A guide to exploring KEGG pathways with KEGGscape

KEGGscape constructs KEGG pathway on Cytoscape3

(formerly known as KGMLReader for Cytoscape 2.*).

In contrast to KEGG web, you can edit the network and map your data as you like.

Installing KEGGscape

KEGGscape source code is distributed as open source software under the Apache License, Version 2.0 and is available at GitHub.

https://github.com/idekerlab/KEGGscape

KEGGscape requires Cytoscape version 3.6. First you need to download Cytoscape from

http://cytoscape.org/download.html

You can install KEGGscape with Cytoscape app manager.

How to import KEGG pathway xml(kgml) to Cytoscape

First we show how to import KEGG pathway xml(kgml) to Cytoscape.

Importing kgml to Cytoscape with REST endpoint

KEGGscape exposes a REST endpoint to directly import a KEGG pathway entry and it is documented in the main Swagger page generated by CyREST (available under: Help -> Automation -> CyREST API).

You can import kgml to Cytoscape with filling the KEGG pathway ID and clicking the “Try it out!” button.

Importing kgml to Cytoscape by manually downloading kgml

Downloading KEGG pathway kgml

You can download KEGG pathway kgml without opening web browser (if you know the KEGG pathway entryID you want to import).

wget http://rest.kegg.jp/get/eco00020/kgml -O eco00020.xml

eco00020.xml is TCA cycle of Escherichia coli K-12 MG1655.

Importing kgml to Cytoscape by GUI

You can import kgml to Cytoscape from menu bar

File -> Import -> Network -> File

and open eco00020.xml.

How to bundle edges

KEGGscape creates two edges for a reversible reaction, if you want to bundle these reversible reactions like KEGG, please select “Bundle edges” from “Layout” menu.

Combination of Python scripts and KEGGscape

Scripting language support is an experimental feature in Cytoscape 3.

Cytoscape 3 supports scripting language. Here we show a sample of Python + KEGGscape.

We import all Ecoli pathways to Cytoscape.

Importing all KEGG pathways of Escherichia coli K-12 MG1655

First we download all Ecoli pathways with the following Python script. This script requires requests Python package.

import requests

ORGANISM = "eco"

pathways = requests.get('http://rest.kegg.jp/list/pathway/' + ORGANISM)
for line in pathways.content.split('\n'):
    pathwayid = line.split('\t')[0].replace('path:', '')
    kgml = requests.get('http://rest.kegg.jp/get/' + pathwayid + '/kgml')
    f = open(pathwayid + '.xml', 'w')
    f.write(kgml.content)
    f.close

You will get all eco KGMLs like this.

Next we show a sample to batch-import kgml files from Python script. To use Python from Cytoscape3, you need to download jython-standalone from here , and move the jython-standalone like this.

Now you can batch-import kgml files with Python. Here we import all carbohydrate metabolism kgml files. (Of course you can import all pathways, but it takes time and cys file get so big.)

mkdir carbohydrate
mv eco00010.xml eco00020.xml eco00030.xml eco00040.xml eco00051.xml
eco00052.xml eco00053.xml eco00500.xml eco00520.xml eco00562.xml
eco00620.xml eco00630.xml eco00640.xml eco00650.xml eco00660.xml

next run cytoscape3, go in osgi shell and run following Python script(save as load_kegg.py).

from java.io import File

KEGG_DIR = "/ABS_PATH_TO/carbohydrate/"
pathways = ["eco00010.xml", "eco00020.xml", "eco00030.xml", "eco00040.xml", "eco00051.xml", "eco00052.xml", "eco00053.xml", "eco00500.xml", "eco00520.xml", "eco00562.xml", "eco00620.xml", "eco00630.xml", "eco00640.xml", "eco00650.xml", "eco00660.xml"]

loadNetworkTF = cyAppAdapter.get_LoadNetworkFileTaskFactory()
taskManager = cyAppAdapter.getTaskManager()

allTasks = None

for pathway in pathways:
    kgmlpath = File(KEGG_DIR + pathway)
    print str(kgmlpath)
    itr = loadNetworkTF.createTaskIterator(kgmlpath)
    if allTasks is None:
        allTasks = itr
    else:
        allTasks.append(itr)

taskManager.execute(allTasks)

Save this Python script as load_kegg.py. To run load_kegg.py, type (from Cytoscape3 OSGi shell)

cytoscape:script python /ABS_PATH_TO_SCRIPT/load_kegg.py

Mapping drug targets on KEGG pathway

Here we show a example of data integration. We map drug targets(from Drugbank) on KEGG pathway. To manage several tables, we use MongoDB and PyMongo.

Importing all data into MongoDB

First we export node attribute table of Alanine, aspartate and glutamate metabolism as alanine_nodes.csv.

Next we download drug targets from Drugbank and id convert table with KEGG REST API.

wget http://www.drugbank.ca/system/downloads/current/all_target_ids_all.csv.zip
unzip all_target_ids_all.csv.zip
wget http://rest.kegg.jp/conv/eco/uniprot
mv uniprot conv_eco_uniprot.tsv

Finally we import these tables into mongodb.

mongoimport --db keggscape --collection alanine_nodes --headerline --type csv --file alanine_nodes.csv
mongoimport --db keggscape --collection all_target_ids_all --headerline --type csv --file all_target_ids_all.csv
mongoimport --db keggscape --collection conv_eco_uniprot -f uniprot_id,kegg_id --type tsv --file conv_eco_uniprot.tsv

Merging tables with PyMongo

We integrate the three table(network nodes, drug targets table, id conversion table). Here we append columns drug target and drug to Cytoscape’s node table.

from pymongo import MongoClient

client = MongoClient()
db = client['keggscape']

node_collection = db['alanine_nodes']
drug_collection = db['all_target_ids_all']
conv_collection = db['conv_eco_uniprot']

gene_table = node_collection.find({"KEGG_NODE_TYPE": "gene"})

for genes in gene_table:
    locuses = genes["KEGG_ID"].split("\r") #newline character depends on your OS, I exported cytoscape table on Mac
    for locus in locuses:
        ids = conv_collection.find_one({"kegg_id": locus})
        drug = drug_collection.find_one({"UniProt ID": ids["uniprot_id"].replace("up:", "")})
        if drug != None:
            node_collection.update({"_id": genes["_id"]}, {"$push": {"drug_ids": drug["Drug IDs"], "target_id": drug["ID"], "target": locus}})
            node_collection.update({"_id": genes["_id"]}, {"$set": {"is_target": 1}})

Next we create fields.txt to export the new node table.

shared name
drug_ids
target_id
target
is_target

and export node table as csv.

mongoexport --db keggscape --collection alanine_nodes --csv --fieldFile fields.txt --out alanine_drugs.csv

import this alanine_drugs.csv into Cytoscape and highlight drug targets as below.

Mapping genome scale metabolic model on KEGG pathway

Here we show the other example of data integration. We map iAF1260(a genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs) on KEGG pathway.

Importing iAF1260 into MongoDB

You can download iAF1260 reaction table from ModelSEED.

and import this table into MongoDB, like this.

mongoimport --db keggscape --collection iaf1260 --type tsv --headerline --file table.tsv

and export Galactose metabolism pathway from Cytoscape and import it like this.

mongoimport --db keggscape --collection galactose_node --headerline --type csv --file galactose_node.csv

This Python script append column which enzyme genes differ between KEGG and iAF1260.

from sets import Set
from pymongo import MongoClient

client = MongoClient()
db = client['keggscape']

node_collection = db['galactose_node']
model_collection = db['iaf1260']

kegggene_table = node_collection.find({"KEGG_NODE_TYPE": "gene"})
modelreaction_table = model_collection.find({"KEGG RID": {"$regex": "R[0-9]{5}"}})

for kegggene in kegggene_table:
    kegggenes = kegggene['KEGG_ID'].split("\r")
    keggonly_genes = []
    modelonly_genes = []

    for keggreaction in kegggene['KEGG_NODE_REACTIONID'].split(" "):
        modelkeggreaction_table = model_collection.find({"KEGG RID": {"$regex": keggreaction.replace("rn:", "")}})

        if modelkeggreaction_table.count() > 0:
            for modelkeggreaction in modelkeggreaction_table:
                modelgenes = modelkeggreaction['iAF1260\r'].strip().replace("<br>", "eco:").split(", ")

                if Set(kegggenes) != Set(modelgenes):
                    keggonly = Set(kegggenes) - Set(modelgenes)
                    modelonly = Set(modelgenes) - Set(kegggenes)
                    if len(keggonly) > 0:
                        node_collection.update({"_id": kegggene["_id"]}, {"$push": {"keggonly": " ".join(keggonly)}})
                    else:
                        node_collection.update({"_id": kegggene["_id"]}, {"$push": {"modelonly": " ".join(modelonly)}})

And export galactose_node collection and reimport to Cytoscape.

mongoexport --db keggscape --collection galactose_nodes --csv --fieldFile genediff_fields.txt --out new_galactose_nodes.csv

Here is the annotation difference between iAF1260 and KEGG

This work was supported by the National Bioscience Database Center(NBDC) program Database Integration Coordination Program (Tool Prototype for Integrated Database Analysis)

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