Genomics, Transcriptomics, and Proteomics: Engaging Biologists

Richard LeDuc

Manager, NCGAS

eScience, Chicago 10/8/2012

Central Dogma of Molecular Biology

DNAATGGC

ATA CC

DNAReplicates

itself

mRNA

DNA istranscribed to

RNA

Protein

RNA istranslated to

protein

Central Dogma of Molecular Biology

DNA mRNA Protein

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

• Genome assembly• RNA-sequencing• ChIP-sequencing• Methyl-sequencing• Shotgun bottom-up

proteomics• 2D gel proteomics• Top-down proteomics

4

5

Zhao et al. BMC Bioinformatics 2011, 12(Suppl 14):S2http://www.biomedcentral.com/1471-2105/12/S14/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 SupportCompute 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 OwnComputational 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

• Biologists Each has to learn to work

with existing systems. Few have specialized in HPC.

• Computer center Support for hundreds of small

projects. Each project has different

needs.

• Funded by National Science Foundation1. Large memory clusters for assembly

2. Bioinformatics consulting for biologists

3. Optimized software for better efficiency

• Open for business at: http://ncgas.org

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

10

GALAXY.NCGAS.ORG Model

Virtual box hosting Galaxy.ncgas.org

The host for each tool is configured individually

Quarry Mason

Data CapacitorArchive

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 CapacitorNO 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 (barnettw@iu.edu)

Rich LeDuc (rleduc@iu.edu)

Le-Shin Wu (lewu@iu.edu)

Carrie Ganote (cganote@iu.edu)

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-1062432

• 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, 2012. Available from: http://hdl.handle.net/2022/14746

• 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 (http://creativecommons.org/licenses/by/3.0/). 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.