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 industy updates delivered to you every week!

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate scientists the ability to use machine learning to identify e Read more…

By Rob Farber

Mellanox Reacts to Activist Investor Pressures in Letter to Shareholders

March 16, 2018

Activist investor Starboard Value has been exerting pressure on Mellanox Technologies to increase its returns. In response, the high-performance networking company on Monday, March 12, published a letter to shareholders outlining its proposal for a May 2018 extraordinary general meeting (EGM) of shareholders and highlighting its long-term growth strategy and focus on operating margin improvement. Read more…

By Staff

Quantum Computing vs. Our ‘Caveman Newtonian Brain’: Why Quantum Is So Hard

March 15, 2018

Quantum is coming. Maybe not today, maybe not tomorrow, but soon enough. Within 10 to 12 years, we’re told, special-purpose quantum systems will enter the commercial realm. Assuming this happens, we can also assume that quantum will, over extended time, become increasingly general purpose as it delivers mind-blowing power. Read more…

By Doug Black

HPE Extreme Performance Solutions

Achieve Optimal Performance at Scale with High Performance Fabrics for HPC

High Performance Computing (HPC) is unlocking a new era of speed and productivity to fuel business transformation. Rapid advancements in HPC capabilities are helping organizations operate faster and more effectively than ever, but in today’s fast-paced marketplace, a new generation of technologies is required to reach greater scalability and cost-efficiency. Read more…

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise IT in its willingness to outsource computational power. The m Read more…

By Chris Downing

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Stephen Hawking, Legendary Scientist, Dies at 76

March 14, 2018

Stephen Hawking passed away at his home in Cambridge, England, in the early morning of March 14; he was 76. Born on January 8, 1942, Hawking was an English theo Read more…

By Tiffany Trader

Hyperion Tackles Elusive Quantum Computing Landscape

March 13, 2018

Quantum computing - exciting and off-putting all at once - is a kaleidoscope of technology and market questions whose shapes and positions are far from settled. Read more…

By John Russell

Part Two: Navigating Life Sciences Choppy HPC Waters in 2018

March 8, 2018

2017 was not necessarily the best year to build a large HPC system for life sciences say Ari Berman, VP and GM of consulting services, and Aaron Gardner, direct Read more…

By John Russell

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

SciNet Launches Niagara, Canada’s Fastest Supercomputer

March 5, 2018

SciNet and the University of Toronto today unveiled "Niagara," Canada's most-powerful supercomputer, comprising 1,500 dense Lenovo ThinkSystem SD530 high-perfor Read more…

By Tiffany Trader

Part One: Deep Dive into 2018 Trends in Life Sciences HPC

March 1, 2018

Life sciences is an interesting lens through which to see HPC. It is perhaps not an obvious choice, given life sciences’ relative newness as a heavy user of H Read more…

By John Russell

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

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

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Leading Solution Providers

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in wha Read more…

By John Russell

World Record: Quantum Computer with 46 Qubits Simulated

December 18, 2017

Scientists from the Jülich Supercomputing Centre have set a new world record. Together with researchers from Wuhan University and the University of Groningen, Read more…

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

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