Recent advances in high-throughput technologies enable quantitative monitoring of the abundance of various biological molecules and allow determination of their variation between biological states on a genomic scale. Two popular platforms are DNA microarrays that measure messenger RNA transcript levels, and gel-free proteomic analyses that quantify protein abundance. Obviously, no single approach can fully unravel the complexities of fundamental biology and it is equally clear that integrative analysis of multiple levels of gene expression would be valuable in this endeavor. However, most integrative transcriptomic and proteomic studies have thus far either failed to find a correlation or only observed a weak correlation. In addition to various biological factors, it is suggested that the poor correlation could be quite possibly due to the inadequacy of available statistical tools to compensate for biases in the data collection methodologies. To address this issue, attempts have recently been made to systematically investigate the correlation patterns between transcriptomic and proteomic datasets, and to develop sophisticated statistical tools to improve the chances of capturing a relationship. The goal of these efforts is to enhance understanding of the relationship between transcriptomes and proteomes so that integrative analyses may be utilized to reveal new biological insights that are not accessible through one-dimensional datasets. In this review, we outline some of the challenges associated with integrative analyses and present some preliminary statistical solutions. In addition, some new applications of integrated transcriptomic and proteomic analysis to the investigation of post-transcriptional regulation are also discussed.

中文翻译：

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转录组和蛋白质组学数据的综合分析：挑战，解决方案和应用。

高通量技术的最新进展使得可以定量监控各种生物分子的丰度，并可以确定它们在基因组规模上生物学状态之间的差异。两种流行的平台是测量信使RNA转录水平的DNA微阵列，以及定量蛋白质丰度的无凝胶蛋白质组学分析。显然，没有任何一种方法可以完全揭示基础生物学的复杂性，并且同样清楚的是，对多种水平的基因表达进行整合分析将在这项工作中有价值。然而，迄今为止，大多数整合转录组学和蛋白质组学研究要么未找到相关性，要么仅观察到了弱相关性。除了各种生物学因素之外，有人指出，相关性差的原因很可能是由于可用的统计工具不足以补偿数据收集方法中的偏差。为了解决此问题，最近已尝试系统地研究转录组和蛋白质组学数据集之间的相关模式，并开发复杂的统计工具以提高捕获关系的机会。这些努力的目的是增强对转录组和蛋白质组之间关系的理解，以便可以利用整合分析揭示一维数据集无法获得的新的生物学见解。在这篇综述中，我们概述了与综合分析相关的一些挑战，并提出了一些初步的统计解决方案。此外，