txStart', 'txEnd']] = df[['txStart', 'txEnd']].astype(int)
genes = pd.Series(np.zeros(vcf_ex_sub.shape[0]))
#gene = hg19.bin_query('refGene', vcf_ex_sub.CHROM[1], vcf_ex_sub.POS[1], vcf_ex_sub.POS[1])
#print vcf_ex_sub.POS.iloc[0]
for i in range(0, vcf_ex_sub.shape[0]):
#genes[i] = df[[df.chrom == str(vcf_ex_sub.CHROM.iloc[i]) and df.txStart >= str(vcf_ex_sub.POS.iloc[i])]].bool() #and df.txStart >= str(vcf_ex_sub.POS.iloc[i]) and df.txEnd <= vcf_ex_sub.POS.iloc[i]].bool(),
chrom = vcf_ex_sub['CHROM'].iloc[i]
location = vcf_ex_sub['POS'].iloc[i]
import csv
from cruzdb import Genome
# DEPENDENCIES:
# python2 -m pip install --upgrade pip
# python2 -m pip install setuptools # first 2 may be already done
# python2 -m pip install cruzdb
# python2 -m pip install sqlalchemy
# MySQLdb stuff:
# sudo apt-get install mysql-server
# sudo apt-get install libmysqlclient-dev # gives us mysql_config
# FINALLY download MySQLdb source, do process described in INSTALL file
hg19 = Genome('hg19')
INPUTFILE = "suggestive.pheno_simple.covar_none.test_wald.csv"
filereader = csv.reader(open(INPUTFILE))
chrom_i = None
pos_i = None
for i, line in enumerate(filereader):
if i == 0:
# CHROM POS REF ALT N_INFORMATIVE Test Beta SE Pvalue PVALUE
chrom_i = line.index('CHROM')
pos_i = line.index('POS')
continue
chrom = 'chr' + str(line[chrom_i])
pos = int(line[pos_i])
start = pos - 50 # kind of arbitrary search 50 back 50 forward.
end = pos + 50
#!/usr/bin/env python
from cruzdb import Genome
import sys
import re
import tqdm
import pandas as pd
limit = 1000
build = sys.argv[1]
fname = sys.argv[2]
mybuild = Genome(build)
if (re.search("hg19", build)):
snpdb = mybuild.snp138
elif (re.search("hg18", build)):
snpdb = mybuild.snp130
with open(sys.argv[3], "w") as f:
counter = 0
f.write("chrom\tstart\tend\tname\tscore\tstrand\n")
rs_ids = pd.read_csv(fname, names=["id"])
for index in tqdm.tqdm(range(0, rs_ids.shape[0], limit)):
result = snpdb.filter(
snpdb.name.in_(list(rs_ids["id"][index:index +
limit]))).limit(limit).all()
f.write("\n".join(map(lambda x: str(x), result)))
f.write("\n")
def pipeline(col_num, step, dist, prefix, threshold, seed, bed_files, mlog=False,
region_filter_p=1, region_filter_n=1, genome_control=False, db=None):
sys.path.insert(0, op.join(op.dirname(__file__), ".."))
from cpv import acf, slk, fdr, peaks, region_p, stepsize, filter
from cpv._common import genome_control_adjust, genomic_control, bediter
import operator
if step is None:
step = stepsize.stepsize(bed_files, col_num)
print >>sys.stderr, "calculated stepsize as: %i" % step
lags = range(1, dist, step)
lags.append(lags[-1] + step)
prefix = prefix.rstrip(".")
#if genome_control:
# with open(prefix + ".adj.bed", "w") as fh:
# genome_control_adjust_bed(bed_files, col_num, fh)
# bed_files = [fh.name]
putative_acf_vals = acf.acf(bed_files, lags, col_num, simple=False,
mlog=mlog)
acf_vals = []
# go out to max requested distance but stop once an autocorrelation
# < 0.05 is added.
for a in putative_acf_vals:
# a is ((lmin, lmax), (corr, N))
# this heuristic seems to work. stop just above the 0.08 correlation
# lag.
if a[1][0] < 0.04 and len(acf_vals) > 2: break
acf_vals.append(a)
if a[1][0] < 0.04 and len(acf_vals): break
# save the arguments that this was called with.
with open(prefix + ".args.txt", "w") as fh:
print >>fh, " ".join(sys.argv[1:]) + "\n"
import datetime
print >>fh, "date: %s" % datetime.datetime.today()
with open(prefix + ".acf.txt", "w") as fh:
acf_vals = acf.write_acf(acf_vals, fh)
print >>sys.stderr, "wrote: %s" % fh.name
print >>sys.stderr, "ACF:\n", open(prefix + ".acf.txt").read()
spvals, opvals = [], []
with open(prefix + ".slk.bed", "w") as fhslk:
for row in slk.adjust_pvals(bed_files, col_num, acf_vals):
fhslk.write("%s\t%i\t%i\t%.4g\t%.4g\n" % row)
opvals.append(row[-2])
spvals.append(row[-1])
print >>sys.stderr, "# original lambda: %.2f" % genomic_control(opvals)
del opvals
gc_lambda = genomic_control(spvals)
print >>sys.stderr, "wrote: %s with lambda: %.2f" % (fhslk.name, gc_lambda)
if genome_control:
fhslk = open(prefix + ".slk.gc.bed", "w")
adj = genome_control_adjust([d['p'] for d in bediter(prefix + ".slk.bed", -1)])
for i, line in enumerate(open(prefix + ".slk.bed")):
print >>fhslk, "%s\t%.5g" % (line.rstrip("\r\n"), adj[i])
fhslk.close()
print >>sys.stderr, "wrote: %s" % fhslk.name
with open(prefix + ".fdr.bed", "w") as fh:
for bh, l in fdr.fdr(fhslk.name, -1):
fh.write("%s\t%.4g\n" % (l.rstrip("\r\n"), bh))
print >>sys.stderr, "wrote: %s" % fh.name
fregions = prefix + ".regions.bed"
with open(fregions, "w") as fh:
list(peaks.peaks(prefix + ".fdr.bed", -1, threshold, seed,
step, fh, operator.le))
n_regions = sum(1 for _ in open(fregions))
print >>sys.stderr, "wrote: %s (%i regions)" % (fregions, n_regions)
with open(prefix + ".regions-p.bed", "w") as fh:
N = 0
fh.write("#chrom\tstart\tend\tmin_p\tn_probes\tslk_p\tslk_sidak_p\n")
# use -2 for original, uncorrected p-values in slk.bed
for region_line, slk_p, slk_sidak_p, sim_p in region_p.region_p(
prefix + ".slk.bed",
prefix + ".regions.bed", -2,
0, step, mlog=mlog):
fh.write("%s\t%.4g\t%.4g\n" % (region_line, slk_p, slk_sidak_p))
fh.flush()
N += int(slk_sidak_p < 0.05)
print >>sys.stderr, "wrote: %s, (regions with corrected-p < 0.05: %i)" \
% (fh.name, N)
regions_bed = fh.name
header = (gzip.open(bed_files[0]) if bed_files[0].endswith(".gz")
else open(bed_files[0])).next().split("\t")
if all(h in header for h in ('t', 'start', 'end')):
with open(prefix + ".regions-t.bed", "w") as fh:
N = 0
for i, toks in enumerate(filter.filter(bed_files[0], regions_bed,
p_col_name=col_num)):
if i == 0: toks[0] = "#" + toks[0]
else:
if float(toks[6]) > region_filter_p: continue
if int(toks[4]) < region_filter_n: continue
N += 1
print >>fh, "\t".join(toks)
print >>sys.stderr, ("wrote: %s, (regions with region-p"
"< %.3f and n-probes >= %i: %i)") \
% (fh.name, region_filter_p, region_filter_n, N)
try:
from cpv import manhattan
regions = manhattan.read_regions(fh.name)
manhattan.manhattan(prefix + ".slk.bed", 3, prefix.rstrip(".") + ".manhattan.png",
False, ['#959899', '#484B4C'], "", False, None,
regions=regions, bonferonni=True)
except ImportError:
pass # they dont have matplotlib
if db is not None:
from cruzdb import Genome
g = Genome(db)
lastf = fh.name
with open(prefix + ".anno.%s.bed" % db, "w") as fh:
g.annotate(lastf, ("refGene", "cpgIslandExt", "cytoBand"), out=fh,
feature_strand=True, parallel=len(spvals) > 500)
print >>sys.stderr, "wrote: %s annotated with %s" % (fh.name, db)
def getSnpInfo(x):
from cruzdb import Genome
hg19 = Genome(db="hg19")
snp151 = hg19.snp151
info = snp151.filter_by(name=x).first()
return info
def setUp(self):
self.db = Genome('hg18')
def snpinfo(infile,
outfile=None,
notfound='ignore',
genome='hg19',
dbsnpver="150",
inopts=None,
outopts=None,
snpcol=None,
cachedir=gettempdir()):
_inopts = Box(skip=0, comment='#', delimit='\t')
_inopts.update(inopts or {})
inopts = _inopts
_outopts = Box(delimit='\t',
headDelimit='\t',
headPrefix='',
headTransform=None,
head=True,
ftype='bed',
cnames='refUCSC, alleles, alleleFreqs, alleleFreqCount')
_outopts.update(outopts)
outopts = _outopts
cnames = alwaysList(outopts['cnames'])
reader = TsvReader(infile, **inopts)
if not reader.meta: reader.autoMeta()
snpcol = snpcol or reader.meta.keys()[0]
snps = list(set([r[snpcol] for r in reader]))
reader.rewind()
dbfile = path.join(cachedir, 'snpinfo_%s_%s.db' % (genome, dbsnpver))
schema = {
'chrom': 'text', # chr8
'chromStart': 'int', # 128700232L
'chromEnd': 'end', # 128700233L
'name': 'text primary key', # rs7005394
'score': 'real', # 0
'strand': 'text', # +
'refNCBI': 'text', # T
'refUCSC': 'text', # T
'observed': 'text', # C/T
'class': 'single', # single
'avHet': 'real', # 0.49
'avHetSE': 'real', # 0.02
'func': 'text', # set(['ncRNA'])
'submitterCount': 'int', # 20
'submitters':
'text', # 1000GENOMES,ABI,BCM-HGSC-SUB,BCM_SSAHASNP,BGI,BL ...
'alleleFreqCount': 'int', # 2
'alleles': 'text', # C,T,
'alleleNs': 'text', # 2634.000000,2374.000000,
'alleleFreqs': 'text', # 0.525958,0.474042,
}
cache = Cache(dbfile, 'snpinfo', schema, 'name')
dummies = {
'func':
dict(query=Cache.DUMMY['array']['query'],
find=Cache.DUMMY['array']['find'],
insert=lambda col, data:
(col, ' // ' + ' // '.join(Cache._uniqueData(list(data), True))),
update=lambda col, data:
(col,
Function.concat(Field(col),
value=' // ' + ' // '.join(
Cache._uniqueData(list(d), True)))),
result=lambda data: data if isinstance(data, list) else list(
filter(None, data.split(' // '))))
}
columns = ['chrom', 'name', 'chromStart', 'chromEnd', 'score', 'strand'
] + cnames
ret, allrest = cache.query(columns, {'name': snps}, dummies)
dbsnp = Genome(db=genome)
dbsnp = getattr(dbsnp, "snp%s" % dbsnpver)
writer = None
if outfile:
head = outopts['head']
headPrefix = outopts['headPrefix']
headDelimit = outopts['headDelimit']
headTransform = outopts['headTransform']
del outopts['head']
del outopts['headPrefix']
del outopts['headDelimit']
del outopts['headTransform']
writer = TsvWriter(outfile, **outopts)
if head:
writer.writeHead(prefix=headPrefix,
delimit=headDelimit,
transform=headTransform)
if writer:
for r in ret.values():
r.CHR = r.chrom
r.START = r.chromStart
r.END = r.chromEnd
r.NAME = r.name
r.SCORE = r.score
r.STRAND = r.strand
writer.write(r)
cached = []
if allrest:
for snp in allrest['name']:
s = dbsnp.filter_by(name=snp).first()
if not s:
if notfound == 'error':
raise RecordNotFound('Record not found: %s' % snp)
elif notfound == 'skip':
continue
else:
stderr.write('Record not found: %s \n' % snp)
continue
cached.append(s)
if writer:
r = TsvRecord()
r.CHR = s.chrom
r.START = s.chromStart
r.END = s.chromEnd
r.NAME = s.name
r.SCORE = s.score
r.STRAND = s.strand
for cname in cnames:
setattr(r, cname, getattr(s, cname))
writer.write(r)
# save cached data
cachedata = {}
for c in cached:
for k in schema.keys():
if not k in cachedata:
cachedata[k] = []
cachedata[k].append(getattr(c, k))
if cachedata:
cache.save(cachedata, dummies)
return {r.name: r for r in ret.values() + cached}
from cruzdb import Genome
import time
from toolshed import nopen
import os
anno_file = "data_c_constant_early.bed"
# sub-sample to get fewer rows.
list(nopen("|awk 'NR == 1 || NR % 4 == 0'" +(" %s > %s.some" % (anno_file, anno_file))))
anno_file += ".some"
nlines = sum(1 for _ in nopen(anno_file))
print "loc\tinstance\tparallel\ttime"
for parallel in (True, False):
for name, args in (('local\tsqlite', ('sqlite:///hg18.db',)),
('remote\tmysql', ('hg18',)),
('local\tmysql', ('hg18', 'brentp', 'localhost'))
):
g = Genome(*args)
out = "%s-%s.anno.txt" % (name.replace("\t", "-"), parallel)
t0 = time.time()
g.annotate(anno_file, ('refGene',), out=out, feature_strand=True,
parallel=parallel)
t1 = time.time()
print "\t".join(map(str, (name, parallel, ("%.1f" % (t1 - t0)))))
assert nlines == sum(1 for _ in nopen(out))
os.unlink(out)
import sys
from cruzdb import Genome
db = sys.argv[1]
#db = Genome('sqlite:////usr/local/src/cruzdb/%s.db' % db)
db = Genome(db)
refGene = db.refGene
for g in refGene.all():
for feat in g.gene_features:
print "\t".join(map(str, feat))
def pipeline(col_num,
step,
dist,
acf_dist,
prefix,
threshold,
seed,
bed_files,
mlog=True,
region_filter_p=1,
region_filter_n=None,
genome_control=False,
db=None,
use_fdr=True):
sys.path.insert(0, op.join(op.dirname(__file__), ".."))
from cpv import acf, slk, fdr, peaks, region_p, stepsize, filter
from cpv._common import genome_control_adjust, genomic_control, bediter
import operator
if step is None:
step = min(acf_dist, stepsize.stepsize(bed_files, col_num))
print >> sys.stderr, "calculated stepsize as: %i" % step
lags = range(1, acf_dist, step)
lags.append(lags[-1] + step)
prefix = prefix.rstrip(".")
putative_acf_vals = acf.acf(bed_files,
lags,
col_num,
simple=False,
mlog=mlog)
acf_vals = []
# go out to max requested distance but stop once an autocorrelation
# < 0.05 is added.
for a in putative_acf_vals:
# a is ((lmin, lmax), (corr, N))
# this heuristic seems to work. stop just above the 0.08 correlation
# lag.
if a[1][0] < 0.04 and len(acf_vals) > 2: break
acf_vals.append(a)
if a[1][0] < 0.04 and len(acf_vals): break
# save the arguments that this was called with.
with open(prefix + ".args.txt", "w") as fh:
print >> fh, " ".join(sys.argv[1:]) + "\n"
import datetime
print >> fh, "date: %s" % datetime.datetime.today()
from .__init__ import __version__
print >> fh, "version:", __version__
with open(prefix + ".acf.txt", "w") as fh:
acf_vals = acf.write_acf(acf_vals, fh)
print >> sys.stderr, "wrote: %s" % fh.name
print >> sys.stderr, "ACF:\n", open(prefix + ".acf.txt").read()
spvals, opvals = [], []
with ts.nopen(prefix + ".slk.bed.gz", "w") as fhslk:
fhslk.write('#chrom\tstart\tend\tp\tregion-p\n')
for row in slk.adjust_pvals(bed_files, col_num, acf_vals):
fhslk.write("%s\t%i\t%i\t%.4g\t%.4g\n" % row)
opvals.append(row[-2])
spvals.append(row[-1])
print >> sys.stderr, "# original lambda: %.2f" % genomic_control(opvals)
del opvals
gc_lambda = genomic_control(spvals)
print >> sys.stderr, "wrote: %s with lambda: %.2f" % (fhslk.name,
gc_lambda)
if genome_control:
fhslk = ts.nopen(prefix + ".slk.gc.bed.gz", "w")
adj = genome_control_adjust(
[d['p'] for d in bediter(prefix + ".slk.bed.gz", -1)])
for i, line in enumerate(ts.nopen(prefix + ".slk.bed.gz")):
print >> fhslk, "%s\t%.5g" % (line.rstrip("\r\n"), adj[i])
fhslk.close()
print >> sys.stderr, "wrote: %s" % fhslk.name
with ts.nopen(prefix + ".fdr.bed.gz", "w") as fh:
fh.write('#chrom\tstart\tend\tp\tregion-p\tregion-q\n')
for bh, l in fdr.fdr(fhslk.name, -1):
fh.write("%s\t%.4g\n" % (l.rstrip("\r\n"), bh))
print >> sys.stderr, "wrote: %s" % fh.name
fregions = prefix + ".regions.bed.gz"
with ts.nopen(fregions, "w") as fh:
list(
peaks.peaks(prefix + ".fdr.bed.gz", -1 if use_fdr else -2,
threshold, seed, dist, fh, operator.le))
n_regions = sum(1 for _ in ts.nopen(fregions))
print >> sys.stderr, "wrote: %s (%i regions)" % (fregions, n_regions)
if n_regions == 0:
sys.exit()
with ts.nopen(prefix + ".regions-p.bed.gz", "w") as fh:
N = 0
fh.write("#chrom\tstart\tend\tmin_p\tn_probes\tz_p\tz_sidak_p\n")
# use -2 for original, uncorrected p-values in slk.bed
for region_line, slk_p, slk_sidak_p, sim_p in region_p.region_p(
prefix + ".slk.bed.gz", prefix + ".regions.bed.gz", -2, step):
fh.write("%s\t%.4g\t%.4g\n" % (region_line, slk_p, slk_sidak_p))
fh.flush()
N += int(slk_sidak_p < 0.05)
print >>sys.stderr, "wrote: %s, (regions with corrected-p < 0.05: %i)" \
% (fh.name, N)
regions_bed = fh.name
header = ts.header(bed_files[0])
#if all(h in header for h in ('t', 'start', 'end')):
if region_filter_n is None: region_filter_n = 0
with ts.nopen(prefix + ".regions-t.bed", "w") as fh:
N = 0
for i, toks in enumerate(
filter.filter(bed_files[0], regions_bed, p_col_name=col_num)):
if i == 0: toks[0] = "#" + toks[0]
else:
if float(toks[6]) > region_filter_p: continue
if int(toks[4]) < region_filter_n: continue
#if region_filter_t and "/" in toks[7]:
# # t-pos/t-neg. if the lower one is > region_filter_t?
# vals = map(int, toks[7].split("/"))
# if min(vals) > region_filter_t: continue
N += 1
print >> fh, "\t".join(toks)
print >>sys.stderr, ("wrote: %s, (regions with region-p "
"< %.3f and n-probes >= %i: %i)") \
% (fh.name, region_filter_p, region_filter_n, N)
try:
from cpv import manhattan
regions = manhattan.read_regions(fh.name)
manhattan.manhattan(prefix + ".slk.bed.gz",
3,
prefix.rstrip(".") + ".manhattan.png",
False, ['#959899', '#484B4C'],
"",
False,
None,
regions=regions,
bonferonni=False)
except ImportError:
pass # they dont have matplotlib
if db is not None:
from cruzdb import Genome
g = Genome(db)
lastf = fh.name
with open(prefix + ".anno.%s.bed" % db, "w") as fh:
fh.write('#')
g.annotate(lastf, ("refGene", "cpgIslandExt"),
out=fh,
feature_strand=True,
parallel=len(spvals) > 500)
print >> sys.stderr, "wrote: %s annotated with %s" % (fh.name, db)
"""
"testing" of bin queries and nearest queries
take a long time to run so not part of standard test suite
"""
import time
from cruzdb import Genome
from random import randrange, seed
#g = Genome('hg18', host='localhost', user='******')
#g.mirror(['refGene'], "sqlite:////tmp/u.db")
g = Genome('sqlite:////tmp/u.db')
# if we choose a huge distance all should have a distance of 0
#assert all(k.dist == 0 for k in g.knearest("refGene", "chr1", 1234, 9915555, k=3))
print g.upstream("refGene", "chr1", 9444, 9555, k=6)
last = g.refGene.order_by(-g.refGene.table().c.txStart)[0]
print last
last.txStart = 1000 + last.txEnd
last.txEnd = last.txStart + 100
last.strand = "-"
print last
print g.upstream("refGene", last, k=6)
import time
from toolshed import nopen
import os
anno_file = "data_c_constant_early.bed"
# sub-sample to get fewer rows.
list(
nopen("|awk 'NR == 1 || NR % 4 == 0'" + (" %s > %s.some" %
(anno_file, anno_file))))
anno_file += ".some"
nlines = sum(1 for _ in nopen(anno_file))
print "loc\tinstance\tparallel\ttime"
for parallel in (True, False):
for name, args in (('local\tsqlite', ('sqlite:///hg18.db', )),
('remote\tmysql', ('hg18', )),
('local\tmysql', ('hg18', 'brentp', 'localhost'))):
g = Genome(*args)
out = "%s-%s.anno.txt" % (name.replace("\t", "-"), parallel)
t0 = time.time()
g.annotate(anno_file, ('refGene', ),
out=out,
feature_strand=True,
parallel=parallel)
t1 = time.time()
print "\t".join(map(str, (name, parallel, ("%.1f" % (t1 - t0)))))
assert nlines == sum(1 for _ in nopen(out))
os.unlink(out)
## Purpose: Extract chrom/pos from rs #
## dbsnp144, build 38
## =================================================
## ************* NOTE ******************************
## This script should be run in virtualenv with these installed:
# pip install six
# pip install cruzdb
# pip install sqlalchemy
# pip install mysql-python
## *************************************************
from cruzdb import Genome
import sys
fname = "variants_rs.csv"
outfile = open('chrom_pos.table', 'w')
lines = [line.rstrip('\r\n') for line in open(fname, "r")]
for rs in lines:
if rs.startswith("rs"):
var_info = Genome('hg38').snp144.filter_by(name=rs).first()
if var_info is not None:
outputstring = str(var_info.chrom.split("chr")[1]) + "\t" + str(
var_info.chromStart + 1) + "\n"
outfile.write(outputstring)
else:
outfile.write(str("Caution:" + rs + "\n"))
outfile.close()
#!/usr/bin/env python2.7
from cruzdb import Genome
#this takes a list of genes on chromsome20 and gets their transcript coords
g = Genome(db="hg19")
genes = [
'AHCY', 'ARFGEF2', 'BMP2', 'DNAJC5', 'EDN3', 'GSS', 'GNAS1', 'JAG1',
'PANK2', 'PRNP', 'tTG', 'SALL4', 'VAPB'
]
for gene in genes:
gene_obj = g.refGene.filter_by(name2=gene).first()
if gene_obj:
#one based intervals
#http://gatkforums.broadinstitute.org/discussion/1204/what-input-files-does-the-gatk-accept-require
print("{0}:{1}-{2}".format(gene_obj.chrom.replace('chr', ''),
gene_obj.txStart, gene_obj.txEnd))
"""
"testing" of bin queries and nearest queries
take a long time to run so not part of standard test suite
"""
import time
from cruzdb import Genome
from random import randrange, seed
#g = Genome('hg18', host='localhost', user='******')
#g.mirror(['refGene'], "sqlite:////tmp/u.db")
g = Genome('sqlite:////tmp/u.db')
# if we choose a huge distance all should have a distance of 0
#assert all(k.dist == 0 for k in g.knearest("refGene", "chr1", 1234, 9915555, k=3))
print g.upstream("refGene", "chr1", 9444, 9555, k=6)
last = g.refGene.order_by(-g.refGene.table().c.txStart)[0]
print last
last.txStart = 1000 + last.txEnd
last.txEnd = last.txStart + 100
last.strand = "-"
print last
print g.upstream("refGene", last, k=6)
1 / 0
seed(1)
#/usr/bin/python
import sys
from cruzdb import Genome
sys.path.insert(0, "/home/mokha/Documents/Krauthammer_Lab/PythonClasses")
from SVSv5 import Exon, Isoform, MultiIsoform, SpliceJunction, IsoformSJ
print "------------ Algorithm: 160919_Isoform_1.py ------------"
""" Reconstruct transcripts based on Splice Junctions """
#assign all splice junctions to specific gene: go through cruzdb & find end points for each gene --> assign
# g = Genome( 'sqlite:////tmp/hg19.db' )
g = Genome('sqlite:////tmp/hg19_v2.db')
Isoform.set_cruzdb(g)
#retrieve gene & print information on it based on
gene = g.refGene.filter_by(name2='BRAF').all()
# all_genes = g.refGene.filter_by( name2 = 'TTN' ).first()
# all_genes = g.refGene.filter_by( name2 = 'AGRN' ).all()
# all_genes = g.refGene.filter_by( name2 = 'AGRN' ).first()
# all_genes = g.refGene.filter_by( name2 = 'DIXDC1' ).all()
for each_isoform in gene:
obj_iso = Isoform(each_isoform.name)
#print name
print obj_iso.isoform_id, ":", obj_iso.gene_sym
print "obj_iso = ", obj_iso
from cruzdb import Genome
from cruzdb.sequence import sequence
# mirror the neede tables from UCSC to a local sqlite db
local = Genome('hg19').mirror(('refGene', 'targetScanS'), 'sqlite:///hg19.mirna.db')
# connect to the newly created local sqlite database instance.
refseq_ids = []
# iterate over the coding in refGene
for gene in (rgene for rgene in local.refGene if rgene.is_coding):
if None in gene.utr3: continue # skip genes with no UTR
utr_start, utr_end = gene.utr3
# query the targetScan miRNA table with efficient bin query
sites = local.bin_query('targetScanS', gene.chrom, utr_start, utr_end)
# print BED file of genes whose 3'UTR contains a miR-96 target site
# with a score > 85.
if any("miR-96" in s.name and s.score > 85 for s in sites):
refseq_ids.append(gene.name) # save the refSeq for later GO analysis
# gene is a python object but its string representation is BED format
# we also print out the UTR sequence.
print gene, sequence('hg19', gene.chrom, utr_start, utr_end)
# open a webbrowser to show enrichment of the genes we've selected in DAVID
Genome.david_go(refseq_ids)
print vcf_ex.head()
# Rename columns to fix the syntax in chromosome number
names = vcf_ex.columns.values
new_names = ['CHROM']
new_names.extend(names[1:])
print "\n", new_names
vcf_ex.columns = new_names
# If QUAL > 0.5, sample passes
vcf_ex_sub = vcf_ex.loc[vcf_ex.QUAL > 0.5, ['CHROM', 'POS']].copy()
print vcf_ex_sub.head()
# Get the Genome object from cruzdb
# connects to MySQL genome browser at UCSC
g = Genome('hg38')
# Convert table 'refGene' to pandas dataframe
# columns of interest 'chrom' (chrX, %s), 'txStart' (number, %s), 'txEnd' (number , %s)
print "Extracting reference genome table (HG38) from UCSC Genome Browser"
df = g.dataframe('refGene')
df[['txStart', 'txEnd']] = df[['txStart', 'txEnd']].astype(int)
genes = pd.Series(np.zeros(vcf_ex_sub.shape[0]))
#gene = hg19.bin_query('refGene', vcf_ex_sub.CHROM[1], vcf_ex_sub.POS[1], vcf_ex_sub.POS[1])
#print vcf_ex_sub.POS.iloc[0]
for i in range(0, vcf_ex_sub.shape[0]):
#genes[i] = df[[df.chrom == str(vcf_ex_sub.CHROM.iloc[i]) and df.txStart >= str(vcf_ex_sub.POS.iloc[i])]].bool() #and df.txStart >= str(vcf_ex_sub.POS.iloc[i]) and df.txEnd <= vcf_ex_sub.POS.iloc[i]].bool(),
chrom = vcf_ex_sub['CHROM'].iloc[i]
#/usr/bin/python
#Script: setup_cruzdb_databases.py
from cruzdb import Genome
arrTables_hg19 = [
"refGene", "knownGene", "ensGene", "ccdsKgMap", "knownGeneMrna",
"kgProtAlias", "knownToEnsembl", "knownToRefSeq", "wgEncodeGencodeBasicV19"
]
#NOTE: "ensGene" is not present in hg38
#NOTE: "ensGene"
arrTables_hg38 = [
"refGene", "knownGene", "ccdsKgMap", "knownGeneMrna", "kgProtAlias",
"knownToEnsembl", "knownToRefSeq"
]
# db_hg19_path = "sqlite:////tmp/hg19.db"
# db_hg38_path = "sqlite:////tmp/hg38.db"
db_hg19_path = "sqlite:////tmp/hg19_v2.db"
db_hg38_path = "sqlite:////tmp/hg38_v2.db"
Genome(db="hg19").mirror(arrTables_hg19, db_hg19_path)
Genome(db="hg38").mirror(arrTables_hg38, db_hg38_path)
