Today’s life scientists are producing genomes galore.
But there’s a problem: The latest DNA sequencing instruments are
burying researchers in trillions of bytes of data and overwhelming existing
tools in biological computing. It doesn’t help that there’s a variety of
sequencing instruments feeding a diverse set of applications.
Iowa State University’s Srinivas Aluru is leading a research
team that’s developing a set of solutions using high-performance computing. The
researchers want to develop core techniques, parallel algorithms and software
libraries to help researchers adapt parallel computing techniques to
high-throughput DNA sequencing, the next generation of sequencing technologies.
Those technologies are now ubiquitous, “enabling single
investigators with limited budgets to carry out what could only be accomplished
by an international network of major sequencing centers just a decade ago,”
says Aluru, the Ross Martin Mehl and Marylyne Munas Mehl Professor of Computer
Engineering at Iowa State.
“Seven years ago we were able to sequence DNA one fragment at a
time,” he says. “Now researchers can read up to 6 billion DNA sequences in one
experiment.
“How do we address these big data issues?”
A three-year, $2 million grant from the BIGDATA program of the
National Science Foundation and the National Institutes of Health will support
the search for a solution by Aluru and researchers from Iowa State, Stanford
University, Virginia Tech, and the University of Michigan. In addition to
Aluru, the project’s leaders at Iowa State are Patrick Schnable, Iowa State’s
Baker Professor of Agronomy and director of the centers for Plant Genomics and Carbon
Capturing Crops, and Jaroslaw Zola, a former research assistant professor in
electrical and computer engineering who recently moved to Rutgers University.
The majority of the grant—$1.3 million—will support research at
Iowa State. And Aluru is quick to say that none of the grant will support
hardware development.
Researchers will start by identifying a large set of building
blocks frequently used in genomic studies. They’ll develop the parallel
algorithms and high-performance implementations needed to do the necessary data
analysis. And they’ll wrap all of those technologies in software libraries
researchers can access for help. On top of all that, they’ll design a domain
specific language that automatically generates computing codes for researchers.
Aluru says that should be much more effective than asking high-performance
computing specialists to develop parallel approaches to each and every
application.
“The goal is to empower the broader community to benefit from
clever parallel algorithms, highly tuned implementations, and specialized high
performance computing hardware, without requiring expertise in any of these,”
says a summary of the research project.
Aluru says the resulting software libraries will be fully
open-sourced. Researchers will be free to use the libraries while developing,
editing and modifying them as needed.
“We’re hoping this approach can be the most cost-effective and
fastest way to gain adoption in the research community,” Aluru says. “We want
to get everybody up to speed using high-performance computing.”
Source: Iowa State University
