Contents

2.12.1. Using BridgeDb web services




Recipe Overview
Reading Time
30 minutes
Executable Code
Yes
Difficulty
Using BridgeDb web services
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Recipe Type
Hands-on
Audience
Data Manager, Data Scientist
Maturity Level & Indicator
[F+MM-1.2C]
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2.12.1.1. Overview

In this notebook I will present two use cases for BridgeDb with the purpose of identifier mapping:

  • Mapping data from a recognized data source by BridgeDb to another recognized data source (see here). For example mapping data identifiers from HGNC to Ensembl.

  • Given a local identifier and a TSV mapping it to one of the BridgeDb data sources, how to map the local identifier to a different data source.

2.12.1.2. Querying the WS

To query the Webservice we define below the url and the patterns for a single request and a batch request. You can find the docs here. We will use Python’s requests library.

url = "https://webservice.bridgedb.org/"

single_request = url+"{org}/xrefs/{source}/{identifier}"

batch_request = url+"{org}/xrefsBatch/{source}{}"

import requests
import pandas as pd

Here we define a method that will turn the web service response into a dataframe with columns corresponding to:

  • The original identifier

  • The data source that the identifier is part of

  • The mapped identifier

  • The data source for the mapped identifier

def to_df(response, batch=False):
    if batch:
        records = []
        for tup in to_df(response).itertuples():
            if tup[3] != None:
                for mappings in tup[3].split(','):
                    target = mappings.split(':', 1)
                    if len(target) > 1:
                        records.append((tup[1], tup[2], target[1], target[0]))
                    else:
                        records.append((tup[1], tup[2], target[0], target[0]))
        return pd.DataFrame(records, columns = ['original', 'source', 'mapping', 'target'])
        
    return pd.DataFrame([line.split('\t') for line in response.text.split('\n')])

Here we define the organism and the data source from which we want to map

source = "H"
org = 'Homo sapiens'

2.12.1.3. Case 1

Here we first load the case 1 example data.

case1 = pd.read_csv("data/case1-example.tsv", header=None)
case1
0
0 A1BG
1 A1CF
2 A2MP1

Then we batch request the mappings

response1 = requests.post(batch_request.format('', org=org, source=source), data = case1.to_csv(index=False, header=False))

And use our to_df method to turn it into a DataFrame

case1_df = to_df(response1, batch=True)
case1_df
original source mapping target
0 A1BG HGNC uc002qsd.5 Uc
1 A1BG HGNC 8039748 X
2 A1BG HGNC GO:0072562 T
3 A1BG HGNC uc061drj.1 Uc
4 A1BG HGNC ILMN_2055271 Il
... ... ... ... ...
109 A2MP1 HGNC 16761106 X
110 A2MP1 HGNC 16761118 X
111 A2MP1 HGNC ENSG00000256069 En
112 A2MP1 HGNC A2MP1 H
113 A2MP1 HGNC NR_040112 Q

114 rows × 4 columns

2.12.1.4. Case 2

Here we first load the case 2 example data and perform the same steps as before

case2 = pd.read_csv('data/case2-example.tsv', sep='\t', names=['local', 'source'])

source_data = case2.source.to_csv(index=False, header=False)
query = batch_request.format('', org=org, source=source)
response2 = requests.post(query, data = source_data)

mappings = to_df(response2, batch=True)
mappings
original source mapping target
0 A1BG HGNC uc002qsd.5 Uc
1 A1BG HGNC 8039748 X
2 A1BG HGNC GO:0072562 T
3 A1BG HGNC uc061drj.1 Uc
4 A1BG HGNC ILMN_2055271 Il
... ... ... ... ...
109 A2MP1 HGNC 16761106 X
110 A2MP1 HGNC 16761118 X
111 A2MP1 HGNC ENSG00000256069 En
112 A2MP1 HGNC A2MP1 H
113 A2MP1 HGNC NR_040112 Q

114 rows × 4 columns

After obtaining the mappings we join with the TSV file on the Affy identifier, obtaining the desired mapping by selecting the columns mapping and local

local_mapping = mappings.join(case2.set_index('source'), on='original')

local_mapping[['mapping', 'local']]
mapping local
0 ENSG00000121410 aa11
1 ENSG00000148584 bb34
2 ENSG00000256069 eg93

2.12.1.5. Using Script

from bridgedb_script import get_mappings

get_mappings("data/case2-example.tsv", "Homo sapiens", "H", case=2, target='En')
original source mapping target local
0 A1BG HGNC ENSG00000121410 En aa11
1 A1CF HGNC ENSG00000148584 En bb34
2 A2MP1 HGNC ENSG00000256069 En eg93 
get_mappings("data/case1-example.tsv", "Homo sapiens", "H", case=1)
original source mapping target
0 A1BG HGNC uc002qsd.5 Uc
1 A1BG HGNC 8039748 X
2 A1BG HGNC GO:0072562 T
3 A1BG HGNC uc061drj.1 Uc
4 A1BG HGNC ILMN_2055271 Il
... ... ... ... ...
109 A2MP1 HGNC 16761106 X
110 A2MP1 HGNC 16761118 X
111 A2MP1 HGNC ENSG00000256069 En
112 A2MP1 HGNC A2MP1 H
113 A2MP1 HGNC NR_040112 Q

114 rows × 4 columns

2. Identifier mapping with BridgeDb 3. Introduction to terminologies and ontologies