def get_descriptions (ensid):
request='/xrefs/id/%s' %(ensid)
decoded = ensembl_rest.get_endpoint(server, request)
desc = []
for xref in decoded:
if (xref['description']) and (xref['dbname']):
string = "DB: %s\tDescription: %s" %(xref['dbname'], xref['description'] )
desc.append(string)
return desc
import json, ensembl_rest
server = 'http://rest.ensembl.org'
vep_endpoint = '/vep/human/id/{}'
vep_post_endpoint = '/vep/human/id'
overlap_endpoint = '/overlap/region/human/{}?feature=variation'
# 1) Print VEP results for rs189863975
# a) For each overlapping transcript (transcript_consequences) print
# variant_allele,
# transcript_id,
# the consequence_terms
# and if available the polyphen_score and polyphen_prediction
variant_effects = ensembl_rest.get_endpoint(server,
vep_endpoint.format('rs189863975'))
for entry in variant_effects:
for consequence in entry['transcript_consequences']:
variant_allele = consequence['variant_allele']
transcript_id = consequence['transcript_id']
polyphen_score = consequence.get('polyphen_score', 'no polyphen score')
polyphen_prediction = consequence.get('polyphen_prediction',
'no polyphen prediction')
consequence_terms = ','.join(consequence['consequence_terms'])
print("Variant allele: {}, Transcript ID: {}, Consequence terms: {}".
format(variant_allele, transcript_id, consequence_terms))
if (polyphen_score != 'no polyphen score'):
print(" PolyPhen score: {}, PolyPhen prediction: {}".format(
polyphen_score, polyphen_prediction))
import json, ensembl_rest server = "http://rest.ensembl.org" # Gene tree endpoint exercises # CG-6a: Get the information for the protein genetree with the stable id ENSGT00390000003602. output should be in the orthoxml format ext = "/genetree/id/ENSGT00390000003602?" content_type = "text/x-orthoxml+xml" endpoint = ensembl_rest.get_endpoint(server, ext, content_type) print (endpoint)
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import requests, json, sys, ensembl_rest
server = "http://rest.ensembl.org"
## Export all microarray platforms that are annotated for humans in Ensembl and their associated information.
request = '/regulatory/species/homo_sapiens/microarray'
decoded = ensembl_rest.get_endpoint(server, request)
print(json.dumps(decoded, indent=4, sort_keys=True))
"""
You have performed a microarray experiment with the array HumanWG_6_V2. The following probes gave you a positive signal:
ILMN_1763508, ILMN_1861090, ILMN_1890175, ILMN_1749304, ILMN_1894173, ILMN_1911643, ILMN_1891089, ILMN_1859810, ILMN_1843473, ILMN_1770856
a) Which transcripts do they map to?
b) Which genes do these transcripts belong to?
"""
# Transcript and gene mappings for different probes
array = 'HumanWG_6_V2'
probes = [
'ILMN_1763508', 'ILMN_1861090', 'ILMN_1890175', 'ILMN_1749304',
'ILMN_1894173', 'ILMN_1911643', 'ILMN_1891089', 'ILMN_1859810',
'ILMN_1843473', 'ILMN_1770856'
]
for probe in probes:
print(probe)
request = '/regulatory/species/homo_sapiens/microarray/%s/probe/%s?content-type=application/json;gene=1;transcript=1' % (
array, probe)
decoded = ensembl_rest.get_endpoint(server, request)
if decoded:
print("Probe length: %sbp Sequence: %s" %
import json, ensembl_rest
server = "http://rest.ensembl.org" #http://ebi-cli-003:3000"
# Alignment endpoint exercise
# CG-1a: Get in json format the LastZ pairwise alignment for taeniopygia_guttata V gallus_gallus for region 2:106041430-106041480:1
ext = "/alignment/region/taeniopygia_guttata/2:106041430-106041480:1?method=LASTZ_NET;species_set=taeniopygia_guttata;species_set=gallus_gallus"
endpoint = ensembl_rest.get_endpoint(
server, ext
) # a third parameter 'content_type' defaults to 'application/json', so no need to define it here
print(json.dumps(endpoint, indent=4, sort_keys=True))
import json, ensembl_rest server = "http://rest.ensembl.org" # Family endpoint exercises # CG-4: Get the information for families predicted for the human gene ENSG00000283087. What do you notice? ext = "/family/member/id/ENSG00000283087?" endpoint = ensembl_rest.get_endpoint(server, ext) print(json.dumps(endpoint, indent=4, sort_keys=True))
import json, ensembl_rest
server = 'http://rest.ensembl.org'
phenotype_endpoint = '/phenotype/term/homo_sapiens/{}'
variation_post_endpoint = '/variation/human?pops=1'
# 1) Get all variants that are associated with the phenotype 'Coffee consumption'. For each variant print
# a) the p-value for the association
# b) the PMID for the publication which describes the association between that variant and Coffee consumption
# c) the risk allele and the associated gene
request = phenotype_endpoint.format('coffee consumption')
associations = ensembl_rest.get_endpoint(server, request)
variation2risk_allele = {}
for association in associations:
variation = association['Variation']
desc = association['description']
source = association['source']
mapped_to_accession = association['mapped_to_accession']
attributes = association['attributes']
p_value = attributes['p_value']
external_reference = attributes['external_reference']
associated_gene = attributes['associated_gene']
risk_allele = attributes.get('risk_allele', '')
if risk_allele != '':
variation2risk_allele[variation] = risk_allele
print(
"Variation: {}, Phenotype: {}, p-value: {}, PMID: {}, Associated gene(s): {}, Risk allele: {}"
.format(variation, desc, p_value, external_reference, associated_gene,
import json, ensembl_rest
server = 'http://rest.ensembl.org'
overlap_region_endpoint = '/overlap/region/human/{}?feature={}'
variation_post_endpoint = '/variation/human'
lookup_id_endpoint = '/lookup/id/{}?expand=1'
overlap_id_endpoint = '/overlap/id/{}?feature={}'
# 1) Print all variants that are located on chromosome 17 between 80348215 and 80348333.
# Use the overlap endpoint to get the location (seq_region_name, start, end),
# alleles, consequence_type and clinical_significance for each variant in the region.
request = overlap_region_endpoint.format('17:80348215..80348333', 'variation')
variants = ensembl_rest.get_endpoint(server, request)
for v in variants:
assembly_name = v['assembly_name']
seq_region_name = v['seq_region_name']
start = v['start']
end = v['end']
alleles = '/'.join(v['alleles'])
consequence_type = v['consequence_type']
clinical_significance = v['clinical_significance']
print(
"Location: {}:{}:{}-{}, Alleles: {}, Consequence: {}, Clinical significance: {}"
.format(assembly_name, seq_region_name, start, end, alleles,
consequence_type, clinical_significance))
# 2) Get the variant class, evidence attributes, source and the most_severe_consequence
# for all variants in that region from the variant endpoint.
for t in efo['_embedded']['terms']:
print("Link(IRI): %s" % (t['iri']))
if t['description']:
for d in t['description']:
print("Description: %s" % (d))
else:
print('No description provided')
print()
## main
# 1. List all Epigenomes available in Ensembl Regulation
server = "http://rest.ensembl.org"
endpoint = '/regulatory/species/homo_sapiens/epigenome'
decoded = ensembl_rest.get_endpoint(server, endpoint, 'application/json')
print(json.dumps(decoded, indent=4, sort_keys=True))
# 2. Find additional information (where available) for each epigenome using the Ontology Lookup Service
efo_server = "http://www.ebi.ac.uk/ols/api/ontologies/efo/terms?obo_id="
for r in decoded:
print("Epigenome name: %s" % r['name'])
# No EFO ID assigned to this epigenome
if not r['efo_id']:
print("No EFO ID assigned: %s\n" % (r['scientific_name']))
continue
request = efo_server + r['efo_id']
efo = ensembl_rest.get_endpoint_efo(efo_server, request)
import json, ensembl_rest
server = 'http://rest.ensembl.org'
ld_region_endpoint = '/ld/human/region/{}/{}'
ld_endpoint = '/ld/human/{}/{}'
# 1) Compute LD in the region 3:196064297-196068186
# for the population 1000GENOMES:phase_3:CEU.
# Print all results with r2=1 and d_prime=1.
ld_values = ensembl_rest.get_endpoint(
server,
ld_region_endpoint.format('3:196064297-196068186',
'1000GENOMES:phase_3:CEU'))
high_ld_pairs = (
ld_value for ld_value in ld_values
if float(ld_value['d_prime']) == 1.0 and float(ld_value['r2']) == 1.0)
for pair in high_ld_pairs:
variation1 = pair['variation1']
variation2 = pair['variation2']
print("Pair: {}-{}".format(variation1, variation2))
print('')
# Compute pairwise LD for all variants that are not further away from rs535797132
# than 500kb.
# Print all variants that are in LD (d_prime >= 0.8) with rs535797132.
# For each pair of variants also print d_prime and r2.
# Use 1000GENOMES:phase_3:FIN as the population.
