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Sponsored by
7:45 am Breakfast Presentation
A Scalable Sequencing Architecture
Peter Robinson, Director, Business Development, Life Sciences, High Performance Computing, DataDirect Networks
It is very difficult to identify the bottlenecks in a poorly architected sequencing system. Despite endemic system issues, the architecture can appear to be functioning at peak performance. In this talk DDN will focuses on the storage components and the impact that a file system can have on the drug discovery pipeline. Data will be presented showing that a greater than 25 percent increase in performance, for an in-place sequencing pipeline, can be realized by the file system alone. DDN will also show how additional gains can be realized over NFS with a parallel file system that enables a scalable pipeline.
8:30 Chairperson’s Remarks
Folker Meyer, Ph.D., Computational Biologist, Mathematics and Computer Science Division, Argonne National Laboratory
8:35 Cloud Computing for Sequence Analysis: A 50,000 Foot View
Narayan Desai, Principal Experimental Systems Engineer, Mathematics and Computer Science; Fellow, Computation Institute, Argonne National Laboratory
Cloud computing is quickly becoming a popular resource for analysis of sequence data. This talk will present an overview of the economic and technical forces driving cloud computing, as well as a conceptual framework for determining when clouds are most effective.
9:10 Cloud-Scale RNA-Sequencing Differential Expression Analysis with Myrna
Jeffrey Leek, Ph.D., Assistant Professor, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
As sequencing throughput approaches dozens of gigabases per day, there is a growing need for efficient software for analysis of transcriptome sequencing (RNA-Seq) data. Myrna is a cloud-computing pipeline for calculating differential gene expression in large RNA-Seq datasets. I will describe Myrna and apply the program to analyze large publicly available data sets and assess the goodness of fit of standard statistical models.
Sponsored by
9:45 Next Gen Data Management for Next Gen Life Sciences
Will McGrath, Business Development Manager, Life Sciences, Quantum
The advent of next generation sequencers are contributing to orders of magnitude more data to sift through, analyze, and share, increasing complexities of genomic sequencing workflows. Added complexity means added risk, jeopardizing time to discovery. A tightly integrated scalable high-performance computing platform with intelligent data management options such as automated archival and retrieval could make all the difference. Learn how an end-to-end data management infrastructure solution solves the most demanding Next Gen Sequencing Workflows so your scientists can strive for the next major medical breakthrough without having to worry about IT.
10:00 Networking Coffee Break with Poster and Exhibit Viewing
10:45 Scaling-Up Application Throughput Using the Cloud
Folker Meyer, Ph.D., Computational Biologist, Mathematics and Computer Science Division, Argonne National Laboratory
11:20 CloVR: Automated Sequence Analysis Using Virtual Machines and
Cloud Computing
Samuel Angiuoli, Institute for Genome Sciences, Center for Bioinformatics and Computational Biology, University of Maryland School of Medicine
CloVR provides automated, push-button sequence analysis pipelines in a portable package that runs on a desktop computer or cloud computing platform. We present the CloVR architecture and provide performance and costs in multiple applications of current generation genome sequencing technologies including whole-genome shotgun assembly and annotation and 16S-RNA and total metagenome analysis.
11:55 Close of Session
Sponsored by
12:15 pm Luncheon Presentation
Selecting A LIMS for Next Generation Sequencing Research
Michael Kuzyk, Ph.D., Product Manager, Omics Labs, GenoLogics Life Sciences Software
Bruce Pharr, VP Products, Marketing and Business Development, GenoLogics Life Sciences Software
No other industry has seen processing speeds rise and costs drop as dramatically as genomics. Modern genomics labs are now struggling to manage the data these techniques generate. A recent survey cites data storage, data management, and informatics as the biggest hurdle to expanding next gen sequencing (NGS). Moreover, analysis costs for sequencing remain high, spotlighting the need for better ways to centralize information and track sample information across experiments.
This talk reviews the informatics challenges presented by NGS and proposes three criteria that labs should assess when selecting an NGS lab information management system (LIMS).
2:00 Chairperson’s Remarks
Neil Miller, Deputy Director, Software Engineering, NCGR
2:05 IT and Software Solutions for Next Gen Data Management at NCGR
Neil Miller, Deputy Director, Software Engineering, NCGR
An overview of hardware and software used for next-generation sequencing data management and analysis at the National Center for Genome Resources (NCGR). NCGR relies on custom developed software including the Grindstone LIMS and Alpheus sequence analysis pipeline as well as commercial solutions to meet the IT challenges of running a sequencing center and to transform sequencing data into scientific discovery.
2:40 Strategies for Scaling Next-Generation Sequencing Analysis Software
and Systems
Craig S. Pohl, Software Engineering Manager, The Genome Center, Washington University
School of Medicine
The Genome Center at Washington University has a high-throughput sequencing and primary analysis pipeline which has required the development of customized software to reach our goals to maximize throughput, increase quality control, minimize costs and use system resources effectively. This presentation will cover the software system and the strategies developed to reach these goals while responding to the challenges of scale due to new sequencing instruments, vendor code releases, system bottlenecks and maintenance outages.
Sponsored by
3:15 IlluminaCompute: Simplified Computation for Production-Scale Sequencing
Jason Blue-Smith, Product Manager, Informatics, Illumina
Illumina’s TruSeq SBS technology has transformed the way genomic research questions are asked and answered. As the output of Illumina’s instruments increases, smaller labs are encountering data management challenges that mandate a simplified computation framework for production-scale sequencing.
3:30 Networking Refreshment Break with Poster and Exhibit Viewing
4:15 Gene Annotation Data in a “Schema-Free” Paradise
Chunlei Wu, Ph.D., Research Investigator II, Genomics Institute of the Novartis Research Foundation
Managing gene annotation data is often complicated due to their heterogeneous data structure. Recently emerged “schema-free” databases show advantages by allowing data stored in their native structure. We recently migrated our gene annotation database from Oracle to “schema-free” CouchDB. This migration greatly simplifies data loading and data queries, and gains impressive query performance.
4:50 Semantic Integration of Data in Systems Biology
Julia Ponomarenko, Senior Research Scientist, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California-San Diego
Mihail Baitaliuc, Programmer/Analyst, San Diego Supercomputing Center
The presented resource, IntegromeDB, is a semantic graph-based 'deep-web' data integration system that automatically captures, integrates and manages publicly available information essential for deciphering and modeling gene regulatory networks. We will demonstrate the application of the system to the analysis of host-pathogen interactions that enabled us to identify novel host genes associated with the influenza infection.
5:25 Close of Session
5:45-9:00 Dinner Short Courses
*Separate registration required.
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