Ruggles Kelly V, Krug Karsten, Wang Xiaojing, Clauser Karl R, Wang Jing, Payne Samuel H, Fenyö David, Zhang Bing, Mani D R
From the ‡Department of Medicine, New York University School of Medicine, New York, New York 10016.
§The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142.
Mol Cell Proteomics. 2017 Jun;16(6):959-981. doi: 10.1074/mcp.MR117.000024. Epub 2017 Apr 29.
With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.
随着高通量下一代测序和基于深度质谱的蛋白质组学技术的联合进步,蛋白质基因组学,即蛋白质组学和基因组学数据的综合分析,已成为一个新的研究领域。该领域早期的工作重点是利用样本特异性基因组和转录组测序数据改进蛋白质鉴定。最近,对基因组学和蛋白质组学研究的定量测量进行综合分析,已经在基因表达调控、细胞信号传导和疾病方面获得了新的见解。已经开发或改编了许多方法和工具,以实现一系列综合蛋白质基因组学方法,在本文中,我们将已发表的方法和工具系统地分为四大类:(1)以序列为中心的蛋白质基因组学;(2)蛋白质基因组学关系分析;(3)蛋白质基因组学数据的综合建模;(4)数据共享和可视化。我们对每一类中的方法和可用工具进行了全面综述,并突出了它们的典型应用。