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Cytoscape: Now and Future 
Keiichiro Ono 
UCSD Trey Ideker Lab 
Cytoscape Core Team 
Lab Meeting 12/1/2014
Now 
Future
Now: Current Status 
- Overall Project Status 
- Cytoscape 3.2 
- cyREST
Future 
Web Technologies 
Cytoscape-as-a-service 
Service-oriented workflow 
Docker 
Reproducible Science 
NIH The Commons
Status
> 700 downloads/month
Cytoscape: Now and Future
Cytoscape: Now and Future
App Store Daily Download Plot
Cytoscape: Now and Future
Cytoscape: Now and Future
Project Status 
Cytoscape Core: 
Apps: 
Publications: 
✔ 
✔ 
?
Status: Cytoscape Core
Cytoscape 3.2
Cytoscape 3.2 
- Released early November 
- What’s New? 
- Chart Editor 
- Export as Web Application 
- Performance Improvements 
- Lots of bug fixes
Chart Editor
Chart Editor
Chart Editor 
- Visualize multiple data points 
to a single view 
- Time series data 
- Multiple GO terms 
- Chart types: Bar, Box, Pie, 
Heat Map, Ring 
- Part of standard Visual Style 
Editor 
- Everything will be saved 
into session files
Gradient Editor
Export as Web Application
Export as Web Application 
Exporting Cytoscape-generated 
visualizations as a 
complete web application 
using Cytoscape.js
Cytoscape: Now and Future
Cytoscape: Now and Future
Cytoscape: Now and Future
cyREST
Users
User Type I 
- Average computing skills 
- Use Excel as their primary 
workbench for data analysis 
- For them, bioinformatics 
means using some of 
NCBI/EBI web tools or 
DAVID 
- Have tons of data not 
analyzed / visualized yet 
- Excel is my friend.
User Type II 
- Advanced computing skills 
- Use Python + SciPy / 
NumPy, R + 
Bioconductor, or 
MATLAB every day 
- If necessary, write their 
own packages 
- Use HPC technologies a lot 
- Manual operation is evil.
Both of them are Important! 
- Type I: “Bench Biologists” 
- Domain experts 
- Data producers 
- Type II: Computational Biologists 
- Experts of large-scale data analysis 
- Especially important for genome-scale 
data analysis 
They are ignored for a long 
time in Cytoscape world…
User Type II 
- Advanced computing skills 
- Use Python + SciPy / 
NumPy, R + 
Bioconductor, or 
MATLAB every day 
- If necessary, write their 
own packages 
- Use HPC technologies a lot 
- Manual operation is evil.
Requests from Type II Users 
- I have 200 networks in my session and I need to create 
one PDF per view. How can I do it with Cytoscape? 
- I need to use igraph for network analysis, but its 
visualization feature is limited. I want to use Cytoscape 
as an external visualization engine for R. 
- Usually I use IPython Notebook to record my work. 
How can I integrate Cytoscape into my workflow? 
- I want to generate Style for each time point and create 
small multiples of networks.
REST
What is cyREST? 
REST 
- Platform-independent, RESTful API 
module for Cytoscape 
- Means you can access basic 
Cytoscape data objects 
programmatically
In-House Databases External Computing Resources 
Interactive Data Analysis 
Environments 
- Graph Layout 
- Statistical Analysis 
- Data Pre-processing 
RStudio 
- NumPy 
- SciPy 
- Pandas 
- NetworkX 
IPython Notebook 
Public Data Repository File / Code Hosting Services 
EBI RDF Platform 
PSICQUIC Services 
Other Bioinformatics Web Applications / Services 
- igraph 
- rCurl 
Command Line Tools 
> sed 
> awk 
> grep 
> curl 
Web Browsers 
Data Repository & Collaboration Service 
Data Bus (Internet) 
Your Workstation 
Cytoscape App Store 
Apps 
REST 
Core 
Cytoscape Desktop
Cytoscape 3.1+ Clients 
REST 
GET 
DELETE 
POST 
PUT
Mapping Cytoscape API to HTTP Methods 
Cytoscape Operations 
Create 
Read 
Update 
Delete 
HTTP Methods 
POST 
GET 
PUT 
DELETE
GET http://localhost:1234/v1/networks/52 
Get full network with unique ID 52 as JSON
http://localhost:1234/v1/networks/52
Demo: 
Cytoscape Controlled 
from IPython Notebook 
REST 
http://bit.ly/1wcKXVV
REST 
Ready to Use Now! 
http://apps.cytoscape.org/apps/cyrest
Future
History
2005
Cytoscape: Now and Future
2005 
- Cytoscape 2.2: Simple Java Application 
- Google released an application called 
Google Maps beta 
- “Re-discovery” of JavaScript, or Ajax
2014
Cytoscape: Now and Future
2014 
- Cytoscape 3.2.0: (Modularized) Java Application 
- Client applications are migrating to the web browsers 
- “Pure” desktop applications are dying slowly… 
- Even desktop applications depend on eternal services 
- JavaScript everywhere 
- Cloud Computing 
- Scale-out over scale-up
Trend in Software Design 
- An application is a collection of smaller services 
- JavaScript is a first-class citizen in the world of 
programming languages 
- Design application with cloud services in mind
http://12factor.net/
In the modern era, software is commonly delivered as a 
service: called web apps, or software-as-a-service. The twelve-factor 
app is a methodology for building software-as-a-service apps that: 
• Use declarative formats for setup automation, to minimize time and 
cost for new developers joining the project 
• Have a clean contract with the underlying operating system, offering 
maximum portability between execution environments 
• Are suitable for deployment on modern cloud platforms, obviating 
the need for servers and systems administration 
• Minimize divergence between development and production, 
enabling continuous deployment for maximum agility 
• And can scale up without significant changes to tooling, 
architecture, or development practices.
Cytoscape: Now and Future
This MANIFESTO counters 
current trends in 
bioinformatics where 
institutes and companies 
are creating monolithic 
software solutions aimed 
mostly at end-users.
Let’s see what’s happening 
in (scientific) computing…
Bioinformatics Open Source Conference (BOSC)
@Facebook HQ @New York Times 
in Boston
What I Have Learned… 
- Python is becoming the standard 
language for “Data Scientists” 
- Python itself is a very slow language, 
but is a perfect glue 
- Lots of tools are made by scientists 
(e.g. Anaconda by Continuum) 
- They do understand current 
problems in modern scientific 
computing, and trying to solve them
Cytoscape: Now and Future
What I Have Learned… 
- Data visualization 
- Visualization needs varies, especially for 
complex data sets like the one from life 
science domain 
- For that purpose, Java is not the best 
language to implement applications 
- Even large-scale data visualization 
applications are moving to the web browsers 
- Canvas (Cytoscape.js), WebGL (Three.js), 
SVG (D3.js) 
- Most of the talented hackers are working on 
the web browsers, i.e., JavaScript
WikiGalaxy: http://wiki.polyfra.me/#
Cytoscape: Now and Future
Problems in Scientific Computing 
- No more free lunch 
- Even if you buy expensive machines, you cannot get free performance gain 
anymore. You have to design your code for massively distributed 
environment. (From Scale-up to Scale-out) 
- Complex Data Analysis Pipeline 
- Need to build pipeline by connecting multiple resources, or services 
- Needs for complex, customized data visualization 
- Reproducibility 
➡ But building, deploying, and maintaining reproducible pipeline is not 
straight-forward
What does this mean to biologists? 
- “Omics-Scale" Data Analysis 
- Need computing power beyond your workstations 
- Need to build pipelines by connecting multiple 
resources, or services 
➡ But developing, deploying, and maintaining 
reproducible, or “portable” pipeline is not straight-forward
What does this mean to biologists? 
- Collaboration between scientists and software 
engineers will be more important 
- Scientists should spend their time on science, not the 
details of JavaScript syntax or how to build large scale 
pipeline 
- In other words, building bioinformatics computing 
environment itself is a research project
What does this mean to Cytoscape team? 
- Cytoscape should work nicely 
with other tools 
- All bioinformatics tools should 
work as a building block of large 
workflows 
- In a long term, Cytoscape 
should be a collection of 
services
Universe of Tools for Bioinformatics 
!
Cytoscape as a Collection of Services
Case Study 1
PANGIA App
Srivas, Rohith et al. “Assembling Global Maps of Cellular Function through 
Integrative Analysis of Physical and Genetic Networks.” Nature Protocols 
6.9 (2011): 1308–1323. PMC. Web. 1 Dec. 2014.
Core algorithm 1 
as Python 
Java Implementation of 
Algorithms 
Cytoscape 2.x Plugin 
Biological 
Problem 
by Sourav 
Cytoscape 3.x App 
Core algorithm 2 
as Python 
Core algorithm n 
as Python 
PanGIA Service 
(Implement in Python again…?) 
by Greg, Rohith 
by Greg, Rothith and Cytoscape Team 
by David 
History of PanGIA Application
Lots of Duplicate Efforts!
Case Study 2
NeXO Web
NeXO Web 
- Term Enrichment Analysis 
- From list of genes, perform 
hypergeometric test over set of 
machine-generated ontology (NeXO) 
terms and display terms with p-values 
- It is independent from all other parts of 
NeXO Web application
NeXO Web RESTful API 
Term 
Enrichment Service API by Flask 
Python Core 
SciPy 
NumPy 
Overview of NeXO Term 
Enrichment Service
NeXO Web RESTful API 
Term 
Enrichment Service API by Flask 
Python Core 
SciPy 
NumPy 
Overview of NeXO Term 
Enrichment Service
Option 1: As a Cytoscape App 
- Re-implement this algorithm as a Cytoscape App 
(Java Application) 
- Pros: 
- Easy to install 
- Cons: 
- A lot of work… 
- Should be written in Java 
- Does not scale-out!
Option 2: As a Service 
- Wrap existing applications and deploy to platform of users’ choice: 
- Laptops, private servers, and commercial cloud services (AWS/Google 
Computing Cloud, etc.) 
- Pros: 
- Scales-out 
- Client-independent 
- Workflow-friendly 
- Cons: 
- Need to adopt to the new way of software design 
- Relatively more complex deployment
Summary 
- Best practice: for future applications, implementing 
them as services and then call them from Cytoscape, 
IPython, RStudio, and other tools 
- To make your algorithms available to both Type I 
(domain experts) and Type II (hardcore computational 
biologists) users, it is better to deploy them as a 
service, instead of an App
Does technology available to implement 
such applications / workflows?
Yes!
Key: Provenance
Data 
Workflow 
Environment
Data 
Workflow 
Environment
Data 
Workflow 
Environment
Data 
Workflow 
Environment
Software Distribution Problem 
- “It-worked-on-my-machine” syndrome 
- This is a serious problem especially when 
you want to share your workflow with 
collaborators.
Cytoscape: Now and Future
Cytoscape: Now and Future
What is Docker? 
- Container to run applications in an isolated 
environment 
- Application = Layer of images 
- Sharable Environments 
- Environments as code
https://www.docker.com/whatisdocker/
Docker Hub 
- Sharing environments as code! 
- Dockerfile - Definition of your container 
- Example: http://bit.ly/15N23P8
Goal: 
Reproducible Science
We (the NIH) Are Working On, But As 
Yet Do Not Have Good Answers To: 
1. Today, how much are we actually 
spending on data and software related 
activities? 
2. How much should we be spending to 
achieve the maximum benefit to 
biomedical science relative to what we 
spend in other areas? 
Biomedical Research as an Open Digital Enterprise by Philip E. Bourne Ph.D. 
Associate Director for Data Science (NIH)
Reproducibility 
! Most of the 27 Institutes and Centers of the NIH are 
currently reviewing the ability to reproduce research 
they are funding 
! The NIH recently convened a meeting with publishers 
to discuss the issue – a set of guiding principles 
arose 
Biomedical Research as an Open Digital Enterprise by Philip E. Bourne Ph.D. 
Associate Director for Data Science (NIH)
The Cytoscape to a Cytoscape 
- Shares Core Concepts 
- Graph Model 
- Table associated with graph 
- Style (Collection of visual mappings) 
- Implemented as different collection of services 
- Desktop Cytoscape 
- Interactive network data visualizer on the web 
- Optimized for ontology browsing (i.e., future version of NeXO Web)
Photo Credits 
• https://flic.kr/p/bFZpyg 
• https://flic.kr/p/bmXUz1
2014 Keiichiro Ono 
kono@ucsd.edu

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Cytoscape: Now and Future

  • 1. Cytoscape: Now and Future Keiichiro Ono UCSD Trey Ideker Lab Cytoscape Core Team Lab Meeting 12/1/2014
  • 3. Now: Current Status - Overall Project Status - Cytoscape 3.2 - cyREST
  • 4. Future Web Technologies Cytoscape-as-a-service Service-oriented workflow Docker Reproducible Science NIH The Commons
  • 9. App Store Daily Download Plot
  • 12. Project Status Cytoscape Core: Apps: Publications: ✔ ✔ ?
  • 15. Cytoscape 3.2 - Released early November - What’s New? - Chart Editor - Export as Web Application - Performance Improvements - Lots of bug fixes
  • 18. Chart Editor - Visualize multiple data points to a single view - Time series data - Multiple GO terms - Chart types: Bar, Box, Pie, Heat Map, Ring - Part of standard Visual Style Editor - Everything will be saved into session files
  • 20. Export as Web Application
  • 21. Export as Web Application Exporting Cytoscape-generated visualizations as a complete web application using Cytoscape.js
  • 26. Users
  • 27. User Type I - Average computing skills - Use Excel as their primary workbench for data analysis - For them, bioinformatics means using some of NCBI/EBI web tools or DAVID - Have tons of data not analyzed / visualized yet - Excel is my friend.
  • 28. User Type II - Advanced computing skills - Use Python + SciPy / NumPy, R + Bioconductor, or MATLAB every day - If necessary, write their own packages - Use HPC technologies a lot - Manual operation is evil.
  • 29. Both of them are Important! - Type I: “Bench Biologists” - Domain experts - Data producers - Type II: Computational Biologists - Experts of large-scale data analysis - Especially important for genome-scale data analysis They are ignored for a long time in Cytoscape world…
  • 30. User Type II - Advanced computing skills - Use Python + SciPy / NumPy, R + Bioconductor, or MATLAB every day - If necessary, write their own packages - Use HPC technologies a lot - Manual operation is evil.
  • 31. Requests from Type II Users - I have 200 networks in my session and I need to create one PDF per view. How can I do it with Cytoscape? - I need to use igraph for network analysis, but its visualization feature is limited. I want to use Cytoscape as an external visualization engine for R. - Usually I use IPython Notebook to record my work. How can I integrate Cytoscape into my workflow? - I want to generate Style for each time point and create small multiples of networks.
  • 32. REST
  • 33. What is cyREST? REST - Platform-independent, RESTful API module for Cytoscape - Means you can access basic Cytoscape data objects programmatically
  • 34. In-House Databases External Computing Resources Interactive Data Analysis Environments - Graph Layout - Statistical Analysis - Data Pre-processing RStudio - NumPy - SciPy - Pandas - NetworkX IPython Notebook Public Data Repository File / Code Hosting Services EBI RDF Platform PSICQUIC Services Other Bioinformatics Web Applications / Services - igraph - rCurl Command Line Tools > sed > awk > grep > curl Web Browsers Data Repository & Collaboration Service Data Bus (Internet) Your Workstation Cytoscape App Store Apps REST Core Cytoscape Desktop
  • 35. Cytoscape 3.1+ Clients REST GET DELETE POST PUT
  • 36. Mapping Cytoscape API to HTTP Methods Cytoscape Operations Create Read Update Delete HTTP Methods POST GET PUT DELETE
  • 37. GET http://localhost:1234/v1/networks/52 Get full network with unique ID 52 as JSON
  • 39. Demo: Cytoscape Controlled from IPython Notebook REST http://bit.ly/1wcKXVV
  • 40. REST Ready to Use Now! http://apps.cytoscape.org/apps/cyrest
  • 43. 2005
  • 45. 2005 - Cytoscape 2.2: Simple Java Application - Google released an application called Google Maps beta - “Re-discovery” of JavaScript, or Ajax
  • 46. 2014
  • 48. 2014 - Cytoscape 3.2.0: (Modularized) Java Application - Client applications are migrating to the web browsers - “Pure” desktop applications are dying slowly… - Even desktop applications depend on eternal services - JavaScript everywhere - Cloud Computing - Scale-out over scale-up
  • 49. Trend in Software Design - An application is a collection of smaller services - JavaScript is a first-class citizen in the world of programming languages - Design application with cloud services in mind
  • 51. In the modern era, software is commonly delivered as a service: called web apps, or software-as-a-service. The twelve-factor app is a methodology for building software-as-a-service apps that: • Use declarative formats for setup automation, to minimize time and cost for new developers joining the project • Have a clean contract with the underlying operating system, offering maximum portability between execution environments • Are suitable for deployment on modern cloud platforms, obviating the need for servers and systems administration • Minimize divergence between development and production, enabling continuous deployment for maximum agility • And can scale up without significant changes to tooling, architecture, or development practices.
  • 53. This MANIFESTO counters current trends in bioinformatics where institutes and companies are creating monolithic software solutions aimed mostly at end-users.
  • 54. Let’s see what’s happening in (scientific) computing…
  • 55. Bioinformatics Open Source Conference (BOSC)
  • 56. @Facebook HQ @New York Times in Boston
  • 57. What I Have Learned… - Python is becoming the standard language for “Data Scientists” - Python itself is a very slow language, but is a perfect glue - Lots of tools are made by scientists (e.g. Anaconda by Continuum) - They do understand current problems in modern scientific computing, and trying to solve them
  • 59. What I Have Learned… - Data visualization - Visualization needs varies, especially for complex data sets like the one from life science domain - For that purpose, Java is not the best language to implement applications - Even large-scale data visualization applications are moving to the web browsers - Canvas (Cytoscape.js), WebGL (Three.js), SVG (D3.js) - Most of the talented hackers are working on the web browsers, i.e., JavaScript
  • 62. Problems in Scientific Computing - No more free lunch - Even if you buy expensive machines, you cannot get free performance gain anymore. You have to design your code for massively distributed environment. (From Scale-up to Scale-out) - Complex Data Analysis Pipeline - Need to build pipeline by connecting multiple resources, or services - Needs for complex, customized data visualization - Reproducibility ➡ But building, deploying, and maintaining reproducible pipeline is not straight-forward
  • 63. What does this mean to biologists? - “Omics-Scale" Data Analysis - Need computing power beyond your workstations - Need to build pipelines by connecting multiple resources, or services ➡ But developing, deploying, and maintaining reproducible, or “portable” pipeline is not straight-forward
  • 64. What does this mean to biologists? - Collaboration between scientists and software engineers will be more important - Scientists should spend their time on science, not the details of JavaScript syntax or how to build large scale pipeline - In other words, building bioinformatics computing environment itself is a research project
  • 65. What does this mean to Cytoscape team? - Cytoscape should work nicely with other tools - All bioinformatics tools should work as a building block of large workflows - In a long term, Cytoscape should be a collection of services
  • 66. Universe of Tools for Bioinformatics !
  • 67. Cytoscape as a Collection of Services
  • 70. Srivas, Rohith et al. “Assembling Global Maps of Cellular Function through Integrative Analysis of Physical and Genetic Networks.” Nature Protocols 6.9 (2011): 1308–1323. PMC. Web. 1 Dec. 2014.
  • 71. Core algorithm 1 as Python Java Implementation of Algorithms Cytoscape 2.x Plugin Biological Problem by Sourav Cytoscape 3.x App Core algorithm 2 as Python Core algorithm n as Python PanGIA Service (Implement in Python again…?) by Greg, Rohith by Greg, Rothith and Cytoscape Team by David History of PanGIA Application
  • 72. Lots of Duplicate Efforts!
  • 75. NeXO Web - Term Enrichment Analysis - From list of genes, perform hypergeometric test over set of machine-generated ontology (NeXO) terms and display terms with p-values - It is independent from all other parts of NeXO Web application
  • 76. NeXO Web RESTful API Term Enrichment Service API by Flask Python Core SciPy NumPy Overview of NeXO Term Enrichment Service
  • 77. NeXO Web RESTful API Term Enrichment Service API by Flask Python Core SciPy NumPy Overview of NeXO Term Enrichment Service
  • 78. Option 1: As a Cytoscape App - Re-implement this algorithm as a Cytoscape App (Java Application) - Pros: - Easy to install - Cons: - A lot of work… - Should be written in Java - Does not scale-out!
  • 79. Option 2: As a Service - Wrap existing applications and deploy to platform of users’ choice: - Laptops, private servers, and commercial cloud services (AWS/Google Computing Cloud, etc.) - Pros: - Scales-out - Client-independent - Workflow-friendly - Cons: - Need to adopt to the new way of software design - Relatively more complex deployment
  • 80. Summary - Best practice: for future applications, implementing them as services and then call them from Cytoscape, IPython, RStudio, and other tools - To make your algorithms available to both Type I (domain experts) and Type II (hardcore computational biologists) users, it is better to deploy them as a service, instead of an App
  • 81. Does technology available to implement such applications / workflows?
  • 82. Yes!
  • 88. Software Distribution Problem - “It-worked-on-my-machine” syndrome - This is a serious problem especially when you want to share your workflow with collaborators.
  • 91. What is Docker? - Container to run applications in an isolated environment - Application = Layer of images - Sharable Environments - Environments as code
  • 93. Docker Hub - Sharing environments as code! - Dockerfile - Definition of your container - Example: http://bit.ly/15N23P8
  • 95. We (the NIH) Are Working On, But As Yet Do Not Have Good Answers To: 1. Today, how much are we actually spending on data and software related activities? 2. How much should we be spending to achieve the maximum benefit to biomedical science relative to what we spend in other areas? Biomedical Research as an Open Digital Enterprise by Philip E. Bourne Ph.D. Associate Director for Data Science (NIH)
  • 96. Reproducibility ! Most of the 27 Institutes and Centers of the NIH are currently reviewing the ability to reproduce research they are funding ! The NIH recently convened a meeting with publishers to discuss the issue – a set of guiding principles arose Biomedical Research as an Open Digital Enterprise by Philip E. Bourne Ph.D. Associate Director for Data Science (NIH)
  • 97. The Cytoscape to a Cytoscape - Shares Core Concepts - Graph Model - Table associated with graph - Style (Collection of visual mappings) - Implemented as different collection of services - Desktop Cytoscape - Interactive network data visualizer on the web - Optimized for ontology browsing (i.e., future version of NeXO Web)
  • 98. Photo Credits • https://flic.kr/p/bFZpyg • https://flic.kr/p/bmXUz1
  • 99. 2014 Keiichiro Ono kono@ucsd.edu