def expected_result_same_strand_intersection_simple_granges():
c = """Chromosome Start End Strand Score
chr1 6 7 - 7"""
df = pd.read_table(StringIO(c), sep="\s+", header=0)
return PyRanges(df)
def read_gtf(f):
"""seqname - name of the chromosome or scaffold; chromosome names can be given with or without the 'chr' prefix. Important note: the seqname must be one used within Ensembl, i.e. a standard chromosome name or an Ensembl identifier such as a scaffold ID, without any additional content such as species or assembly. See the example GFF output below.
# source - name of the program that generated this feature, or the data source (database or project name)
feature - feature type name, e.g. Gene, Variation, Similarity
start - Start position of the feature, with sequence numbering starting at 1.
end - End position of the feature, with sequence numbering starting at 1.
score - A floating point value.
strand - defined as + (forward) or - (reverse).
# frame - One of '0', '1' or '2'. '0' indicates that the first base of the feature is the first base of a codon, '1' that the second base is the first base of a codon, and so on..
attribute - A semicolon-separated list of tag-value pairs, providing additional information about each feature."""
dtypes = {"Chromosome": "category", "Feature": "category", "Strand": "category"}
df = pd.read_table(f, sep="\t", comment="#", usecols=[0, 2, 3, 4, 5, 6, 8], header=None, names="Chromosome Feature Start End Score Strand Attribute".split(), dtype=dtypes)
if sum(df.Score == ".") == len(df):
cols_to_concat = "Chromosome Start End Strand Feature".split()
else:
cols_to_concat = "Chromosome Start End Strand Feature Score".split()
extract = _fetch_gene_transcript_exon_id(df.Attribute)
print("extract")
print(extract)
extract.columns = "GeneID TranscriptID ExonNumber ExonID".split()
extract.ExonNumber = extract.ExonNumber.astype(float)
df = pd.concat([df[cols_to_concat],
extract], axis=1)
return PyRanges(df)
def expected_result_unstranded():
c = """Chromosome Start End Name Score Strand Start_b End_b Name_b Score_b Strand_b Distance
0 chr1 3 6 h 0 + 6 7 f 0 - 1
1 chr1 5 7 h 0 - 6 7 f 0 - 0"""
return PyRanges(pd.read_table(StringIO(c), sep=" "))
def hyp4():
c = """chr1 0 3 +
chr1 3 4 +
chr1 1 2 +"""
return PyRanges(pd.read_table(StringIO(c), sep="\s+", header=None, names="Chromosome Start End Strand".split()))
def expected_result_subtract_simple_granges():
c = """Chromosome Start End Strand Score
chr1 3 6 + 5
chr1 8 9 + 1"""
df = pd.read_table(StringIO(c), sep="\s+", header=0)
return PyRanges(df)
def expected_result_counterexample5():
c = """chr2 0 1 + 1 2 + 1
chr2 2 3 + 1 3 + 0"""
return PyRanges(pd.read_table(StringIO(c), sep="\s+", header=None,
names="Chromosome Start End Strand Start_b End_b Strand_b Distance".split()))
def expected_result_regular_intersection():
c = """chr1 226987603 226987617 U0 0 +
chr8 38747236 38747251 U0 0 -
chr15 26105515 26105518 U0 0 +"""
return PyRanges(pd.read_table(StringIO(c), header=None, names="Chromosome Start End Name Score Strand".split()))
def expected_result_same_stranded():
c = """Chromosome Start End Name Score Strand Start_b End_b Name_b Score_b Strand_b Distance
0 chr1 10241 10440 HWI-ST216_313:3:1302:4516:156396 1 - 9988 10187 HWI-ST216:427:D29R1ACXX:2:1205:6095:16532 1 - 55
1 chr1 110246 110445 HWI-ST216_313:3:1207:4315:142177 1 + 16109 16308 HWI-ST216:427:D29R1ACXX:2:2110:12286:25379 1 + 93939"""
return PyRanges(pd.read_table(StringIO(c), sep=" "))
def expected_result_opposite_stranded():
c = """Chromosome Start End Name Score Strand Start_b End_b Name_b Score_b Strand_b Distance
0 chr1 10246 10445 HWI-ST216_313:3:1207:4315:142177 1 + 9988 10187 HWI-ST216:427:D29R1ACXX:2:1205:6095:16532 1 - 60
1 chr1 110246 110445 HWI-ST216_313:3:1207:4315:142177 1 + 19958 20157 HWI-ST216:427:D29R1ACXX:2:1313:6283:67310 1 - 90090"""
return PyRanges(pd.read_table(StringIO(c), sep=" "))
def read_bed(f, output_df=False):
columns = "Chromosome Start End Name Score Strand ThickStart ThickEnd ItemRGB BlockCount BlockSizes BlockStarts".split(
)
first_start = open(f).readline().split()[1]
header = None
try:
int(first_start)
except ValueError:
header = 0
df = pd.read_csv(f,
dtype={
"Chromosome": "category",
"Strand": "category"
},
header=header,
sep="\t")
df.columns = columns[:df.shape[1]]
if not output_df:
return PyRanges(df)
else:
return df
def expected_result_overlap_same_strand():
c = """Chromosome Start End Name Score Strand Start_b End_b Name_b Score_b Strand_b
chr15 26105515 26105540 U0 0 + 26105493 26105518 U0 0 +"""
df = pd.read_table(StringIO(c), header=0, sep="\s+")
return PyRanges(df)
def read_bam(f,
sparse=True,
output_df=False,
mapq=0,
required_flag=0,
filter_flag=1540):
try:
import bamread
except ModuleNotFoundError as e:
print(
"bamread must be installed to read bam. Use `conda install -c bioconda bamread` or `pip install bamread` to install it."
)
sys.exit(1)
if sparse:
df = bamread.read_bam(f, mapq, required_flag, filter_flag)
else:
try:
df = bamread.read_bam_full(f, mapq, required_flag, filter_flag)
except AttributeError:
print(
"bamread version 0.0.6 or higher is required to read bam non-sparsely."
)
if output_df:
return df
else:
return PyRanges(df)
def simple_gr2():
c = """Chromosome Start End Strand Score
chr1 1 2 + 1
chr1 6 7 - 2"""
df = pd.read_table(StringIO(c), sep="\s+", header=0)
return PyRanges(df)
def expected_result_overlap_opposite_strand():
c = """Chromosome Start End Name Score Strand Start_b End_b Name_b Score_b Strand_b
chr8 38747226 38747251 U0 0 - 38747236 38747261 U0 0 +
chr1 226987592 226987617 U0 0 + 226987603 226987628 U0 0 -"""
return PyRanges(pd.read_table(StringIO(c), sep="\s+"))
def read_gtf_restricted(f,
annotation=None,
output_df=False,
skiprows=0,
nrows=None):
"""seqname - name of the chromosome or scaffold; chromosome names can be given with or without the 'chr' prefix. Important note: the seqname must be one used within Ensembl, i.e. a standard chromosome name or an Ensembl identifier such as a scaffold ID, without any additional content such as species or assembly. See the example GFF output below.
# source - name of the program that generated this feature, or the data source (database or project name)
feature - feature type name, e.g. Gene, Variation, Similarity
start - Start position of the feature, with sequence numbering starting at 1.
end - End position of the feature, with sequence numbering starting at 1.
score - A floating point value.
strand - defined as + (forward) or - (reverse).
# frame - One of '0', '1' or '2'. '0' indicates that the first base of the feature is the first base of a codon, '1' that the second base is the first base of a codon, and so on..
attribute - A semicolon-separated list of tag-value pairs, providing additional information about each feature."""
dtypes = {
"Chromosome": "category",
"Feature": "category",
"Strand": "category"
}
df_iter = pd.read_csv(
f,
sep="\t",
comment="#",
usecols=[0, 2, 3, 4, 5, 6, 8],
header=None,
names="Chromosome Feature Start End Score Strand Attribute".split(),
dtype=dtypes,
chunksize=int(1e5),
nrows=nrows)
dfs = []
for df in df_iter:
# Since Start is 1-indexed
df.Start -= 1
if sum(df.Score == ".") == len(df):
cols_to_concat = "Chromosome Start End Strand Feature".split()
else:
cols_to_concat = "Chromosome Start End Strand Feature Score".split(
)
extract = _fetch_gene_transcript_exon_id(df.Attribute, annotation)
extract.columns = "gene_id transcript_id exon_number exon_id".split()
extract.exon_number = extract.exon_number.astype(float)
df = pd.concat([df[cols_to_concat], extract], axis=1, sort=False)
dfs.append(df)
df = pd.concat(dfs, sort=False)
df.loc[:, "Start"] = df.Start - 1
if not output_df:
return PyRanges(df)
else:
return df
def test_instantiation_without_strand(chip_10_plus_one):
# mix series, lists and array
seqnames = chip_10_plus_one.Chromosome.values
starts = chip_10_plus_one.Start.values
ends = chip_10_plus_one.End
pr = PyRanges(seqnames=seqnames, starts=starts, ends=ends)
def expected_result_same_strand():
c = """Chromosome Start End Name Score Strand Start_b End_b Name_b Score_b Strand_b
chr1 2 4 U0 0 + 1 9 U0 0 +"""
df = pd.read_table(StringIO(c), header=0, sep="\s+")
return PyRanges(df)
def load_dataset(basename):
full_path = pkg_resources.resource_filename("pyranges", "example_data/{}.bed".format(basename))
df = pd.read_table(full_path, header=None,
names="Chromosome Start End Name Score Strand".split())
return PyRanges(df)
def expected_result_regular_overlap_intersection():
c = """Chromosome Start End Start_a End_a Name_a Score_a Strand Start_b End_b Name_b Score_b Strand_b
chr1 226987603 226987617 226987592 226987617 U0 0 + 226987603 226987628 U0 0 -
chr8 38747236 38747251 38747226 38747251 U0 0 - 38747236 38747261 U0 0 +
chr15 26105515 26105518 26105515 26105540 U0 0 + 26105493 26105518 U0 0 +"""
return PyRanges(pd.read_table(StringIO(c), sep="\s+"))
def simple_gr1():
c = """Chromosome Start End Strand Score
chr1 3 6 + 5
chr1 5 7 - 7
chr1 8 9 + 1"""
df = pd.read_table(StringIO(c), sep="\s+", header=0)
return PyRanges(df)
def simple_gr1():
c = """Chromosome Start End Score Strand
chr1 3 6 5 +
chr1 5 7 7 -
chr1 8 9 1 +"""
df = pd.read_table(StringIO(c), sep="\s+", header=0)
return PyRanges(df)
def read_gff3(f, annotation=None, as_df=False, nrows=None, skiprows=0):
"""Read files in the General Feature Format.
Parameters
----------
f : str
Path to GFF file.
as_df : bool, default False
Whether to return as pandas DataFrame instead of PyRanges.
nrows : int, default None
Number of rows to read. Default None, i.e. all.
See Also
--------
pyranges.read_gtf : read files in the Gene Transfer Format
"""
dtypes = {
"Chromosome": "category",
"Feature": "category",
"Strand": "category"
}
names = "Chromosome Source Feature Start End Score Strand Frame Attribute".split(
)
df_iter = pd.read_csv(f,
comment="#",
sep="\t",
header=None,
names=names,
dtype=dtypes,
chunksize=int(1e5),
skiprows=skiprows,
nrows=nrows)
dfs = []
for df in df_iter:
extra = to_rows_gff3(df.Attribute.astype(str))
df = df.drop("Attribute", axis=1)
ndf = pd.concat([df, extra], axis=1, sort=False)
dfs.append(ndf)
df = pd.concat(dfs, sort=False)
df.loc[:, "Start"] = df.Start - 1
if not as_df:
return PyRanges(df)
else:
return df
def test_instantiation_with_str_for_strands_seqnames(chip_10_plus_one):
# mix series, lists and array
seqnames = "chr1"
starts = chip_10_plus_one.Start.values
ends = chip_10_plus_one.End
strands = "+"
pr = PyRanges(seqnames=seqnames, starts=starts, ends=ends, strands=strands)
def read_bam(f, output_df=False, mapq=0, required_flag=0, filter_flag=1540):
df = bamread.read_bam(f, mapq, required_flag, filter_flag)
if output_df:
return df
else:
return PyRanges(df)
return bamread.read_bam(f, mapq, required_flag, filter_flag)
def background():
c = """Chromosome Start End Name Score Strand
chr1 226987603 226987628 U0 0 -
chr8 38747236 38747261 U0 0 +
chr15 26105493 26105518 U0 0 +"""
df = pd.read_table(StringIO(c), sep="\s+", header=0)
print(df)
return PyRanges(df)
def expected_result_self_unstranded(names):
c = """chr1 9916 9988 HWI-ST216_313:3:1203:10227:6568 1
chr1 9939 9988 HWI-ST216_313:3:2301:15791:16298 1
chr1 9951 9988 HWI-ST216_313:3:2205:20086:33508 1
chr1 9953 9988 HWI-ST216_313:3:1305:6975:102491 1
chr1 9978 9988 HWI-ST216_313:3:1204:5599:113305 1"""
df = pd.read_table(StringIO(c), sep="\s+", names=names[:-1], header=None)
return PyRanges(df)
def chip():
c = """Chromosome Start End Name Score Strand
chr1 226987592 226987617 U0 0 +
chr8 38747226 38747251 U0 0 -
chr15 26105515 26105540 U0 0 +"""
df = pd.read_table(StringIO(c), sep="\s+", header=0)
# print(df)
return PyRanges(df)
def expected_result_no_strand_plus_one(names):
c = """chr1 9916 9988 HWI-ST216_313:3:1203:10227:6568 1 -
chr1 9939 9988 HWI-ST216_313:3:2301:15791:16298 1 +
chr1 9951 9988 HWI-ST216_313:3:2205:20086:33508 1 -
chr1 9953 9988 HWI-ST216_313:3:1305:6975:102491 1 +
chr1 9978 9988 HWI-ST216_313:3:1204:5599:113305 1 -
chr1 110246 110445 HWI-ST216_313:3:1207:4315:142177 1 +"""
df = pd.read_table(StringIO(c), sep="\s+", names=names, header=None)
return PyRanges(df)
def expected_result_counterexample13():
c = """chr1 3538885 3832293 + 0 1 + 3538885
chr1 4426346 9655531 + 0 1 + 4426346"""
return PyRanges(
pd.read_table(
StringIO(c),
sep="\s+",
header=None,
names="Chromosome Start End Strand Start_b End_b Strand_b Distance"
.split()))
def expected_result_minus():
c = """0 chr1 10241 10440 HWI-ST216_313:3:1302:4516:156396 1 - 9988 10187 HWI-ST216:427:D29R1ACXX:2:1205:6095:16532 1 - 55"""
return PyRanges(
pd.read_table(
StringIO(c),
sep="\s+",
header=None,
names=
"Chromosome Start End Name Score Strand Start_b End_b Name_b Score_b Strand_b Distance"
.split()))
