Data analysis with Galaxy on the Cloud
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The document outlines the process of data analysis using Galaxy on the Cloud, including account creation, instance launching, and the use of CloudMan's web interface features like manual and auto-scaling, SSH access, and customization. It provides a plan for analyzing coding exons and SNPs on human chromosome 22, along with detailed instructions for managing instances and submitting jobs. Additional topics cover customizing instances, controlling the Galaxy application, and sharing analysis setups.
![e over
Use the terminal (or install Secure Shell for Chrome)
SSH using user ubuntu and the password you chose
when launching an instance:
[local machine]$ ssh ubuntu@<instance IP address>](https://image.slidesharecdn.com/cloudmanworkshop-140610094941-phpapp01/85/Data-analysis-with-Galaxy-on-the-Cloud-23-320.jpg)
![Management
Console
Application(s)
(eg, Galaxy)
1°
2°
3°
6°, 8°
9°
Persistent
data
repository
Start CloudMan
Setup services
5°
4°
7°
10°
CM-w
CM-w
CM-w
...
FS 2 FS ...
Instance block storage
Contextualize
image
CloudMan MI
CloudMan MI
...
CloudMan MI
CloudMan machine image
11°
S3/Swift
CloudMan instance
Snap2 Snap..
/mnt/galaxy[Indices]
/mnt/cm/paster.log
cm-<hash>
/usr/bin/ec2autorun.log
/tmp/cm/cm_boot.log
Troubleshooting
1
°
2
°
3
°](https://image.slidesharecdn.com/cloudmanworkshop-140610094941-phpapp01/85/Data-analysis-with-Galaxy-on-the-Cloud-29-320.jpg)
![Use the new tool in the cluster
mode
1. Create a new sample shell file to run the tool; call it job_script.sh
with the following content:
#$ -cwd
./dnaclust -l -s 0.9 /mnt/workshop-data/mtDNA.fasta
2. Submit single job to SGE queue
qsub job_script.sh
3. Check the queue: qstat -f
4. Job output will be in the local directory in file job_script.sh.o#
5. Start a number of instances of the script:
qsub job_script.sh (*10)
watch qstat –f
1. See all jobs lined up
6. See auto-scaling in (using /cloud) [1.5-2 mins]
7. Go back to command prmopt, see jobs being distributed](https://image.slidesharecdn.com/cloudmanworkshop-140610094941-phpapp01/85/Data-analysis-with-Galaxy-on-the-Cloud-33-320.jpg)
Data analysis with Galaxy on the Cloud
- 1. Data analysis with Galaxy on the Cloud Enis Afgan
- 2. 100sGB 100+
- 3. How to use the cloud? 1. Get an account on the supported cloud 2. Start a master instance via a launcher app 3. Use CloudMan’s web interface on the master instance to manage the platform
- 4. Agenda details • Launch an instance • Demonstrate the following CloudMan features and prepare for the data analysis part: • Manual & Auto-scaling • Using an S3 bucket as a data source • Accessing an instance over ssh • Customizing an instance • Controlling Galaxy • Sharing-an-instance • Perform data analysis in Galaxy Interactionflow
- 6. Launch an instance 1. Slides @ bit.ly/goc-ws 2. Load biocloudcentral.org 3. Enter the access key and secret key provided at http://bit.ly/ws-creds 4. Provide your email address 5. Use your initials as the cluster name 6. Set any password (and remember it) 7. Keep Large instance type 8. Start your instance Wait for the instance to start (~2-3 minutes) For more details, see http://cloudman.irb.hr
- 8. Agenda details • Launch an instance ✓ • Demonstrate the following CloudMan features and prepare for the data analysis part: • Manual & Auto-scaling • Using an S3 bucket as a data source • Accessing an instance over ssh • Customizing an instance • Controlling Galaxy • Sharing-an-instance • Perform data analysis in Galaxy Interactionflow
- 9. Manual scaling • Explicitly add 1 worker node to your cluster • Node type corresponds to node processing capacity • Research use of Spot instances
- 10. Auto-scaling
- 11. Agenda details • Launch an instance ✓ • Demonstrate the following CloudMan features and prepare for the data analysis part: • Manual & Auto-scaling ✓ • Using an S3 bucket as a data source • Accessing an instance over ssh • Customizing an instance • Controlling Galaxy • Sharing-an-instance • Perform data analysis in Galaxy Interactionflow
- 12. On human chromosome 22, which coding exons have the most SNPs in them?
- 13. A Rough Plan •Get some data •Coding exons on chromosome 22 •SNPs on chromosome 22 •Mess with it •Identify which exons have SNPs •Count SNPs per exon •Visualize our results
- 14. Exons, from UCSC SNPs, from UCSC
- 15. Exons, from UCSC SNPs, from UCSC Exons, from UCSC SNPs, from UCSC Overlap pairings
- 16. Exons, from UCSC SNPs, from UCSC 1 1 2 Exons, from UCSC SNPs, from UCSC Overlap pairings Exon overlap counts
- 17. Exons, from UCSC 1 1 2 Exon overlap counts
- 18. Exons, from UCSC 1 1 2 Exon overlap counts 1 1 2 Join on exon name 0 0 0
- 19. Exons, from UCSC 1 1 2 Exon overlap counts 1 1 2 Join on exon name 0 0 0 1 1 2 Rearrange columns w/ cut
- 20. Your turn http://usegalaxy.org/galaxy101 Slides @ http://bit.ly/gxy-ws
- 21. Agenda details • Launch an instance ✓ • Demonstrate the following CloudMan features and prepare for the data analysis part: • Manual & Auto-scaling ✓ • Using an S3 bucket as a data source • Accessing an instance over ssh • Customizing an instance • Controlling Galaxy • Sharing-an-instance • Perform data analysis in Galaxy Interactionflow
- 22. ource
- 23. e over Use the terminal (or install Secure Shell for Chrome) SSH using user ubuntu and the password you chose when launching an instance: [local machine]$ ssh ubuntu@<instance IP address>
- 24. Once logged in • You have full system access to your instance, including sudo; use it as any other system • galaxy user exists on the system and should be used when manipulating Galaxy (sudo su galaxy) • Can submit any jobs via the standard qsub command
- 25. • Edit Galaxy’s configuration $ sudo su galaxy $ cd /mnt/galaxy/galaxy-app $ vi universe_wsgi.ini allow_library_path_paste = True
- 26. Controlling Galaxy • Start/stop Galaxy application • Add an admin user • Use the email you registered with
- 27. S3 bucket as a data library • Within Galaxy, create a Data Library, using S3 bucket path as the data source (/mnt/workshop-data) • This will import all the datasets into the Data Library • Import that dataset into a history
- 28. Sharing-an-Instance • Share the entire CloudMan platform • Includes all of user data and even the customizations • Publish a self-contained analysis • Make a note of the share-string and send it to your neighbor
- 29. Management Console Application(s) (eg, Galaxy) 1° 2° 3° 6°, 8° 9° Persistent data repository Start CloudMan Setup services 5° 4° 7° 10° CM-w CM-w CM-w ... FS 2 FS ... Instance block storage Contextualize image CloudMan MI CloudMan MI ... CloudMan MI CloudMan machine image 11° S3/Swift CloudMan instance Snap2 Snap.. /mnt/galaxy[Indices] /mnt/cm/paster.log cm-<hash> /usr/bin/ec2autorun.log /tmp/cm/cm_boot.log Troubleshooting 1 ° 2 ° 3 °
- 30. CLUSTER ON THE COMMAND LINE
- 31. Distributed Job Manager (DRM) • Controls job execution on a (set of) resource(s) • A job: an invocation of a program • Manages job load • Provides ability to monitor system and job status • Popular DRMs: Sun Grid Engine (SGE), Portable Batch Scheduler (PBS), TORQUE, Condor, Load Sharing Facility (LSF)
- 32. Customize your instance - install a new tool $ cd /mnt/galaxy/export $ wget http://heanet.dl.sourceforge.net/project/dnaclust/parallel_relea se_3/dnaclust_linux_release3.zip $ unzip dnaclust_linux_release3.zip $ cd dnaclust_linux_release3 $ chmod +x * $ cp /mnt/workshop-data/mtDNA.fasta . Get a copy of a sample dataset Don’t forget
- 33. Use the new tool in the cluster mode 1. Create a new sample shell file to run the tool; call it job_script.sh with the following content: #$ -cwd ./dnaclust -l -s 0.9 /mnt/workshop-data/mtDNA.fasta 2. Submit single job to SGE queue qsub job_script.sh 3. Check the queue: qstat -f 4. Job output will be in the local directory in file job_script.sh.o# 5. Start a number of instances of the script: qsub job_script.sh (*10) watch qstat –f 1. See all jobs lined up 6. See auto-scaling in (using /cloud) [1.5-2 mins] 7. Go back to command prmopt, see jobs being distributed