COMPUTER GENERATES COMPARATIVE GENE MAPS LIBRARY

January 5, 2001

SCIENCE & ENGINEERING NEWS

Ithaca, N.Y. — Comparing the genomes of two related species of a plant or animal often helps to locate important genes that have been identified in one species but not in another, and can provide clues about how both species evolved from a common ancestor. But making these “comparative gene maps” has been a slow, painstaking process, something biologists do by hand over weeks, months or years, using data painstakingly collected in “wet labs” and analyzed with software designed to interpret only one map at a time.

Now, Cornell University researchers have come up with a way to do the comparison step in a few hours on a computer. In early tests, a computer-generated comparison of the genomes of rice and maize (corn) closely matched a similar map made by hand, and even suggested some relationships that had not shown up in the handmade map.

Debra Goldberg, Cornell graduate student in applied mathematics, developed the new method in collaboration with Susan McCouch, Cornell professor of plant breeding, and Jon Kleinberg, Cornell assistant professor of computer science. Goldberg described their work at the Gene Order Dynamics, Comparative Maps and Multigene Families (DCAF) workshop held September in Sainte-Ad•le, Quebec, and will present a later version at the Plant and Animal Genome IX conference in San Diego in January. Their paper, “Algorithms for Constructing Comparative Maps,” appears in Comparative Genomics (David Sankoff and Joseph H. Nadeau, Eds., Kluwer Academic Publishers, 2000). A software implementation of the new method soon will be available to geneticists.

“The point of this isn’t just to compare rice and corn, but to be able to do it with any two species,” Goldberg says. “Ideally we’d like to be able to find new evolutionary pathways.”

Every so often, as reproductive cells divide, genes and segments of chromosomes get shuffled around. One chromosome meets another and pieces of DNA are moved or swapped. If those particular cells then happen to be involved in reproduction, the new arrangement is passed on to the next generation and may spread through the population. It doesn’t happen very often, but over evolutionary time scales many such events show up. Related species descended from a common ancestor have many genes in common, but they occur in different arrangements. A strand of DNA that used to be on chromosome 2 in some common ancestor ends up on chromosome 10, in between two pieces that used to belong to ancestral chromosomes 3 and 5. The relocated genes often continue to do the same jobs, and often several genes move together, retaining their ancestral order along a segment of DNA.

By comparing genomes, scientists can trace the evolutionary paths, and there are immediate practical applications. If it’s known that genes A and B are near each other in the rice genome, and the location of gene A in maize also is known, then a comparative map could help locate gene B in maize. In plant breeding, such a discovery could help to breed corn with better disease resistance or improved nutritional value. In medicine, clues from the genome of the mouse are being used to help find genes associated with human diseases.

The idea of comparative mapping is to align genes in the order they are found along the chromosomes of the first or “base” species with those found in the same order on a single chromosome of the second or “target” species. The raw data consists of ordered lists of the genes and gene markers of both species that have been identified in “wet lab” experiments.

At the simplest level, a computer could look at each gene or marker of the base species, find where it is (on which arm of which chromosome) on the target genome, and label it accordingly. But geneticists want to step back to get a larger view, identifying segments of the base genome that contain arrays of genes that also are found together on the target genome. The catch is what McCouch calls “noise” in the data: the target genome can contain long arrays of genes that look like those on the base genome except that there are a few extra genes here and there that come from somewhere else in the genome. How does the computer decide whether or not to ignore the out-of-place genes? When are two similar linear arrays of genes close enough to be called a match?

In early stages of the work, Goldberg applied constraints, called “penalties,” both for out-of-place genes and for breaks between segments. The computer was directed to minimize both the number of segments it created and the number of out-of-place genes in each segment. While promising, when applied to a comparison between rice and maize this approach still didn’t produce a map close enough to one made by hand, Goldberg says. Among other things, the computer often introduced too few breaks where a small part of one sequence appeared in the middle of another.

So, Goldberg added a procedure that remembers the labels of genes as it goes along, making decisions about what sequences go together on the basis of an overall trend rather than considering just one gene at a time. Based on the sequence it remembered, the computer was allowed to reduce the penalties for breaks between segments. In other words, if a small but meaningful sequence of out-of place genes appeared in the middle of another matching sequence, it would be marked as a separate segment. But if just a few out-of-place genes turned up and didn’t have a meaningful relationship, the overall sequence still would be listed as a single segment.

In computer-science terms, the label for each gene is pushed onto a stack in memory, and popped back off when it gets to be too unlikely. This procedure, the researchers say in their paper, draws on computer methods for parsing sentences in natural-language processing, in which a program remembers words until the end of a sentence and only then decides what the sentence means.

Each chromosome in a living organism consists of two adjacent arms, and the algorithm also was modified to give special consideration to related orders of genes that appear on different arms of the same chromosome. In some cases biologists know which chromosome a gene is on, but not on which arm, so special consideration also was given to those “ambiguous” genes.

The researchers tested their computer method by comparing a computer-generated comparative map of rice and maize with a handmade map prepared in 1999 by William A. Wilson (a postdoctoral fellow in the Department of Plant Breeding at Cornell and now in private industry), and several colleagues at Cornell and Iowa State University. The computer mapping done by Goldberg was based on Wilson’s original data. The results, the researchers say, were remarkably similar, although in their paper they note some minor differences. They also point out that handmade maps usually are made with reference to additional information that biologists hold in their memories, such as the order of genes along the chromosomes of other related species.

The computer also found a small “footprint” of an ancestral chromosome in maize that did not turn up in the handmade map, McCouch says. This will be investigated further in the lab, she says.

Besides rice and maize, the algorithm has been tested on a comparison between the mouse and human genomes. “It appears to work well in both cases,” McCouch says “It is certainly our intention to present this algorithm as a replacement for the construction of hand-crafted comparative maps.”

Related World Wide Web sites: The following sites provide additional information on this news release. Some might not be part of the Cornell University community, and Cornell has no control over their content or availability.

o Cornell Center for Applied Mathematics: http://www.cam.cornell.edu/ o Susan McCouch home page: http://www.plbr.cornell.edu/PBBweb/McCouch.html o Jon Kleinberg home page: http://www.cs.cornell.edu/annual_report/99-00/Kleinberg.htm

============================================================

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