def get_transcript(self, ensg):
"""Takes ENSG and RefSeq codes to find Ensembl transcript ID and peptide ID.
:param ensg: ENSG number.
:param refseq: RefSeq NM_ code.
:return: transcript and protein IDs.
"""
# 1) Connect to BioMart server.
server = BiomartServer("http://www.ensembl.org/biomart")
server.verbose = True # provides setting up details
new_list = []
# 2) Select dataset to check against. Not essential but quicker so BioMart doesn't need to search.
hs_genes = server.datasets['hsapiens_gene_ensembl']
# 3) This is like clicking Results on the BioMart website. Filter by ENSG and RefSeq to get transcript & peptide
results = hs_genes.search({
'filters': {'ensembl_gene_id': '%s' % ensg,
'refseq_mrna': '%s' % self.refseq},
'attributes': ['ensembl_transcript_id', 'ensembl_peptide_id']
}, header=1)
# 4) Convert to readable format and add relevant information to a list.
for line in results.iter_lines():
line = line.decode('utf-8')
new_list.append(line.split())
uni_transcript = new_list[1]
transcript = uni_transcript[0]
protein = uni_transcript[1]
return transcript, protein
def serverConnection(proxy=None, verbose=True):
server = BiomartServer("http://ensembl.gramene.org/biomart")
# if you are behind a proxy
if proxy is not None:
server.http_proxy = os.environ.get('http_proxy', proxy)
# set verbose to True to get some messages
server.verbose = verbose
return server
def server_connect(self):
connected = False
while not connected:
try:
server = BiomartServer("http://grch37.ensembl.org/biomart")
connected = True
except requests.exceptions.ConnectionError:
connected = False
print('Connection error')
server.verbose = True
self.hsapiens_snp = server.datasets['hsapiens_snp']
def GOquery(query, organism, gene_id):
# connect to server
server = BiomartServer("http://www.biomart.org/biomart")
# set verbose to True to get some messages
server.verbose = True
db = server.datasets[organism + '_gene_ensembl']
response = db.search({
'filters': {
gene_id: query
},
'attributes': [gene_id, 'with_go']
})
return response
def vb_request(path: str, ds: tuple):
"Access VectorBase API to get gene positions of requested species"
# Init server
server = BiomartServer('http://biomart.vectorbase.org/biomart/')
# Iterate over requests for gene positions of requested species
attr = ('chromosome_name', 'start_position', 'end_position', 'strand',
'ensembl_gene_id')
for i, n in enumerate(ds):
name = f'{path}/{i}_gene.tsv'
with open(name, 'a') as f:
rqst = server.datasets[n + '_eg_gene'].search({
'filters': {},
'attributes': attr
})
for line in rqst.iter_lines():
f.write(line.decode('utf-8') + '\n')
# Request for orthologous genes of requested species
attr = ['ensembl_gene_id'] + [
f'{ds[i]}_eg_homolog_ensembl_gene' for i in range(1, len(ds))
]
name = path + 'homology_genes.tsv'
with open(name, 'w') as f:
s = server.datasets[ds[0] + '_eg_gene'].search(
{
'filters': {},
'attributes': attr
}, header=1)
for line in s.iter_lines():
f.write(line.decode('utf-8') + '\n')
return print('VB done.')
def _fetchFromServer(ensemble_server, attributes):
server = BiomartServer(ensemble_server)
ensmbl = server.datasets['hsapiens_gene_ensembl']
res = pd.read_csv(io.StringIO(ensmbl.search({
'attributes': attributes
}, header=1).content.decode()), sep='\t')
return res
def QueryBioMart(dataset, attributes, host=host):
server = BiomartServer(host)
organism = server.datasets[dataset]
response = organism.search({'attributes': attributes})
response = response.content.split("\n")
response = [s.split("\t") for s in response]
response = pd.DataFrame(response, columns=attributes)
return response
def get_transcript(ensg, refseq):
server = BiomartServer("http://www.ensembl.org/biomart")
server.verbose = True
new_list = []
hs_genes = server.datasets['hsapiens_gene_ensembl']
results = hs_genes.search({
'filters': {'ensembl_gene_id': '%s' % ensg,
'refseq_mrna': '%s' % refseq},
'attributes': ['ensembl_transcript_id', 'ensembl_peptide_id']
}, header=1)
for line in results.iter_lines():
line = line.decode('utf-8')
new_list.append(line.split())
uni_transcript = new_list[1]
transcript = uni_transcript[0]
protein = uni_transcript[1]
return transcript, protein
def queryBM(query_attributes,
query_dataset,
query_filter=None,
query_items=None,
query_dic=None,
host=biomart_host):
"""
Queries BioMart.
:param query_attributes: list of attributes to recover from BioMart
:param query_dataset: dataset to query
:param query_filter: one BioMart filter associated with the items being queried
:param query_items: list of items to be queried (must assoiate with given filter)
:param query_dic: for complex queries this option should be used instead of 'filters' and 'items' and a dictionary of filters provided here eg. querydic={"filter1":["item1","item2"],"filter2":["item3","item4"]}. If using querydic, don't query more than 350 items at once.
:param host: address of the host server, default='http://www.ensembl.org/biomart'
:returns: a Pandas dataframe of the queried attributes
"""
server = BiomartServer(host)
d = server.datasets[query_dataset]
res = []
if not query_dic:
if query_items:
chunks = [
query_items[x:x + 350]
for x in xrange(0, len(query_items), 350)
]
for c in chunks:
response = d.search({
'filters': {
query_filter: c
},
'attributes': query_attributes
})
for line in response.iter_lines():
line = line.decode('utf-8')
res.append(line.split("\t"))
else:
response = d.search({'attributes': query_attributes})
for line in response.iter_lines():
line = line.decode('utf-8')
res.append(line.split("\t"))
elif query_dic:
response = d.search({
'filters': query_dic,
'attributes': query_attributes
})
for line in response.iter_lines():
line = line.decode('utf-8')
res.append(line.split("\t"))
res = pd.DataFrame(res)
res.columns = query_attributes
return (res)
def main(newfile):
atts = ['ensembl_gene_id', 'ensembl_peptide_id']
url = 'http://www.ensembl.org/biomart'
server = BiomartServer(url)
hge = server.datasets['hsapiens_gene_ensembl']
with open(newfile, 'w') as outF:
s = hge.search({'attributes': atts}, header=0)
for l in s.iter_lines():
(gene_id, peptide_id) = l.split('\t')
if len(peptide_id) > 0:
outF.write('%s\t%s\n' % (gene_id.strip(), peptide_id.strip()))
def get_region_from_rsid(rsid_list):
region_list = []
if rsid_list:
from biomart import BiomartServer
server = BiomartServer("http://uswest.ensembl.org/biomart/")
db = server.datasets['hsapiens_snp']
response = db.search({'filters': {'variation_source': 'dbSNP',
'snp_filter': rsid_list},
'attributes': ['refsnp_id', 'chr_name', 'chrom_start', 'chrom_end']})
output = dict()
for line in response.iter_lines():
line = line.decode('utf-8').split("\t")
try:
if line[0] in output.keys():
print('Multiple entries for: {}'.format(line[0]))
if line[1] in CONTIG:
output[line[0]] = {'chr': line[1],
'start': line[2],
'stop': line[3]}
except:
if line[0] not in output.keys():
print('Error getting chr pos for: {}'.format(line[0]))
pass
present = set(output.keys())
if len(present) < len(rsid_list):
missing = [x for x in rsid_list if x not in present]
print("Missing following rsID: " + ', '.join(missing))
response = db.search({'filters': {'variation_source': 'dbSNP',
'snp_synonym_filter': missing},
'attributes': ['refsnp_id', 'chr_name', 'chrom_start', 'chrom_end']})
print('Converted the following rsID:')
for i, line in enumerate(response.iter_lines()):
line = line.decode('utf-8').split("\t")
try:
if line[1] in CONTIG:
output[line[0]] = {'chr': line[1],
'start': line[2],
'stop': line[3]}
print(missing[i] + ' -> ' + line[0])
except:
print("Error getting chr pos for: {}".format(line[0]))
pass
region_list = [str(output[x]["chr"]) + ":" + str(output[x]["start"]) + "-" + str(output[x]["stop"]) for x in output
if output[x]["chr"] and output[x]["start"] and output[x]["stop"]]
return region_list
def datasetsBM(host=biomart_host):
"""
Lists BioMart datasets.
:param host: address of the host server, default='http://www.ensembl.org/biomart'
:returns: nothing
"""
stdout_ = sys.stdout #Keep track of the previous value.
stream = StringIO()
sys.stdout = stream
server = BiomartServer(biomart_host)
server.show_datasets()
sys.stdout = stdout_ # restore the previous stdout.
variable = stream.getvalue()
v = variable.replace("{", " ")
v = v.replace("}", " ")
v = v.replace(": ", "\t")
print(v)
def searchDataset(chrom, start, end):
from biomart import BiomartServer
server = BiomartServer("http://asia.ensembl.org/biomart")
server.verbose = True
ccrigri = server.datasets["ccrigri_gene_ensembl"]
response = ccrigri.search({
"filters": {
"chromosome_name": chrom,
"start": start,
"end": end
},
"attributes": [
"ensembl_gene_id", "ensembl_transcript_id", "refseq_mrna",
"external_gene_name", "start_position", "end_position",
"wikigene_description"
]
})
for i in response.iter_lines():
i = i.decode("utf-8")
return i
def __init__(self, **kwargs):
"""
Connect the annotator to Biomart's server. Set self.VERBOSE=True to get
info of the queries as they're made.
"""
super().__init__(**kwargs)
self.server = BiomartServer('http://grch37.ensembl.org/biomart')
self.server.verbose = self.VERBOSE
if self.proxies and 'http' in self.proxies:
self.server.http_proxy = self.proxies['http']
self.database = self.server.databases['ENSEMBL_MART_SNP']
self.human_snps = self.database.datasets['hsapiens_snp']
def __init__(self):
# GRCh37 is also known as hg19
# server = BiomartServer("http://useast.ensembl.org/biomart")
self.server = BiomartServer("http://grch37.ensembl.org/biomart")
# set verbose to True to get some messages
# server.verbose = True
# server.show_databases()
self.database = self.server.databases["ENSEMBL_MART_SNP"]
# db.show_datasets()
self.dataset = self.database.datasets["hsapiens_snp"]
def get_ENSEMBL_data(listID, dataset, transcr_expr_file=None):
"""Function to connect to ENSEBL and retrieve data.
The functions follows two modes of working:
1) Transcript selection scheme acording to ENSEMBL/HAVANA, TSL and APPRIS. (Default)
2) Transcript selection by the best expressed transcript if ther is an externaly provided file.
Return: Pandas data frame of the transcripts and their ENSEMBL features.
"""
print("Connection to ENSEMBL server.", file=sys.stderr)
server = BiomartServer("http://www.ensembl.org/biomart/")
dt = server.datasets[dataset]
print("Retrieve the dataset...", file=sys.stderr)
listAttrib = ['ensembl_gene_id', 'ensembl_transcript_id', 'external_gene_name', 'transcript_length', 'transcript_biotype', 'cdna_coding_start', 'cdna_coding_end', 'cdna', 'description']
listAttrib2 = ['ensembl_gene_id', 'ensembl_transcript_id', 'transcript_tsl', 'transcript_appris', 'transcript_source', 'transcript_length', 'transcript_biotype']
print("Fetch data from: {}".format(str(server)), file=sys.stderr)
# Collect data from the ENSEMBL datasets.
dfFeat = pd.DataFrame()
dfTrans = pd.DataFrame()
for chunk in chunks(listID, 100):
print('Fetching...', file=sys.stderr)
res1 = dt.search({'filters': {'ensembl_gene_id': chunk}, 'attributes': listAttrib}, header=1)
res2 = dt.search({'filters': {'ensembl_gene_id': chunk}, 'attributes': listAttrib2}, header=1)
# Reading stream to a pandas data frame.
dataf = pd.read_csv(io.StringIO(res1.text), sep='\t', encoding='utf-8')
datat = pd.read_csv(io.StringIO(res2.text), sep='\t', encoding='utf-8')
# Cleanup data frame lines that do not correspond to protein coding genes.
dataf = dataf[dataf['Transcript type'] == 'protein_coding']
datat = datat[datat['Transcript type'] == 'protein_coding']
# Concatenate data frames.
dfFeat = pd.concat([dfFeat, dataf], axis=0, sort=False)
dfTrans = pd.concat([dfTrans, datat], axis=0, sort=False)
print("...fetch done!", file=sys.stderr)
# Function to select transcripts.
transcripts = select_transcripts(dfTrans, dfFeat, transcr_expr_file)
# Set the size of the UTRs and the CDS by using the information of the coding exon.
for index, row in transcripts.iterrows():
# For 5'UTR end take the smallest coordinate in the coding exons.
coding_start = min([int(x) for x in row["cDNA coding start"].split(";")])
coding_end = max([int(x) for x in row["cDNA coding end"].split(";")])
# REPLACE the right CDS start and end
transcripts.at[index, "cDNA coding start"] = coding_start
transcripts.at[index, "cDNA coding end"] = coding_end
# Clean up the TSL value.
tsl = row["Transcript support level (TSL)"].split()[0]
transcripts.at[index, "Transcript support level (TSL)"] = tsl
return transcripts
def get_region_from_gene(gene_list):
from biomart import BiomartServer
server = BiomartServer("http://uswest.ensembl.org/biomart/")
db = server.datasets['hsapiens_gene_ensembl']
response = db.search({
'filters': {
'source': 'ensembl_havana',
'hgnc_symbol': gene_list
},
'attributes':
['hgnc_symbol', 'chromosome_name', 'start_position', 'end_position']
})
output = dict()
for line in response.iter_lines():
line = line.decode('utf-8').split("\t")
try:
if line[0] in output.keys():
print('Multiple entries for: {}'.format(line[0]))
print(line)
output[line[0]] = {
'chr': int(line[1]),
'start': int(line[2]),
'stop': int(line[3])
}
except:
if line[0] not in output.keys():
print('Error getting chr pos for: {}'.format(line[0]))
pass
present = set(output.keys())
if len(present) < len(gene_list):
missing = [x for x in gene_list if x not in present]
print("Missing following genes: " + ', '.join(missing))
region_list = [
str(output[x]["chr"]) + ":" + str(output[x]["start"]) + "-" +
str(output[x]["stop"]) for x in output
if output[x]["chr"] and output[x]["start"] and output[x]["stop"]
]
return region_list
def gene_name_to_ensembl_id():
atts = ['external_gene_name', 'ensembl_gene_id']
# server = BiomartServer( "http://www.ensembl.org/biomart" ) # latest: not using to keep everything in sync with build 37
server = BiomartServer("http://grch37.ensembl.org/biomart")
hge = server.datasets['hsapiens_gene_ensembl']
# collect data from server as list of lists
s = hge.search({'attributes': atts}, header=1)
biomart_list = list()
for l in s.iter_lines():
line = l.decode("utf-8").split("\t")
biomart_list.append(line)
# convert to pandas df and fix the colnames
biomart_table = pd.DataFrame(biomart_list)
biomart_table.columns = biomart_table.iloc[0]
biomart_table = biomart_table.drop([
0,
]).drop_duplicates()
return biomart_table
def getEnsemblAnnotation(species="hsapiens", filePath=None):
from biomart import BiomartServer
import pandas as pd
server = BiomartServer("http://www.biomart.org/biomart")
ensembl = server.databases["ensembl"]
genes = ensembl.datasets["{0}_gene_ensembl".format(species)]
attributes = ['mgi_symbol'] if species == "mmusculus" else ["hsapiens"]
attributes += ['ensembl_gene_id', 'ensembl_transcript_id']
response = genes.search({'attributes': attributes})
results = list()
for line in response.iter_lines():
results.append(line.split("\t"))
annotation = pd.DataFrame(results, columns=attributes)
if filePath is not None:
annotation.to_csv(filePath, index=False)
else:
return annotation
def attributesBM(dataset, host=biomart_host):
"""
Lists BioMart attributes for a specific dataset.
:param dataset: dataset to list attributes of.
:param host: address of the host server, default='http://www.ensembl.org/biomart'
:returns: nothing
"""
stdout_ = sys.stdout #Keep track of the previous value.
stream = StringIO()
sys.stdout = stream
server = BiomartServer(host)
d = server.datasets[dataset]
d.show_attributes()
sys.stdout = stdout_ # restore the previous stdout.
variable = stream.getvalue()
v = variable.replace("{", " ")
v = v.replace("}", " ")
v = v.replace(": ", "\t")
print(v)
def convert_entrez_to_gene_symbol(keyword_list):
"""
Convert Refseq ID to Gene Symbol and description using Biomart
"""
#print("\nConverting Refseq to Gene Symbol\n")
server = BiomartServer("http://mar2017.archive.ensembl.org/biomart")
#server.verbose = True
#server.show_databases()
#server.show_datasets()
hsapiens = server.datasets['hsapiens_gene_ensembl']
#hsapiens.show_filters()
#hsapiens.show_attributes()
# run a search with custom filters and attributes (no header)
response = hsapiens.search({
'filters': {
'entrezgene': keyword_list
},
'attributes': ['external_gene_name', 'description', 'entrezgene']
})
return response
from biomart import BiomartServer
atts = ['external_gene_name','external_gene_source','ensembl_gene_id',
'ensembl_transcript_id','ensembl_peptide_id']
server = BiomartServer( "http://www.biomart.org/biomart" )
hge = server.datasets['hsapiens_gene_ensembl']
print(server.databases['ensembl'])
s = hge.search({'attributes': atts}, header=1)
for l in s.iter_lines():
print(l)
#Code: Access Biomart
#!/usr/bin/python
#import BiomartServer from the
#biomart module
from biomart import BiomartServer
#connect to the biomart server
server = BiomartServer("http://www.biomart.org/biomart")
#show all the server databases on the console
server.show_databases()
#show all the server datasets on the console
server.show_datasets()
#use the 'uniprot' dataset
uniprot = server.datasets['uniprot']
#run a search with custom filters and attributes
#such as searching for results with the
#protein name "Dystrophin"
#the UniProt accession number,
#the protein name and the
#gene name are give for
#each search result
#!/usr/bin/env python # https://pypi.python.org/pypi/biomart from biomart import BiomartServer #Connect to a Biomart Server server = BiomartServer( "http://www.biomart.org/biomart" ) # set verbose to True to get some messages server.verbose = True
from biomart import BiomartServer
server = BiomartServer( "http://www.biomart.org/biomart" ) # if not behind a proxy, otherwise see: https://pypi.python.org/pypi/biomart/0.8.0
server.verbose = True # set verbose to True to get some messages
# run a search with custom filters and attributes (no header)
response = uniprot.search({
'filters': {
'accession': ['Q9FMA1', 'Q8LFJ9']
},
'attributes': [
'accession', 'protein_name'
]
})
# FUNCTIONS ====================================================================
# RUN ==========================================================================
# GENE LIST
# ------------------------------------------------------------------------------
# Read gene list
gene_list = pd.read_csv(gene_list_file, header=None)[0].tolist()
# BioMaRt
# Connect to the server and prepare for querying
# ------------------------------------------------------------------------------
# Connect to biomart
server = BiomartServer("http://www.ensembl.org/biomart")
server.verbose = False
# Check available databases
#server.show_databases()
# Select Genes database
db = server.databases['ENSEMBL_MART_ENSEMBL']
# Check available datasets (species)
#db.show_datasets()
# Select H. sapiens dataset
ds = db.datasets['hsapiens_gene_ensembl']
# show all available filters and attributes of the 'uniprot' dataset
def biomart_anno(url, loca):
urlp = urlparse(url)
species = urlp.path.split('/')[1]
code = species.split('_')[0][0].lower() + species.split('_')[1]
server = BiomartServer('%s://%s/biomart' % (urlp.scheme, urlp.netloc))
datasets = server.datasets
print("Connecting to Ensembl biomart...")
_ = ['metazoa', 'plants', 'fungi', 'bacteria', 'protists']
dn = code + ('_eg_gene'
if urlp.netloc.split('.')[0] in _ else '_gene_ensembl')
ds = datasets[dn]
os.system('mkdir -p %s' % loca)
S = ds.search({'attributes': ['ensembl_gene_id', 'gene_biotype', \
'external_gene_name', 'description', 'chromosome_name', 'start_position', \
'end_position', 'strand']})
gene_infor = pd.DataFrame.from_records(
[str(i, encoding = 'utf-8').split('\t') for i in S.iter_lines()], \
columns = ['gene', 'gene_biotype', 'gene_name', 'gene_description', \
'chromosome_name', 'start_position', 'end_position', 'strand'])
m = {"-1": "-", "1": "+"}
gene_infor['strand'] = [
m[i] if i in m else str(i) for i in gene_infor['strand']
]
gene_infor['gene_position'] = gene_infor.loc[:, ['chromosome_name', \
'start_position', 'end_position', 'strand']].apply(\
lambda x: ':'.join(x), axis=1)
gene_infor.drop(['chromosome_name', 'start_position', 'end_position', \
'strand'], axis = 1, inplace=True)
####
gene2GO = getGO(ds, loca)
if gene2GO.shape[0] != 0:
g = gene2GO.groupby('gene')['GO_id'].apply(lambda x: ', '.join(x))
gene_infor['GO_id'] = [
g[i] if i in g else '' for i in gene_infor['gene']
]
####
gene2kegg = getKEGG(ds, loca)
if gene2kegg.shape[0] != 0:
k = gene2kegg.groupby('gene')['KEGG_enzyme'].apply(
lambda x: ', '.join(x))
gene_infor['KEGG_enzyme'] = [
k[i] if i in k else '' for i in gene_infor['gene']
]
####
gene2entrez = getEntrezgene(ds, loca)
if gene2entrez.shape[0] != 0:
e = gene2entrez.groupby('gene')['entrez'].apply(lambda x: ', '.join(x))
gene_infor['entrez'] = [
e[i] if i in e else '' for i in gene_infor['gene']
]
####
gene2swissprot = getSwissProt(ds, loca)
if gene2swissprot.shape[0] != 0:
s = gene2swissprot.groupby('gene')['SwissProt'].apply(
lambda x: ', '.join(x))
gene_infor['SwissProt'] = [
s[i] if i in s else '' for i in gene_infor['gene']
]
gene_infor.to_csv(loca + '/gene.infor.tsv', sep='\t', index=False)
print('saved %d records to %s/gene.infor.tsv' %
(gene_infor.shape[0], loca))
import os
from glob import glob
from tqdm import tqdm
import pandas as pd
import click
from sklearn.linear_model import LogisticRegression
import numpy as np
from biomart import BiomartServer
import readquant
print('Connecting to biomart...')
SERVER = BiomartServer("http://www.ensembl.org/biomart")
ENSEMBL = SERVER.datasets['hsapiens_gene_ensembl']
def get_ERCC():
return readquant.data.ERCC()['concentration in Mix 1 (attomoles/ul)']
def get_MT():
print('Get MT genes from biomart...')
r = ENSEMBL.search({
'filters': {
'chromosome_name': 'MT'
},
'attributes': ['ensembl_gene_id']
})
idx = pd.Index(r.text.split())
#Code: Access Biomart
#!/usr/bin/python
#import BiomartServer from the
#biomart module
from biomart import BiomartServer
#connect to the biomart server
server = BiomartServer("http://www.biomart.org/biomart")
#show all the server databases on the console
server.show_databases()
#show all the server datasets on the console
server.show_datasets()
#use the 'uniprot' dataset
uniprot = server.datasets['uniprot']
#run a search with custom filters and attributes
#such as searching for results with the
#protein name "Dystrophin"
#the UniProt accession number,
#the protein name and the
#gene name are give for
#each search result
'''
from biomart import BiomartServer
urls = { '92': 'http://apr2018.archive.ensembl.org/biomart', '91': 'http://dec2017.archive.ensembl.org/biomart',
'90': 'http://aug2017.archive.ensembl.org/biomart', '89': 'http://may2017.archive.ensembl.org/biomart',
'88': 'http://mar2017.archive.ensembl.org/biomart', '87': 'http://dec2016.archive.ensembl.org/biomart',
'86': 'http://oct2016.archive.ensembl.org/biomart', '85': 'http://jul2016.archive.ensembl.org/biomart',
'84': 'http://mar2016.archive.ensembl.org/biomart', '83': 'http://dec2015.archive.ensembl.org/biomart',
'82': 'http://sep2015.archive.ensembl.org/biomart', '81': 'http://jul2015.archive.ensembl.org/biomart',
'80': 'http://may2015.archive.ensembl.org/biomart', '79': 'http://mar2015.archive.ensembl.org/biomart',
'78': 'http://dec2014.archive.ensembl.org/biomart', '77': 'http://oct2014.archive.ensembl.org/biomart',
'76': 'http://aug2014.archive.ensembl.org/biomart', '75': 'http://feb2014.archive.ensembl.org/biomart',
'74': 'http://dec2013.archive.ensembl.org/biomart' }
server = BiomartServer( urls['${ENSEMBL_VERSION}'] )
tags = { 'pfam': 'Pfam', 'scanprosite': 'Prosite', 'prosite': 'Prosite' }
ensembl = server.datasets['hsapiens_gene_ensembl']
response = ensembl.search({
'attributes': [ 'ensembl_transcript_id', 'transcript_version', '${DB}', '${DB}_start', '${DB}_end' ]
})
with open('${DB}_features.tsv', 'w') as OUT:
for line in response.iter_lines():
line = line.decode('utf-8')
t,v,f,s,e = line.split("\\t")
if f:
OUT.write('{}.{}\\t{}\\t{}\\t{}\\t{}\\n'. format(t, v, tags['${DB}'], f, s, e))
def __init__(self):
self.__server = BiomartServer("http://www.ensembl.org/biomart")
from biomart import BiomartServer
import json
server = BiomartServer('http://biomart.vectorbase.org/biomart')
steph = server.datasets['astephensi_eg_gene']
response = steph.search({'attributes': ['ensembl_gene_id']})
genes = []
for line in response.iter_lines():
line = line.decode('utf-8')
genes.append(line)
with open('./data/genes.json', 'w') as file:
json.dump(genes, file)
from biomart import BiomartServer
from Clean import clean
server = BiomartServer( "http://biomart.vectorbase.org/biomart/" )
#'vb_gene_mart_1708': VectorBase Genes database
#'agambiae_eg_gene': Anopheles gambiae genes (AgamP4) dataset
#'agambiae_eg_genomic_sequence': Anopheles gambiae sequences (AgamP4)
# show server databases 'server.show_databases()'
# show server datasets 'server.show_datasets()'
# show all available filters and attributes of the 'uniprot' dataset:
# 'uniprot.show_filters()','uniprot.show_attributes()'
#use 'a_gambiae' dataset
#'ensembl_gene_id': 'Gene stable ID', 'strand': 'Strand',
#'start_position': 'Gene start (bp)', 'end_position': 'Gene end (bp)','chromosome_name': 'Chromosome/scaffold name'
def vb_import(ds):
r = server.datasets[ds].search({
'filters': {},
'attributes': [ 'chromosome_name', 'start_position', 'end_position', 'strand', 'ensembl_gene_id' ]
}, header = 1 )
file = open('test_response.txt', 'r')
# response format is TSV
for line in r.iter_lines():
line = line.decode('utf-8')
print(line)
file.close()
#vb_import('agambiae_eg_gene')
abc = server.datasets['agambiae_eg_gene']
#abc.show_attributes()
import getopt, sys
from biomart import BiomartServer
from io import StringIO
from os import listdir
from os import walk
import glob
import pandas as pd
server = BiomartServer('http://www.ensembl.org/biomart')
hsap = server.datasets['hsapiens_gene_ensembl']
def strand_fix(i):
if i == 1:
return ('+')
elif i == -1:
return ('-')
else:
return ('invalid strand')
def get_bed_data(extGeneNames):
response = hsap.search(
{
'filters': {
'external_gene_name': extGeneNames
},
'attributes': [
"chromosome_name", "start_position", "end_position", "strand",
"external_gene_name"
]
