转录组
蛋白质组
计算生物学
分类
蛋白质组学
生物
信使核糖核酸
计算机科学
蛋白质表达
生物信息学
基因表达
基因
人工智能
遗传学
作者
Saad Haider,Ranadip Pal
出处
期刊:Current Genomics
[Bentham Science]
日期:2013-02-01
卷期号:14 (2): 91-110
被引量:368
标识
DOI:10.2174/1389202911314020003
摘要
Until recently, understanding the regulatory behavior of cells has been pursued through independent analysis of the transcriptome or the proteome. Based on the central dogma, it was generally assumed that there exist a direct correspondence between mRNA transcripts and generated protein expressions. However, recent studies have shown that the correlation between mRNA and Protein expressions can be low due to various factors such as different half lives and post transcription machinery. Thus, a joint analysis of the transcriptomic and proteomic data can provide useful insights that may not be deciphered from individual analysis of mRNA or protein expressions. This article reviews the existing major approaches for joint analysis of transcriptomic and proteomic data. We categorize the different approaches into eight main categories based on the initial algorithm and final analysis goal. We further present analogies with other domains and discuss the existing research problems in this area.
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