aGOtool has two application programming interfaces(APIs) which enable you to get data without using the graphical user interface of the web page. The APIs are convenient if you need programmatic access. Please have a look at the examples. Why are there two APIs? Beacause, one already existed (and remains for backwards compatibility) and the other was created to facilitate easy Cytoscape integration.

A.) This API is analogous to STRING's API.

Please follow the STRING API help link for instructions of how to use this API and
replace the STRING URL https://string-db.org/api with the following aGOtool URL

https://agotool.org/api

B.) The second API is described in examples below.

Imports

import requests
import pandas as pd
from io import StringIO

url = r"https://agotool.org/api_orig"

call api_help for help and to get default arguments (parameters are described in detail below)

response = requests.get(r"https://agotool.org/api_help")
print(response.json())

Example #1

Protein name: trpA; Organism: Escherichia coli K12_MG1655

ENSPs = ['511145.b1260', '511145.b1261', '511145.b1262', '511145.b1263', '511145.b1264', '511145.b1812',
     '511145.b2551', '511145.b3117', '511145.b3360', '511145.b3772', '511145.b4388']
fg = "%0d".join(ENSPs)
result = requests.post(url,
                   params={"output_format": "json",
                           "enrichment_method": "genome",
                           "taxid": 83333},
            # UniProt reference proteomes uses "Escherichia coli (strain K12)"
            # with Taxid 83333 as a pan proteome instead of 511145 (TaxID on taxonomic rank of species).
                   data={"foreground": fg})
result_json = result.json()
print("Enrichment results for {} number of terms".format(len(result_json)))
print(result_json[0])

Example #2

Use iPython or Jupyter notebook to interactively view results as a pandas data frame.

Protein name: CDC15; Organism: Saccharomyces cerevisiae

ENSPs = ['4932.YAR019C', '4932.YFR028C', '4932.YGR092W', '4932.YHR152W', '4932.YIL106W', '4932.YJL076W',
     '4932.YLR079W', '4932.YML064C', '4932.YMR055C', '4932.YOR373W', '4932.YPR119W']
fg = "%0d".join(ENSPs)
result = requests.post(url,
                   params={"output_format": "tsv",
                           "enrichment_method": "genome",
                           "taxid": 559292},
            # UniProt reference proteomes uses "Saccharomyces cerevisiae S288C"
            # with Taxid 559292 as a pan proteome instead of 4932 (TaxID on taxonomic rank of species).
                   data={"foreground": fg})
df = pd.read_csv(StringIO(result.text), sep='\t')
print(df.head())

Example #3

Protein name: smoothened; Organism: Mus musculus

ENSPs = ['10090.ENSMUSP00000001812', '10090.ENSMUSP00000002708', '10090.ENSMUSP00000021921',
     '10090.ENSMUSP00000025791', '10090.ENSMUSP00000026474', '10090.ENSMUSP00000030443',
     '10090.ENSMUSP00000054837', '10090.ENSMUSP00000084430', '10090.ENSMUSP00000099623',
     '10090.ENSMUSP00000106137', '10090.ENSMUSP00000107498']
fg = "%0d".join(ENSPs)
result = requests.post(url,
                   params={"output_format": "json",
                           "enrichment_method": "compare_samples",
                           "limit_2_entity_type": "-56;-51;-57",
                   # comma separate desired entity_types (e.g. PMIDs, UniProt Keywords, and Reactome).
                           "p_value_cutoff": 1,
                           "FDR_cutoff": 1,
                           "taxid": 10090},
                   data={"foreground": fg,
                         "background": fg})
# since the foreground equals the background completely no significant enrichment will result from this analysis
# it is meant to show how to show how to use the method call, and to apply a sanity check in case you get no results (try using p value or FDR cutoff of 1)
result_json = result.json()
print(result_json[0])

Example #4

Characterize foreground, get functional information

ENSPs = ["9606.ENSP00000266970"]
fg = "%0d".join(ENSPs)
result = requests.post(url,
                   params={"output_format": "tsv",
                           "filter_foreground_count_one": False,
                           "enrichment_method": "characterize_foreground"},
                   data={"foreground": fg})
df = pd.read_csv(StringIO(result.text), sep='\t')
print(df.head())