def test_genome_arg_to_load_vcf_cached_75():
eq_(load_vcf(HG19_VCF_FILENAME),
load_vcf(HG19_VCF_FILENAME,
genome=cached_release(75), convert_ucsc_contig_names=True))
assert load_vcf(HG19_VCF_FILENAME) != load_vcf(
HG19_VCF_FILENAME,
genome=cached_release(75),
convert_ucsc_contig_names=False)
def infer_genome(genome_object_string_or_int):
"""
If given an integer, return associated human EnsemblRelease for that
Ensembl version.
If given a string, return latest EnsemblRelease which has a reference
of the same name.
If given a PyEnsembl Genome, simply return it.
"""
if isinstance(genome_object_string_or_int, Genome):
return genome_object_string_or_int
if is_integer(genome_object_string_or_int):
return cached_release(genome_object_string_or_int)
elif is_string(genome_object_string_or_int):
# first infer the canonical reference name, e.g. mapping hg19 -> GRCh37
# and then get the associated PyEnsembl Genome object
reference_name = infer_reference_name(genome_object_string_or_int)
return genome_for_reference_name(reference_name)
else:
raise TypeError(
("Expected genome to be an int, string, or pyensembl.Genome "
"instance, got %s : %s") % (
str(genome_object_string_or_int),
type(genome_object_string_or_int)))
def infer_genome(genome_object_string_or_int):
"""
If given an integer, return associated human EnsemblRelease for that
Ensembl version.
If given a string, return latest EnsemblRelease which has a reference
of the same name.
If given a PyEnsembl Genome, simply return it.
"""
if isinstance(genome_object_string_or_int, Genome):
return genome_object_string_or_int
if is_integer(genome_object_string_or_int):
return cached_release(genome_object_string_or_int)
elif is_string(genome_object_string_or_int):
# first infer the canonical reference name, e.g. mapping hg19 -> GRCh37
# and then get the associated PyEnsembl Genome object
reference_name = infer_reference_name(genome_object_string_or_int)
return genome_for_reference_name(reference_name)
else:
raise TypeError(
("Expected genome to be an int, string, or pyensembl.Genome "
"instance, got %s : %s") % (
str(genome_object_string_or_int),
type(genome_object_string_or_int)))
def __setstate__(self, fields):
# This field require special logic.
self.ensembl = cached_release(fields.pop("release"))
# Remaining fields are simple properties that just get set.
for (key, value) in fields.items():
setattr(self, key, value)
def test_pandas_and_pyvcf_implementations_equivalent():
paths = [
{'path': data_path("somatic_hg19_14muts.vcf")},
{'path': data_path("somatic_hg19_14muts.space_in_sample_name.vcf")},
{'path': "/" + data_path("somatic_hg19_14muts.vcf")},
{'path': data_path("somatic_hg19_14muts.vcf.gz")},
{'path': data_path("multiallelic.vcf")},
{'path': data_path("mutect-example.vcf")},
{'path': data_path("strelka-example.vcf")},
{'path': data_path("mutect-example-headerless.vcf"),
'genome': cached_release(75)},
]
if RUN_TESTS_REQUIRING_INTERNET:
paths.append({'path': VCF_EXTERNAL_URL})
paths.append({'path': VCF_EXTERNAL_URL + ".gz"})
def do_test(kwargs):
vcf_pandas = load_vcf_fast(**kwargs)
vcf_pyvcf = load_vcf(**kwargs)
eq_(vcf_pandas, vcf_pyvcf)
eq_(len(vcf_pandas), len(vcf_pyvcf))
eq_(vcf_pandas.elements, vcf_pyvcf.elements)
eq_(vcf_pandas.metadata, vcf_pyvcf.metadata)
assert len(vcf_pandas) > 1
assert len(vcf_pyvcf) > 1
for kwargs in paths:
yield (do_test, kwargs)
def test_genome_arg_to_load_vcf():
variants = load_vcf(VCF_FILENAME)
eq_(variants, load_vcf(VCF_FILENAME, genome=75))
eq_(variants, load_vcf(VCF_FILENAME, genome=cached_release(75)))
eq_(variants, load_vcf(VCF_FILENAME, genome="grch37"))
eq_(variants, load_vcf(VCF_FILENAME, genome="GRCh37"))
eq_(variants, load_vcf(VCF_FILENAME, genome="b37"))
# TODO: actually make hg19 different from b37! They should use
# different MT sequences
eq_(variants, load_vcf(VCF_FILENAME, genome="hg19"))
def test_genome_arg_to_load_vcf():
variants = load_vcf(VCF_FILENAME)
eq_(variants, load_vcf(VCF_FILENAME, genome=75))
eq_(variants, load_vcf(VCF_FILENAME, genome=cached_release(75)))
eq_(variants, load_vcf(VCF_FILENAME, genome="grch37"))
eq_(variants, load_vcf(VCF_FILENAME, genome="GRCh37"))
eq_(variants, load_vcf(VCF_FILENAME, genome="b37"))
# TODO: actually make hg19 different from b37! They should use
# different MT sequences
eq_(variants, load_vcf(VCF_FILENAME, genome="hg19"))
def test_transcript_support_level():
""" The Transcript Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript
models for users, based on the type and quality of the alignments used to annotate the transcript.
In the Ensembl database, it can be assigned to a value 1 through 5, or reported as NA, or missing, or missing
completely in older releases. We translate it to an integer value, otherwise to None.
"""
ensembl93 = cached_release(93)
transcript = ensembl93.transcripts_by_name("DDX11L1-202")[0]
eq_(transcript.support_level, 1)
# For this transcript, the transcript_support_level value is missing in the database record:
transcript = ensembl93.transcripts_by_name("OR4G11P-202")[0]
eq_(transcript.support_level, None)
# Some features are reported as "NA" in Ensembl: those are features like pseudogenes, single exon transcripts,
# HLA, T-cell receptor and Ig transcripts that are not analysed in terms of TSL and therefore not given any
# of the TSL categories. We translate NA to None as well.
transcript = ensembl93.transcripts_by_name("MIR1302-2-201")[0]
eq_(transcript.support_level, None)
# Transcript_support_level column was missing completely in GRCh37 and older releases of GRCh38:
ensembl77 = cached_release(77)
transcript = ensembl77.transcripts_by_name("DDX11L1-002")[0]
eq_(transcript.support_level, None)
def test_transcript_support_level():
""" The Transcript Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript
models for users, based on the type and quality of the alignments used to annotate the transcript.
In the Ensembl database, it can be assigned to a value 1 through 5, or reported as NA, or missing, or missing
completely in older releases. We translate it to an integer value, otherwise to None.
"""
ensembl93 = cached_release(93)
transcript = ensembl93.transcripts_by_name("DDX11L1-202")[0]
eq_(transcript.support_level, 1)
# For this transcript, the transcript_support_level value is missing in the database record:
transcript = ensembl93.transcripts_by_name("OR4G11P-202")[0]
eq_(transcript.support_level, None)
# Some features are reported as "NA" in Ensembl: those are features like pseudogenes, single exon transcripts,
# HLA, T-cell receptor and Ig transcripts that are not analysed in terms of TSL and therefore not given any
# of the TSL categories. We translate NA to None as well.
transcript = ensembl93.transcripts_by_name("MIR1302-2-201")[0]
eq_(transcript.support_level, None)
# Transcript_support_level column was missing completely in GRCh37 and older releases of GRCh38:
ensembl77 = cached_release(77)
transcript = ensembl77.transcripts_by_name("DDX11L1-002")[0]
eq_(transcript.support_level, None)
def test_ensembl_releases(*versions):
"""
Run a unit test which takes an EnsemblRelease as an argument
for multiple releases (most recent for each reference genome)
"""
if len(versions) == 0:
ensembl_releases = major_releases
else:
ensembl_releases = [cached_release(version) for version in versions]
def decorator(test_fn):
@functools.wraps(test_fn)
def new_test_fn():
for ensembl in ensembl_releases:
test_fn(ensembl)
return new_test_fn
return decorator
def test_pandas_and_pyvcf_implementations_equivalent():
paths = [
{
'path': data_path("somatic_hg19_14muts.vcf")
},
{
'path': data_path("somatic_hg19_14muts.space_in_sample_name.vcf")
},
{
'path': "/" + data_path("somatic_hg19_14muts.vcf")
},
{
'path': data_path("somatic_hg19_14muts.vcf.gz")
},
{
'path': data_path("multiallelic.vcf")
},
{
'path': data_path("mutect-example.vcf")
},
{
'path': data_path("strelka-example.vcf")
},
{
'path': data_path("mutect-example-headerless.vcf"),
'genome': cached_release(75)
},
]
if RUN_TESTS_REQUIRING_INTERNET:
paths.append({'path': VCF_EXTERNAL_URL})
paths.append({'path': VCF_EXTERNAL_URL + ".gz"})
def do_test(kwargs):
vcf_pandas = load_vcf_fast(**kwargs)
vcf_pyvcf = load_vcf(**kwargs)
eq_(vcf_pandas, vcf_pyvcf)
eq_(len(vcf_pandas), len(vcf_pyvcf))
eq_(vcf_pandas.elements, vcf_pyvcf.elements)
eq_(vcf_pandas.metadata, vcf_pyvcf.metadata)
assert len(vcf_pandas) > 1
assert len(vcf_pyvcf) > 1
for kwargs in paths:
yield (do_test, kwargs)
def test_ensembl_releases(*versions):
"""
Run a unit test which takes an EnsemblRelease as an argument
for multiple releases (most recent for each reference genome)
"""
if len(versions) == 0:
ensembl_releases = major_releases
else:
ensembl_releases = [cached_release(version) for version in versions]
def decorator(test_fn):
@functools.wraps(test_fn)
def new_test_fn():
for ensembl in ensembl_releases:
test_fn(ensembl)
return new_test_fn
return decorator
def test_ensembl_releases(*versions):
"""
Run a unit test which takes an EnsemblRelease as an argument
for multiple releases (most recent for each reference genome)
"""
if len(versions) == 0:
ensembl_releases = major_releases
else:
if any(version > MAX_ENSEMBL_RELEASE for version in versions):
raise ValueError("Invalid ensembl release numbers: %s" % (versions,))
ensembl_releases = [cached_release(version) for version in versions]
def decorator(test_fn):
@functools.wraps(test_fn)
def new_test_fn():
for ensembl in ensembl_releases:
test_fn(ensembl)
return new_test_fn
return decorator
def test_ensembl_releases(*versions):
"""
Run a unit test which takes an EnsemblRelease as an argument
for multiple releases (most recent for each reference genome)
"""
if len(versions) == 0:
ensembl_releases = major_releases
else:
if any(version > MAX_ENSEMBL_RELEASE for version in versions):
raise ValueError("Invalid ensembl release numbers: %s" %
(versions, ))
ensembl_releases = [cached_release(version) for version in versions]
def decorator(test_fn):
@functools.wraps(test_fn)
def new_test_fn():
for ensembl in ensembl_releases:
test_fn(ensembl)
return new_test_fn
return decorator
"""
Test all methods which return collections of gene IDs that aren't converting
from some other type of name or ID.
TODO: Implement tests for EnsemblRelease.gene_ids
"""
from __future__ import absolute_import
from nose.tools import assert_raises, ok_
from pyensembl import ensembl_grch38, cached_release
from .common import test_ensembl_releases
ensembl77 = cached_release(77, "human")
def test_gene_ids_grch38_hla_a():
# chr6:29,945,884 is a position for HLA-A
# Gene ID = ENSG00000206503
# based on:
# http://useast.ensembl.org/Homo_sapiens/Gene/
# Summary?db=core;g=ENSG00000206503;r=6:29941260-29945884
ids = ensembl_grch38.gene_ids_at_locus(6, 29945884)
expected = "ENSG00000206503"
assert ids == ["ENSG00000206503"], \
"Expected HLA-A, gene ID = %s, got: %s" % (expected, ids)
def test_gene_ids_of_gene_name_hla_grch38():
hla_a_gene_ids = ensembl_grch38.gene_ids_of_gene_name("HLA-A")
assert 'ENSG00000206503' in hla_a_gene_ids, hla_a_gene_ids
def __init__(self,
contig,
start,
ref,
alt,
ensembl=ensembl_grch38,
allow_extended_nucleotides=False,
normalize_contig_name=True):
"""
Construct a Variant object.
Parameters
----------
contig : str
Chromosome that this variant is on
start : int
1-based position on the chromosome of first reference nucleotide
ref : str
Reference nucleotide(s)
alt : str
Alternate nucleotide(s)
ensembl : Genome or EnsemblRelease
Object used for determining gene/transcript annotations
allow_extended_nucleotides : bool
Extended nucleotides include 'Y' for pyrimidies or 'N' for any base
normalize_contig_name : bool
By default the contig name will be normalized by trimming a 'chr'
prefix and converting all letters to upper-case. If we don't want
this behavior then pass normalize_contig_name=False.
"""
# first initialize the _genes and _transcripts fields we use to cache
# lists of overlapping pyensembl Gene and Transcript objects
self._genes = self._transcripts = None
# user might supply Ensembl release as an integer, reference name,
# or pyensembl.Genome object
if isinstance(ensembl, Genome):
self.ensembl = ensembl
elif isinstance(ensembl, int):
self.ensembl = cached_release(ensembl)
elif isinstance(ensembl, str):
self.ensembl = genome_for_reference_name(ensembl)
else:
raise TypeError(
("Expected ensembl to be an int, string, or pyensembl.Genome "
"instance, got %s : %s") % (type(ensembl), str(ensembl)))
self.normalize_contig_name = normalize_contig_name
self.allow_extended_nucleotides = allow_extended_nucleotides
self.original_contig = contig
self.contig = normalize_chromosome(
contig) if normalize_contig_name else contig
if ref != alt and ref in STANDARD_NUCLEOTIDES and alt in STANDARD_NUCLEOTIDES:
# Optimization for common case.
self.original_ref = self.ref = ref
self.original_alt = self.alt = alt
self.original_start = self.start = self.end = int(start)
return
# we want to preserve the ref/alt/pos both as they appeared in the
# original VCF or MAF file but also normalize variants to get rid
# of shared prefixes/suffixes between the ref and alt nucleotide
# strings e.g. g.10 CTT>T can be normalized into g.10delCT
#
# The normalized variant properties go into fields
# Variant.{original_ref, original_alt, original_pos}
# whereas the trimmed fields are:
# Variant.{ref, alt, start, end}
# the original entries must preserve the number of nucleotides in
# ref and alt but we still want to normalize e.g. '-' and '.' into ''
self.original_ref = normalize_nucleotide_string(
ref, allow_extended_nucleotides=allow_extended_nucleotides)
self.original_alt = normalize_nucleotide_string(
alt, allow_extended_nucleotides=allow_extended_nucleotides)
self.original_start = int(start)
# normalize the variant by trimming any shared prefix or suffix
# between ref and alt nucleotide sequences and then
# offset the variant position in a strand-dependent manner
(trimmed_ref, trimmed_alt, prefix,
suffix) = (trim_shared_flanking_strings(self.original_ref,
self.original_alt))
self.ref = trimmed_ref
self.alt = trimmed_alt
if len(trimmed_ref) == 0:
# insertions must be treated differently since the meaning of a
# position for an insertion is:
# "insert the alt nucleotides after this position"
#
# Aside: what if both trimmed ref and alt strings are empty?
# This means we had a "null" variant, probably from a VCF
# generated by force-calling mutations which weren't actually
# found in the sample.
# Null variants are interepted as inserting zero nucleotides
# after the whole reference sequence.
#
# Start and end both are base-1 nucleotide position before
# insertion.
self.start = self.original_start + max(0, len(prefix) - 1)
self.end = self.start
else:
# for substitutions and deletions the [start:end] interval is
# an inclusive selection of reference nucleotides
self.start = self.original_start + len(prefix)
self.end = self.start + len(trimmed_ref) - 1
StartLoss,
AlternateStartCodon,
PrematureStop,
FrameShift,
ExonLoss,
ExonicSpliceSite,
FrameShiftTruncation,
# TODO: SpliceDonor, SpliceReceptor
)
from pyensembl import ensembl_grch37, cached_release
from .common import expect_effect
# tried using more recent releases but found that many of them
# are very specific to Ensembl data between releases 77-81
ensembl_grch38 = cached_release(81)
def test_incomplete():
# transcript EGFR-009 (ENST00000450046 in Ensembl 78)
# has an incomplete 3' end
# chrom. 7 starting at 55,109,723
# first exon begins: ATCATTCCTTTGGGCCTAGGA
# change the first nucleotide of the 5' UTR A>T
variant = Variant("7", 55109723, "A", "T", ensembl=ensembl_grch38)
expect_effect(
variant,
transcript_id="ENST00000450046",
effect_class=IncompleteTranscript,
modifies_coding_sequence=False,
modifies_protein_sequence=False)
"""Make sure we're getting correct transcritp sequence from Ensembl and that
it's a sequence type which correctly implements `complement`
and `reverse_complement`
"""
from __future__ import absolute_import
from nose.tools import eq_
from pyensembl import cached_release
ensembl54 = cached_release(54)
ensembl83 = cached_release(83)
def test_transcript_sequence_ensembl54():
seq = ensembl54.transcript_sequence("ENST00000321606")
assert len(seq) == 414, \
"Expected transcript ENST00000321606 to have 414nt, got %s : %d" % (
seq, len(seq))
nucleotide_lines = [
"CATGTCACCCACCTTCAGGCGGCCCAAGACACTGCGACTCCGGAGGCAGCCCAGATATCCTCGGAAGAG",
"CACCCCCAGGAGAAACAAGCTTGGCCACTATGCTATCATCAAGTTTCCGCTGACCACTGAGTCGGCCGT",
"GAAGAAGATAGAAGAAAACAACACGCTTGTGTTCACTGTGGATGTTAAAGCCAACAAGCACCAGATCAG",
"ACAGGCTGTGAAGAAGCTCTATGACAGTGATGTGGCCAAGGTCACCACCCTGATTTGTCCTGATAAAGA",
"GAACAAGGCATATGTTCGACTTGCTCCTGATTATGATGCTTTCGATGTTGTAACAAAATTGGGATCACC",
"TAAACTGAGTCCAGCTGGCTAACTCTAAATATATGTGTATCTTTTCAGCATAAAAAAATAATGTTTTTC"
]
full_transcript_sequence = "".join(nucleotide_lines)
eq_(str(seq), full_transcript_sequence)
# now get the same sequence via a Transcript object
eq_(ensembl54.transcript_by_id("ENST00000321606").sequence, seq)
from pyensembl import cached_release, genome_for_reference_name
grcm38 = genome_for_reference_name("grcm38")
grch38 = cached_release(87)
StartLoss,
AlternateStartCodon,
PrematureStop,
FrameShift,
ExonLoss,
ExonicSpliceSite,
FrameShiftTruncation,
# TODO: SpliceDonor, SpliceReceptor
)
from pyensembl import ensembl_grch37, cached_release
from .common import expect_effect
# tried using more recent releases but found that many of them
# are very specific to Ensembl data between releases 77-81
ensembl_grch38 = cached_release(81)
def test_incomplete():
# transcript EGFR-009 (ENST00000450046 in Ensembl 78)
# has an incomplete 3' end
# chrom. 7 starting at 55,109,723
# first exon begins: ATCATTCCTTTGGGCCTAGGA
# change the first nucleotide of the 5' UTR A>T
variant = Variant("7", 55109723, "A", "T", ensembl=ensembl_grch38)
expect_effect(variant,
transcript_id="ENST00000450046",
effect_class=IncompleteTranscript,
modifies_coding_sequence=False,
modifies_protein_sequence=False)
"""
Test all methods which return collections of gene IDs that aren't converting
from some other type of name or ID.
TODO: Implement tests for EnsemblRelease.gene_ids
"""
from __future__ import absolute_import
from nose.tools import assert_raises, ok_
from pyensembl import ensembl_grch38, cached_release
from .common import test_ensembl_releases
ensembl77 = cached_release(77, "human")
def test_gene_ids_grch38_hla_a():
# chr6:29,945,884 is a position for HLA-A
# Gene ID = ENSG00000206503
# based on:
# http://useast.ensembl.org/Homo_sapiens/Gene/
# Summary?db=core;g=ENSG00000206503;r=6:29941260-29945884
ids = ensembl_grch38.gene_ids_at_locus(6, 29945884)
expected = "ENSG00000206503"
assert ids == ["ENSG00000206503"], \
"Expected HLA-A, gene ID = %s, got: %s" % (expected, ids)
def test_gene_ids_of_gene_name_hla_grch38():
hla_a_gene_ids = ensembl_grch38.gene_ids_of_gene_name("HLA-A")
assert 'ENSG00000206503' in hla_a_gene_ids, hla_a_gene_ids
hla_b_gene_ids = ensembl_grch38.gene_ids_of_gene_name("HLA-B")
def __init__(
self,
contig,
start,
ref,
alt,
ensembl=ensembl_grch38,
allow_extended_nucleotides=False):
"""
Construct a Variant object.
Parameters
----------
contig : str
Chromosome that this variant is on
start : int
1-based position on the chromosome of first reference nucleotide
ref : str
Reference nucleotide(s)
alt : str
Alternate nucleotide(s)
ensembl : Genome or EnsemblRelease
Object used for determining gene/transcript annotations
info : dict, optional
Extra metadata about this variant
"""
self.contig = normalize_chromosome(contig)
# user might supply Ensembl release as an integer, reference name,
# or pyensembl.Genome object
if isinstance(ensembl, Genome):
self.ensembl = ensembl
elif isinstance(ensembl, int):
self.ensembl = cached_release(ensembl)
elif isinstance(ensembl, str):
self.ensembl = genome_for_reference_name(ensembl)
else:
raise TypeError(
("Expected ensembl to be an int, string, or pyensembl.Genome "
"instance, got %s : %s") % (type(ensembl), str(ensembl)))
if (ref in STANDARD_NUCLEOTIDES and
alt in STANDARD_NUCLEOTIDES and
ref != alt):
# Optimization for common case.
self.original_ref = self.ref = ref
self.original_alt = self.alt = alt
self.original_start = self.start = self.end = int(start)
return
# we want to preserve the ref/alt/pos both as they appeared in the
# original VCF or MAF file but also normalize variants to get rid
# of shared prefixes/suffixes between the ref and alt nucleotide
# strings e.g. g.10 CTT>T can be normalized into g.10delCT
#
# The normalized variant properties go into fields
# Variant.{original_ref, original_alt, original_pos}
# whereas the trimmed fields are:
# Variant.{ref, alt, start, end}
# the original entries must preserve the number of nucleotides in
# ref and alt but we still want to normalize e.g. '-' and '.' into ''
self.original_ref = normalize_nucleotide_string(ref,
allow_extended_nucleotides=allow_extended_nucleotides)
self.original_alt = normalize_nucleotide_string(alt,
allow_extended_nucleotides=allow_extended_nucleotides)
self.original_start = int(start)
# normalize the variant by trimming any shared prefix or suffix
# between ref and alt nucleotide sequences and then
# offset the variant position in a strand-dependent manner
(trimmed_ref, trimmed_alt, prefix, suffix) = (
trim_shared_flanking_strings(self.original_ref, self.original_alt))
self.ref = trimmed_ref
self.alt = trimmed_alt
# insertions must be treated differently since the meaning of a
# position for an insertion is
# "insert the alt nucleotides after this position"
if len(trimmed_ref) == 0:
# start and end both are nucleotide before insertion
self.start = self.original_start + max(0, len(prefix) - 1)
self.end = self.start
else:
# for substitutions and deletions the [start:end] interval is
# an inclusive selection of reference nucleotides
self.start = self.original_start + len(prefix)
self.end = self.start + len(trimmed_ref) - 1
from .common import test_ensembl_releases
from .data import (
FOXP3_001_transcript_id,
CTNNBIP1_004_transcript_id,
CTNNBIP1_004_UTR5,
CTNNBIP1_004_UTR3,
CTNNBIP1_004_CDS,
CTNNBIP1_004_locus,
CTTNNIP1_004_exon_lengths,
CTTNNIP1_004_exon_ids,
EGFR_001_protein_sequence,
TP53_gene_id,
)
ensembl77 = cached_release(77)
def test_transcript_start_codon():
"""
test_transcript_start_codon : Check that fields Transcript
(for transcript named CTNNBIP1-004) matches known values.
"""
CTNNBIP1_004_transcript = ensembl77.transcript_by_id(
CTNNBIP1_004_transcript_id)
assert Locus.__eq__(CTNNBIP1_004_locus, CTNNBIP1_004_transcript), \
"Expected locus %s but got %s" % (
CTNNBIP1_004_locus, Locus.__str__(CTNNBIP1_004_transcript))
start_offsets = CTNNBIP1_004_transcript.start_codon_spliced_offsets
assert len(start_offsets) == 3, \
"Wrong length for start codon: %d (%s)" % (
"""
Exon IDs of the TP53 gene and one of its transcripts (TP53-026) were copied
from the Ensembl website, make sure same IDs are found by pyensembl.
"""
from __future__ import absolute_import
from pyensembl import cached_release
ensembl = cached_release(77)
# all exons associated with TP53 gene in Ensembl release 77
TP53_EXON_IDS_RELEASE_77 = [
'ENSE00002337729', 'ENSE00002419584',
'ENSE00003625790', 'ENSE00003518480',
'ENSE00003723991', 'ENSE00003712342',
'ENSE00001657961', 'ENSE00003725258',
'ENSE00003740946', 'ENSE00002204316',
'ENSE00002064269', 'ENSE00003750554',
'ENSE00003634848', 'ENSE00003492844',
'ENSE00003735852', 'ENSE00003545950',
'ENSE00003605891', 'ENSE00002051192',
'ENSE00002084733', 'ENSE00003726882',
'ENSE00001146308', 'ENSE00002667911',
'ENSE00003752869', 'ENSE00003739898',
'ENSE00003753508', 'ENSE00002034209',
'ENSE00002030826', 'ENSE00001596491',
'ENSE00002037735', 'ENSE00003736616',
'ENSE00002672443', 'ENSE00002226620',
'ENSE00003715195', 'ENSE00003750794',
'ENSE00003745267', 'ENSE00003746220',
'ENSE00003656695', 'ENSE00003669712',
# Read the exported Differential Gene Expression find to find the Drug Candidates
df = pd.read_csv("diff_exp_results.csv")
#Stripping Ensembl ID name
df['Gene'] = df['Gene'].str[5:]
df = df[df.log2FoldChange > 3]
#Store the Newly found Drugs for the thresholded Genes in df_DG
df_DG = pd.DataFrame(columns=('Gene', 'Drug'))
#Import annotations. You will need to install:
#pyensembl install --release 100 --species homo_sapiens
#if the file doesn't work
geneDB = cached_release(100, "human")
#Reading finding drugs available for the genes
print("finding drugs available for the genes...")
genes_list = []
drugs_list = []
for i in range(len(df['Gene'])):
try:
genename = geneDB.gene_name_of_gene_id(df['Gene'][i])
drugs = drugsfinder(genename)
genes_list = genes_list + [genename] * len(drugs)
drugs_list = drugs_list + drugs
except:
pass
df_DG['Gene'] = genes_list
def __init__(
self,
contig,
start,
ref,
alt,
ensembl=ensembl_grch38,
allow_extended_nucleotides=False,
normalize_contig_name=True):
"""
Construct a Variant object.
Parameters
----------
contig : str
Chromosome that this variant is on
start : int
1-based position on the chromosome of first reference nucleotide
ref : str
Reference nucleotide(s)
alt : str
Alternate nucleotide(s)
ensembl : Genome or EnsemblRelease
Object used for determining gene/transcript annotations
allow_extended_nucleotides : bool
Extended nucleotides include 'Y' for pyrimidies or 'N' for any base
normalize_contig_name : bool
By default the contig name will be normalized by trimming a 'chr'
prefix and converting all letters to upper-case. If we don't want
this behavior then pass normalize_contig_name=False.
"""
# first initialize the _genes and _transcripts fields we use to cache
# lists of overlapping pyensembl Gene and Transcript objects
self._genes = self._transcripts = None
# user might supply Ensembl release as an integer, reference name,
# or pyensembl.Genome object
if isinstance(ensembl, Genome):
self.ensembl = ensembl
elif isinstance(ensembl, int):
self.ensembl = cached_release(ensembl)
elif isinstance(ensembl, str):
self.ensembl = genome_for_reference_name(ensembl)
else:
raise TypeError(
("Expected ensembl to be an int, string, or pyensembl.Genome "
"instance, got %s : %s") % (type(ensembl), str(ensembl)))
self.normalize_contig_name = normalize_contig_name
self.allow_extended_nucleotides = allow_extended_nucleotides
self.original_contig = contig
self.contig = normalize_chromosome(contig) if normalize_contig_name else contig
if ref != alt and ref in STANDARD_NUCLEOTIDES and alt in STANDARD_NUCLEOTIDES:
# Optimization for common case.
self.original_ref = self.ref = ref
self.original_alt = self.alt = alt
self.original_start = self.start = self.end = int(start)
return
# we want to preserve the ref/alt/pos both as they appeared in the
# original VCF or MAF file but also normalize variants to get rid
# of shared prefixes/suffixes between the ref and alt nucleotide
# strings e.g. g.10 CTT>T can be normalized into g.10delCT
#
# The normalized variant properties go into fields
# Variant.{original_ref, original_alt, original_pos}
# whereas the trimmed fields are:
# Variant.{ref, alt, start, end}
# the original entries must preserve the number of nucleotides in
# ref and alt but we still want to normalize e.g. '-' and '.' into ''
self.original_ref = normalize_nucleotide_string(
ref,
allow_extended_nucleotides=allow_extended_nucleotides)
self.original_alt = normalize_nucleotide_string(
alt,
allow_extended_nucleotides=allow_extended_nucleotides)
self.original_start = int(start)
# normalize the variant by trimming any shared prefix or suffix
# between ref and alt nucleotide sequences and then
# offset the variant position in a strand-dependent manner
(trimmed_ref, trimmed_alt, prefix, suffix) = (
trim_shared_flanking_strings(self.original_ref, self.original_alt))
self.ref = trimmed_ref
self.alt = trimmed_alt
if len(trimmed_ref) == 0:
# insertions must be treated differently since the meaning of a
# position for an insertion is:
# "insert the alt nucleotides after this position"
#
# Aside: what if both trimmed ref and alt strings are empty?
# This means we had a "null" variant, probably from a VCF
# generated by force-calling mutations which weren't actually
# found in the sample.
# Null variants are interepted as inserting zero nucleotides
# after the whole reference sequence.
#
# Start and end both are base-1 nucleotide position before
# insertion.
self.start = self.original_start + max(0, len(prefix) - 1)
self.end = self.start
else:
# for substitutions and deletions the [start:end] interval is
# an inclusive selection of reference nucleotides
self.start = self.original_start + len(prefix)
self.end = self.start + len(trimmed_ref) - 1
from .common import test_ensembl_releases
from .data import (
FOXP3_001_transcript_id,
CTNNBIP1_004_transcript_id,
CTNNBIP1_004_UTR5,
CTNNBIP1_004_UTR3,
CTNNBIP1_004_CDS,
CTNNBIP1_004_locus,
CTTNNIP1_004_exon_lengths,
CTTNNIP1_004_exon_ids,
EGFR_001_protein_sequence,
TP53_gene_id,
)
ensembl77 = cached_release(77)
def test_transcript_start_codon():
"""
test_transcript_start_codon : Check that fields Transcript
(for transcript named CTNNBIP1-004) matches known values.
"""
CTNNBIP1_004_transcript = ensembl77.transcript_by_id(
CTNNBIP1_004_transcript_id)
assert Locus.__eq__(CTNNBIP1_004_locus, CTNNBIP1_004_transcript), \
"Expected locus %s but got %s" % (
CTNNBIP1_004_locus, Locus.__str__(CTNNBIP1_004_transcript))
start_offsets = CTNNBIP1_004_transcript.start_codon_spliced_offsets
assert len(start_offsets) == 3, \
"""Make sure we're getting correct transcritp sequence from Ensembl and that
it's a sequence type which correctly implements `complement`
and `reverse_complement`
"""
from __future__ import absolute_import
from nose.tools import eq_
from pyensembl import cached_release
ensembl54 = cached_release(54)
ensembl83 = cached_release(83)
def test_transcript_sequence_ensembl54():
seq = ensembl54.transcript_sequence("ENST00000321606")
assert len(seq) == 414, \
"Expected transcript ENST00000321606 to have 414nt, got %s : %d" % (
seq, len(seq))
nucleotide_lines = [
"CATGTCACCCACCTTCAGGCGGCCCAAGACACTGCGACTCCGGAGGCAGCCCAGATATCCTCGGAAGAG",
"CACCCCCAGGAGAAACAAGCTTGGCCACTATGCTATCATCAAGTTTCCGCTGACCACTGAGTCGGCCGT",
"GAAGAAGATAGAAGAAAACAACACGCTTGTGTTCACTGTGGATGTTAAAGCCAACAAGCACCAGATCAG",
"ACAGGCTGTGAAGAAGCTCTATGACAGTGATGTGGCCAAGGTCACCACCCTGATTTGTCCTGATAAAGA",
"GAACAAGGCATATGTTCGACTTGCTCCTGATTATGATGCTTTCGATGTTGTAACAAAATTGGGATCACC",
"TAAACTGAGTCCAGCTGGCTAACTCTAAATATATGTGTATCTTTTCAGCATAAAAAAATAATGTTTTTC"
]
full_transcript_sequence = "".join(nucleotide_lines)
eq_(str(seq), full_transcript_sequence)
# now get the same sequence via a Transcript object
eq_(ensembl54.transcript_by_id("ENST00000321606").sequence, seq)
"""
Exon IDs of the TP53 gene and one of its transcripts (TP53-026) were copied
from the Ensembl website, make sure same IDs are found by pyensembl.
"""
from __future__ import absolute_import
from pyensembl import cached_release
ensembl = cached_release(77)
# all exons associated with TP53 gene in Ensembl release 77
TP53_EXON_IDS_RELEASE_77 = [
'ENSE00002337729', 'ENSE00002419584', 'ENSE00003625790', 'ENSE00003518480',
'ENSE00003723991', 'ENSE00003712342', 'ENSE00001657961', 'ENSE00003725258',
'ENSE00003740946', 'ENSE00002204316', 'ENSE00002064269', 'ENSE00003750554',
'ENSE00003634848', 'ENSE00003492844', 'ENSE00003735852', 'ENSE00003545950',
'ENSE00003605891', 'ENSE00002051192', 'ENSE00002084733', 'ENSE00003726882',
'ENSE00001146308', 'ENSE00002667911', 'ENSE00003752869', 'ENSE00003739898',
'ENSE00003753508', 'ENSE00002034209', 'ENSE00002030826', 'ENSE00001596491',
'ENSE00002037735', 'ENSE00003736616', 'ENSE00002672443', 'ENSE00002226620',
'ENSE00003715195', 'ENSE00003750794', 'ENSE00003745267', 'ENSE00003746220',
'ENSE00003656695', 'ENSE00003669712', 'ENSE00002051873', 'ENSE00002048269',
'ENSE00002670535', 'ENSE00002677565', 'ENSE00003532881', 'ENSE00003520683',
'ENSE00002076714', 'ENSE00002062958', 'ENSE00002073243', 'ENSE00003670707',
'ENSE00002065802', 'ENSE00002362269'
]
def test_exon_ids_of_gene_id():
"""
test_exon_ids_of_gene_id: Ensure that gene_id ENSG00000141510 (name=TP53),
