Hernandez-Valladares Maria, Vaudel Marc, Selheim Frode, Berven Frode, Bruserud Øystein
a Department of Clinical Science, Faculty of Medicine and Dentistry , University of Bergen , Bergen , Norway.
b Proteomics Unit, Department of Biomedicine, Faculty of Medicine and Dentistry , University of Bergen , Bergen , Norway.
Expert Rev Proteomics. 2017 Aug;14(8):649-663. doi: 10.1080/14789450.2017.1352474. Epub 2017 Jul 27.
Mass spectrometry (MS)-based proteomics has become an indispensable tool for the characterization of the proteome and its post-translational modifications (PTM). In addition to standard protein sequence databases, proteogenomics strategies search the spectral data against the theoretical spectra obtained from customized protein sequence databases. Up to date, there are no published proteogenomics studies on acute myeloid leukemia (AML) samples. Areas covered: Proteogenomics involves the understanding of genomic and proteomic data. The intersection of both datatypes requires advanced bioinformatics skills. A standard proteogenomics workflow that could be used for the study of AML samples is described. The generation of customized protein sequence databases as well as bioinformatics tools and pipelines commonly used in proteogenomics are discussed in detail. Expert commentary: Drawing on evidence from recent cancer proteogenomics studies and taking into account the public availability of AML genomic data, the interpretation of present and future MS-based AML proteomic data using AML-specific protein sequence databases could discover new biological mechanisms and targets in AML. However, proteogenomics workflows including bioinformatics guidelines can be challenging for the wide AML research community. It is expected that further automation and simplification of the bioinformatics procedures might attract AML investigators to adopt the proteogenomics strategy.
基于质谱(MS)的蛋白质组学已成为表征蛋白质组及其翻译后修饰(PTM)不可或缺的工具。除了标准蛋白质序列数据库外,蛋白质基因组学策略还将光谱数据与从定制蛋白质序列数据库获得的理论光谱进行比对。截至目前,尚无关于急性髓系白血病(AML)样本的蛋白质基因组学研究发表。涵盖领域:蛋白质基因组学涉及对基因组和蛋白质组数据的理解。两种数据类型的交叉需要先进的生物信息学技能。本文描述了一种可用于AML样本研究的标准蛋白质基因组学工作流程。详细讨论了定制蛋白质序列数据库的生成以及蛋白质基因组学中常用的生物信息学工具和流程。专家评论:借鉴近期癌症蛋白质基因组学研究的证据,并考虑到AML基因组数据的公开可得性,使用AML特异性蛋白质序列数据库对当前和未来基于MS的AML蛋白质组学数据进行解读,可能会发现AML新的生物学机制和靶点。然而,包括生物信息学指南在内的蛋白质基因组学工作流程对广大AML研究群体而言可能具有挑战性。预计生物信息学程序的进一步自动化和简化可能会吸引AML研究人员采用蛋白质基因组学策略。