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At Beyond Genome, Naylor Identifies 10 Challenges Facing Proteomic Scientists

SAN FRANCISCO, June 23 (GenomeWeb News) - Kicking off the proteomics track at the Beyond Genome conference here today, Stephen Naylor from MIT's CSBi program introduced a number of challenges he sees facing proteomics scientists.


The good news, he noted, is that proteomics is getting more attention from more researchers, noting that the majority of talks and papers at this year's ASMS meeting last month involved proteomics. He also assured conference attendees that there have been plenty of advances moving the field forward.


But there's some bad news too, he said, including what he called the "graveyard" of embattled or completely failed proteomics companies.


In a call for better technology and analytical capabilities, Naylor identified proteomics as the current "technology hole" in the systems biology spectrum. Acknowledging that "there's been some tremendous work done in the past several years" in proteomics, he contended that the incredible complexity of the discipline makes it far more problematic than other systems biology components such as the more established genomics or the longer-lived metabolomics.


Naylor's talk started the day for the proteomics part of the conference, and the subsequent talks in that track for the day will be devoted to elucidating each of the 10 challenges he listed. To illustrate just one of the challenges -- issues related to dynamic range -- Naylor demonstrated that because current technologies detect the most abundant proteins, which tend to be the same over and over, studies of protein patterns haven't been able to distinguish between, say, a patient with Alzheimer's and one with colon cancer. He added that while there are differences in patterns between the proteins found, it hasn't yet been proven that those differences stem from biological variation rather than platform variation.


Today's talks will address 10 challenges in proteomics, which are: complex mixture analysis; differential proteomics; relative and absolute quantitation; dynamic range; membrane proteins; post-translational modification; throughput; protein arrays and multiplexing; protein expression and production; protein informatics.


What's needed to face these issues, Naylor said, include routine analysis; robustness; reproducibility; sensitivity; specificity; selectivity; speed; and capability for stoichiometric quantitation.

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