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.

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