Abstract:
BACKGROUND:Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function. RESULTS:In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aβ peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels. CONCLUSION:Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods.The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Brauner JM,Groemer TW,Stroebel A,Grosse-Holz S,Oberstein T,Wiltfang J,Kornhuber J,Maler JMdoi
10.1186/1471-2105-15-181subject
Has Abstractpub_date
2014-06-11 00:00:00pages
181issn
1471-2105pii
1471-2105-15-181journal_volume
15pub_type
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