Bioinformatics: the next frontier of metabolomics.
Bioinformatics: the next frontier of metabolomics.
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DOI：
10.1021/ac5040693
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发表时间：
2015-01-06
影响因子：
7.4
通讯作者：
Siuzdak, Gary
中科院分区：
文献类型：
--
作者：
Johnson, Caroline H.;Ivanisevic, Julijana;Benton, H. Paul;Siuzdak, Gary
需要生物信息工具来执行统计分析和数据库功能等基本功能。现在，最困难的任务之一也需要它们，帮助研究人员确定哪些代谢物最具生物学意义。这可以通过帮助识别过程、减少特征冗余、为串联质谱 (MS/MS) 提出更好的候选物、加速或自动化工作流程、通过荟萃分析或多组分析对特征列表进行去卷积或使用稳定同位素和路径图谱来实现。因此，本综述重点关注识别代谢物的最新和创新的生物信息学进展。除了生物标志物发现之外，代谢组学的主要目标是确定与疾病发病机制或其他代谢扰动相关的最有意义的代谢物。代谢物在生物途径中发挥重要作用；它们的通量或差异调节（失调）可以揭示对疾病和环境影响的新见解。因此，代谢组学分析最重要的目标之一是分配代谢物特性，以便它们可用于进一步的统计和知情途径分析。 1, 2 在过去几年中，通过非靶向或靶向代谢组学分析代谢物的技术已经取得了广泛的改进。为实验设计、样品提取技术和数据采集建立最有效的协议的努力已经得到回报，提供了强大的复杂数据集。 3− 9 随着对这些数据集的需求越来越多，例如为特征分配身份和生物学意义，生物信息学是目前最需要增长的代谢组学领域。通常情况下，代谢组学分析会产生一系列对所研究的疾病或刺激物特异性较低的代谢物（图 1）。其中一些代谢物似乎在多种疾病中失调，例如酰基肉碱 10−13 和脂肪酸。 14−17 它们可能更能表明受干扰的全身原因（食欲、体力活动、昼夜节律变化等）、样本污染或仪器/生物信息噪音，而不是疾病的特定生物标志物。这方面的一个例子可以在电离辐射尿液生物标志物的分析中看到，其中在辐射暴露后，大鼠体内的二羧酸含量下调。事实证明，这一观察结果实际上是由辐射暴露扰乱β-氧化途径后食欲下降引起的，而不是由辐射引起的细胞变化引起的。 18, 19 此外，二羧酸可能在提取过程中从塑料中浸出，进一步增加了它们在电离辐射中的作用的模糊性。 20 除了确定生物标志物的正确来源外，确定其生理作用以及如何将其用作治疗靶点也很重要。这首先必须从代谢物的识别开始，并通过过滤用户设置的固有偏差阈值来确定。这些阈值包括倍数变化和 p 值的阈值，它们高度依赖于实验；体外
Bioinformatic tools are required to carry out essential functions such as statistical analyses and database functionalities. Now, they are also needed for one of the most difficult tasks, helping researchers decide which metabolites are the most biologically meaningful. This can be achieved through aiding the identification process, reducing feature redundancy, putting forward better candidates for tandem mass spectrometry (MS/MS), speeding up or automating the workflow, deconvolving the feature list through meta-analysis or multigroup analysis, or using stable isotopes and pathway mapping. This review thus focuses on the most recent and innovative bioinformatic advancements for identifying metabolites. A primary objective of metabolomics beyond biomarker discovery is to identify the most meaningful metabolites that correlate with disease pathogenesis or other perturbations of metabolism. Metabolites play important roles in biological pathways; their flux or differential regulation (dysregulation) can reveal novel insights into disease and environmental influences. Therefore, one of the most important goals of metabolomic analysis has been to assign metabolite identity so they can be used for further statistical and informed pathway analysis. 1, 2 Over the past few years, technologies for analyzing metabolites by untargeted or targeted metabolomics have undergone extensive improvements. Strides to establish the most efficient protocols for experimental design, sample extraction techniques, and data acquisition have paid off providing robust complex data sets. 3− 9 As more is being required of these data sets such as assigning identity and biological meaning to the features, bioinformatics is the area of metabolomics which is currently undergoing the most needed growth. It is often the case that metabolomic analysis results in a list of metabolites with low specificity for the disease or stimulus being studied (Figure 1). Some of these metabolites seem to be dysregulated in a variety of diseases such as acylcarnitines 10− 13 and fatty acids. 14− 17 They may be more indicative of a perturbed systemic cause (appetite, physical activity, diurnal rhythm changes, etc..), sample contamination, or instrumental/bioinformatic noise, rather than a specific biomarker of disease. An example of this can be seen in the analysis of urinary biomarkers of ionizing radiation, where dicarboxylic acids were downregulated in the rat after radiation exposure. It was proven that this observation was actually caused by a decreased appetite after radiation exposure perturbing the β-oxidation pathway and not from radiation-induced cellular changes. 18, 19 Furthermore, dicarboxylic acids can leach out from plastics during the extraction process, further adding to the ambiguity of their role in ionizing radiation. 20 As well as identifying the correct source of the biomarkers, it is also important to identify their physiological role and how to utilize them as therapeutic targets. This first has to start with the identification of the metabolite and is determined by filtering thresholds set by the user which is intrinsically biased. These thresholds include those for fold change and p-value, which are highly dependent on the experiment; in vitro
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DOI：
10.1038/nrmicro1949
发表时间：
2009-02
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1093/bioinformatics/btu370
发表时间：
2014-10
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Daly R;Rogers S;Wandy J;Jankevics A;Burgess KE;Breitling R
通讯作者：
Breitling R
影响因子：
7.4
作者：
Broeckling, C. D.;Afsar, F. A.;Prenni, J. E.
通讯作者：
Prenni, J. E.
影响因子：
4.7
作者：
Bi, Hui-Chang;Pan, Yu-Zhuo;Yu, Ai-Ming
通讯作者：
Yu, Ai-Ming
DOI：
10.1073/pnas.0610772104
发表时间：
2007-02-06
影响因子：
11.1
作者：
Duarte, Natalie C.;Becker, Scott A.;Palsson, Bernhard O.
通讯作者：
Palsson, Bernhard O.