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Apr 1, 2013 (Vol. 33, No. 7)

Computational Bio Problem Solving

  • Adolescence

    Based on his experience integrating genomic technologies into clinical practice “in the early days” of the late 1980s, Dr. Popovic cited four interrelated issues that the field will have to grapple with on its way to maturity.

    “People’s credentials define their turf in medicine, and people in bioinformatics currently don’t have any turf defined.” This is the first of the issues, he said. Although genetics and genomics are applied in pathology labs, bioinformatics is not part of lab medicine or pathology—it is “essentially a group of outsiders trying to break into somebody else’s turf.”

    Second, the bioinformatics community “hasn’t really developed algorithms and had to put them through a clinical sieve to become the standard of care.” This is a long and laborious process, and it is important to understand that the road from a cool algorithm helping to pinpoint the meaning of a VUS, to becoming standard of care, is a long one.

    Third, the field needs to grapple with how to get reimbursed for their efforts: “You have to up front reconcile what you’re going to do to get paid for that. You truly need a business plan to show how you’re going to execute this and actually put it into clinical practice.”

    And last, there is a need to understand where to aim the technology to answer questions that are relevant to the physician who will be ordering the test. Just because something can be done doesn’t mean it will be.

    Dr. Karsan pointed out that from the user’s perspective, even a targeted panel of 40–50 genes—let alone a whole genome or whole exome sequence—is still a lot of information coming in. It’s important to have tools for visualization of that data, and to have an easy way to interface with the databases that are currently available.

    Ultimately the pathologist would have the sequencer hooked up to the hospital information system, which can then kick out a report saying a test is positive or it’s negative—not detailing the VUSs that may be interesting to follow. Because, explained Dr. Popovic, “that’s absolutely meaningless to the doc that has 30 seconds to read the report and give an answer to the patient. They need a yes/no.”

  • All that Data

    Huge amounts of data are the curse of modern biology—eclipsing the Library of Congress in terms of volume of data. “We’re running out of room to add new stuff,” pointed out Dr. Michaels. In addition, the sheer computing power required by some new algorithms is testing the limits of present technology.

    Intel is beginning to devote more resources to studying the energetics of computing. Many algorithmic approaches, they have found, are extremely inefficient, moving lots of data around, which is “the most expensive part, energy-wise,” he said. This is driving new memory technologies and methodologies. “The systems that we develop in the future are going to be very different, from an architectural standpoint, but also with very different programming issues associated with them.”

    For its part, Illumina “is committed to making the data as small and portable as possible,” said Dr. Stockton. The firm is collaborating with the community on ways to represent the genome that is “vastly more compact,” including working with EMBL’s European Bioinformatics Institute on the CRAM compression format, as well as “streamlining the data that comes directly off of the instrument.”

    Space issues aside, there is talk of whether it will make more sense to store a person’s genomic sequence or to have the patient resequenced later if necessary. Among the unresolved issues, noted Dr. Popovic, who co-authored an Oregon law that paved the way for the federal Genetic Information Nondiscrimination Act of 2008, are security of the data, privacy concerns (including the right for the patient not to know), and how to prevent the data from being used to discriminate against the patient.

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