Literature DB >> 19133722

Comparison of three commercially available DIGE analysis software packages: minimal user intervention in gel-based proteomics.

Yunyi Kang1, Tanasit Techanukul, Anthanasios Mantalaris, Judit M Nagy.   

Abstract

The success of high-performance differential gel electrophoresis using fluorescent dyes (DIGE) depends on the quality of the digital image captured after electrophoresis, the DIGE enabled image analysis software tool chosen for highlighting the differences, and the statistical analysis. This study compares three commonly available DIGE enabled software packages for the first time: DeCyder V6.5 (GE-Healthcare), Progenesis SameSpots V3.0 (Nonlinear Dynamics), and Dymension 3 (Syngene). DIGE gel images of cell culture media samples conditioned by HepG2 and END2 cell lines were used to evaluate the software packages both quantitatively and subjectively considering ease of use with minimal user intervention. Consistency of spot matching across the three software packages was compared, focusing on the top fifty spots ranked statistically by each package. In summary, Progenesis SameSpots outperformed the other two software packages in matching accuracy, possibly being benefited by its new approach: that is, identical spot outline across all the gels. Interestingly, the statistical analysis of the software packages was not consistent on account of differences in workflow, algorithms, and default settings. Results obtained for protein fold changes were substantially different in each package, which indicates that in spite of using internal standards, quantification is software dependent. A future research goal must be to reduce or eliminate user controlled settings, either by automatic sample-to-sample optimization by intelligent software, or by alternative parameter-free segmentation methods.

Mesh:

Year:  2009        PMID: 19133722     DOI: 10.1021/pr800588f

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  8 in total

Review 1.  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

2.  Core Canonical Pathways Involved in Developing Human Glioblastoma Multiforme (GBM).

Authors:  Somiranjan Ghosh; Sisir Dutta; Gabriel Thorne; Ava Boston; Alexis Barfield; Narendra Banerjee; Rayshawn Walker; Hirendra Nath Banerjee
Journal:  Int J Sci Res Sci Eng Technol       Date:  2017-02-01

3.  Highlights on the capacities of "Gel-based" proteomics.

Authors:  François Chevalier
Journal:  Proteome Sci       Date:  2010-04-28       Impact factor: 2.480

4.  Feature detection techniques for preprocessing proteomic data.

Authors:  Kimberly F Sellers; Jeffrey C Miecznikowski
Journal:  Int J Biomed Imaging       Date:  2010-05-05

5.  Comparative proteomics of inner membrane fraction from carbapenem-resistant Acinetobacter baumannii with a reference strain.

Authors:  Vishvanath Tiwari; Jitendraa Vashistt; Arti Kapil; Rajeswari R Moganty
Journal:  PLoS One       Date:  2012-06-26       Impact factor: 3.240

Review 6.  Proteomic profiling of the dystrophin-deficient mdx phenocopy of dystrophinopathy-associated cardiomyopathy.

Authors:  Ashling Holland; Kay Ohlendieck
Journal:  Biomed Res Int       Date:  2014-03-20       Impact factor: 3.411

7.  Application of fluorescence two-dimensional difference in-gel electrophoresis as a proteomic biomarker discovery tool in muscular dystrophy research.

Authors:  Steven Carberry; Margit Zweyer; Dieter Swandulla; Kay Ohlendieck
Journal:  Biology (Basel)       Date:  2013-12-02

Review 8.  The impact of growth hormone on proteomic profiles: a review of mouse and adult human studies.

Authors:  Silvana Duran-Ortiz; Alison L Brittain; John J Kopchick
Journal:  Clin Proteomics       Date:  2017-06-29       Impact factor: 3.988

  8 in total

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