高通量蛋白质组学流程中二维凝胶电泳的自动图像对齐

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

作者信息

Dowsey Andrew W, Dunn Michael J, Yang Guang-Zhong

机构信息

Institute of Biomedical Engineering, Imperial College London, United Kingdom.

出版信息

Bioinformatics. 2008 Apr 1;24(7):950-7. doi: 10.1093/bioinformatics/btn059. Epub 2008 Feb 28.

DOI:10.1093/bioinformatics/btn059

PMID:18310057

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/.

摘要

动机

近年来，对高通量蛋白质组学的探索揭示了一些挑战。虽然在液相色谱和质谱联用（LC/MS），即所谓的“鸟枪法”蛋白质组学的自动化蛋白质分离方面已取得了显著进展，但诸如人类蛋白质组组织（HUPO）脑蛋白质组计划等大规模开放项目表明，只有当LC/MS辅以二维凝胶电泳（2-DE）研究时，才能实现最大程度的蛋白质组覆盖。此外，这两种分离方法都需要自动比对和差异分析，以缓解生物信息学瓶颈，从而使高通量蛋白质生物标志物的发现成为现实。本文的目的是描述一个全自动图像比对框架，用于将2-DE整合到高通量差异表达蛋白质组学流程中。

结果

所提出的方法基于稳健的自动图像归一化（RAIN），以规避传统方法的缺点。传统方法在分析的早期阶段使用符号表示，由于建模和比对不准确而引入了持续的误差。在RAIN中，一个三阶体积不变B样条模型被纳入多分辨率模式，以校正多个尺度上的几何和表达不均匀性。然后可以在图像域中直接比较归一化后的图像，进行定量差异分析。通过与现有最先进方法在真实和合成扭曲的二维凝胶上进行评估，所提出的分析框架在匹配精度和差异灵敏度方面有显著提高。通过加速的通用图形处理器（GPGPU）实现建立了高通量分析。