def test_knownGene(base_url):
# To speed up testing, we'll download the file and reuse the downloaded copy
knownGene_url = base_url + 'knownGene.txt.gz'
# To speed up testing, we'll download the file and reuse the downloaded copy
knownGene_file, headers = urlretrieve(knownGene_url)
knownGene_localurl = 'file:///%s' % os.path.abspath(knownGene_file)
knownGene = GenomeIntervalTree.from_table(
url=knownGene_localurl, decompress=True
) # Py3 downloads .gz files to local files with names not ending with .gz
assert len(knownGene) == 82960
result = knownGene[b'chr1'].search(100000, 138529)
assert len(result) == 1
assert list(result)[0].data['name'] == b'uc021oeg.2'
knownGene = GenomeIntervalTree.from_table(url=knownGene_localurl,
mode='cds',
decompress=True)
assert len(knownGene) == 82960
assert not knownGene[b'chr1'].overlaps(100000, 138529)
knownGene = GenomeIntervalTree.from_table(url=knownGene_localurl,
mode='exons',
decompress=True)
assert len(knownGene) == 742493
result = list(knownGene[b'chr1'].search(134772, 140566))
assert len(result) == 3
assert result[0].data == result[1].data and result[0].data == result[2].data
def test_knownGene():
# To speed up testing, we'll download the file and reuse the downloaded copy
knownGene_url = 'http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/knownGene.txt.gz'
# Mirror. Slightly faster and more stable, I believe:
knownGene_url = 'http://kt.era.ee/distribute/pyintervaltree/knownGene.txt.gz'
# To speed up testing, we'll download the file and reuse the downloaded copy
knownGene_file, headers = urlretrieve(knownGene_url)
knownGene_localurl = 'file:///%s' % os.path.abspath(knownGene_file)
knownGene = GenomeIntervalTree.from_table(url=knownGene_localurl, decompress=True) # Py3 downloads .gz files to local files with names not ending with .gz
assert len(knownGene) == 82960
result = knownGene[b'chr1'].search(100000, 138529)
assert len(result) == 1
assert list(result)[0].data['name'] == b'uc021oeg.2'
knownGene = GenomeIntervalTree.from_table(url=knownGene_localurl, mode='cds', decompress=True)
assert len(knownGene) == 82960
assert not knownGene[b'chr1'].overlaps(100000, 138529)
knownGene = GenomeIntervalTree.from_table(url=knownGene_localurl, mode='exons', decompress=True)
assert len(knownGene) == 742493
result = list(knownGene[b'chr1'].search(134772, 140566))
assert len(result) == 3
assert result[0].data == result[1].data and result[0].data == result[2].data
def _test_promotorsearch():
# Realistic example: find a promotor of a given gene ('NANOG', for example)
# It is slow, so you don't want to run it too much.
from intervaltree_bio import GenomeIntervalTree, UCSCTable
# Download refGene table
refGene = GenomeIntervalTree.from_table(
url=
'http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/refGene.txt.gz',
parser=UCSCTable.REF_GENE)
# Find the NANOG gene
nanog = [
i for chrom in refGene for i in refGene[chrom]
if i.data['name2'] == 'NANOG'
]
nanog = nanog[0]
# Download genome segmentation table
e = Encode()
segments = e.AwgSegmentation.CombinedHepg2.fetch().read_as_intervaltree()
# Find the segmentation of the NANOG transcript +- 10kb
results = segments[nanog.data['chrom']].search(nanog.begin - 10000,
nanog.end + 10000)
# Leave the promotor/promotor flanking segments only
results = [i for i in results if i.data[0] in ['PF', 'P']]
print results
def test_ensGene(base_url):
# Smoke-test we can at least read ensGene.
ensGene_url = base_url + 'ensGene.txt.gz'
ensGene = GenomeIntervalTree.from_table(url=ensGene_url,
mode='cds',
parser=UCSCTable.ENS_GENE)
assert len(ensGene) == 204940
def test_genepred():
# Smoke-test for output from gtfToGenePred
testdir = os.path.join(os.path.dirname(__file__), 'test_data')
kg = open(os.path.abspath(os.path.join(testdir, "test_genepred.txt")))
gtree = GenomeIntervalTree.from_table(fileobj=kg,
mode='cds',
parser=UCSCTable.GENEPRED)
assert len(gtree) == 100
def test_refGene(base_url):
# Smoke-test for refGene
refGene_url = base_url + 'refGene.txt.gz'
refGene = GenomeIntervalTree.from_table(url=refGene_url,
mode='tx',
parser=UCSCTable.REF_GENE)
assert len(
refGene
) == 52350 # NB: Some time ago it was 50919, hence it seems the table data changes on UCSC and eventually the mirror and UCSC won't be the same.
def gtf2tree(gtf_path):
genepred_annot = os.path.splitext(gtf_path)[0] + ".genePred"
ucsc_annot = os.path.splitext(gtf_path)[0] + ".UCSCTable.gz"
gtf_to_genepred(gtf_path, genepred_annot)
genepred_to_UCSCtable(genepred_annot, ucsc_annot)
kg = gzip.open(ucsc_annot)
gtree = GenomeIntervalTree.from_table(fileobj=kg,
mode='tx',
parser=UCSCTable.ENS_GENE)
return gtree
def _test_promotorsearch():
# Realistic example: find a promotor of a given gene ('NANOG', for example)
# It is slow, so you don't want to run it too much.
from intervaltree_bio import GenomeIntervalTree, UCSCTable
# Download refGene table
refGene = GenomeIntervalTree.from_table(url='http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/refGene.txt.gz', parser=UCSCTable.REF_GENE)
# Find the NANOG gene
nanog = [i for chrom in refGene for i in refGene[chrom] if i.data['name2'] == 'NANOG']
nanog = nanog[0]
# Download genome segmentation table
e = Encode()
segments = e.AwgSegmentation.CombinedHepg2.fetch().read_as_intervaltree()
# Find the segmentation of the NANOG transcript +- 10kb
results = segments[nanog.data['chrom']].search(nanog.begin-10000, nanog.end+10000)
# Leave the promotor/promotor flanking segments only
results = [i for i in results if i.data[0] in ['PF', 'P']]
print results
class AnnoTestCase(unittest.TestCase):
"""Tests annotation"""
mygtf = os.path.realpath("test_data/test.gtf.gz")
genepred_annot = os.path.splitext(mygtf)[0] + ".genePred"
ucsc_annot = os.path.splitext(mygtf)[0] + ".UCSCTable.gz"
su.gtf_convert.gtf_to_genepred(mygtf, genepred_annot)
su.gtf_convert.genepred_to_UCSCtable(genepred_annot, ucsc_annot)
su.gtf_convert.gtf_to_genepred(mygtf, genepred_annot)
su.gtf_convert.genepred_to_UCSCtable(genepred_annot, ucsc_annot)
kg = gzip.open(ucsc_annot)
global gtree
gtree = GenomeIntervalTree.from_table(fileobj=kg, mode='tx', parser=UCSCTable.ENS_GENE)
def test_get_jxnside_anno_v1(self):
"""test get_jxnside_anno"""
jxn_filt = pd.DataFrame({'name': 'chr1:871160:+:chr1:985950:-:0:0',
'jxn_reads': 'a1,a2,a3',
'jxn_counts': 3,
'spans': 5,
'spanreads': 's1,s2,s3,s4,s5',
'dist': 114790,
'ann_format': 'Symbol:Transcript:Strand:Exon_No:Dist_to_Exon:Frame:CDS_Length'},
index=pd.Series(0))
jxn_filt['left_symbol'], jxn_filt['left_annot'], jxn_filt['left_strand'], jxn_filt['left_cdslen'] = zip(*jxn_filt.apply(lambda x: su.annotate_sv.get_jxnside_anno(x['name'], gtree, 1), axis=1))
jxn_filt['right_symbol'], jxn_filt['right_annot'], jxn_filt['right_strand'], jxn_filt['right_cdslen'] = zip(*jxn_filt.apply(lambda x: su.annotate_sv.get_jxnside_anno(x['name'], gtree, 2), axis=1))
assert(jxn_filt['left_symbol'].iloc[0] == 'SAMD11')
assert(jxn_filt['right_symbol'].iloc[0] == 'AGRN')
assert(jxn_filt['left_strand'].iloc[0] == '+')
assert(jxn_filt['right_strand'].iloc[0] == '+')
assert(jxn_filt['left_cdslen'].iloc[0] == 9852)
assert(jxn_filt['right_cdslen'].iloc[0] == 3395)
def test_get_jxnside_anno_v2(self):
"""test get_jxnside_anno.. Tests where no transcripts found"""
jxn_filt = pd.DataFrame({'name': 'chr19:560462:+:chr8:560462:-:0:0',
'jxn_reads': 'a1,a2,a3',
'jxn_counts': 3,
'spans': 5,
'spanreads': 's1,s2,s3,s4,s5',
'dist': 13082084,
'ann_format': 'Symbol:Transcript:Strand:Exon_No:Dist_to_Exon:Frame:CDS_Length'},
index=pd.Series(0))
jxn_filt['left_symbol'], jxn_filt['left_annot'], jxn_filt['left_strand'], jxn_filt['left_cdslen']= zip(*jxn_filt.apply(lambda x: su.annotate_sv.get_jxnside_anno(x['name'], gtree, 1), axis=1))
assert(jxn_filt['left_symbol'].iloc[0] == 'NA')
assert(jxn_filt['left_strand'].iloc[0] == 'NA')
assert(jxn_filt['left_cdslen'].iloc[0] == 'NA')
def test_get_jxnside_anno_v3(self):
"""test get_jxnside_anno.. Tests non-coding transcripts for sorting"""
jxn_filt = pd.DataFrame({'name': 'chr12:15704606:+:chr1:11918527:-:2:1',
'jxn_reads': 'a1,a2,a3',
'jxn_counts': 3,
'spans': 5,
'spanreads': 's1,s2,s3,s4,s5',
'dist': 'NA',
'ann_format': 'Symbol:Transcript:Strand:Exon_No:Dist_to_Exon:Frame:CDS_Length'},
index=pd.Series(0))
jxn_filt['left_symbol'], jxn_filt['left_annot'], jxn_filt['left_strand'], jxn_filt['left_cdslen'] = zip(*jxn_filt.apply(lambda x: su.annotate_sv.get_jxnside_anno(x['name'], gtree, 1), axis=1))
assert(jxn_filt['left_symbol'].iloc[0] == 'NA')
assert(jxn_filt['left_strand'].iloc[0] == 'NA')
assert(jxn_filt['left_cdslen'].iloc[0] == 'NA')
def test_get_jxn_genes(self):
"""test_get_jxn_genes"""
jxn_filt = pd.DataFrame({'name': 'chr1:871160:+:chr1:985950:-:0:0',
'jxn_reads': 'a1,a2,a3',
'jxn_counts': 3,
'spans': 5,
'spanreads': 's1,s2,s3,s4,s5',
'dist': 114790,
'ann_format': 'Symbol:Transcript:Strand:Exon_No:Dist_to_Exon:Frame:CDS_Length'},
index=pd.Series(0))
jxn_filt['left_all'], jxn_filt['right_all'] = zip(*jxn_filt.apply(lambda x: su.annotate_sv.get_jxn_genes(x['name'], gtree), axis=1))
assert(jxn_filt['left_all'].iloc[0] == ['SAMD11'])
assert(jxn_filt['right_all'].loc[0] == ['AGRN'])
def test_get_pos_genes(self):
"""test_get_pos_genes"""
assert(su.annotate_sv.get_pos_genes('chr1', 871160, gtree) == ['SAMD11'])
def test_refGene():
# Smoke-test for refGene
refGene_url = 'http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/refGene.txt.gz'
refGene_url = 'http://kt.era.ee/distribute/pyintervaltree/refGene.txt.gz'
refGene = GenomeIntervalTree.from_table(url=refGene_url, mode='tx', parser=UCSCTable.REF_GENE)
assert len(refGene) == 52350 # NB: Some time ago it was 50919, hence it seems the table data changes on UCSC and eventually the mirror and UCSC won't be the same.
def test_ensGene():
# Smoke-test we can at least read ensGene.
ensGene_url = 'http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/ensGene.txt.gz'
ensGene_url = 'http://kt.era.ee/distribute/pyintervaltree/ensGene.txt.gz'
ensGene = GenomeIntervalTree.from_table(url=ensGene_url, mode='cds', parser=UCSCTable.ENS_GENE)
assert len(ensGene) == 204940