Two New Data Analysis Tools Developed for Proteomics Researchers


Proteomics researchers from the Pacific Northwest National Laboratory (PNNL), the University of Texas, and the University of Wisconsin-Madison have collaborated to develop and deploy new data analysis tools to further the field of proteomics research. Better tools for protein identification are vital to solving intractable problems such as converting agricultural waste into fuels, detecting bio-based threats and quickly detecting and treating disease. These tools are available free of charge through a publicly available website (link expired). Making new proteomics tools available at no cost to the scientific community allows more researchers to enter the proteomics field without investing in expensive tools or needing to develop their own. DAnTE (Data Analysis Tool Extension) was developed as a statistical and visualization software tool that scientists can use to perform data analysis steps on large-scale proteomics data, but it also performs well on genomics microarray data. The second tool, a “bottom-up” data analysis strategy that can detect thousands of peptides over time, has been demonstrated on data from a time-course study of Rhodobacter sphaeroides, an environmentally important photosynthetic microorganism under study in DOE’s Genomics:GTL program. These tools were funded by several of the National Institutes of Health as well as DOE’s Office of Science. Portions of the research for both tools were performed in the Environmental Molecular Sciences Laboratory, a DOE scientific user facility located at PNNL.

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Du X, SJ Callister, NP Manes, JN Adkins, RA Alexandridis, X Zeng, JH Roh, WE Smith, TJ Donohue, S Kaplan, RD Smith, and MS Lipton. 2008. “A Computational Strategy to Analyze Label-Free Temporal Bottom-Up Proteomics Data.” Journal of Proteome Research 7(7):2595-604.

Polpitiya AD, WJ Qian, N Jaitly, VA Petyuk, JN Adkins, DG Camp II, GA Anderson, and RD Smith. 2008. “DAnTE: A Statistical Tool for Quantitative Analysis of -omics Data.” Bioinformatics 24(13):1556-8.