Cloud to Improve Genomic Research at Spanish National Cancer Research Centre

By Paul Parsons and Alfonso Olias

January 13, 2011

Over the last few years, a new global trend has emerged in the field of genomic studies. With the advent of a new generation of analytical instruments, the cost of determining the order of the nucleotides in a DNA molecule (DNA sequencing) has dramatically decreased, resulting in a significant acceleration of a number of basic and applied related biomedical areas.

While a typical sequencing project (de novo determination of an organism genome, for example) used to last several years and millions of dollars in reagents and resources, nowadays even small laboratories are able to sequence the complete genomes of simple organisms in hours, for just a small fraction of the cost.

Big sequencing projects have shifted to the determination of the specific sequences of populations of individuals, which will give us the ability to associate the differences at the sequence level between them (variants) to specific individual traits (those causing diseases like cancer, for example). Consequently, the bottleneck in sequencing projects has shifted from obtaining DNA reads to the alignment and post-processing of the huge amount of read data now available.

To minimize both processing time and memory requirements, specialized algorithms and high-throughput analysis pipelines are being constantly developed.

The need to analyze increasingly large amounts of genomics and proteomics data has meant that research institutions such as the Spanish National Cancer Research Centre (CNIO) allocate an increasing part of their time and budget provisioning, managing and maintaining their scientific computing infrastructure, areas that not their core business.

The Server Labs, a European IT company focused on IT architectures, software engineering and cloud architecture and services, is working with the Bioinformatics Unit, Structural Biology and Biocomputing Programme at CNIO, to develop a cloud-based solution that would meet their genomic processing needs.

With its pay-per-use concept CNIO would benefit from the Cloud saving time and money maintaining and upgrading their internal IT department. Fixed costs will be translated to variable costs in terms of infrastructure, purchases and upgrades of computational resources, software licenses, as well as expert admins and external resources. 

As the number of sequencing experiments which the CNIO runs can also be variable, the cloud not only eliminates potential over-provisioning, but it also prevents the under-provisioning of resources at peak times, which would result in the inability to run scheduled experiments. CNIO is thus able to pass on the risks associated with the planning and allocation of resources to the cloud provider.

Without the need to provide and manage computational resources themselves, CNIO can focus on their core business, scientific research in genomics and proteomics applied to cancer. In addition to providing the elasticity to run experiments on an on-demand basis the cloud also reduces the time to supply the hardware infrastructure and its configuration based on an automated installation and customization of the software running on top of the hardware. A controlled computational environment for the post-processing of experiments allows results to be more easily reproduced, a key objective to researchers across all disciplines.

Data management cloud services facilitate publishing of data over the Internet enabling researchers to easily share results whilst controlling their access. Data storage in the Cloud was designed from the ground-up with high-availability and durability as key objectives.

By storing their experiment data in the cloud, researchers can ensure their data is safely replicated among data centres. These advantages free researchers from time-consuming operational concerns, such as in-house backups and the provisioning and management of servers from which to share their experiment results.

The vast potential benefits of the cloud will enable the Spanish National Cancer Research Centre to speed up its pace of innovation and bring them a faster ROI on their current research efforts.

An Environment for Genomic Processing in the Cloud

The first step towards carrying out genomic processing in the cloud is to identify the requirements that fulfill a suitable computational environment. These include the hardware architecture, the operating system and the genomic processing tools. Together with CNIO we identified the following software packages employed in their typical genomic processing workflows:

  • Burrows-Wheeler Alignment Tool: BWA aligns short DNA sequences (reads) to a reference sequence such as the whole human genome.
  •  Novoalign: Novoalign is a DNA short read mapper implemented by Novocraft Technologies. The tool uses spaced-seed indexing to align either single or paired-end reads by means of Needleman-Wunsch algorithm. The source code is not available for download. However, anybody may download and use these programs free of charge for their research and any other non-profit activities as long as results are published in open journals.
  • SAM tools: After reads alignment, one might want to call variants or view the alignments against the reference genome. SAM tools is an open-source package of software applications which includes an alignments viewer and a consensus base caller tool to provide lists of variants (somatic mutations, SNPs and indels).
  • BEDTools: This software facilitates common genomics tasks for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (.BED) format. BEDTools supports the comparison of sequence alignments allowing the user to compare next-generation sequencing data with both public and custom genome annotation tracks. BEDTools source code in freely available.

Note that, except for Novoalign, all software packages listed above are open source and freely available.

For our initial proof of concept, we decided to run a configured image with Ubuntu 9.10 x64. This ensures that no additional setup tasks are required when launching new instances in the Cloud, and provides a controlled and reproducible environment for genomic processing.  The Amazon EC2 instance type required was a large instance with 7.5 GB of memory, 4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each) and 850 GB of local instance storage.

With this minimum set up we executed some typical genomic workflows suggested to us by CNIO. We found that for their typical workflow with a raw data input between 3 and 20 GB, the total processing time on the cloud would range between 1 and 4 hours, depending on the size of the raw data and whether the sequencing experiment was single or paired-end. With an EC2 instance pricing at 38 cents per hour for large instances, and ignoring additional time required for customization of the workflow, the cost of pure processing tasks totalled less than $2 for a single experiment.

CNIO’s genomic facilities are able to process up to 20-25 sequencing runs in an Illumina GAII sequencer. On average, they expect to analyse about 150 sequencing lanes per year, generating each 30 gigabyte of entry data (average), and totalling up to 3-4.5 terabytes in storage / processing requirements p.a.

We also found the processing times to be comparable to running the same workflow in-house on similar hardware. However, when processing in the cloud, we found that transferring the raw input data from the lab to the Amazon cloud could become a bottleneck, depending on the bandwidth available. We were able to work around this limitation by processing our data on Amazon’s European data centre and avoiding peak-hours for the data uploads. In future a high-speed file-transfer protocol such as Aspera’s could be leveraged to optimize this step.

Maximizing the Advantages of the Cloud

We demonstrated that genomic processing in the Cloud is feasible and cost-effective, while providing a performance on par with in-house hardware. The true benefits of the cloud will become apparent when processing tens or hundreds of experiment jobs in parallel. This would allow researchers, for instance, to run algorithms with slightly different parameters to analyse the impact on their experiment results. At the same time, the resulting framework should incorporate all of the strengths of the cloud, in particular data durability, publishing mechanisms and audit trails to make experiment results reproducible.

For more detailed information please have a look at The Server Labs’ technical blog.
 

—–

Paul Parsons is CTO and chief architect at The Server Labs, Alfonso Olias, also from The Server Labs serves at Senior Consultant.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in 2017 with scale-up production for enterprise datacenters and Read more…

By Tiffany Trader

Fine-Tuning Severe Hail Forecasting with Machine Learning

July 20, 2017

Depending on whether you’ve been caught outside during a severe hail storm, the sight of greenish tinted clouds on the horizon may cause serious knots in the pit of your stomach, or at least give you pause. There’s g Read more…

By Sean Thielen

Trinity Supercomputer’s Haswell and KNL Partitions Are Merged

July 19, 2017

Trinity supercomputer’s two partitions – one based on Intel Xeon Haswell processors and the other on Xeon Phi Knights Landing – have been fully integrated are now available for use on classified work in the Nationa Read more…

By HPCwire Staff

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's output. The Japanese multinational has made a raft of HPC and A Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the computer we use most (hopefully) and understand least. This mon Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee of the House of Representatives voted to accept the recomme Read more…

By Alex R. Larzelere

Summer Reading: IEEE Spectrum’s Chip Hall of Fame

July 17, 2017

Take a trip down memory lane – the Mostek MK4096 4-kilobit DRAM, for instance. Perhaps processors are more to your liking. Remember the Sh-Boom processor (1988), created by Russell Fish and Chuck Moore, and named after Read more…

By John Russell

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provides participants the opportunity to network with industry lea Read more…

By Tiffany Trader

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fine-Tuning Severe Hail Forecasting with Machine Learning

July 20, 2017

Depending on whether you’ve been caught outside during a severe hail storm, the sight of greenish tinted clouds on the horizon may cause serious knots in the Read more…

By Sean Thielen

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series Read more…

By Ken Chiacchia and Tiffany Jolley

Intel Skylake: Xeon Goes from Chip to Platform

July 13, 2017

With yesterday’s New York unveiling of the new “Skylake” Xeon Scalable processors, Intel made multiple runs at multiple competitive threats and strategic Read more…

By Doug Black

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This