Genomics, Transcriptomics, and Proteomics: Engaging Biologists Richard LeDuc Manager, NCGAS eScience, Chicago 10/8/2012

Central Dogma of Molecular Biology DNA ATGGC ATAC C DNA Replicates itself mRNA DNA is transcribed to RNA Protein RNA is translated to protein

Central Dogma of Molecular Biology DNAmRNAProtein Genomics Transcriptomics Proteomics

Tools of the Trade Instruments Next-Generation Sequencers  Illumina  454  PacBio Mass Spectrometers  5 kinds of mass analyzers  Hybrid analyzers + separation technology Techniques 4

5 Zhao et al. BMC Bioinformatics 2011, 12(Suppl 14):S2 Figure © Vincent Montoya / wikipedia

Analysis as Data Reduction Proteomics Shotgun Bottom-up  3.4 GB of instrument data  172 MB (x1/20) of unstructured files (5,219 files in 67 folders)  13 MB of publishable results (x1/260).  Improved technology increases the size of the instrument files, but not usually the intermediate or final file sizes. DNA Sequencing  Often on the order of x1/2500 from start to finish Instrument Data

Options for Computational Support Compute at the Instrument Supercomputer in a box  Many commercial venders are entering with turn-key solutions to specific problems.  Limited variety of analytic expertise. Build Your Own Computational Center  A rack or two, a few servers, and you are good to go.  Only a subset of HPC skills are present in staff. Computer Centers

Funded by National Science Foundation 1. Large memory clusters for assembly 2. Bioinformatics consulting for biologists 3. Optimized software for better efficiency Open for business at:

Making it easier for Biologists Web interface to NCGAS resources Supports many bioinformatics tools Available for both research and instruction. Common Rare Computational Skills LOW HIGH

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GALAXY.NCGAS.ORG Model Virtual box hosting Galaxy.ncgas.org The host for each tool is configured individually Quarry Mason Data Capacitor Archive NCGAS establishes tools, hardens them, and moves them into production. Custom Galaxy tools can be made for moving data Individual projects can get duplicate boxes – provided they support it themselves. Policies on the DC guarantee that untouched data is removed with time.

NCGAS Sandbox Demo at SC 11 STEP 1: data pre- processing, to evaluate and improve the quality of the input sequence STEP 2: sequence alignment to a known reference genome STEP 3: SNP detection to scan the alignment result for new polymorphisms

10 Gbps 100 Gbps NCGAS Mason (Free for NSF users) IU POD (12 cents per core hour) Data Capacitor NO data storage Charges Your Friendly Neighborhood Sequencing Center Your Friendly Neighborhood Sequencing Center Your Friendly Neighborhood Sequencing Center Moving Forward Other NCGAS XSEDE Resources… Lustre WAN File System Globus On-line and other tools Optimized Software

How would this work at scale? 1.Biologists use Galaxy and other web portals to move data and execute workflows 2.Instrument data transferred across Internet2 3.Data Capacitor flows data into Mason or other computational clusters 4.Data reduction allows “compute in place” to work 5.Data Capacitor mounts or mirrors reference data from NCBI or other sources

In Sum… Modern molecular biology – specifically the omics such as genomics, transcriptomics, and proteomics, provides many tools for answering many questions, but no single solution meets all needs. The amount of data generated decreases along a workflow. This has implications in both storage and analysis. NCGAS can provide a national scale infrastructure to better serve the needs of biologists who cannot become bioinformaticians to accomplish their research. Increasingly specialized skills are needed to provide best-practice solutions at all steps in a workflow.

Thank You Questions? Bill Barnett Rich LeDuc Le-Shin Wu Carrie Ganote

NCGAS Cyberinfrastructure at IU Mason large memory cluster (512 GB/node) Quarry cluster (16 GB/node) Data Capacitor (1 PB at 20 Gbps throughput) Research File System (RFS) for data storage Research Database Cluster for managing data sets. All interconnected with a high speed internal network (40 Gbps)

Acknowledgements & disclaimer This material is based upon work supported by the National Science Foundation under Grants No. ABI This work was supported in part by the Lilly Endowment, Inc. and the Indiana University Pervasive Technology Institute Any opinions presented here are those of the presenter(s) and do not necessarily represent the opinions of the National Science Foundation or any other funding agencies

License terms Please cite as: LeDuc, R.D., Genomics, Transcriptomics, and Proteomics: Engaging Biologists, presented at Extending High-Performance Computing Beyond its Traditional User Communities, Co-located with the 8th IEEE International Conference on eScience, Chicago, USA, October 8, Available from: Items indicated with a © are under copyright and used here with permission. Such items may not be reused without permission from the holder of copyright except where license terms noted on a slide permit reuse. Except where otherwise noted, contents of this presentation are copyright 2011 by the Trustees of Indiana University. This document is released under the Creative Commons Attribution 3.0 Unported license ( This license includes the following terms: You are free to share – to copy, distribute and transmit the work and to remix – to adapt the work under the following conditions: attribution – you must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). For any reuse or distribution, you must make clear to others the license terms of this work.