(Under construction!!)

Integrate Networks and Annotation by Web Services

TableOfContents([3])

Introduction

From version 2.6, Cytoscape works as a web service client for public biological databases. In this tutorial, you will learn how to use Cytoscape as a data integration platform using public databases.

What is a Web Service?

Web Service is a standardized mechanism for computers to exchange data. These days, there are lots of public biological databases accessible over the Internet. Many of them start supporting web services and accessible from client programs. This means you can search and retrieve interactions and annotations directly from client programs. Cytoscape works as a web service client from 2.6, so you can access those databases directly from your Cytoscape Desktop.

Goal of this Tutorial

You can learn the following functions of Cytoscape from this tutorial:

1. How to import networks from public databases
2. How to import annotations and map IDs
3. Merge networks from multiple data sources
4. Map known pathways onto interaction networks

This is a fairly complicated tutorial to use multiple plugins and multiple data sources, so I assume you already know basics of Cytoscape. If not, please finish the basic tutorials first.

Tutorial: Integrate Known Information about PPAR-Gamma

Setup

To do this tutorial, you need to install the following plugins:

Network/Attribute Import Clients

1. Pathway Commons Plugin (installed by default)
2. NCBIClient Plugin
3. NCBIEntrezGeneUserInterface Plugin
4. IntActWSClient Plugin

5. BiomartClient Plugin (0.80 and later. No GUI plugin required.)

6. AgilentLiteratureSearch Plugin

7. MiMI Plugin
8. GPML Plugin

Data Merge

1. AdvancedNetworkMerge Plugin

Scripting

1. RubyScriptingEngine Plugin

2. ScriptingEngineManager Plugin

Search

1. Enhanced Search Plugin

Note: Out of Memory Problem

When you load a lot of plugins at once, sometimes Cytoscape crashes even if you have a lot of memory in your machine. This is because Java heap called Permanent region is full. To avoid this problem, you need to edit the following file:

• # for Mac/Linux
  cytoscape.sh
  
  # for Windows
  cytoscape.bat

In the file, you can see the following options:

 -Xss5M -Xmx1024M -XX:MaxPermSize=128m

In general, if you increase -XX:MaxPermSize, you can load many plugins at once. Default size is 64M, so probably 128M is enough in most cases.

To run Cytoscape with these options, execute the modified file from command line, simply type

cytoscape.sh

for Windows, double-click cytoscape.bat.

Tutorial 1: Search by Keyword

In this exercise, you are going to learn how to search interactions by keyword.

Import Interactions and Annotation from NCBI Entrez Gene

1. Select File-->Import-->Network from Web Services...

2. Set Data Source to NCBI Entrez EUtilities Web Service Client

3. In the Query window, type pparg AND human[ORGN]. This query means search Entrez Gene database by PPARG for human.

4. Press Search button. This process takes a while.

5. When the client finds matched entries, it pops up a confirmation dialog. Press Yes to proceed.

6. You will be asked to type network name. Type PPAR-Gamma from NCBI.

7. Select Layout-->yFiles-->Organic and apply the layout.

8. In the main desktop, you can see a network generated from the NCBI Entrez Gene data sets. Entrez Gene stores interaction data from three databases: BIND, BioGRID, and HPRD. Edge color represents source of the interaction data. attachment:ncbi1.png attachment:ncbi2.png

9. Select File-->Import-->Import Attributes from NCBI Entrez Gene

10. Make Sure Attribute is set to ID and check all attributes on the list attachment:ncbi3.png

11. Press Import. This takes several minutes depends on network speed

12. Now you have a network annotated with Entrez Gene database attachment:ncbi35.png

Import Known Pathways and Interactions from Pathway Commons

1. Use Pathway Commons and search PPARG for human attachment:ncbi4.png

2. Load all pathways and merge them into one big network

Import Binary Interactions from IntAct

1. Select File-->Import-->Network from Web Services...

2. Set Data Source to IntAct Web Service Client

3. In the Query box, type PPARG AND species:human

4. Press Search

5. At this point, IntAct client only imports direct interactions to PPAR-Gamma.

6. On the new network view, select all nodes. Then right-click one of the selected nodes and select Use Web Services-->IntAct Web Service Cleint-->Get neighbours by ID(s) (see the screenshot below)

   • attachment:intact1.png
7. Now the network includes nodes within two hops from PPAR-Gamma.
   • attachment:intact2.png

8. At this point, your workspace should look like the following (use View-->Arrange Network Windows-->Tiled to arrange network views):

   • attachment:intact3.png

Import KEGG Pathway using BioRuby

1. Download the following [http://www.ruby-lang.org/en/ Ruby] script to your current working directory. attachment:kegg_relation_mapper_for_bioruby_console.rb

2. Select Plugins-->Scripting Language Consoles-->Open Ruby Console. This command initializes [http://bioruby.org/ BioRuby] Console and takes several moments

3. Check the location of your script file. cd to the location if necessary. attachment:kegg1.png

4. Search the pathway related to PPAR-Gamma. From the console, type:

   keggapi.bfind('pathway pparg human')

   This command invokes BioRuby's KEGG API, and it takes a while to be initialized. The command means search KEGG Pathway database using keyword pparg and human. The result should look like the following:

   bioruby> keggapi.bfind('pathway pparg human')
   JRuby limited openssl loaded. gem install jruby-openssl for full support.
   http://wiki.jruby.org/wiki/JRuby_Builtin_OpenSSL
     ==> "path:hsa03320 PPAR signaling pathway - Homo sapiens (human); Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that are activated by fatty acids and their derivatives. PPAR has three subtypes (PPARalpha, beta/delta, and gamma) showing different expression patterns in vertebrates. Each of them is encoded in a separate gene and binds fatty acids and eicosanoids. PPARalpha plays a role in the clearance of circulating or cellular lipids via the regulation of gene expression involved in lipid metabolism in liver and skeletal muscle. PPARbeta/delta is involved in lipid oxidation and cell proliferation. PPARgamma promotes adipocyte differentiation to enhance blood glucose uptake.\n"
   bioruby> 

   Now we found a pathway path:hsa03320 PPAR signaling pathway in KEGG related to PPAR-Gamma gene. Let's Import this pathway to Cytoscape.

5. Type:

    pathway_id = 'path:hsa03320'

6. Then run the script:

   source 'YOUR_SCRIPT_NAME'

   If the script is not in your current working directory, you need to use the full path. After few moments, you can see the following relation diagram of genes on pathway path:hsa03320 (custom VizMapper applied to the following screenshot). attachment:kegg2.png

   This script creates two additional attributes KEGG ID and Entrez Gene ID. You can use them for node labels to make the diagram more meaningful. attachment:kegg3.png

Import Pathways from WikiPathways

[http://www.wikipathways.org/index.php/WikiPathways WikiPathways] is a database of curated pathways using Wiki-style interface. Pathway data files are available as GPML format (standard data file format for [http://www.genmapp.org/default.html GenMAPP]) and they are readable in Cytoscape. In addition, GPML plugin supports direct pathway import from WikiPathways using Cytoscape's web service client framework.

1. Install [http://www.pathvisio.org/Cytoscape_plugin GPML Plugin]. You can install it from Plugin Manager.

2. Select File-->Import-->Network from Web Services...

3. Set Data Source to WikiPathways Web Service Client

4. Type pparg in the Search text box.

5. Set organism to Homo Sapience and press Search.

6. You can see several candidate pathways. To import them, just double-click the pathway name. attachment:gpml1.png attachment:gpml2.png

Extract Interactions from Publications by Agilent Literature Search (not finished yet!)

1. Start Agilent Literature Search from Plugins-->Agilent Literature Search

2. In the Terms window, type pparg

3. Increase the Max Engine Matches to 1000

4. Check Use Aliases

5. Start search by pressing blue triangle icon. attachment:agilent1.png

6. After few minutes, new network will be generated from publication data. Right-click the network name and name it PPAR-Gamma Network by Litelature search. attachment:agilent2.png

At this point, this network does not have annotations/ID sets for nodes. To add some more information for this network, you can use BioMart web service client. Before importing annotations from BioMart, you need to make a new attribute from node IDs. Node IDs generated by Agilent Literature Search is all lower-case. However, human Gene Symbols are usually represented as upper case. Since Cytoscape is case-sensitive, the following process is required.

1. Create new String attribute by clicking Create New Attribute icon in Attribute Browser.

2. Name it Gene Symbol

3. Select all nodes

4. Click the Batch Attribute Editor icon on the Node Attribute Browser.

5. Select Copy tab and copy canonicalName to Gene Symbol

6. Select Operation tab and set command to To upper-case and Gene Symbol

7. Press Go. attachment:agilent3.png

Now you are ready to add some annotations to the generated network.

1. Select File-->Import-->Import Attributes from BioMart...

2. Set Data Source to ENSEMBL GENES (Homo Sapience)

3. Select Gene Symbol as the key attribute, and Data Type should be HGNC Symbol

4. Select some annotations from the list.

5. Press Import

6. Matched attributes will be mapped to the nodes on the current network. attachment:agilent4.png

In this example, I imported some ID sets including NCBI Entrez Gene ID. Let's import some more annotations from NCBI by using Entrez Gene ID as the key.

1. Select

How can we annotate genes not found in BioMart?

Usually, Gene Symbols are a bit more meaningful for biologists than unique database IDs (for example, PPARG is an abbreviation of peroxisome proliferator-activated receptor gamma for human and its Entrez Gene ID is a simple digit [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=gene&Cmd=ShowDetailView&TermToSearch=5468&ordinalpos=2&itool=EntrezSystem2.PEntrez.Gene.Gene_ResultsPanel.Gene_RVDocSum 5468]). However, this string is not always unique. This causes ID mapping problems. In this example, some of the Gene Symbols are not annotated by BioMart, although you can find some data files in other databases.

In such case, you can use some command line tools to create ID mapping table for Cytoscape.

Create Gene Symbol Mapping Table by BioRuby

1. Select all node.

2. In the Attribute Browser, select EntrezGene ID and Gene Symbol

3. Sort the attributes by EntrezGene ID. Then you can see nodes which are not annotated by BioMart.

4. From the table, select all of the Gene Symbols which are not annotated.
5. Copy those Gene Symbols by right-click selected items
   • attachment:entrez_ruby1.png

6. Open Ruby Console. Create a string variable symbols and paste the Gene Symbols

   bioruby> symbols
     ==> "COX1\nTRG@\nCEBPD\nTCF1\nIL17\nIGAN\nDSH\nIV\nTITF1\nBIRC4\nNGFB\nFRIZZLED\n1.9.3.1\nGPR40\nMAFD2\nMHC2TA\nBCATENIN\nTNFSF6\nNF-KAPPAB\nPBEF1\nCTAA1\nMCP\nCEBPA\nTNFRSF6\nIFN-ALPHA\n1.13.11.17\nFOXO1A\nAPLN\nRN7SK\nKRAS2\n3.6.3.14\nTHRAP4\nTPRT\nCTSL\nTNFA\nKRTHBP1\nPDR\nHIS1\nCSE\nPKB\nTGFB\nG22P1\nPPARBP\nCOPD\nTNFRSF5\nMCS\nRNH\nCUTL1\nCSEN\nVEGF\nACDC\nMALAT1\nPKC\nIPF1\nPAP\nAXPC1\nZNF42\n1.3.99.1\nCOX2\nACF\nPMPS\n4.1.1.38\nDSCR1\nACTIN\nNA\nHYPB\n6.2.1.7\nNBS1\nPGS\nBTF3L1\n2.7.1.64"

7. Replace new line character

   bioruby> symbols = symbols.gsub(/\n/, '[SYM] OR ' )
     ==> "COX1[SYM] OR TRG@[SYM] OR CEBPD[SYM] OR TCF1[SYM] OR IL17[SYM] OR IGAN[SYM] OR DSH[SYM] OR IV[SYM] OR TITF1[SYM] OR BIRC4[SYM] OR NGFB[SYM] OR FRIZZLED[SYM] OR 1.9.3.1[SYM] OR GPR40[SYM] OR MAFD2[SYM] OR MHC2TA[SYM] OR BCATENIN[SYM] OR TNFSF6[SYM] OR NF-KAPPAB[SYM] OR PBEF1[SYM] OR CTAA1[SYM] OR MCP[SYM] OR CEBPA[SYM] OR TNFRSF6[SYM] OR IFN-ALPHA[SYM] OR 1.13.11.17[SYM] OR FOXO1A[SYM] OR APLN[SYM] OR RN7SK[SYM] OR KRAS2[SYM] OR 3.6.3.14[SYM] OR THRAP4[SYM] OR TPRT[SYM] OR CTSL[SYM] OR TNFA[SYM] OR KRTHBP1[SYM] OR PDR[SYM] OR HIS1[SYM] OR CSE[SYM] OR PKB[SYM] OR TGFB[SYM] OR G22P1[SYM] OR PPARBP[SYM] OR COPD[SYM] OR TNFRSF5[SYM] OR MCS[SYM] OR RNH[SYM] OR CUTL1[SYM] OR CSEN[SYM] OR VEGF[SYM] OR ACDC[SYM] OR MALAT1[SYM] OR PKC[SYM] OR IPF1[SYM] OR PAP[SYM] OR AXPC1[SYM] OR ZNF42[SYM] OR 1.3.99.1[SYM] OR COX2[SYM] OR ACF[SYM] OR PMPS[SYM] OR 4.1.1.38[SYM] OR DSCR1[SYM] OR ACTIN[SYM] OR NA[SYM] OR HYPB[SYM] OR 6.2.1.7[SYM] OR NBS1[SYM] OR PGS[SYM] OR BTF3L1[SYM] OR 2.7.1.64"
   
    1. Create a NCBI EUtilities Web Service Client and build query
    {{{
   bioruby> ncbi = Bio::NCBI::SOAP.new
   JRuby limited openssl loaded. gem install jruby-openssl for full support.
   http://wiki.jruby.org/wiki/JRuby_Builtin_OpenSSL
   ignored element: {http://www.w3.org/2001/XMLSchema}sequence of WSDL::XMLSchema::Sequence
   ignored element: {http://www.w3.org/2001/XMLSchema}choice of WSDL::XMLSchema::Sequence
   ignored element: {http://www.w3.org/2001/XMLSchema}choice of WSDL::XMLSchema::Sequence
   ignored element: {http://www.w3.org/2001/XMLSchema}sequence of WSDL::XMLSchema::Sequence
   ignored element: {http://www.w3.org/2001/XMLSchema}sequence of WSDL::XMLSchema::Sequence
   ignored element: {http://www.w3.org/2001/XMLSchema}choice of WSDL::XMLSchema::Sequence
   ignored element: {http://www.w3.org/2001/XMLSchema}sequence of WSDL::XMLSchema::Sequence
     ==> #<Bio::NCBI::SOAP:0x7342054 @log=nil, @wsdl="http://www.ncbi.nlm.nih.gov/entrez/eutils/soap/eutils.wsdl", @driver=#<SOAP::RPC::Driver:#<SOAP::RPC::Proxy:http://www.ncbi.nlm.nih.gov/entrez/eutils/soap/soap_adapter_1_5.cgi>>>
   
   bioruby> query = '(' + symbols + ') AND human[ORGN]'
     ==> "(COX1[SYM] OR TRG@[SYM] OR CEBPD[SYM] OR TCF1[SYM] OR IL17[SYM] OR IGAN[SYM] OR DSH[SYM] OR IV[SYM] OR TITF1[SYM] OR BIRC4[SYM] OR NGFB[SYM] OR FRIZZLED[SYM] OR 1.9.3.1[SYM] OR GPR40[SYM] OR MAFD2[SYM] OR MHC2TA[SYM] OR BCATENIN[SYM] OR TNFSF6[SYM] OR NF-KAPPAB[SYM] OR PBEF1[SYM] OR CTAA1[SYM] OR MCP[SYM] OR CEBPA[SYM] OR TNFRSF6[SYM] OR IFN-ALPHA[SYM] OR 1.13.11.17[SYM] OR FOXO1A[SYM] OR APLN[SYM] OR RN7SK[SYM] OR KRAS2[SYM] OR 3.6.3.14[SYM] OR THRAP4[SYM] OR TPRT[SYM] OR CTSL[SYM] OR TNFA[SYM] OR KRTHBP1[SYM] OR PDR[SYM] OR HIS1[SYM] OR CSE[SYM] OR PKB[SYM] OR TGFB[SYM] OR G22P1[SYM] OR PPARBP[SYM] OR COPD[SYM] OR TNFRSF5[SYM] OR MCS[SYM] OR RNH[SYM] OR CUTL1[SYM] OR CSEN[SYM] OR VEGF[SYM] OR ACDC[SYM] OR MALAT1[SYM] OR PKC[SYM] OR IPF1[SYM] OR PAP[SYM] OR AXPC1[SYM] OR ZNF42[SYM] OR 1.3.99.1[SYM] OR COX2[SYM] OR ACF[SYM] OR PMPS[SYM] OR 4.1.1.38[SYM] OR DSCR1[SYM] OR ACTIN[SYM] OR NA[SYM] OR HYPB[SYM] OR 6.2.1.7[SYM] OR NBS1[SYM] OR PGS[SYM] OR BTF3L1[SYM] OR 2.7.1.64) AND human[ORGN]"

8. Send the query to ther service

   bioruby> search_result = ncbi.run_eSearch('db' => 'gene', 'term' => query )
     ==> #<SOAP::Mapping::Object:0x94 {http://www.ncbi.nlm.nih.gov/soap/eutils/esearch}Count="87" {http://www.ncbi.nlm.nih.gov/soap/eutils/esearch}RetMax="20" {http://www.ncbi.nlm.nih.gov/soap/eutils/esearch}RetStart="0" {http://www.ncbi.nlm.nih.gov/soap/eutils/esearch}IdList=#<SOAP::Mapping::Object:0x96 {http://www.ncbi.nlm.nih.gov/soap/eutils/esearch}Id=["378938", "3605", "5743", "388732", "7124", "2308", "7080", "7040", "4683", "4663", "9370", "6965", "6932", "6927", "4513", "4512", "2185", "30818", "23590", "4261"]> {http://www.ncbi.nlm.nih.gov/soap/eutils/esearch
   .
   .
   .

9. Extract ID list

   bioruby> id_list = search_result.idList
     ==> #<SOAP::Mapping::Object:0x96 {http://www.ncbi.nlm.nih.gov/soap/eutils/esearch}Id=["378938", "3605", "5743", "388732", "7124", "2308", "7080", "7040", "4683", "4663", "9370", "6965", "6932", "6927", "4513", "4512", "2185", "30818", "23590", "4261"]>
   bioruby> ids = id_list.id.join(" ")
     ==> "378938 3605 5743 388732 7124 2308 7080 7040 4683 4663 9370 6965 6932 6927 4513 4512 2185 30818 23590 4261"

10. Get summary
    • attachment:entrez_ruby2.png

Import Interactions from MiMI Database (not finished yet!)

1. Prepare list of genes. In this example, we are going to use the following:

   10062 10580 10998 10999 11001 116519 126129 1374 1375 1376 1579 1581 1582 1593 1622 1962 2167 2168 2169 2170 2171 2172 2173 2180 2181 2182 23305 2710 2712 284541 28965 30 3158 33 335 336 34 345 3611 364 376497 4023 4199 4312 4973 51 5105 5106 51129 5170 51703 5346 5360 5465 5467 5468 6256 6257 6258 6319 6342 642956 7316 7350 8309 8310 9370 9415 948

   In this case, ID set is Entrez Gene ID. You can use other ID sets as a query, but if you use Entrez Gene ID, you can minimize the search time.

2. Select File-->Import-->Network from Web Service

3. Set Data Source to NCBI Entrez EUtilities Web Service Client

4. Paste the gene ID list to the Query box

5. Press Search. Again, this process takes several minutes (depends on network status)

6. Name the network and press OK.

7. After applying Organic layout, the network looks like the following:

   • attachment:ncbi_gene_list1.png
8. Next, import annotation for them. It is same as the protocol described in the first section.
9. When import is done, annotations about the genes are imported like the following:
   • attachment:ncbi_gene_list2.png
10. You can check the location of the genes you entered as the query by using Enhanced Search plugin. Paste the list of gene IDs in the search ESP window on the toolbar and press enter. Genes in the original query will he selected.
    • attachment:ncbi_gene_list3.png attachment:ncbi_gene_list4-1.png

Mark Original Nodes

1. Assume nodes in the original query are already selected.

2. In the Node Attribute Browser window, you can see the icon to create new attribute. Press the icon

3. Select String Attribute

4. Name the new attribute.

5. On the right side of the Browser, you can see an icon called Batch Attribute Editor. Press the icon

6. In the Operation tab, select Set and then select the attribute name you created from the combo box.

7. Type the value. In this example, I use query1 as the attribute value

   • attachment:ncbi_gene_list4.png

8. Press Go. New attributes are set to the selected nodes. Close the window.

9. Now you can use it in the Visual Style to see the nodes in the original list more intuitively. The following is an example to use the new attribute to control node size, shape, and color.
   • attachment:ncbi_gene_list5.png attachment:ncbi_gene_list6.png

Tutorial 3: Merge Multiple Networks (not finished yet!)

Example visualization of the integrated network. All networks are merged, and big red nodes are genes on PPAR signaling pathway (path:hsa03320) in KEGG. PPAR-Gamma is selected.

Optional