The development of Next-Generation Sequencing (NGS) technologies in the late 2000’s led to a dramatic decrease in the cost of DNA sequencing. The advent of NGS coupled to the advancements in HPC storage and computing technologies at the time created the perfect storm for a deluge of genomics data. This confluence of events led to a pressing question: how best to put all this data to use?

A genome is an organism’s complete set of DNA and, as such, it ultimately determines all biological functions and the myriad of variations that make some of us susceptible while others immune to different diseases. Therefore, it is of great interest to the biomedical community to determine an individual’s genome, which is a process much like deciphering scrambled letters (genome sequencing) so one can assemble them into words (genome assembly) to write a book, (variant analysis).

Given the new affordability of NGS methods and the increased computing and storage capacities of the last decade, genomics can now be performed at the population level. Large national genomics initiatives such as the “UK Biobank,” the “All of Us program” in the US, Singapore’s “GenomeAsia,” “Genomics Thailand,” etc. are emerging all around the world. With goals of sequencing 500K to over 1M participants in a few years’ time, these country-wide efforts aim to capture the genetic variation of their people to make Precision Medicine a reality. With Precision Medicine the hope is to deliver individualized prevention, diagnosis, and treatment by leveraging knowledge from a person’s genetic background.

The greatest challenge such population-level genomics efforts face is scale: scaling up in input data from exomes (the portions of a genome that code information for protein synthesis) to whole genomes, as well as scaling up production (from a handful to tens of thousands of samples), and the corresponding challenges it creates for the HPC infrastructure. Exomes correspond to 1% of whole genomes and are small regions in genes dictating important biological functions. Yet, exomes cannot provide the comprehensive picture found in the remaining 99% of whole genomes. Exomes were traditionally sequenced because of their smaller size, lower cost, and faster processing. Today, many genomics centers around the world are making the transition from exome to whole-genome sequencing, while also trying to tackle unprecedented volumes of data from hundreds of thousands of patients.

Three out of the four analysis stages in genomics take place in the HPC environment of a cluster or supercomputer, including genome assembly (assembling the DNA letters into words), variant analyses (comparing how a word/gene is spelled in different people), and downstream bioinformatics (measuring effect of variations on function/disease). Therefore, scaling out genomics productions largely depends on the HPC technologies made available to the genomics applications.

With these dependencies in mind, Lenovo set out to identify which technologies bring the most acceleration to genomics workflows. To that end, we conducted a systematic study of the performance of hundreds of parameters on 30+ tools in the Broad Institute’s Genome Analysis Tool Kit (GATK) Germline Variant Calling Workflow against hundreds of permutations of hardware building blocks, system tunings, data types (exomes, whole genomes), execution modes (latency vs. throughput), and software implementations (e.g. standard vs. Spark, etc.).

Today, it still takes a typical datacenter 150-160 hrs. to process a single whole genome and 4-6 hrs. for an exome. In 2017, Intel’s work on BIGstack (Intel’s reference architecture for GATK workflows) reduced processing times to 10.8 hr. and 25 min, respectively. As a result of Lenovo’s permutation tests of the hardware, software, and system factors affecting the performance of genomics workflows we identified an optimized architecture that can process 1 whole genome in 5.5 hrs. and 1 exome in 4 minutes with no specialty hardware. With Lenovo’s genomics optimized hardware, a data center can expect to process 4.5 genomes or 343 exomes per node per day. Some genomics solutions out there promise processing times around 3-4 hr. for a whole genome but require expensive, specialized hardware that does not scale well for large volumes and licensing proprietary software. Lenovo’s optimized genomics architecture on the other hand, provides a 27X to 40X performance improvement on non-specialty hardware, and does so in a manner that is more affordable, more scalable, and reduces costs by leveraging open-source software that is validated and widely-accepted by the scientific community.

Another byproduct of Lenovo’s systematic genomics performance testing was the ability to generate a fluid rather than a static refence architecture for genomics as is the norm in the HPC industry. Every genomics data center adopts a different mix of workloads, analyses workflows, has different active and archiving storage needs, a different mix of research types to support, and therefore needs a customized architecture tailored to their specific needs. Thus, we converted the lessons learned from our genomics benchmarking and systematic testing into formulas captured in an industry-first Genomics Sizing Tool.

Lenovo’s Genomics Sizing Tool calculates the projected HPC usage for an expected workload; for example, it outputs the compute nodes, active, and archive storage needed to meet a workload quota (e.g., 50K genomes/yr.). The Sizing Tool can also be used to size the current production capabilities of an existing cluster: e.g., to answer the questions of “[H]ow many genomes can I process with my current cluster?,” Or, “[H]ow many genomes/yr. can this year’s budget afford me?”

We are leveraging both Lenovo’s optimized architecture and the Genomics Sizing Tool to help data centers around the world accelerate their workflows and plan their HPC resources more effectively as they embark on ever increasing workloads from cohort-level and population-level genomics projects. Lenovo’s team of Genomics experts work together with the data center’s researchers, developers, and HPC experts to create custom HPC usage designs projecting data growth over time, designing data flow, storage, and management across the cluster. These exercises in HPC usage and projections are proving invaluable in workload management, budget planning, IT expenditure justification and allocation, and resource accountability. Through its commitment to developing and adopting cutting-edge technological innovation, Lenovo is enabling the worldwide movement to sequence entire populations, bringing such initiatives closer to making precision medicine a reality, and delivering on its promise of Smarter Technology for All. A white paper will soon follow with a detailed description of the systematic permutation tests and benchmarks alluded to here as well as the resulting optimizations and Genomics Sizing Tool accelerating and sizing the HPC resources for deploying genomics at scale.