Visit additional Tabor Communication Publications
August 31, 2007
Harvard grad student devises an efficient method to sort and organize billions of genomic matches
A DOE graduate fellow has developed an algorithm that will dramatically slash the time it takes to sort and catalog billions of genome sequences from the Joint Genome Institute and other research centers.
The algorithm, developed by Ben Campbell Smith from Harvard University, can search and organize billions of genomic sequence comparisons in a day instead of a month. The efficiency will enable staff at the Biological Data Management and Technology Center (BDMTC) at Berkeley Lab to massage raw data into materials that scientists can easily use for genomic analyses.
BDMTC develops informatics tools and provides data management for the Joint Genome Institute (JGI), UC San Francisco, Berkeley Lab's Life Sciences and Physical Biosciences divisions and the California Institute of Quantitative Biomedical Research (QB3). The Integrated Microbial Genomes (IMG) system, created by BDMTC, integrates microbial data from the JGI and other public sources and enables comparative analyses across species, something researchers look for in hunting for clues about evolution, for example.
"Ben is an outstanding worker. Bioinformatics is now flooded with a huge amount of data to be analyzed and cross compared. Scalability is a major issue especially for a tightly integrated system such as IMG," said Ernest Szeto, a BDMTC researcher who works closely with Smith. "Ben has applied solid computer science skills to help deal with some of these most pressing disk oriented processing scalability issues."
Smith is working in BDMTC this summer as a DOE Computational Science Graduate Fellow. The fellowship, funded by the DOE Office of Science and the National Nuclear Security Administration, not only pays for each fellow's tuition and other school fees, it also provides an annual stipend of $31,200 and other funds for research-related expenses.
Bioinformatics is not Smith's research focus in school. In fact, the Harvard graduate student is partial to high-energy physics. His Ph.D. work involves hunting for the elusive Higgs boson particle, whose existence can validate a theory on how fundamental particles such as electrons and quarks acquire mass.
But working with biological data isn't new for Smith. He recalled fondly the time he worked in his father's bioinformatics lab at the University of British Columbia, where the elder Smith is a hematologist/oncologist.
"Before I started graduate school, my dad said, 'Come work for me and write some code.' I had a lot of fun doing that," said Smith, who searched Berkeley Lab's Computing Sciences web site for research ideas and learned about the work by Markowitz and his group. "I thought it would be cool to work on something that people use all the time."
Genomics and high-energy physics share one similarity -- they both generate an incredible amount of research data that must be culled to obtain useful information for research. With that in mind, Smith said he was able to immerse himself quickly in the informatics project at Berkeley Lab.
Every time a microbe's genome is sequenced, that information goes to the Pacific Northwest National Laboratory (PNNL), which uses a supercomputer and a software called Basic Local Alignment Search Tool (BLAST) to look for matching sequences among the roughly 3.2 million microbial sequences in the database.
Instead of looking for matches only between the newly sequenced genome and those already in the database, however, the PNNL computer carries out the "all versus all" BLAST search, spitting out results that show all the matches among various microbes' genomes. When each microbe's genome can produce thousands of gene sequences, the process of matching them with each other will produce an enormous set of data. As a result, BDMTC staff aren't able to update the IMG system frequently.
Smith's task is to organize and format those results so that researchers can quickly find specific comparisons among the two sequences or microbes they are studying. The dataset he is working with contains 20 billion lines, each corresponding with a match.
The 20 billion matches aren't in any particular order, making it even more difficult to sort them by taxons and then "score," which refers to a statistical analysis of the quality of the matches (some matches could have been made in error).
Before Smith devised the new method, BDMTC staff used a brute force algorithm that read the output a single line at a time and wrote the match to a file based upon the two taxons involved. Because of the inefficiency inherent in accessing a different file for each of the nearly 20 billion matches, this process would take approximately 30 days.
Smith's algorithm, on the other hand, first breaks down the data into thousands of smaller files. Using a cluster with 35 dual core CPUs, the smaller chunks of data are catalogued by the genomes they contain. This allows a sorting program to focus only on very small subsets of the data corresponding to the genome of interest. The process is further sped up through the use of a binary search tree, which allows the sorting to remain computationally efficient, even for very large datasets.
"The process now takes a day. You take all the data and run and sort it. Then anyone who needs it again can quickly look up the results," Smith said.
With the new technique, the IGM system can be updated four times a year instead of two. The algorithm will be used in the next release of IMG/M, the metagenomics version of IMG, which is accompanied with a big batch of computational results from PNNL. The next release is scheduled for December or January.
Learn more about the IMG system at http://crd.lbl.gov/html/BDMTC. Information about the Computational Science Graduate Fellowship program can be found at http://www.krellinst.org/csgf/index.shtml.
Source: Lawrence Berkeley National Laboratory, Computational Research Division. This article was originally published in the August 2007 issue of the Computational Research Division Report which can be found at http://crd.lbl.gov/html/news/CRDreport.html.
May 23, 2013 |
The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
May 22, 2013 |
At some point in the not-too-distant future, building powerful, miniature computing systems will be considered a hobby for high schoolers, just as robotics or even Lego-building are today. That could be made possible through recent advancements made with the Raspberry Pi computers.
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
May 15, 2013 |
Supercomputers at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) have worked on important computational problems such as collapse of the atomic state, the optimization of chemical catalysts, and now modeling popping bubbles.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.