Staff Awards & Honors
Two Journal Articles Make ACS Most-Cited List
Two articles by Pacific Northwest National Laboratory staff that appeared in the February 2006 issue of the Journal of Proteome Research are being featured as an American Chemical Society 2006 Most-Cited Article. These are articles published in ACS journals during 2006 that received the most citations in the same year, based on citation data obtained from Thomson Scientific. The two articles are:
"Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics," by Stephen Callister, Richard Barry, Josh Adkins, Ethan Johnson, Wei-Jun Qian, Bobbie-Jo Webb-Robertson, Dick Smith,
and Mary Lipton.
Researchers investigated four techniques for normalizing peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry. The peptides came from a protein sample, two Deinococcus radiodurans samples, and two mouse striatum samples. Before normalization, replicate runs from each sample set were statistically different, and after normalization, they were not. For most LC-FTICR MS analyses, linear regression normalization ranked either first or second among the four techniques, suggesting that it was more generally suitable for reducing systematic biases.
"Characterization of the mouse brain proteome using global proteomic analysis complemented with cysteinyl-peptide enrichment," by Haixing Wang, Wei-Jun Qian, Mark Chin, Vladislav Petyuk, Richard Barry, Tao Liu, Marina Gritsenko, Heather Mottaz,
Ron Moore, Dave Camp, Arshad Khan, Desmond Smith, and Dick Smith.
Scientists from PNNL and the University of California-Los Angeles completed the first comprehensive characterization of the whole mouse brain proteome and the most comprehensive proteome coverage for the mammalian brain to date. They took a global proteomic approach for comprehensive profiling of the brain tissue using liquid chromatography-tandem mass spectrometry and an extensive protein database for the whole mouse brain. The database generated from this study will be the basis for future quantitative brain proteomic studies using mouse models.
A most-cited article represents critical, new research results influencing the direction of scientific discovery. Congratulations to our PNNL authors.