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1. M Transfer first line as column names Start Import Row 2 Comment Line Refresh Preview Preview Text File Left Click Enable Disable Column Right Click Edit Column f X EntrezID1 w Symbol 1 X Entrez D2 le Symbol2 Data Source R Rual et al S Stelzl e 1 AIBG 10321 CRISP3 L 2 A2M 259 AMBP L 2 A2M 348 APOE L 2 A2M 351 APP L 2 A2M 354 KLK3 L 2 A2M 567 B2M L 2 A2M 1508 CTSB L 2 A2M 1990 ELAL L Figure 7 Network import panel parameters Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Page 27 IIIIIIIIIIIIIII Figure 8 Force based view on a very large network Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Control Pane ox X Network ER select ses defaut GR 7 G E ve 6 4 os 04 D 0 os os 0 oO i Fil Color wo Height boo bel g Label Color 4 A Label Font Face AN 12 Label Font Stas lt PN NAmE aad s 1112111111111 255 Label Transparency moa Label wath wwe Nested Network image Visible i NI ox Oo iE 4 o D 9 oo D fz D s size 64 Tooltip 100 Transparency coe nemor L nere Network Table al menor x Figure 9 Using viz mapper to add annotation with attributes Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 082. 1duosnue v JOUINY vd HIN 1duosnue A Jouinv Vd HIN 1duosnue v JOuINY Vd HIN Su et al Figure 10 A sub cluster of LSM genes Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Page 29 Su et al Page 30 Select a Network Collection Network Collection Create new network collection B Mapping Column for Existing Network shared name j Interaction Definition Source Interaction Interaction Type Target Interaction Column 1 p gt Default Interaction m Column2_ p a Q Columns in BLUE will be loaded as EDGE ATTRIBUTES Advanced M Show Text File Import Options Text File Import Options Delimiter Preview Options M Tab Comma _ Semicolon Y Space _ Other gt Show all entries in the file Show first 100 entries Column Names Network Import Options IM Transfer first line as column names Start Import Row 1 Comment Line Default Interaction pp Refresh Preview Preview Text File Left Click Enable Disable Column Right Click Edit Column OK Came Figure 11 Attribute import configurations Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Page 31 aS EupA I R EKOLA MY AA ttp D 33 ele b DD D D DD D D D Tbb bbb n bbb R e ee Pete Pe nn nnn ua ER n ua ER e ILL ILL EP Figure 12 Force b
3. illustrate how to visualize the human disease network 29 30 31 32 33 34 35 36 Start a new session in Cytoscape by go to menu File New Session Go to menu File Import Network File choose previously downloaded disease net txt In the Interaction Definition tab within the Import Network From Table dialog choose Column 1 as the Source Interaction and Column 2 as the Target Interaction Check Show Text File Import Options in the Advanced tab and check Transfer first line as column names Click on the Weight column header in the newTable preview to use Weight as an edge attribute Your import dialog should look like Figure 11 Click on the OK button Go to Layout Apply Preferred Layout to apply a layout to the network Your network view should be similar to Figure 12 Note that there are many duplicate edges The diseases are linked by shared gene mutations and each link is documented twice in this file We can remove the duplicated edges by going to menu Edit Remove Duplicated Edges Click on the network you would like to remove duplicated edges and then check Ignore edge direction Click on the OK button If the view is not automatically refreshed pan or zoom the network view and the duplicate edges will disappear We can import additional attribute data to overlay on the
4. 1duosnue v JOuINY Vd HIN Su et al Page 2 STRING Franceschini et al 2013 GeneMANIA Zuberi et al 2013 host a large amount of data that can be readily represented as a network and then analyzed On the other hand rapid technological advances in high throughput technology have improved the feasibility of constructing networks automatically from tens of thousands of molecular profiles Dutkowski et al 2014 Dutkowski and Kramer 2012 Margolin et al 2006 Cytoscape supports visualization analysis and interpretation of these networks and helps better understand the biological systems they model Cytoscape was developed as a response to this network data explosion and the visualization and analytical challenges it poses starting in 2001 Shannon et al 2003 While many tools can be used for general purpose network visualization or analysis e g Pajek Batagelj and Mrvar 1998 Gephi Bastian et al 2009 Jung Fisher et al 2005 GraphStream Dutot et al 2007 igraph Csardi and Nepusz 2006 Cytoscape aims to satisfy the unique needs of biologists needing to interactively explore biological networks such as metabolic pathways or gene regulatory networks in the context of corresponding experimental data For example gene expression changes obtained from a transcriptomics experiment can be used to color nodes in a disease pathway so that researchers can study genes of interest e g the most differentially expressed ge
5. Doncheva NT et al Analyzing and visualizing residue networks of protein structures Trends Biochem Sci 2011 36 179 82 PubMed 21345680 Doncheva NT et al Topological analysis and interactive visualization of biological networks and protein structures Nat Protoc 2012 7 670 85 PubMed 22422314 Dutkowski J et al NeXO Web the NeXO ontology database and visualization platform Nucleic Acids Res 2014 42 D1269 74 PubMed 24271398 Dutkowski J Kramer M A gene ontology inferred from molecular networks Nat 2012 Dutot A et al GraphStream A Tool for bridging the gap between Complex Systems and Dynamic Graphs In Emergent Properties in Natural and Artificial Complex Systems Satellite Conference within the 4th European Conference on Complex Systems ECCS 2007 2007 Edgar R et al Gene Expression Omnibus NCBI gene expression and hybridization array data repository Nucleic Acids Res 2002 30 207 10 PubMed 11752295 Fisher D et al Analysis and Visualization of Network Data Using JUNG J Stat 2005 Franceschini A et al STRING v9 1 protein protein interaction networks with increased coverage and integration Nucleic Acids Res 2013 41 D808 15 PubMed 23203871 Gao J et al Metscape a Cytoscape plug in for visualizing and interpreting metabolomic data in the context of human metabolic networks Bioinformatics 2010 26 971 3 PubMed 20139469 Gasch AP et al Genomic Expression Programs in the Response of Yeast Cells to
6. Open MCODE The MCODE panel should now be visible in the control panel In Find Cluster s tab choose in Whole Network then click on the Analyze current network Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al 26 27 28 Page 11 After some processing the MCODE panel should open and the identified clusters should be displayed Each of the clusters can be exported as a sub network Select the second cluster and click on the Create Sub Network button You should now see a network like Figure 10 There are mostly LSM genes associated with small nuclear RNAs In this case MCODE identified a functionally homogenous cluster from a large interaction network purely based on how densely interconnected the nodes were You can grow or shrink the discovered local clusters by adjusting the Size Threshold slider The clusters will change accordingly with regard to the seed genes Drag the slider several notches and the cluster will expand You can create a new sub network and check the functional term association using BiNGO following similar procedures in protocol 3 Explore the Human Disease Network Although Cytoscape is often used to visualize gene protein and metabolic networks it can be used to visualize other biomedical networks as well In the following example we will
7. PubMed 14597658 Su G et al GLay community structure analysis of biological networks Bioinformatics 2010 26 3135 7 PubMed 21123224 Tarcea VG et al Michigan molecular interactions r2 from interacting proteins to pathways Nucleic Acids Res 2009 37 D642 6 PubMed 18978014 Turinsky AL et al Structural Genomics Chen YW editor Humana Press Totowa NJ 2014 Vailaya A et al An architecture for biological information extraction and representation Bioinformatics 2005 21 430 8 PubMed 15608051 Yu H et al The importance of bottlenecks in protein networks correlation with gene essentiality and expression dynamics PLoS Comput Biol 2007 3 e59 PubMed 17447836 Zuberi K et al GeneMANIA prediction server 2013 update Nucleic Acids Res 2013 41 W115 22 PubMed 23794635 Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Page 20 Import Colu rom Table Target Table Data Where to Import Table Data LT 0 a Network Collection Select a Network Collection Network Collection BIOGRID ORGANISM Saccharomyces_cerevisiae 3 2 105 mitab i Key Column for Network shared name i Importing Type Import Data as Node Table Columns i V Show Mapping Options V Show Text File Import Options V Case Sensitive Annotation File to Table Mapping Select the primary key column in table Text File Import Options Delimiter Pre
8. al 2009 which calculates an even larger number of network parameters In addition to the traditional hierarchical clustering and heat map visualization clusterMaker2 may be used to create a co expression network where the edges between nodes represent expression profile similarities The resulting similarity network can be further clustered to partition the network into modules of genes with similar expression patterns Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N JouIn y Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al Page 14 To explore the concept of modules in more detail Cytoscape apps such as jActiveModules Ideker et al 2002 can be used to find subnetworks where nodes show significant changes in expression levels Unlike the co expression network approach mentioned above jActiveModules takes the network topology into account This can highlight subnetworks where the genes in that subnetwork experience similar expression patterns A plausible biological explanation for co expression of genes or proteins is functional relatedness This is especially true in prokaryotes where functionally related genes may be organized into the same operons in the genome Genes involved in a complex can exhibit just in time assembly where one highly regulated critical gene controls the overall activity of the entire complex de Lichtenberg et al 2005 Comparing different e
9. and utilize such information to aid network visualization Such data can also be imported directly from many external sources As mentioned above there are many other Cytoscape 3 apps that enables data importing from data repositories such as Reactome Joshi Tope and Gillespie 2005 KEGG Kanehisa 2002 WikiPathways Kelder et al 2012 MetScape Karnovsky et al 2012 Agilent Literature Search Vailaya et al 2005 Using Cytoscape users can also integrate their own experimental data with existing network data and functional annotations Commentary Background Information The term biological network usually refers to two types of data those human curated from the literature and those that are experimentally derived The former is built on curated and verified knowledge such as those stored in pathway and protein interaction databases The latter is derived from experiments such as protein interaction screens or gene expression correlations Combining these two data sources and other functional annotations enables Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al Page 15 researchers to support their experiment and identify new patterns from the data Visual exploration tools are required for this especially if the data are large The omics era has brought many opportunities and challenges for network analy
10. as reference set E Select ontology file GO Biological Process E Select namespace biological process Select organism annotation Saccharomyces cerevisiae Discard the following evidence codes a Check box for saving D Save BINGO Data file Start BINGO Bingo parameter configurations Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Page 24 Su et al cluster freq total freq genes sis 6 4101E 76 22613 ribonucleoprotein complex biogenesis 2 7423E 71 34470 ncRNA processing 2 9965E 66 2 4548E 64 L 34660 ncRNA metabolic process 1 3755E 63 LO 16072 rRNA metabolic process 7 6707E 63 6396 RNA processing 1 9044E 50 16070 RNA metabolic process 1 2770E 48 44085 cellular component biogenesis 1 4678E 39 42273 ribosomal large subunit biogenesis 3 3481E 36 L 462 maturation of SSU rRNA from tricistronic rRNA transcript SSU rRNA 5 85 r 1 5290E 32 30490 maturation of SSU rRNA 2 1988E 31 447 endonucleolytic cleavage in ITS1 to separate SSU rRNA from 5 8S rRNA an 8 3839E 28 478 endonucleolytic cleavage involved in rRNA processing 4 4947E 27 4 6153E 73 9 8724E 69 7 1915E 64 4 4186E 62 1 9807E 61 9 2048E 61 1 9588E 48 1 1493E 46 1 1743E 37 2 4107E 34 1 0008E 30 1 3193E 29 4 6434E 26 2 1574E 25 23524F 25 98 178 55 096 99 178 55 696 89 178 50 096 78 178 43 896 92 178 51 696 78 178 43 896 90 178 50 596 106 178
11. at java com by default the 64bit version is available at http java com en download manual jsp and is recommended For Linux systems Oracle Java 7 has caused Cytoscape crashes on some platforms and OpenJDK7 http openjdk java net is a good alternative For Mac systems the Cytoscape installer automatically triggers JVM6 to install if no other Java version is present For additional information select the Release Notes button on the Cytoscape web site http cytoscape org Note if your computer does not meet the above requirements earlier versions of Cytoscape are available from the Cytoscape website and they may run on older computer hardware 1 Use a web browser to load the Cytoscape web page http cytoscape org and select the Download Cytoscape button 2 Fill in your name organization and e mail address to register as a user user registration helps with Cytoscape grant renewals Check to agree the terms of use Lesser GNU Public License LGPL and optionally join the email list Click on the Proceed to Download button 3 Click on the name of the distribution that matches your operating system OS choose Windows 32bit only if you have 32bit Windows e g Windows XP 4 Execute the downloaded Cytoscape bundle to install the instructions for this depend on your web browser 5 Launch Cytoscape the instructions for this depend on your OS For Mac or Linux double click on the Cytoscape ic
12. by the experiment Right click on the first entry and select Destroy Network to save memory on your system If a view was not created for the new network right click on the new and only remaining network and select Create View Otherwise just click on that network to select it Layout the network by going to Layout Prefuse Force Directed Layout This may take some time after which you can add graphical details by going to View Show Graphics Details This may take some time too The result should look similar to Figure 3 Go to menu Layout Bundle Edges All Nodes and Edges In the dialog click OK Edge bundling is a new Cytoscape feature that simplifies the view of a complex network by bundling edges that are close to each other like ropes This may take some time Using Apps for Additional Analysis A unique strength of Cytoscape is its rich collection of Apps that can perform various analyses We will now use some of these Apps to analyze our data Ensure you have the specific App installed as described in Support Protocol 2 before you attempt the protocol Identify Network Modules One common task in biological network analysis is to identify clusters or modules of biological molecules that share similar properties For instance a cluster of genes whose expression changes similarly to external stimuli may have related function and participate in the same biolo
13. disease network Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al 37 38 39 40 41 Page 12 Open supplementary tableS2 txt with a text editor On the second row change Name to Disease Name Cytoscape attribute tables come preconfigured with a name column when importing a network importing this attribute again may cause a conflict Go to menu File Import Table File and choose previous downloaded supplementary tableS2 txt In the Import Column From Table dialog a Make sure the disease network is selected in the Network Collection combo b Check Show Mapping Options and Show Text File Import Options in the Advanced tab c In the Text File Import Options tab check Transfer first line as column names set Start Import Row to 2 click on the Refresh Preview button d Select 1 9 Disease ID for Select the primary key column in table e Your import dialog should look like Figure 13 Click on the OK button After import in the Table Panel click on the first button Change Table Mode and check Show all in the pop up menu You can check the imported disease attributes as in Figure 14 Click on the Change Table Mode button again to switch back to sh
14. 1duosnue y Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue y Jouiny Vd HIN c on wr Nop Crews NIH Public Access Author Manuscript Published in final edited form as Curr Protoc Bioinformatics 47 8 13 1 8 13 24 doi 10 1002 0471250953 bi0813s47 BIOLOGICAL NETWORK EXPLORATION WITH CYTOSCAPE 3 Gang Su John H Morris Barry Demchak and Gary D Badert Molecular Behavioral Neuroscience Institute University of Michigan Ann Arbor United States Resource for Biocomputing Visualization and Informatics University of California San Francisco United States 3Department of Medicine University of California San Diego United States The Donnelly Centre University of Toronto Toronto Canada Abstract Cytoscape is one of the most popular open source software tools for the visual exploration of biomedical networks composed of protein gene and other types of interactions It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data thereby facilitating research tasks such as predicting gene function and pathway construction Cytoscape provides core functionality to load visualize search filter and save networks and hundreds of Apps extend this functionality to address specific research needs The latest generation of Cytoscape version 3 0 and later has substantial improvements in function use
15. 59 100 78 56 36 178 20 296 36 78 20 2 36 178 20 296 24178 13 496 24 178 13 496 24072 12 A 372 6208 5 9 423 6208 6 8 350 6208 5 6 252 6208 4 0 409 6208 6 5 262 6208 4 2 532 6208 8 5 837 6208 13 4 903 6208 14 5 77 6208 1 2 93 6208 1 4 99 6208 1 5 40 6208 0 6 42 6208 0 6 4216202 N 60 851963 850688 851963 850688 850688 851665 850688 851665 850688 851665 850688 851665 850688 851665 851963 850688 851963 850688 851497 856488 850422 853458 850422 853458 854881 850414 854881 850414 Figure 6 Force based view of a Cytoscape network Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Select nodes Su et al Select a Network Collection Page 26 Network Collection Create new network collection Mapping Column for New Network shared name Mapping Column for Existing Network shared name nem lar Interaction Definition Source Interaction Interaction Type Target Interaction Column 2 Column 5 Column 4 Q Columns in BLUE will be loaded as EDGE ATTRIBUTES Advanced V Show Text File Import Options Text File Import Options r Delimiter Preview Options M Tab C Comma C Semicolon O Space C Other Show all entries in the file Show first 100 2 entries Column Names Network Import Options Default Interaction
16. 85 O1 0074 085 0453 Sarip GR oa Som FAUL O16 0587 giam ARG hagl 854222 T Tawid 0 0 0273 Bip BUDS 1 065 0449 Hpa p HPA2 1 c 0189 0135 Welip MREN ma Figure 3 Initial display of loaded network Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al View Status No status info 1 Ja 551924 854487 854157 850872 850893 854381 855591 251678 852060 Export Graphics Map Colors Onto Network Close Figure 4 Heatmap and dendrogram display Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Page 23 Su et al Figure 5 BiNGO Settings BiNGO settings Save settings as default Cluster name Cluster 2 d Get Cluster from Network L Paste Genes from Text Do you want to assess over or underrepresentation Overrepresentation L Underrepresentation E Visualization O No Visualization Select a statistical test Hypergeometric test Select a multiple testing correction Benjamini amp Hochberg False Discovery Rate FDR correction i Choose a significance level 0 05 Select the categories to be visualized Overrepresented categories after correction Y Select reference set Use whole annotation
17. Be Be Be N Lu dmi EE N m LE Ll Lg W Be Be Be Be Ld Be lE Li N um 9 oum UR m N m N m N Ra W m D m UN ee N un UD m N m m UU m Figure 15 Visualize closely associated human diseases using attributes and vizmapper Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Figure 16 Fully annotated Human Disease Network HDN Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08
18. Environmental Changes Mol Biol Cell 2000 11 4241 4257 PubMed 11102521 Goel R et al Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis Mol Biosyst 2012 8 453 63 PubMed 22159132 Goh K I et al The human disease network Proc Natl Acad Sci U S A 2007 104 8685 90 PubMed 17502601 Hamosh A et al Online Mendelian Inheritance in Man OMIM a knowledgebase of human genes and genetic disorders Nucleic Acids Res 2005 33 D514 7 PubMed 15608251 Ideker T et al Discovering regulatory and signalling circuits in molecular interaction networks Bioinformatics 2002 18 Suppl 1 S233 40 PubMed 12169552 Joshi Tope G Gillespie M 2005Reactome a knowledgebase of biological pathways Nucleic acids Kanehisa M The KEGG database Novartis Found Symp 2002 247 91 101 discussion 101 3 119 28 244 52 PubMed 12539951 Karnovsky A et al Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data 2012 Kelder T et al WikiPathways building research communities on biological pathways Nucleic Acids Res 2012 40 D1301 7 PubMed 22096230 Kerrien S et al The IntAct molecular interaction database in 2012 Nucleic Acids Res 2012 40 D841 6 PubMed 22121220 De Lichtenberg U et al Comparison of computational methods for the identification of cell cycle regulated genes Bioinformatics 2005 21 1164 71 PubMed 15513999 Loti
19. Table S4 List of human protein protein interactions http www barabasilab com pubs CCNR ALB Publications 200705 14 PNAS HumanDisease Suppl supplementary tableS4 txt Download Network data Human Disease Network http www barabasilab com pubs CCNR ALB_Publications 200705 14_PNAS HumanDisease Suppl disease net w and Disease Gene Network http www barabasilab com pubs CCNR ALB_Publications 200705 14_PNAS HumanDisease Suppl gene net w Rename the human disease network file from disease net w to disease net txt and human gene network file from gene net w to gene net txt Explore the Protein protein Interaction Network 4 0 NN 11 12 13 14 Launch Cytoscape 3 Go to menu File Import Network File choose supplementary_tableS4 txt The file import dialog should be shown In the Delimiter tab check only Tab In the Column Names tab check Transfer first line as column names In the Start Import Row text box click the spinner to change number to 2 We want to skip the comment lines Click on Refresh Preview In the Interaction Definition tab choose Column 2 as the Source Interaction and choose Column 4 as the Target Interaction In the Interaction Type column choose Column 5 There are three interaction sources R and S indicate two literature sources and L indicates literature cur
20. a S et al Cytoscape app store Bioinformatics 2013 29 1350 1 PubMed 23595664 Maere S et al BINGO a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks Bioinformatics 2005 21 3448 9 PubMed 15972284 Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al Page 19 Margolin AA et al Reverse engineering cellular networks Nat Protoc 2006 1 662 71 PubMed 17406294 Merico D et al How to visually interpret biological data using networks Nat Biotechnol 2009 27 921 4 PubMed 19816451 Montojo J et al GeneMANIA Cytoscape plugin fast gene function predictions on the desktop Bioinformatics 2010 26 2927 8 PubMed 20926419 Morris JH et al clusterMaker a multi algorithm clustering plugin for Cytoscape BMC Bioinformatics 2011 12 436 PubMed 22070249 Morris JH et al structureViz linking Cytoscape and UCSF Chimera Bioinformatics 2007 23 2345 7 PubMed 17623706 Saito R et al A travel guide to Cytoscape plugins Nat Methods 2012 9 1069 76 PubMed 23132118 Scardoni G et al Analyzing biological network parameters with CentiScaPe Bioinformatics 2009 25 2857 9 PubMed 19729372 Shannon P et al Cytoscape a software environment for integrated models of biomolecular interaction networks Genome Res 2003 13 2498 504
21. ased view of the human disease network HDN Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Page 32 nrc wUG T T Target Table Data Where to Import Table Data To a Network Collection P Select a Network Collection Network Collection disease net txt Key Column for Network 1 shared name Importing Type Import Data as Node Table Columns Advanced M Show Mapping Options Y Show Text File Import Options Case Sensitive Annotation File to Table Mapping Select the primary key column in table 1 9 Disease ID Text File Import Options Delimiter Preview Options M Tab Comma Semicolon Space Other Show all entries in the file Show first 100 7 entries Column Names IM Transfer first line as column names Start Import Row 2 5 Comment Line Preview Text File Left Click Enable Disable Column Right Click Edit Column emm i s Disease ID s Disease Name Disorder class Jy Size s Degree k Class degree k 7 Genes implicated Entrez ID comm 1 17 20 lyase deficiency Endocrine 1 0 0 CYP17A1 1586 6 L 2 methyl 3 hydroxybutyryl CoA deh Metabolic 1 0 0 HADH2 3028 4 2 methylbutyrylglycinuria Metabolic 1 0 0 ACADSB 36 5 3 beta hydroxysteroid_dehydrogenas Meta
22. ation Check the Show Text File Import Options checkbox If any of the columns are shown as blue click on the column header to make them grey We don t want to import the gene IDs as edge attributes If all settings are correct the import dialog should look similar to Figure 7 Click on the OK button Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al Page 10 Layout and Overlay Information on the Network When a network is imported from a file it has no layout information by default Cytoscape lays out the network as a grid We will demonstrate different layouts 15 16 17 18 19 20 21 22 23 Go to menu Layout Apply Preferred Layout or hit the corresponding button in the toolbar to apply a force directed layout Depending on your computer this step may take a few minutes Your view should look similar to Figure 8 Click on the Styles tab to reveal the visual styles manager in the control panel Click on the Edge tab on the bottom to reveal edge attribute mappings Click on the Properties drop down button to reveal the popup menu Click on Show all to reveal all possible mappings Scroll down to reveal Stroke Color Unselected Click on the arrow on the right to reveal possible options In the Column field click and select intera
23. bolic 1 0 0 HSD3B2 3284 6 3 hydroxyacyl CoA dehydrogenase de Metabolic 1 0 0 HADHSC 3033 7 3 Methylcrotonyl CoA carboxylase de Metabolic 2 0 0 MCCC1 56922 MCCC2 64087 8 3 methylglutaconic aciduria Metabolic 1 0 0 AUH 5491 9 3 methylglutaconicaciduria Metabolic 1 1 4 OPA3 80207 10 3 M syndrome multiple 1 0 0 CUL7 9820 12 6 mercaptopurine sensitivity Metabolic 1 0 0 TPMT 7172 File Size Unknown OK Cancel Figure 13 Node attribute import parameters Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Page 33 Ten ORM e 3 OLIAI 1277 1 4 Cannectwe tissue disorder 5428 Name Genes implicated Entrez ID comma deliminated Dissection_of_cervical_arteries Alpers_syndrome Wolff Parkinson White syndrome HELLP syndrome Type String KAG2 51422 ADHA 3030 Shared Column m WN RR e Ke e e N MP22 5376 EGR2 1959 P 4 3 multiple Dejerine Sottas disease ASR 846 1 2 Endocrine Hypocalcemia AP2 6891 1 1 Immunological Wegener_granulomatosis NTP1A3 478 TORIA 1861 6 2 Neurological Dystonia SHR 2492 BMP15 9210 2 3 Endocrine Ovarian_dysgenesis PRT 3531 SIC34A1 A569 2 1 Metabolic Lralithiasise Figure 14 Using attribute table to verify data were imported correctly Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Page 34 e e La ee E Be Be Ll ld x Ei
24. bolic pathway This helps visualize multiple types of data in the same plot to help identify patterns and relationships between data of diverse types In this protocol we will use a yeast protein protein interaction network and a classic gene expression experiment to illustrate the process of integrative data visualization using attribute mapping We will also use a Cytoscape App to identify regions sub networks that could be biologically important We begin by retrieving the gene expression dataset from a classic yeast experiment by Gasch et al Gasch et al 2000 that explored how yeast gene expression changes in response to environmental stimuli The data that reflects gene expression changes in response to temperature shock will be used for subsequent exploration The dataset is stored in the NCBI Gene Expression Omnibus GEO data repository Edgar et al 2002 Barrett et al 2007 with the accession number GDS112 Fetch Expression Data from GEO 1 The full GEO data SOFT format can be directly downloaded via a web browser from this link ftp ftp ncbi nlm nih gov geo datasets GDS nnn GDS112 soft GDS112_full soft gz You can also interactively explore this data using online tools via http www ncbi nlm nih gov sites GDSbrowser acc GDS112 The downloaded file is compressed in gzip format with a gz extension You can decompress the file directly by using the 7 Zip utility http 7 zip org on Windows or the Arc
25. bove demonstrate common workflows though many other workflows are possible New protocols are regularly posted at http tutorials cytoscape org Also new Apps are regularly posted to the Cytoscape App store many enabling new workflows A good way to find popular Apps is to rank all Apps by popularity Number of downloads on the App store website From the App store homepage click All Apps at the top left then click the downloads button at the top to sort Apps by number of downloads Critical Parameters and Troubleshooting Out of memory errors Symptoms When loading a network from either a database source or from a Cytoscape session file a loading message box is displayed the progress animation continues but no network is displayed either in the Networks tab or a network window The Memory button in the lower right of the Cytoscape window contains Low instead of OK Possible causes Cytoscape has run out of memory to load the network or the operating system is swapping RAM to the hard disk because your Cytoscape vmoptions file allocates more RAM to Java than your workstation has Remedies Add more memory then register the new memory with Cytoscape per the Note on Memory Consumption section of the Cytoscape user manual If you have installed 32 bit Java and 32 bit Cytoscape and already have 4GB of RAM consider using 64 bit Java 64 bit Cytoscape adding more RAM Mismatch between Java and Cytoscape Symptom
26. ction In the Mapping Type field choose Discrete Mapping Choose a color for each of the interaction data sources for L R and S For example you can use Red Green and Blue for the three data sources and use the mixed color Teal Purple Yellow and Grey for LR RS and so on Now let s make the nodes a little bit more transparent to reveal the distribution of data sources Click on the Node tab Click on the Properties drop down and click on Show All Scroll to the Fill Color tab and choose a shade of grey Scroll to Transparency and click on 255 then change the value to 100 Your view should look similar to Figure 9 Cytoscape sometimes hide labels node graphics and other information to improve visualization speed You can force Cytoscape to draw all Graphics detail by going to menu View Show Graphics Details You can see that even though this is a very dense network protein interactions from the same source tend to cluster with each other Discover Local Gene Clusters Using MCODE Identifying densely connected nodes e g genes from a very densely connected network is useful for identifying biological modules such as complexes pathways or other related sets of nodes MCODE Bader and Hogue 2003 is one of many Cytoscape Apps that identifies local clusters that can be used to identify interesting modules 24 25 Launch MCODE by going to menu Apps MCODE
27. d HIN 1duosnue N JOUINY Vd HIN Su et al Page 13 increment to 3 The network should look like Figure 15 Disorders that are connected with thick edges indicate more shared number of genetic mutations For example the Leigh syndrome is closely connected to mitochondrial complex deficiency 48 Click on the Node tab Find Label and choose Disease_Name Now Disease name will be displayed in each disorder instead of disease IDs 49 Double Click on Height and choose Degree k Choose Continuous Mapping Double click on the mapping bar and drag vertical ticks to make the node size range from 30 300 mapped to degree 1 50 Repeat this step for Width 50 We could now reproduce the visual style used in the poster at http www barabasilab com pubs CCNR ALB Publications 200705 14 PNAS HumanDisease Suppl Goh etal poster pdf Your network view should look similar to Figure 16 The two selected nodes are Leukemia and Deafness classes respectively Diseases in the same class tend to be placed near each other and form clusters that share similar gene mutations Guidelines for Understanding Results The protocols provided here can stand alone as methods for analyzing biological networks and also serve as a starting point for more in depth analysis using various Cytoscape analysis and visualization apps The two basic protocols have focused on protein protein interaction networks but Cy
28. gical processes The clusterMaker App Morris et al 2011 provides many frequently used network clustering algorithms For instance we can choose to find clusters in a gene network based on expression profiles using hierarchical or K means clustering or identify densely intra connected sub networks using Markov clustering or community clustering Su et al 2010 19 20 Launch the clusterMaker App and the hierarchical cluster dialog Apps clusterMaker Hierarchical cluster First apply Support Protocol 2 to install the clusterMaker App if you haven t already done so Select all of the expression data columns GSM 1029 GSM 1030 GSM 1032 GSM 1033 and GSM 1034 Since this is a time series data we probably don t Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue N Jouinv Vd HIN Su et al Page 8 want to cluster the attributes so deselect Cluster attributes as well as nodes and select Show TreeViewer when complete Select OK 21 A clustered heatmap should now be shown similar to Figure 4 Some well defined clustering patterns can be identified The first cluster has a single protein HSP12 but the next cluster contains 178 genes The corresponding nodes in a cluster can be selected by clicking on the horizontal lines in the dendrogram as shown in Figure 4 These 178 genes are all characterized by a
29. he Resource for Biocomputing Visualization and Informatics P41 GM103311 Cytoscape development is a large community effort We thank all of the core Cytoscape developers and App developers who have enriched the Cytoscape user experience with their ideas LITERATURE CITED Atkinson HJ et al Using sequence similarity networks for visualization of relationships across diverse protein superfamilies PLoS One 2009 4 e4345 PubMed 19190775 Bader GD et al BIND The Biomolecular Interaction Network Database Nucleic Acids Res 2001 29 242 5 PubMed 11125103 Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al Page 18 Bader GD Hogue CWV An automated method for finding molecular complexes in large protein interaction networks BMC Bioinformatics 2003 4 2 PubMed 12525261 Barrett T et al NCBI GEO mining tens of millions of expression profiles database and tools update Nucleic acids 2007 Bastian M et al 2009Gephi an open source software for exploring and manipulating networks ICWSM Batagelj V Mrvar A Pajek program for large network analysis Connections 1998 Chatr Aryamontri A et al The BioGRID interaction database 2013 update Nucleic Acids Res 2012 41 D816 23 PubMed 23203989 Csardi G Nepusz T The igraph software package for complex network research InterJournal Complex Syst 2006
30. hive utility on Macintosh Load the Protein protein Interaction Network into Cytoscape 3 4 Launch Cytoscape At the Welcome screen click on the S cerevisiae yeast button under From Organism Network This will load a BioGRID Chatr Aryamontri et al 2012 interaction network for Saccharomyces cerevisiae baker s yeast This network contains approximately 6 600 nodes and 340 000 edges depending on the network version your Cytoscape loads Note that due to its large size no network view is created automatically This saves computer resources Also very large networks are often too dense to effectively visualize The imported network can be found listed in the Network tab Import the Gene Expression Data 5 Go to menu File Import Table File and select the unzipped SOFT dataset GDS112_full soft that was downloaded above Note that there are a number of comment lines at the start of the file We can skip over those comments by selecting Show Text File Import Options and setting Start Import Row to 83 and then click on Refresh Preview To associate the experimental gene expression data with the network the same gene identifiers must be used in both datasets The BioGRID yeast dataset uses Entrez Gene identifiers IDs as the node primary identifiers Conveniently we Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N JouIn y Vd HIN 1duosnue
31. ms OS In order to use Cytoscape the following hardware and software requirements should be met Hardware Requirements Minimum Requirements 1Ghz CPU or higher dedicated graphics card 500MB hard drive space 1GB free physical memory a display that supports 1024 x 768 or higher resolution Certain Apps such as literature search or pathway retrieval require an Internet connection Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al Recommended Requirements dual core or quad core CPU at 2Ghz or higher dedicated graphics card with 512MB or more video memory 1GB or more available hard drive space 4GB or more physical memory two high definition HD displays 1920x1080 or 1366x768 high speed Internet connection For best performance we recommend using a solid state disk SSD instead of a hard disk Operating System OS Windows Windows 8 7 XP or Vista 64bit OS is recommended for large networks Macintosh OSX 10 7 or later with Intel CPUs recommended 10 8 or later Linux Ubuntu 13 x 12 x 11 x or Fedora Java Runtime JRE Installation 64bit Java Virtual Machine JVM is recommended the latest Oracle distribution can be found at http www java com en As of this writing Cytoscape has been tested with Java 6 and 7 but not later versions For Windows systems the 32bit JVM is supplied
32. nd fast search functions that enable the user to quickly find sets of nodes or edges with custom criteria such as a molecular function GO term connectivity to many external data repositories e g Pathway Commons Reactome e amore user friendly online local App management mechanism e anew network property table that supports complex annotations from a variety of sources e comprehensive support for many network formats for import export e anda metanode subnetwork mechanism that enables the user to build hierarchy in a network a network can be a node in another network enabling a high level functional or structural view of big networks instead of a flat hairball of tens of thousands of nodes or edges With these new capabilities users can e explore a network obtained from experiments or annotations in Cytoscape e overlay the nodes or edges with experimental values using Styles e obtain additional functional annotation from external sources such as Gene Ontology GO Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue N Jouinv Vd HIN Su et al Page 16 e partition the network for potential functional modules e g using MCODE or clusterMaker Apps e or merge a network with existing curated networks e g using Michigan Molecular Interaction MiMI or GeneMANIA databases Our protocols a
33. nes Basic Protocol 2 Explore a Human Disease Network In this protocol we import data from the human disease network constructed by Goh et al Goh et al 2007 This study constructed a global association network between diseases and genes using curated mutation data from the Online Mendelian Inheritance in Man OMIM Hamosh er al 2005 database A human disease network HDN was constructed by connecting diseases that share the same gene mutations and a disease gene network DGN was constructed similarly via associated diseases Some of the resulting functional modules were interpreted quantitatively using microarray and protein protein interaction networks We will now explore the network from this paper Obtain Human Disease Network Dataset 1 Use a browser to load the http www barabasilab com pubs CCNR ALB Publications 200705 14 PNAS HumanDisease Suppl webpage Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al Page 9 Download Supporting Table S2 Network characteristics of diseases http www barabasilab com pubs CCNR ALB_Publications 200705 14_PNAS HumanDisease Suppl supplementary tableS2 txt Supporting Table S3 Network characteristics of disease genes http www barabasilab com pubs CCNR ALB Publications 200705 14 PNAS HumanDisease Suppl supplementary tableS3 txt Supporting
34. nes in the context of existing pathway knowledge and then infer potential novel gene functionality A variety of text and numeric data such as gene function annotations pathways and expression profiles can be imported and projected onto Cytoscape networks In addition to these core capabilities Cytoscape differentiates itself from other network visualization tools by enabling and encouraging active third party development of add on visualization and analysis Apps A large number of Cytoscape Apps known previously as plugins designed for biological networks are available from the Cytoscape App Store providing functionality including data import from external repositories functional annotation and discovery module detection literature search network layouts and network filtering Lotia et al 2013 Saito et al 2012 This rapidly growing App ecosystem makes Cytoscape very attractive for users who need easy to use analysis tools for biological network data In the following text we introduce several analysis protocols addressing frequently encountered Cytoscape usage scenarios Following these protocols you will learn how to set up Cytoscape install and use Apps import biological networks use visual styles to map attribute data find highly inter connected clusters and generate network layouts to aid visualization Support Protocol 1 Set up Cytoscape Cytoscape 3 was developed on Oracle Java and can be run on most operating syste
35. nfiguration 3 For user Bob using Windows 7 the directory would be C Users Bob CytoscapeConfiguration 3 If restarting Cytoscape fails in the same way delete the Apps too by removing lt userdir gt CytoscapeConfiguration and restarting Cytoscape again No View Window after Large Network Load Symptoms After loading a large network the network name appears in the Network tab but there is no window showing the network Possible causes For large networks Cytoscape shortens the overall load time by not drawing the network view window Remedies Right click on the network in the Network tab and choose the Create View menu item The network window will appear within a few seconds You can create a more manageable subnetwork by using the procedure in Step 14 of Basic Protocol 1 Data Integration Errors Symptoms Expression or attribute data files are not properly integrated with the loaded network Possible causes The gene identifier columns that synchronize the two files do not match exactly or the files may not be in the correct format Remedies Use the Node Table or Edge Table tabs in the Table Panel to check that the network identifiers match the identifiers in the expression or attribute data file per the tutorial at http opentutorials cgl ucsf edu index php Tutorial Network Loading And ID Mapping Acknowledgments Work on this protocol was funded by the National Resource for Network Biology P41 GM103504 and t
36. on in the installation folder For Windows open the Cytoscape folder through the Start Button and All Programs list then click on the Cytoscape icon Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue N Jouinv Vd HIN Su et al Page4 6 The Cytoscape desktop and the welcome screen should now appear Support Protocol 2 Search and Install Apps Cytoscape Apps are optional extensions to the Cytoscape software that provide specific additional features In Cytoscape 2 x Apps were called plugins Cytoscape 2 x plugins are not compatible with Cytoscape 3 x Many of these apps are available for public browsing and download at the Cytoscape App Store http apps cytoscape org Apps can be searched by keyword or browsed by function tags Each App has its own App page containing a feature description details on usage download statistics links to available tutorials and user rating information Apps can be installed directly from the App Store or from within Cytoscape using the Cytoscape App Manager To install an App e g the BINGO gene set enrichment analysis App directly from the App Store website 1 Launch Cytoscape and keep it running Use a browser to load the BINGO web page http apps cytoscape org apps bingo Click on the Install button A dialog should pop out showing the progress UD ode D Oe When installed the butt
37. on on the BINGO web page will change to Installed To install an App e g the MCODE network module detection App from within Cytoscape using the Cytoscape App Manager 1 Launch Cytoscape close the Welcome screen if it is still visible Go to menu Apps App Manager In the Search text box type MCODE no quotes Select the MCODE App Click on the Install button A wu FF amp BS When installed the Installed button will become grey Multiple Apps can be installed one after the next For instance you can follow either of the above protocols to install the cluster Maker network clustering App and the enhanced Graphics App that provides additional rich information visualization features for Cytoscape network nodes Note Apps should be tested after being installed before installing another App to enable tracing any issue that may arise from a particular App When installation is complete click on the Currently Installed tab All installed apps should be shown Basic Protocol 1 Analyzing Gene Expression Data in Cytoscape One common use of Cytoscape is to map attribute data such as experimental data or text annotations onto a biological network such as a protein protein interaction network or Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al Page 5 meta
38. ow only attributes associated with selected nodes To search for diseases with keywords you can type the search terms in the top right corner of the toolbar Type Neuro to select all diseases associated with Neurological disorders they are quite close to each other in the network We can overlay the network with the attributes using visual styles 42 43 44 45 46 47 Click on the Style tab to edit your visual styles Click on the Node tab to reveal Node properties Click on the Properties drop down and click on Show All Scroll to Fill Color In the Column field choose Disorder class and then choose Discrete Mapping Since the network contains many disease classes we can right click and choose Mapping Value Generators Random Color in the popup menu Each disease class is then automatically assigned a random color as node fill Click on the Edge tab Click on the Properties button and click on Show All Find the Width field and choose Weight for the Column field This value indicates how many gene mutations different diseases share Choose Discrete Mapping right click to activate popup menu choose Mapping Value Generators and choose Number Series Set the start width to 1 and set the Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N JouIn y Vd HIN 1duosnue y Jouiny V
39. r interface and performance relative to previous versions This protocol aims to jump start new users with specific protocols for basic Cytoscape functions such as installing Cytoscape and Cytoscape Apps loading data visualizing and navigating the network visualizing network associated data attributes and identifying clusters It also highlights new features that benefit experienced users Keywords Cytoscape Interactive Network Visualization Network Analysis Introduction A network model or graph in mathematics represents associations between entities in a system It is commonly used to study complex systems in many disciplines including computer science social science and life sciences The molecules in a biological system interact with each other and form molecular complexes modules or pathways that carry out various biological functions In a biological network nodes or vertices often represent proteins genes or metabolites while edges often represent relationships such as physical interactions or gene expression regulation Merico et al 2009 These networks can be generated from prior knowledge as well as deduced from experimental data Many online repositories such as KEGG Kanehisa 2002 UNIT 1 12 Reactome UNIT 8 7 BIND Bader et al 2001 HPRD Goel et al 2012 IntAct Kerrien et al 2012 iRefWeb Turinsky et al 2014 MiMI Tarcea et al 2009 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN
40. rching http en wikipedia org wiki Regular_expression as the way to match the genes we would like to select In the text box type the term without the quotes A Z0 9 This selects all entries that have upper case letters or numbers Note that all of the gene expression data has gene symbols that match that regular expression but if the gene only exists in the BioGRID interaction network that field will be blank as in Figure 2 Click on Apply to apply the selection This should select approximately 5 500 nodes as indicated at the bottom of the Select panel Create a New Network with the Selected Subset Now with the selected nodes we would like to create a sub network from the original BioGRID network Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouiny Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue v JOuINY Vd HIN Su et al 14 15 16 17 18 Page 7 Go to menu File New Network From selected nodes all edges to create a new network Depending on the default settings a network view may be created This may take some time Click on the Network tab in the Control Panel There should now be two network entries listed BIOGRID ORGANISM Saccharomyces cerevisiae 3 2 105 mitab and BIOGRID ORGANISM Saccharomyces cerevisiae 3 2 105 mitab 1 The first one is the original network and the second one is the sub network filtered
41. s When starting Cytoscape on Windows you receive messages indicating that the JMV could not be found is defective or the maximum heap size is too large Possible causes You may have inadvertently installed 32 bit Java which is the default download from java com on all Windows systems If you have installed a 64 bit Cytoscape the 32 bit Java is inappropriate Remedies Uninstall 32 bit Java and install 64 bit Java instead Oracle maintains the latest Java at http java com en download manual jsp When you restart Cytoscape you should see its splash screen Cytoscape Freezes during Startup Symptoms When starting Cytoscape the splash screen appears and nothing more happens or it shows the names of Cytoscape modules being loaded but then freezes before showing a Cytoscape window Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 1duosnue N Jouin y Vd HIN 1duosnue y Jouiny Vd HIN 1duosnue N Jouinv Vd HIN Su et al Page 17 Possible causes Cytoscape and its code cache may have become unsynchronized possibly as a result of installing a newer or older Cytoscape Alternatively a new Cytoscape installation could be taking extra time up to 3 minutes to build its code cache Remedies If a Cytoscape windows hasn t appeared after 3 minutes use your OS to terminate the executing Cytoscape then delete the Cytoscape cache directory maintained in your user directory at lt userdir gt CytoscapeCo
42. sis The sharp decline in the cost of high throughput technology has made it possible to efficiently measure tens of thousands of molecular profiles at once for hundreds of different sample groups and experimental conditions Such rich repositories of experimental data along with the human curated annotations from the literature enable researchers to quickly identify novel connections between their observations and existing knowledge thereby enabling testing of new hypotheses In addition to the traditional genomics transcriptomics and proteomics accurate metabolomics phenomics lipidomics are also becoming more accessible Together these data offer different snapshots of a target organism Even though robust and scalable computational and statistical methods have been developed to mine new signals it is often difficult for the researcher to explore such data without high performance versatile and interactive visualization software Cytoscape was originally designed as a simple tool to visualize networks with hundreds or maybe a few thousands of nodes Thanks to the continuous community support it has expanded its capabilities and scope to handle bigger more complex data and evolved into a sophisticated platform that can be used for many network analysis purposes New features include e improved performance of network layouts and visualizations of very large networks using edge curving and bundling to reduce edge clutter flexible a
43. tendency to have elevated expression at the time of the temperature change and significantly decreased expression after 15 minutes Some of the other genes also show a tendency towards decreased expression after 30 minutes Perform an Enrichment Analysis using BINGO When we obtain gene clusters from a network a natural follow up question is how do these clusters map to known gene function BiNGO is a Cytoscape App that identifies statistically over represented Gene Ontology GO gene function annotation terms in a gene set or sub network Maere et al 2005 We will now use BiNGO to identify enriched functions in the previously identified clusters 22 Launch the BINGO App Apps BiNGO 23 Select a name for the cluster e g Cluster 2 and make sure the Get Cluster from Network option is selected as in Figure 5 24 Click on Start BINGO to run BINGO The results are displayed both as a table ordered by p value of term enrichment and a network of ontology terms where the node color represents the p value of the over represented terms as in Figure 6 As might be expected the top scoring p values are all related to ribosome biogenesis and RNA processing As the cell is shocked the first step is to ramp up its ability to make proteins to respond to the new conditions Later on the cell s response the cell no longer requires additional ribosomes and RNA processing machinery so it ramps down the expression of these ge
44. toscape has been used to explore structural networks Morris ef al 2007 Doncheva et al 2011 protein protein similarity networks Atkinson ef al 2009 and biological pathways see the Wikipathways App the CyKEGG Parser App and the MetScape App Gao et al 2010 If the starting point is a list of genes and no source network is available apps like the AgilentLiteratureSearch App Vailaya et al 2005 and GeneMANIA Montojo et al 2010 can provide useful starting points as well as sources for information to augment existing networks Additional Apps are available to extend the analysis and visualization we present here and delve further into the biological meaning of the interactions they are available with documentation at http apps cytoscape org Basic Protocol 1 demonstrates the annotation of a protein protein interaction network with expression data although the approach could be used to annotate networks with a wide variety of additional data The hierarchical clusters derived from the expression data provide just one approach to exploring this data set For instance critical genes and proteins tend to be hubs nodes connected to many other nodes or part of the shortest path through the network between two other nodes Yu et al 2007 Various network parameters may be calculated using Cytoscape s built in NetworkAnalyzer via Tools NetworkAnalyzer Doncheva et al 2012 or through Cytoscape apps such as CentiScaPe Scardoni et
45. view Options V Tab Comma J Semicolon _ Space _ other O Show all entries in the file Show first 100 Jj entries Column Names i Transfer first line as column names Start Import Row 83 E Comment Line T Left Click Enable Disable Column Right Click Edit Column Aui oo S2 ID REF V IDENTIFIER GSM1029 GSM1030 GSM1032 GSM1033 V GSM1034 V Gene title 1 GENOMIC 1X 0 192 0 404 0 149 0 38 0 074 a 2 3XSSC 1193 0 38 0 534 2 055 0 379 3 GENOMIC 0 5X 0 653 0 353 0219 0 097 0 165 4 3XSSC 0 465 0 064 0 387 0 678 0 144 5 GENOMIC 0 25X 0 556 0 389 0 028 0 035 0 041 6 3XSSC 1493 0222 0 0 911 0 051 7 EMPTY 0 799 0 385 0 478 0 756 0 322 8 null null null 0 null IZ 5 File Size Unknown Figure 1 Prepare parameters to import data into Cytoscape Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al Page 21 Control Panel Defaut fiter i X Node Gene symbol matches regex i A Z0 9 4 D Case sensitive LJ Apply Automatically Lappy J Figure 2 Entering regular expressions for Cytoscape Curr Protoc Bioinformatics Author manuscript available in PMC 2015 September 08 Su et al flle f sers scooter Tile Users seoote Table Panel ea Genet Genes Ha unien Une Wk 1880 semi O
46. xpression patterns across experimental conditions can also reveal different mechanisms that cause the same end result As we saw the BINGO app finds significantly over represented Gene Ontology terms annotated to the genes of interest This helps identify functions enriched in a set of genes including sets of genes that are co expressed Basic Protocol 2 demonstrates the workflow of using Cytoscape to visualize and annotate large biomedical networks One important and useful Cytoscape feature is its Style Manager formerly called VizMapper which allows researchers to translate a variety of attribute data such as gene expression profiles functional gene groups and pathways and protein protein interaction types to intuitive graphic representations that facilitate exploratory knowledge discovery In our examples we used BINGO clusterMaker and MCODE to identify closely associated gene and protein clusters These clusters can be immediately visualized in the Network view which is especially helpful for visualizing and understanding the local topologies and functional features in very large networks such as the Human Disease Network having thousands of nodes and edges The Cytoscape App store contains many other examples http apps cytoscape org apps with tag datavisualization tailored for visualizing data from various biological sources In our protocol we imported disease network and additional annotation data containing disease categories
47. y Jouiny Vd HIN 1duosnue N Jouinv Vd HIN Su et al Page 6 already have that data in our SOFT dataset labeled as Gene ID To link these datasets select Show Mapping Options and under Select the primary key column in table and select Gene ID as in Figure 1 Select OK to import the data as attributes The imported data includes fold changes at five different time points after shifting the temperature from 30 C to 37 C Sample Accession Time Point GSM1029 0 minutes GSM1030 5 minutes GSM1032 15 minutes GSM1033 30 minutes GSM1034 60 minutes These attributes include not only a number of numeric attributes but also various symbols We will use that in the next step Filter the Network with the Genes that have Expression Data The BioGRID network contains too many nodes and edges to visually explore effectively To explore a biologically relevant subset we can obtain a sub network using only genes in the experimental data 9 10 11 12 13 Click on the Select tab in the left side panel Control Panel in Cytoscape Click to create a new filter and select Column Filter A new selection box should be shown with the label Choose column Click on Choose column select Node Gene Symbol click on the contains button and change it to matches regex This allows us to define a regular expression a way to define text patterns for sea
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