Lecture Slides More information will be available through our wiki:

1. VIZBI 2015 Tutorial Session
3/24/2015 @Broad Institute
Keiichiro Ono
UCSD Trey Ideker Lab
Cytoscape Core Team
Cytoscape, IPython, Docker, and Reproducible
Network Data Visualization Workflows

2. Thanks for Attending!
You will learn a lot today….
REST

3. Who am I?

4. Keiichiro Ono

5. Keiichiro Ono
Background
Bioinformatics
Computer Science
Work
Research
Bioinformatics workflow
Visualization pipeline
Data
Visualization
Networks
Other Biological Data
Integration
Molecular Interactions
Pathways
Annotations
Software Development
Cytoscape
NeXO
Cyberinfrastructure
All kinds of small tools
Like
Art
Kandinsky
Mondrian
Music
Electronica
Techno
Minimal
Detroit
Jazz
Sci-fi
Movie
Novel
Life
US
San Diego
San Francisco Bay Area
Los Angeles
Orange County
Japan
Gifu
Tokyo

6. Keiichiro Ono
Background
Bioinformatics
Computer Science
Work
Art
Kandinsky
Mondrian
Electronica

7. Keiic
Work
Research
Bioinformatics workflow
Visualization pipeline
Data
Visualization
Networks
Other Biological Data
Integration
Molecular Interactions
Pathways
Annotations
Software Development
Cytoscape
NeXO
Cyberinfrastructure
All kinds of small tools

8. Keiichiro Ono
Background
Bioinformatics
Computer Science
Work
Research
Bioinformatics workflow
Visualization pipeline
Data
Visualization
Networks
Other Biological Data
Integration
Molecular Interactions
Pathways
Annotations
Software Development
Cytoscape
NeXO
Cyberinfrastructure
All kinds of small tools
Like
Art
Kandinsky
Mondrian
Music
Electronica
Techno
Minimal
Detroit
Jazz
Sci-fi
Movie
Novel
Life
US
San Diego
San Francisco Bay Area
Los Angeles
Orange County
Japan
Gifu
Tokyo

9. Computer Science Biology

10. Agenda

11. Cytoscape, IPython, Docker,
and Reproducible Workflow
Overview of Cytoscape 3.2

12. Cytoscape, IPython, Docker,
and Reproducible Workflow
Overview of Cytoscape 3.2

13. Overview of Cytoscape 3.2

14. Quick Introduction to
Cytoscape

15. What is Cytoscape?

16. An Open Source Platform for Biological Network Data
Integration, Analysis and Visualization
Cytoscape

17. Cytoscape
- Open Source (LGPL)
- Free for both commercial and academic use
- Developed and maintained by universities,
companies, and research institutions
- De-facto standard software in biological network
research community
- Expandable by Apps

18. Is it really a de-facto
standard?

21. Basic Workflow

22. 1. Data Integration
(Load Networks and Tables)
2. Data Analysis
3. Visualization
Basic Workflow
4. Prepare for Publication

23. Network Data
Annotated
Networks
Attributes
Analyzed Data

24. Real World
Use Cases
http://cytoscape-publications.tumblr.com/

25. Introduction to v3.2

26. Cytoscape 3.2

27. Cytoscape 3.2
- New Features:
- Export as Web Application
- Chart Editor
- Performance Improvements
- Lots of bug fixes

28. Export as Web Application

29. Export as Web Application
Exporting Cytoscape-
generated visualizations as a
complete web application
using Cytoscape.js

30. Cytoscape.js?

31. http://www.cytoscape.org/
screenshots.html

35. Exercise:
Export as Web Application

36. Export View As Web App
- Open Cytoscape
- Load a sample network (Small ones)
- Apply layout
- File → Export → Network Views as Web Page…
- Open in browser
- python -m SimpleHTTPServer 8000

37. Chart Editor

38. Chart Editor

39. 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

40. Gradient Editor

41. Exercise:
Create a Simple Chart

42. Create a Simple Chart
- Download this file: http://cl.ly/aLmm
- Create node chart with expression values

44. http://cl.ly/aLjS

45. Part I Summary
- Cytoscape 3.2 includes new features for advanced
network visualization
- More integration to Cytoscape.js
- Build prototype web-based visualization in
Cytoscape
- v3.3
- Not finalized yet… Feature preview in summer

46. Cytoscape, IPython, Docker,
and Reproducible Workflow
Overview of Cytoscape 3.2

47. Cytoscape, IPython, Docker,
and Reproducible Workflow

48. Background

49. Illumina HiSeqX (http://systems.illumina.com/content/dam/illumina-marketing/documents/products/datasheets/datasheet-hiseq-x-ten.pdf)

50. Biology = Data-Driven Science

51. Problems in Bioinformatics
- 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

52. Problems in Bioinformatics
- 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

53. Goal:
Reproducible Science

54. https://flic.kr/p/5fJ4U4

55. >gi|10313991|ref|NC_002549.1| Zaire ebolavirus isolate Ebola virus/
H.sapiens-tc/COD/1976/Yambuku-Mayinga, complete genome
CGGACACACAAAAAGAAAGAAGAATTTTTAGGATCTTTTGTGTGCGAATAACTATGAGGAAGATTAATAA
TTTTCCTCTCATTGAAATTTATATCGGAATTTAAATTGAAATTGTTACTGTAATCACACCTGGTTTGTTT
CAGAGCCACATCACAAAGATAGAGAACAACCTAGGTCTCCGAAGGGAGCAAGGGCATCAGTGTGCTCAGT
TGAAAATCCCTTGTCAACACCTAGGTCTTATCACATCACAAGTTCCACCTCAGACTCTGCAGGGTGATCC
AACAACCTTAATAGAAACATTATTGTTAAAGGACAGCATTAGTTCACAGTCAAACAAGCAAGATTGAGAA
TTAACCTTGGTTTTGAACTTGAACACTTAGGGGATTGAAGATTCAACAACCCTAAAGCTTGGGGTAAAAC
ATTGGAAATAGTTAAAAGACAAATTGCTCGGAATCACAAAATTCCGAGTATGGATTCTCGTCCTCAGAAA
ATCTGGATGGCGCCGAGTCTCACTGAATCTGACATGGATTACCACAAGATCTTGACAGCAGGTCTGTCCG
TTCAACAGGGGATTGTTCGGCAAAGAGTCATCCCAGTGTATCAAGTAAACAATCTTGAAGAAATTTGCCA
ACTTATCATACAGGCCTTTGAAGCAGGTGTTGATTTTCAAGAGAGTGCGGACAGTTTCCTTCTCATGCTT
TGTCTTCATCATGCGTACCAGGGAGATTACAAACTTTTCTTGGAAAGTGGCGCAGTCAAGTATTTGGAAG
GGCACGGGTTCCGTTTTGAAGTCAAGAAGCGTGATGGAGTGAAGCGCCTTGAGGAATTGCTGCCAGCAGT
ATCTAGTGGAAAAAACATTAAGAGAACACTTGCTGCCATGCCGGAAGAGGAGACAACTGAAGCTAATGCC
GGTCAGTTTCTCTCCTTTGCAAGTCTATTCCTTCCGAAATTGGTAGTAGGAGAAAAGGCTTGCCTTGAGA
AGGTTCAAAGGCAAATTCAAGTACATGCAGAGCAAGGACTGATACAATATCCAACAGCTTGGCAATCAGT
AGGACACATGATGGTGATTTTCCGTTTGATGCGAACAAATTTTCTGATCAAATTTCTCCTAATACACCAA
GGGATGCACATGGTTGCCGGGCATGATGCCAACGATGCTGTGATTTCAAATTCAGTGGCTCAAGCTCGTT
TTTCAGGCTTATTGATTGTCAAAACAGTACTTGATCATATCCTACAAAAGACAGAACGAGGAGTTCGTCT
CCATCCTCTTGCAAGGACCGCCAAGGTAAAAAATGAGGTGAACTCCTTTAAGGCTGCACTCAGCTCCCTG
GCCAAGCATGGAGAGTATGCTCCTTTCGCCCGACTTTTGAACCTTTCTGGAGTAAATAATCTTGAGCATG
GTCTTTTCCCTCAACTATCGGCAATTGCACTCGGAGTCGCCACAGCACACGGGAGTACCCTCGCAGGAGT
AAATGTTGGAGAACAGTATCAACAACTCAGAGAGGCTGCCACTGAGGCTGAGAAGCAACTCCAACAATAT
GCAGAGTCTCGCGAACTTGACCATCTTGGACTTGATGATCAGGAAAAGAAAATTCTTATGAACTTCCATC
AGAAAAAGAACGAAATCAGCTTCCAGCAAACAAACGCTATGGTAACTCTAAGAAAAGAGCGCCTGGCCAA

56. Goal: Reproducible Science
REST

57. Why Reproducibility is Important?
- Saving your time
- Reusability
- Make your sponsors (funding agencies) happy

58. 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)

59. 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)

60. Reproducible
or Lose your Funding
(maybe)

61. Overview of Modern
Computing Resources

62. Modern Computing
= Stratum

63. Modern Computing
Bare Metal Machine
OS
Virtual Machine
Frameworks
Application

64. Bare Metal Machine
OS
Virtual Machine
Frameworks
Your App

65. Problems
- Complex Dependency
- OS-dependent code
- Network of dependencies to run popular library X
- Library version numbers
- It-worked-on-my-machine syndrome
- Installation Hell

66. Software Distribution Problem
- “It-worked-on-my-machine” syndrome
- This is a serious problem especially when
you want to share your workflow with
collaborators.

68. Setting up Software is
not Trivial Anymore…

69. Basic Workflow
of Data Visualization

70. Data
Preparation
Analysis Visualization

71. Data Preparation

72. Data
Preparation
- Cleansing
- Normalization
- Missing values
- Corrupted values
- Reformat
- Conversion

73. Data
Preparation
Analysis Visualization

74. Analysis

75. Analysis
- Filtering
- Standard graph
statistics
- Density
- Betweenness
- Centrality
- Clustering
- Community Detection
- GO enrichment analysis

76. Data
Preparation
Analysis Visualization

77. Visualization

78. Visualization
- Mapping
- Data points to
visual variables
- Layout
- For graphs:
- Force-directed
- Tree

79. Data
Preparation
Analysis Visualization

80. Data
Preparation
Analysis Visualization

81. Data
Preparation
Analysis Visualization

82. Data
Preparati
on
Analysis Visualizati
on

83. Preparation Analysis
Visualization

84. Preparation
Some Black
Magic
Visualization

85. Preparation ?
Visualization

86. Sharing Results
😐

87. Sharing Results and Process
😃

88. Technology for Reproducibility

89. 可視化技術のレイヤ
Source Code
Environment
Data

90. 可視化技術のレイヤData

91. Source Code
Environment
Data

92. Source Code

94. The Need for Openness
in Data Journalism
http://bit.ly/1ipxwXP
Brian Keegan, Ph.D.

95. Source Code
Environment
Data

96. Environment

97. REST

98. Git/GitHub For Sharing Code/Notebooks

99. Git/GitHub For Sharing Code/Notebooks
- Git - Distributed Source Code Management
System
- GitHub - (Public) Remote repository + great user
interface for working with OSS code

100. - Create a new repository from existing one
- Complete copy of the original + your full access
- Pull Request
Forking

101. Exercise:
Fork Repository

102. Fork My Repo.
https://github.com/
idekerlab/vizbi-2015

103. REST

104. Docker as Portable Data Analysis Environment

105. Bare Metal Machine
OS
Virtual Machine
Frameworks
Your App

106. Bare Metal Machine
OS (Linux)
Docker
Frameworks
Application
Frameworks
Application
Frameworks
Application
Frameworks
Application
Frameworks
Application

108. What is Docker?
- Container to run applications in an isolated
environment
- Application = Layer of images
- Sharable Environments
- Environments as code

109. Virtual Machine vs Container

110. https://www.docker.com/whatisdocker/

111. Low Performance Penalty
Efficient Use of Resource

112. How Docker-nized
World Works?
Container 1:
IPython + SciPy Stack
Your Machine
Container 2:
R + Bioconductor
Container 3:
Neo4j

113. Your Machine
Container 1:
IPython + SciPy Stack
Container 2:
R + Bioconductor
Container 3:
Neo4j

114. Your Machine
Container 1:
IPython + SciPy Stack
Container 2:
R + Bioconductor
Container 3:
Neo4j

115. Docker Hub
- Sharing environments as code!
- Dockerfile - Definition of your container
- “GitHub of Images”

116. Union File System

117. https://docs.docker.com/terms/layer/

118. Our Dockerfile
#
# Docker image for VIZBI 2015 Tutorial
#
# This is a generic setup for network data analysis and visualization.
# This Distribution includes:
# - Python
# - IPython Notebook
# - Standard data analysis tools
# - NetworkX, igraph, and graph-tool
#
FROM ipython/scipyserver
MAINTAINER Keiichiro Ono <kono@ucsd.edu>
RUN mkdir /graph-tool
WORKDIR /graph-tool
ADD . /graph-tool
RUN echo "deb http://downloads.skewed.de/apt/trusty trusty universe" >>/etc/apt/sources.list
RUN echo "deb-src http://downloads.skewed.de/apt/trusty trusty universe" >>/etc/apt/
sources.list
RUN apt-key add graph-tool-pub-key.txt
RUN apt-get update &&
apt-get install -y build-essential libxml2-dev libxslt1-dev
python-dev libzmq3-dev libcurl4-openssl-dev python3-graph-tool
RUN pip install networkx python-igraph py2cytoscape requests bokeh
WORKDIR /notebooks

119. Our Dockerfile
#
# Docker image for VIZBI 2015 Tutorial
#
# This is a generic setup for network data analysis and visualization.
# This Distribution includes:
# - Python
# - IPython Notebook
# - Standard data analysis tools
# - NetworkX, igraph, and graph-tool
#
FROM ipython/scipyserver
MAINTAINER Keiichiro Ono <kono@ucsd.edu>
RUN mkdir /graph-tool
WORKDIR /graph-tool
ADD . /graph-tool
RUN echo "deb http://downloads.skewed.de/apt/trusty trusty universe" >>/etc/apt/sources.list
RUN echo "deb-src http://downloads.skewed.de/apt/trusty trusty universe" >>/etc/apt/
sources.list
RUN apt-key add graph-tool-pub-key.txt
RUN apt-get update &&
apt-get install -y build-essential libxml2-dev libxslt1-dev
python-dev libzmq3-dev libcurl4-openssl-dev python3-graph-tool
RUN pip install networkx python-igraph py2cytoscape requests bokeh
WORKDIR /notebooks

120. FROM ipython/scipyserver
Extend this image from existing one
in Docker Hub

121. Image B
Image C
Image A

122. Data Analyst’s Toolbox
Basic Python
Graph Analysis

123. Docker in Practice

124. Run a Container

125. Run Options
- -p: Publish port
- -p 80:8080 - Publish container’s port 8080 to 80
- -v: Mount local volume
- -v $PWD:/myapp - Mount current working
directory to container’s /myapp directory

126. Run Options
- -n: Name the instance
- -n graph-analysis
- -d: Run in background (as daemon)

127. docker run -p 80:3000 -d
-v $PWD:/webapp/contents node
Example

128. Exercise:
Run a Docker Image

129. Run Docker Image
- Publish port 80
- Run in background
- Mound forked repository to /
notebook
- Add environment variables
- Password

130. docker run -d -v $PWD:/notebooks
-p 80:8888 -e "PASSWORD=yourpass"
-e "USE_HTTP=1" idekerlab/
vizbi-2015
Actual Command to Run Our Image (one-line)
Current directory should be under your home
(e.g. /Users/foo/Documents/vizbi-2015)

131. Exercise:
Build a Docker Image

132. docker build -t network .

133. Exercise:
Edit Dockerfile

134. Build Docker Image
- Add a instruction to install new
software
- For example, wget
- Build it

135. REST

136. Jupyter as a Lab Notebook for Dry Experiments

137. Interactive Command-Line
+
Markdown-based Documents

138. IPython Notebook?
Jupyter?

139. IPython
Notebook
Notebook UI
+ Python Kernel
Jupyter
Notebook UI
+
Language Kernel
(R/Julia/etc.)

140. Language-Agnostic
- From next version (4.x), Python Notebook will be an
implementation of Jupyter
- You can switch to other language kernels

142. Exercise:
Learn Jupyter Basics

143. Access Notebook Server
Running in a Docker Container
- We will use a extended version of official ipython/
SciPy server image
- By default, it uses secure connection (https)
- /notebooks is the root directory of notebooks
- Mount local file system to share Notebooks
between container and your laptop

144. Try Lesson 0

145. REST

146. REST
cyREST for Cytoscape-as-a-service

147. REST Why We Need cyREST?

148. Users

149. 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.

150. 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.

151. 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…

152. 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.

153. 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.

154. REST

155. What is cyREST?
- Platform-independent, RESTful API
module for Cytoscape
- Means you can access basic
Cytoscape data objects
programmatically
REST

156. Interactive Data Analysis
Environments
In-House Databases External Computing Resources
- Graph Layout
- Statistical Analysis
- Data Pre-processing
RStudio
- NumPy
- SciPy
- Pandas
- NetworkX
IPython Notebook
File / Code Hosting ServicesPublic Data Repository
PSICQUIC Services
EBI RDF Platform
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
Cytoscape Desktop
Apps
Core
REST

157. REST
Cytoscape 3.1+
Clients
POST
PUT
DELETE
GET

158. Mapping Cytoscape API to HTTP Methods
Create
Read
Update
Delete
Cytoscape Operations
POST
GET
PUT
DELETE
HTTP Methods

159. Get full network with unique ID 52 as JSON
GET http://localhost:1234/v1/networks/52

160. http://localhost:1234/v1/networks/52

161. Try Lesson 1, 3, and then 2

162. Future

163. History

164. 2005

166. 2005
- Cytoscape 2.2: Simple Java Application
- Google released an application called
Google Maps beta
- “Re-discovery” of JavaScript, or Ajax

167. 2014

169. 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

170. 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

171. http://12factor.net/

172. 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.

174. This MANIFESTO counters
current trends in
bioinformatics where
institutes and companies
are creating monolithic
software solutions aimed
mostly at end-users.

175. by Peter Wang @PyData 2014

176. 2015 Keiichiro Ono
kono@ucsd.edu

177. • https://flic.kr/p/bFZpyg
• https://flic.kr/p/bmXUz1
Photo Credits

178. • https://www.flickr.com/photos/23629083@N03/15409436041/in/photolist-ptFotK-9uS2gj-hypkSp-hypk9F-hypjha-99c472-9Xkuuc-huNmqB-7NMxMz-rg2Xh2-qYABcA-qjnGoB-rg2WVF-
rdQYMf-qjaxy7-rg5Aoo-rg2Wre-qYAAt1-rg2Wev-qYAAaA-rg2W1V-rdQXT1-qjawtS-rg9ePH-rg5zb3-qjnEtV-qYHAvc-qYBA9d-rg2V7F-qYHAeF-qYAySA-rg5ys9-rg9dLF-rg2Utg-rg9drH-qYAyew-
rg9dmc-rg5xP5-rg5xDA-qYAxV5-rg2TLe-rg5xp7-rg5xfQ-aq32tC-hba7em-hbafzE-gbeABq-gck7Dv-7PoYg1-fkisQL
• https://www.flickr.com/photos/nebulux/10000066526/in/photolist-geEXo7-58r1VP-6GioJH-9juEda-53HFiR-4sq7n3-4gyg7e-8ag9VV-8uqK43-4E89Gc-
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aAY6cH-76QCEX-7f6mdp-RntfW-eFuVBC-5nY8Vc-7utTA2-brdj8F-92k6n3-5KdCfh-83uVKy-8unxG8-3d3zxi-cdz8S7-4HT5qQ-99SwEn-7Akbcb-8y7ds9-fvo9zH-9zZky3
• https://www.flickr.com/photos/stratman2/8613731520/in/photolist-e8aChq-7LLUoQ-8s8eBL-6uGRmE-77wKJF-
dqo6ar-6hffGK-7rykRT-6fG8WV-8unyFa-8AeF8A-93Xpo2-9XLXCj-7GVMym-5Tu3dJ-7v58RC-5K9nBF-2MbvpL-2M77nV-et54Ce-6hfgvr-6hffQa-67wNj5-9FDGTz-49NmoE-eFXB7u-76QB7H-
brdbSP-brcYHT-22zYYv-6fFZoM-ckuXNC-a8UZ3D-dzGXYU-6nf4MN-4j7TzA-47fYur-2kutoV-56catX-apUJgr-cSJHkG-88w1ie-6Nbj1a-8MYxve-6xL3SF-6fL87j-4G6x71-
dUL16b-7auq8Q-6hwbVB
• https://www.flickr.com/photos/gcwest/281385801/in/photolist-5mFJtX-4o3Ria-hD9E92-qSbck-9abnoA-7hsWoU-ntEmgy-oSAQtv-nx5Chg-iuZJCa-j7eWKk-hD7JTZ-4iECHX-j8M2r7-bSrWHc-
prpFcX-db7xd-jLmzoF-75mqRx-pnSzL-6gVcao-9F5bop-j77HEs-73Umq1-5kRyNp-hD9cR2-mTvNB8-gyXWaf-Lkro7-idQBY4-fRYu1-5eR2cn-3EK4k-nnxH8u-9uDMLx-4NY3Yi-kDQagt-
ioGRSb-75qid1-82RzYt-5qQuwt-n8hvL6-ifemz5-3iYUQG-aJnNiX-mzirX2-23rDNy-qx3KEd-h5UnGW-hD7Jqz