Sifting through the vast treasure trove of data spilling from modern life science instruments is perhaps the defining challenge for biomedical research today. NIH, for example, generates about 1.5PB of data a month, and that excludes NIH-funded external research. Not only have DNA sequencers become extraordinarily powerful, but also they have proliferated in size and type, from big workhorse instruments like the Illumina HiSeq X Ten, down to reliable bench-top models (MiSeq) suitable for small labs, and there are now in advanced development USB-stick sized devices that plug into a USB port.
“The flood of sequence data, human and non-human that may impact human health, is certainly growing and in need of being integrated, mined, and understood. Further, there are emerging technologies in imaging and high resolution structure studies that will be generating a huge amount of data that will need to be analyzed, integrated, and understood,”[i] said Jack Collins, Director of the Advanced Biomedical Computing Center at the Frederick National Laboratory for Cancer Research, NCI.
Here are just a few of the many feeder streams to the data deluge:
- DNA Sequencers. An Illumina (NASDAQ: ILMN) top-of-the-line HiSeq Ten Series can generate a full human genome in just 18 hours (and generate 3TB) and deliver 18000 genomes in a year. File size for a single whole genome sample may exceed 75GB.
- Live cell imaging. High throughput imaging in which robots screen hundreds of millions of compounds on live cells typically generate tens of terabytes weekly.
- Confocal imaging. Scanning 100s of tissue section, with sometimes many scans per section, each with 20-40 layers and multiple fluorescent channels can produce on the order of 10TB weekly.
- Structural Data. Advanced investigation into form and structure is driving huge and diverse datasets derived from many sources.
Broadly, the flood of data from various LS instruments stresses virtually every part of most research computational environment (cpu, network, storage, system and application software). Indeed, important research and clinical work can be delayed or not attempted because although generating the data is feasible, the time required to perform the data analysis can be impractical. Faced with these situations, research organizations are forced to retool the IT infrastructure.
“Bench science is changing month to month while IT infrastructure is refreshed every 2-7 years. Right now IT is not part of the conversation [with life scientists] and running to catch up,” noted Ari Berman, GM of Government Services, the BioTeam consulting firm and a member of Tabor EnterpriseHPC Conference Advisory Board.
The sheer volume of data is only one aspect of the problem. Diversity in files and data types further complicates efforts to build the “right” infrastructure. Berman noted in a recent presentation that life sciences generates massive text files, massive binary files, large directories (many millions of files), large files ~600Gb and very many small files ~30kb or less. Workflows likewise vary. Sequencing alignment and variant calling offer one set of challenges; pathway simulation presents another; creating 3D models – perhaps of the brain and using those to perform detailed neurosurgery with real-time analytic feedback
“Data piles up faster than it ever has before. In fact, a single new sequencer can typically generate terabytes of data a day. And as a result, an organization or lab with multiple sequencers is capable of producing petabytes of data in a year. The data from the sequencers must be analyzed and visualized using third-party tools. And then it must be managed over time,” said Berman.
An excellent, though admittedly high-end, example of the growing complexity of computational tools being contemplated and developed in life science research is presented by the European Union Human Brain Project[ii] (HBP). Among its lofty goals are creation of six information and communications technology (ICT) platforms intended to enable “large-scale collaboration and data sharing, reconstruction of the brain at different biological scales, federated analysis of clinical data to map diseases of the brain, and development of brain-inspired computing systems.”
- Neuroinformatics: a data repository, including brain atlases.
- Brain Simulation: building ICT models and simulations of brains and brain components.
- Medical Informatics: bringing together information on brain diseases.
- Neuromorphic Computing: ICT that mimics the functioning of the brain.
- Neurorobotics: testing brain models and simulations in virtual environments.
- HPC Infrastructure: hardware and software to support the other Platforms.
(Tellingly HBP organizers have recognized the limited computational expertise of many biomedical researchers and also plan to develop technical support and training programs for users of the platforms.)
There is broad agreement in the life sciences research community that there is no single best HPC infrastructure to handle the many LS use cases. The best approach is to build for the dominant use cases. Even here, said Berman, building HPC environments for LS is risky, “The challenge is to design systems today that can support unknown research requirements over many years.” And of course, this all must be accomplished in a cost-constrained environment.
“Some lab instruments know how to submit jobs to clusters. You need heterogeneous systems. Homogeneous clusters don’t work well in life sciences because of the varying uses cases. Newer clusters are kind of a mix and match of things we have fat nodes with tons of cpus and thin nodes with really fast cpus, [for example],” said Berman.
Just one genome, depending upon the type of sequencing and the coverage, can generate 100GB of data to manage. Capturing, analyzing, storing, and presenting the accumulating data requires a hybrid HPC infrastructure that blends traditional cluster computing with emerging tools such as iRODS (Integrated Rule-Oriented Data System) and Hadoop. Unsurprisingly the HPC infrastructure is always a work in progress
Here’s a snapshot of the two of the most common genomic analysis pipelines:
- DNA Sequencing. DNA extracted from tissue samples is run through the high-throughput NGS instruments. These modern sequencers generate hundreds of millions of short DNA sequences for each sample, which must then be ‘assembled’ into proper order to determine the genome. Researchers use parallelized computational workflows to assemble the genome and perform quality control on the reassembly—fixing errors in the reassembly.
- Variant Calling. DNA variations (SNPs, haplotypes, indels, etc) for an individual are detected, often using large patient populations to help resolve ambiguities in the individual’s sequence data. Data may be organized into a hybrid solution that uses a relational database to store canonical variations, high-performance file systems to hold data, and a Hadoop-based approach for specialized data-intensive analysis. Links to public and private databases help researchers identify the impact of variations including, for example, whether variants have known associations with clinically relevant conditions.
The point is that life science research – and soon healthcare delivery – has been transformed by productivity leaps in the lab that now are creating immense computational challenges. (next Part 2: Storage Strategies)