Image analysis tools and emerging algorithms for expression proteomics.
Image analysis tools and emerging algorithms for expression proteomics.
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DOI：
10.1002/pmic.200900635
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发表时间：
2010-12
期刊：
影响因子：
3.4
通讯作者：
Dunn MJ
中科院分区：
文献类型：
--
作者：
Dowsey AW;English JA;Lisacek F;Morris JS;Yang GZ;Dunn MJ
自从 20 世纪 70 年代学术界兴起以来，计算分析工具已经成熟为许多成熟的商业软件包，支撑着表达蛋白质组学的研究。在本文中，我们描述了已建立的蛋白质分离二维凝胶电泳 (2-DE) 技术的图像分析流程，并首先介绍了质谱 (MS) 的信号分析，我们还解释了新兴的液相色谱与 MS (LC/MS) 耦合的高通量“鸟枪”蛋白质组学平台的当前图像分析工作流程。说明和比较了这两种方法的生物信息学挑战，同时从用户和技术的角度描述了现有的商业和学术包及其工作流程。在寻找差异表达时，人们注意到对结果量化进行合理统计处理的重要性。尽管蛋白质组学软件已广泛使用，但在算法准确性、客观性和自动化方面仍有许多挑战需要克服，这通常是由于确定性的以点为中心的方法在流程早期丢弃信息，传播错误。我们回顾了 2-DE、MS、LC/MS 和成像 MS 中信号和图像分析算法的最新进展。特别关注小波技术、2-DE 中基于图像的自动对齐和差异分析、MS 中的贝叶斯峰值混合模型和功能混合建模以及 LC/MS 的分组一致性对齐方法。
Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-D Gel Electrophoresis (2-DE) technique of protein separation, and by first covering signal analysis for Mass Spectrometry (MS), we also explain the current image analysis workflow for the emerging high-throughput ‘shotgun’ proteomics platform of Liquid Chromatography coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whilst existing commercial and academic packages and their workflows are described from both a user’s and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.
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