Pinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data. Motivation: One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust and reproducible methods for detecting, matching and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels and finally quantifying each spot by calculating normalized spot volumes. This approach is time consuming, error-prone and frequently requires extensive manual editing, which can unintentionally introduce bias into the results. Results: We introduce a new method for spot detection and quantification called Pinnacle that is automatic, quick, sensitive and specific and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller coefficiant of variations (CVs). More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences. Availability: Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Melnykov, Volodymyr; Maitra, Ranjan; Nettleton, Dan: Accounting for spot matching uncertainty in the analysis of proteomics data from two-dimensional gel electrophoresis (2011)
- Morris, Jeffrey S.; Baladandayuthapani, Veerabhadran; Herrick, Richard C.; Sanna, Pietro; Gutstein, Howard: Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data (2011)
- Karpievitch, Yuliya V.; Polpitiya, Ashoka D.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.: Liquid chromatography mass spectrometry-based proteomics: biological and technological aspects (2010)
- Melnykov, Volodymyr; Maitra, Ranjan: Finite mixture models and model-based clustering (2010)
- Morris, Jeffrey S.; Clark, Brittan N.; Gutstein, Howard B.: \textitpinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data. (2008) ioport