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Its always  sunny  on top of the Cloud! An intro to Amazon Web Services ,[object Object],[object Object],ViPDAC, a stand-alone Proteomics Analysis Suite in the Cloud
‘ How the humble pipette tip helped us rethink our computing strategy...’
Meet Joe ‘the’ Researcher...
Proteomics - Finding and identifying proteins DB Rat/Tissue Sample LC MS/MS Peptide Identification Results & Analysis
Current architecture Windows (head node, preprocessing, storage) Raw File .dtas Protein IDs IBM Blade Cluster (Sequest)
Finite Resource, wait your turn 1 MCW Cluster
Here’s the lab’s pipette tip, Let me have it when you’re done...
What would you do if there was only one tip? ,[object Object],[object Object],[object Object],[object Object],[object Object]
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
Virtual Proteomics Analysis Cluster (ViPDAC) http://proteomics.mcw.edu/vipdac + +
Current architecture with Sequest Raw File .dtas Protein IDs IBM Blade Cluster (Sequest) Windows (head node, preprocessing, storage)
ViPDAC & Amazon Components S3 (Data Store) Raw File .dtas Protein IDs EC2 (OMSSA, !XTandem)
ViPDAC & Amazon Components S3 (Data Store) Raw File .dtas Protein IDs 2x 3x 20x
ViPDAC: Create a new analysis job
Job in progress
Wait in line vs On Demand vs 1 MCW Cluster Molly’s ViPDAC Shama’s ViPDAC Brian’s ViPDAC Bassam’s ViPDAC
Equal-opportunity computing - Clusters for All vs 1 PC 1 ViPDAC or n ViPDACs
Observations Sign up & Start up is hard for biologists. http://www.directthought.com / http://www.elasticpod.com /
Now what? ,[object Object],[object Object],[object Object],[object Object],[object Object],Molly’s ViPDAC Shama’s ViPDAC Brian’s ViPDAC Bassam’s ViPDAC
Internal Hybrid Solution – Local and Cloud Scale up/down/off
Clouds & Bioinformatics: Our observations so far ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Acknowledgements ,[object Object],[object Object],[object Object],[object Object],Low Cost, Scalable Proteomics Data Analysis Using Amazon’s Cloud Computing Services and Open Source Search Algorithms. J. Proteome Res., 2009, 8 (6), pp 3148–3153

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Virtual Proteomics Analysis Cluster in the Cloud

Editor's Notes

  1. Internally we now utilize a hybrid solution – Sequest and mascot running on local clusters, X!Tandem and OMSSA are run on AWS. Raw data can be sent to any and all of these algorithms through an integrated workflow system