Literature DB >> 18194961

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

Jeffrey S Morris1, Brittan N Clark, Howard B Gutstein.   

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.

Mesh:

Substances:

Year:  2008        PMID: 18194961      PMCID: PMC2662725          DOI: 10.1093/bioinformatics/btm590

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

1.  Quantitative and reproducible two-dimensional gel analysis using Phoretix 2D Full.

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2.  Similarity of the Escherichia coli proteome upon completion of different biopharmaceutical fermentation processes.

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3.  A novel approach to spot detection for two-dimensional gel electrophoresis images using pixel value collection.

Authors:  Paul Cutler; Geoffrey Heald; Ian R White; Jason Ruan
Journal:  Proteomics       Date:  2003-04       Impact factor: 3.984

4.  Quantitative evaluation of proteins in one- and two-dimensional polyacrylamide gels using a fluorescent stain.

Authors:  Julie C Nishihara; Kathleen M Champion
Journal:  Electrophoresis       Date:  2002-07       Impact factor: 3.535

5.  Normalization and analysis of residual variation in two-dimensional gel electrophoresis for quantitative differential proteomics.

Authors:  Jonas S Almeida; Romesh Stanislaus; Ed Krug; John M Arthur
Journal:  Proteomics       Date:  2005-04       Impact factor: 3.984

6.  Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum.

Authors:  Jeffrey S Morris; Kevin R Coombes; John Koomen; Keith A Baggerly; Ryuji Kobayashi
Journal:  Bioinformatics       Date:  2005-01-26       Impact factor: 6.937

7.  High resolution two-dimensional electrophoresis of proteins.

Authors:  P H O'Farrell
Journal:  J Biol Chem       Date:  1975-05-25       Impact factor: 5.157

8.  A comparison of selected mRNA and protein abundances in human liver.

Authors:  L Anderson; J Seilhamer
Journal:  Electrophoresis       Date:  1997 Mar-Apr       Impact factor: 3.535

9.  Intermittent lumbar puncture in rats: a novel method for the experimental study of opioid tolerance.

Authors:  Jijun J Xu; Brittan C Walla; Miguel F Diaz; Gregory N Fuller; Howard B Gutstein
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10.  Correlation between protein and mRNA abundance in yeast.

Authors:  S P Gygi; Y Rochon; B R Franza; R Aebersold
Journal:  Mol Cell Biol       Date:  1999-03       Impact factor: 4.272

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  17 in total

1.  Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data.

Authors:  Veerabhadran Baladandayuthapani; Yuan Ji; Rajesh Talluri; Luis E Nieto-Barajas; Jeffrey S Morris
Journal:  J Am Stat Assoc       Date:  2010-12       Impact factor: 5.033

2.  AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA.

Authors:  Jeffrey S Morris; Veerabhadran Baladandayuthapani; Richard C Herrick; Pietro Sanna; Howard Gutstein
Journal:  Ann Appl Stat       Date:  2011-01-01       Impact factor: 2.083

3.  Statistical contributions to proteomic research.

Authors:  Jeffrey S Morris; Keith A Baggerly; Howard B Gutstein; Kevin R Coombes
Journal:  Methods Mol Biol       Date:  2010

4.  Detection and Quantification of Protein Spots by Pinnacle.

Authors:  Jeffrey S Morris; Howard B Gutstein
Journal:  Methods Mol Biol       Date:  2016

5.  Differential expression of leaf proteins in four cultivars of peanut (Arachis hypogaea L.) under water stress.

Authors:  Padmavathi A V Thangella; Srinivas N B S Pasumarti; Raghu Pullakhandam; Bhanuprakash Reddy Geereddy; Manohar Rao Daggu
Journal:  3 Biotech       Date:  2018-03-02       Impact factor: 2.406

Review 6.  Laser capture sampling and analytical issues in proteomics.

Authors:  Howard B Gutstein; Jeffrey S Morris
Journal:  Expert Rev Proteomics       Date:  2007-10       Impact factor: 3.940

Review 7.  Image analysis tools and emerging algorithms for expression proteomics.

Authors:  Andrew W Dowsey; Jane A English; Frederique Lisacek; Jeffrey S Morris; Guang-Zhong Yang; Michael J Dunn
Journal:  Proteomics       Date:  2010-12       Impact factor: 3.984

8.  Informatics and statistics for analyzing 2-d gel electrophoresis images.

Authors:  Andrew W Dowsey; Jeffrey S Morris; Howard B Gutstein; Guang-Zhong Yang
Journal:  Methods Mol Biol       Date:  2010

9.  Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.

Authors:  Jeffrey S Morris; Veerabhadran Baladandayuthapani
Journal:  Stat Modelling       Date:  2017-06-15       Impact factor: 2.039

Review 10.  Microproteomics: analysis of protein diversity in small samples.

Authors:  Howard B Gutstein; Jeffrey S Morris; Suresh P Annangudi; Jonathan V Sweedler
Journal:  Mass Spectrom Rev       Date:  2008 Jul-Aug       Impact factor: 10.946

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