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Bioinformatics Advance Access originally published online on January 14, 2008
Bioinformatics 2008 24(4):529-536; doi:10.1093/bioinformatics/btm590
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Pinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data

Jeffrey S. Morris 1,*, Brittan N. Clark 2 and Howard B. Gutstein 2

1Department of Biostatistics and 2Department of Anesthesiology and Molecular Genetics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd Unit 447, Houston, TX 77030-4009, USA

*To whom correspondence should be addressed.


  Abstract

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.

Contact: jefmorris{at}mdanderson.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on April 30, 2007; revised on August 22, 2007; accepted on November 25, 2007

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