This talk was presented at Super Computing '09 in the Cloud Computing for Systems and Computational Biology workshop. It describes the proteomics analysis package we built using Amazon's cloud computing architecture. More information in our paper in J. Proteome Research http://pubs.acs.org/doi/abs/10.1021/pr800970z

2. ‘ How the humble pipette tip helped us rethink our computing strategy...’

3. Meet Joe ‘the’ Researcher...

4. Proteomics - Finding and identifying proteins DB Rat/Tissue Sample LC MS/MS Peptide Identification Results & Analysis

5. Current architecture Windows (head node, preprocessing, storage) Raw File .dtas Protein IDs IBM Blade Cluster (Sequest)

6. Finite Resource, wait your turn 1 MCW Cluster

7. Here’s the lab’s pipette tip, Let me have it when you’re done...

9. OK, more computers might be better... but... we dont have the money! we dont have an IT guy/gal we dont have a sysadmin we dont know how to install a cluster we wont use it all the time

10. Virtual Proteomics Analysis Cluster (ViPDAC) http://proteomics.mcw.edu/vipdac + +

11. Current architecture with Sequest Raw File .dtas Protein IDs IBM Blade Cluster (Sequest) Windows (head node, preprocessing, storage)

12. ViPDAC & Amazon Components S3 (Data Store) Raw File .dtas Protein IDs EC2 (OMSSA, !XTandem)

13. ViPDAC & Amazon Components S3 (Data Store) Raw File .dtas Protein IDs 2x 3x 20x

14. ViPDAC: Create a new analysis job

15. Job in progress

16. Wait in line vs On Demand vs 1 MCW Cluster Molly’s ViPDAC Shama’s ViPDAC Brian’s ViPDAC Bassam’s ViPDAC

17. Equal-opportunity computing - Clusters for All vs 1 PC 1 ViPDAC or n ViPDACs

18. Observations Sign up & Start up is hard for biologists. http://www.directthought.com / http://www.elasticpod.com /

20. Internal Hybrid Solution – Local and Cloud Scale up/down/off