doing research in the cloud - nih workshop dennis gannon

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A presentation at the NIH Workshop on Advanced Networking for Data-Intensive Biomedical Research. The talk covers our work with the science community on using cloud computing to enhance and improve basic research for data analysis and scientific discovery

TRANSCRIPT

6

Melbourne

Sydney

Brazil

• Many Examples

• The Challenge: sustainability Data

Acquisition &

modelling

Collaboration

and

visualisation

Analysis &

data mining

Dissemination

& sharing

Archiving and

preserving

• The network is fast but …• The first mile problem

• FedX net.

• Use parallel streams to upload

• Don’t try to move it all

• Manage locality • Keep the hot data local on cloud disk

• Manage the working set over time

• The rest is archival

• Let the data accrue • Stream data directly to the cloud

• The Internet of Things = the Internet of Instruments

The Windows Azure for Research program:

·Free access to Windows Azure cloud computing and storage

(submit proposals for Windows Azure Research Awards)

· Windows Azure for Research training classes (20 classes

worldwide. )

· Support and technical resources

azure4research.com.

Real-time Catastrophe Risk Management on Windows Azure

Open assembly and analysis of large sequencing data sets

Text mining for identifying disease-gene-biological relationships

Bing for Genomes –Genomic Search and Comparison

Inference of gene networks studying human cancers on the cloud

Using data science approaches to map biologic processes to clinical

outcome

Cloud Based Drug Discovery for Malaria

Towards an interactive secondary analysis of RNA sequencing data

service in Widows Azure cloud with Apache Spark framework

User-Steering Phylogenetic Workflows in the Cloud

the use of the cloud as computational platform for genomic analysis

Enabling Data Parallelism for large-scale Biomedical Ontology Matching

Analysis and interpretation of human exome sequencing for clinical

diagnosis

Alzheimer Bio Project

Data analysis services for the molecular detection of emerging

pathogens

Azure-based Text Mining Tools for Genome-wide Association Studies

Scalable Protein Sequence Similarity Search for Metagenomics

Cloud-based Platform for Genome-scale Prediction of Protein Functional

Complex Structures at Experimental Quality

Data Analytics in Bio projects

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