Data merging for integrated microarray and proteomic analysis
Data merging for integrated microarray and proteomic analysis
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DOI：
10.1093/bfgp/ell019
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发表时间：
2006-12-01
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影响因子：
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通讯作者：
Thrall, Brian D.
中科院分区：
文献类型：
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作者：
Waters, Katrina M.;Pounds, Joel G.;Thrall, Brian D.
即使是一个简单的生物系统，其功能也比其基因、蛋白质和代谢物的总和复杂得多。系统生物学的一个前提是分子谱分析将有助于发现和表征重要的疾病途径。然而，由于多个水平的效应通路调控似乎是常态而不是例外，高通量基因组学和蛋白质组学技术提出的一个重大挑战是提取复杂数据的生物学含义。因此，整合来自不同全球技术平台的异质类型数据是开发细胞和组织反应预测建模所需的必要基础数据库的第一个挑战。考虑到在定义基因表达和蛋白质丰度之间的对应关系方面存在明显的困难，我们如何理解这些数据并设计下一个实验？在这篇综述中，我们强调了与异构数据集集成和分析相关的当前方法和挑战，重点是通过高通量技术获得的全局分析。
The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.