Computing Personal Genomics

By Nicole Hemsoth

June 2, 2011

Personal genomics is critical to advancing our ability to treat and preemptively diagnose genetic diseases. However, despite the possibilities of personalizing medicine, it remains tethered, in large part, to the weight of some significant computational-side problems. This includes everything from storage to compute to code, all of which were issues on the table at the National Center for Supercomputing Applications’ (NCSA) Private Sector Program Annual Meeting .

During the event, Dr. Victor Jongeneel, Senior Research Scientist at NCSA and the Institute for Genomic Biology at the University of Illinois detailed some of the bottlenecks and potential solutions that keep expectations for personal genomics grounded.

In the case of personal genomics, the problem is not the scientific understanding of the genome itself, it’s how to reconstruct, compare and make sense of the massive data from sequencers. He claims that the disruptive part of this technology as a whole is rooted in our ability to actually acquire the data. According to Jongeneel, the amount of DNA sequence data generated last year was more than what had been generated over the entire history of sequencing before that.

Personal genomics is anything but a reality right now Jongeneel says. He notes that the range of new services that offer to sequence your genome for a few hundred dollars are far from complete service. These simply take DNA from a saliva kit, probe for a certain number of positions in genomes that are known to be variable and then try to deduce personal characteristics from that information. He claims that this is not personal genomics because in such a case, all you’re examining are known differences between individuals in the population—not your own genome. Besides, to do what is required for a genuine look at one’s personal genomics is far more computationally-intensive and would entail far more than a measly few hundred dollars.

To realize true personal genomics, all differences between individuals need to be analyzed. Jongeneel explained that we are moving toward this more comprehensive genomic sampling via well-funded projects like the 1000 Genomes Initiative, which aims to allow the generation of all necessary data for $1000. He says this soon will be possible but again the computational bottlenecks are the main limitation.

Jongeneel cites three of the main technology vendors that are providing next-generation sequencing and says that while their approaches differ, on average, for a sequenced genome they’re running for 8 days for 200 gigabases worth of information. This translates into well over one terabyte per human genome.

When it’s human genomes sequences are the result of several hundred million (or even a billion) reads—a number that depends on the technology vendor. From there, researchers need to determine where they come from in the genome relative to common reference genomes. This “simple” alignment process whereby the individual genome is compared via alignment with the reference genome is incredibly demanding computationally—as is the next step where one must interpret this alignment to document individual differences and to make sure there is consistency.

Jongeneel says that this alignment step typically takes several days just for the processing of a single sample as it is aligned to the reference genome. To further complicate the process, we all have pieces of DNA that aren’t necessarily found in the DNA of others. While these are small differences he says these can make a very big difference. Analysis of these unique pieces require a complete piecing together of individual reads to allow researchers to see what the larger structure of the genome might look like. And it gets even more demanding.

Rebuilding genomes requires the construction of highly complex graphs, which itself is a strain on computational resources. This is even more demanding when one must disambiguate the graph to make sense of it in terms of an actual genome sequence. After all, there are pieces of sequence rolling off the machines that are on the order of between 75-100 nucleotides long—and you’re trying to reconstitute genomes that are in the millions or billions of nucleotides long. This is the scientific equivalent of fitting a cell-sized piece into a massive tabletop puzzle.

More concretely than the puzzle image, consider this: Jongeneel says that if you wanted to reconstruct an entire genome from this kind of information you’re talking about the construction of a graph would likely have over 3 billon nodes with in excess of 10 billion edges to it. This is, of course, assuming there are no errors in your data which, he apologizes, there probably are. The raw time taken for an algorithm on a medium-sized cluster the assembly properly takes several weeks for each genome.

Jongeneel says that this is the kind of bottleneck that prevents some interesting genomic projects from taking off. For instance, there is currently an effort to sequence the entire range of DNA for several hundred common vertebrates. However, storing that information and spending several weeks for each individual species makes that out of reach—for now, at least. He says that there is hope on the horizon, but it is going to take a rethinking of code and computing.

He says that the problem lies, in large part, in the software itself. His team ran a test on the widely-used genome assembler ABySS, which has broad appeal since it uses MPI and can leverage a much-needed cluster environment. They undertook assembly for a modest-sized genome of a yeast and noted that it was clear, based on wall clock and memory requirements, that this was not a scalable code.

He says this hints at a much deeper problem—many of those developing genomics software aren’t professional developers. Even though they integrate some complex algorithmic ideas, the code they write “isn’t up to the standards of the HPC community.”

He commented on this further, saying that what is needed most is a highly parallel genome assembler. He pointed to some progress in the arena from a group at Iowa State but says that unfortunately, “their software is not in the public domain so it isn’t available, we can’t test it and it’s not in the community.”

A representative from Microsoft in the audience asked Jongeneel about what the solution might be to this problem, inquiring if it was a simple need for more parallel programmers, better tools or languages for developing these, or some other new type of scalable solution. Jongeneel responded that since most of the code being produced is research grade and the technology moves so quickly that it renders “new” code obsolete in very little time. He says that commercial attempts have failed for the same reason—as soon as they’ve produced a viable, scalable solution they’ve been left behind by the swift movement toward new solutions.

Jongeneel said that if you think about personal genomics, if we even wanted to move toward the goal of one million people, we’re going to hit the exabyte range in no time. He feels that in addition these datasets need to be analyzed using workflows with multiple complex steps, thus we require a fundamental rethinking of compute architectures that can enable this kind of research.

That aside, he claims that one side question is what we should do with the massive amount of raw data that is valuable for future research (and sometimes legally sticky to dispose of now anyway). With this raw data in vast volume he says that extraction of ‘relevant’ information is the problem. Jongeneel notes, Data analytics and pattern discovery on large numbers of genomes will be required to produce meaningful results.

View full video from Jongeneel’s talk here.

Subscribe to HPCwire's Weekly Update!

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

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire