Challenges Related to Analysis of Protein Spot Volumes from Two-Dimensional Gel Electrophoresis As Revealed by Replicate Gels

Grove, Harald; Hollung, Kristin; Uhlen, Anne Kjersti; Martens, Harald; Færgestad, Ellen Mosleth

doi:10.1021/pr0603250

Cited by 53 publications

(49 citation statements)

References 20 publications

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“…On the other hand, most recent publications (Millioni et al 2010a;Silva et al 2010;Dowsey et al 2006;Grove et al 2006;Berth et al 2007;Clark and Gutstein 2008) are in agreement in concluding that 4th generation pixel-based software workflow (Fig. 1) has some advantages over spotbased software, as explained in the following sections.…”

Section: Software-related Variabilitysupporting

confidence: 73%

Operator- and software-related post-experimental variability and source of error in 2-DE analysis

et al. 2011

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In the field of proteomics, several approaches have been developed for separating proteins and analyzing their differential relative abundance. One of the oldest, yet still widely used, is 2-DE. Despite the continuous advance of new methods, which are less demanding from a technical standpoint, 2-DE is still compelling and has a lot of potential for improvement. The overall variability which affects 2-DE includes biological, experimental, and post-experimental (software-related) variance. It is important to highlight how much of the total variability of this technique is due to post-experimental variability, which, so far, has been largely neglected. In this short review, we have focused on this topic and explained that post-experimental variability and source of error can be further divided into those which are software-dependent and those which are operator-dependent. We discuss these issues in detail, offering suggestions for reducing errors that may affect the quality of results, summarizing the advantages and drawbacks of each approach.

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Section: Software-related Variabilitysupporting

confidence: 73%

Operator- and software-related post-experimental variability and source of error in 2-DE analysis

et al. 2011

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“…Matching 2-DE patterns of gliadins from different varieties, therefore, is a challenging matter, as there is a risk that a large proportion or in fact all the proteins may be different. This is also the reason why we set zeros when proteins were not detected, although there is a risk that failure to detect proteins may result from mismatch of proteins rather than the absence of proteins [25]. Despite these challenges, the different strategies were able to unravel the same underlying pattern in the data.…”

Section: Discussionmentioning

confidence: 99%

Multivariate analysis of 2‐DE protein patterns – Practical approaches

Jacobsen

Grove

Jensen³

et al. 2007

Electrophoresis

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Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.

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“…The pitfalls of erroneously estimated spot volumes and inserted missing values are lately well described by Faergestad et al [9]. The number of spots containing a missing value in a match set can be quite large, as shown in a recent study by Grove et al [10]. It was found that as much as 80% of the spots collected in the match table for analysis are subject to missing values.…”

Section: Introductionmentioning

confidence: 90%

A new method for assigning common spot boundaries for multiple gels in two‐dimensional gel electrophoresis

Rye

Færgestad²,

Alsberg³

2008

Electrophoresis

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The benefits of defining common spot boundaries when several gels from 2-DE are compared and analyzed have lately been stressed by both commercial software producers and users of this software. Though the importance of common spot boundaries is clearly stated, few reports exist that target this issue explicitly. In this study a method for defining common spots boundaries is developed, called the spot density method. The method consists of the following steps: segmentation and spot identification on each individual gel, transferring the spot-center coordinates for all gels onto a single new gel, collecting spot centers clustered together in the new gel and finally assigning pixels and new spot boundaries based on the spots in each cluster. The method is compared to a synthetic gel approach, and validated by visual inspection of three representative areas in the gels. The gel images need to be aligned prior to segmentation and spot identification, but the method can be used regardless of the choice of segmentation procedure. This makes the method an easy extension to existing methods for spot identification and matching. Conclusions based on the visual inspection are that the spot density method identifies partly overlapping spots and low-intensity spots better than the synthetic gel approach.

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Challenges Related to Analysis of Protein Spot Volumes from Two-Dimensional Gel Electrophoresis As Revealed by Replicate Gels

Cited by 53 publications

References 20 publications

Operator- and software-related post-experimental variability and source of error in 2-DE analysis

Operator- and software-related post-experimental variability and source of error in 2-DE analysis

Multivariate analysis of 2‐DE protein patterns – Practical approaches

A new method for assigning common spot boundaries for multiple gels in two‐dimensional gel electrophoresis

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