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Bioinformatics Advance Access originally published online on February 28, 2008
Bioinformatics 2008 24(7):950-957; doi:10.1093/bioinformatics/btn059
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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.

Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline

Andrew W. Dowsey 1, Michael J. Dunn 2 and Guang-Zhong Yang 1,*

1Institute of Biomedical Engineering, Imperial College London, United Kingdom and 2UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Ireland

*To whom correspondence should be addressed.


  Abstract

Motivation: The quest for high-throughput proteomics has revealed a number of challenges in recent years. Whilst substantial improvements in automated protein separation with liquid chromatography and mass spectrometry (LC/MS), aka ‘shotgun’ proteomics, have been achieved, large-scale open initiatives such as the Human Proteome Organization (HUPO) Brain Proteome Project have shown that maximal proteome coverage is only possible when LC/MS is complemented by 2D gel electrophoresis (2-DE) studies. Moreover, both separation methods require automated alignment and differential analysis to relieve the bioinformatics bottleneck and so make high-throughput protein biomarker discovery a reality. The purpose of this article is to describe a fully automatic image alignment framework for the integration of 2-DE into a high-throughput differential expression proteomics pipeline.

Results: The proposed method is based on robust automated image normalization (RAIN) to circumvent the drawbacks of traditional approaches. These use symbolic representation at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in modelling and alignment. In RAIN, a third-order volume-invariant B-spline model is incorporated into a multi-resolution schema to correct for geometric and expression inhomogeneity at multiple scales. The normalized images can then be compared directly in the image domain for quantitative differential analysis. Through evaluation against an existing state-of-the-art method on real and synthetically warped 2D gels, the proposed analysis framework demonstrates substantial improvements in matching accuracy and differential sensitivity. High-throughput analysis is established through an accelerated GPGPU (general purpose computation on graphics cards) implementation.

Availability: Supplementary material, software and images used in the validation are available at http://www.proteomegrid.org/rain/

Contact: g.z.yang{at}imperial.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on September 11, 2007; revised on February 8, 2008; accepted on February 11, 2008

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