首页> 外文期刊>Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal Science and Biotechnologies >Bioinformatics Tools for Metabolomic Data Processing and Analysis Using Untargeted Liquid Chromatography Coupled With Mass Spectrometry
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Bioinformatics Tools for Metabolomic Data Processing and Analysis Using Untargeted Liquid Chromatography Coupled With Mass Spectrometry

机译:生物信息学工具,用于无目标液相色谱与质谱联用的代谢组学数据处理和分析

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?? Metabolomics is an important a€?omicsa€? technology, complementary to genomics and proteomics, as parts of systems biology, giving information (qualitative fingerprints and quantitative profiling) as a mirror of cell and extracellular metabolic activity. A cohort of small metabolites are involved in the control and regulation of cellular functions, as intermediates or final products, their presence or levels being useful for the early diagnosis of different pathologies. Bioinformatics tools are mandatory for a future a€?computationala€? metabolomics, needed to manage large number of experimentally acquired data obtained from biological samples (plants, animal or human tissues). This review presents updated information about different high-throughput analytical techniques and data acquisition?? software (1-2), the pre-processing of data, converted to specific matrices, further processed by specific normalization and alignment?? procedures (3), then analysed by statistical univariate and multivariate chemometric and /or statistical techniques (4), identifying biomarkers by comparison with databases (5), and finally elucidating the networks and pathways (6). New software is available for data conversion, pre-processing, alignment algorithms, bucketing, normalization, underlying the challenges and comparisons with international data bases. Finally, the accurate identification of individual molecules as biomarkers, either evaluated by untargeted metabolomics techniques (Principal Component Analysis - PCA), Cluster Analysis - CA) or supervised ones (Partial Least Square Discriminant Analysis (PLS-DA) is presented. The accurate identification of metabolites and their involvement in metabolic networks and pathways became possible by well-established databases (HMDB, LIPID MAPS, KEGG, etc.), to validate all experimental data. Bioinformatics is a sine-qua-non tool, to be used and valorised by untargeted or targeted metabolomics, as an integrated technology in systems biology. ?? ?? ?
机译:??代谢组学是一个重要的组学方法作为系统生物学的一部分,作为基因组学和蛋白质组学的补充的技术,提供信息(定性指纹和定量分析)作为细胞和细胞外代谢活动的镜像。一组小代谢物作为中间产物或终产物参与细胞功能的控制和调节,它们的存在或水平对于早期诊断不同的病理是有用的。生物信息学工具对于将来的计算是必不可少的。代谢组学,需要管理从生物样品(植物,动物或人体组织)获得的大量实验获得的数据。这篇评论介绍了有关不同高通量分析技术和数据采集的最新信息?软件(1-2),对数据进行预处理,转换为特定的矩阵,然后通过特定的归一化和对齐进行进一步处理?程序(3),然后通过统计单变量和多元化学计量学和/或统计技术进行分析(4),通过与数据库比较来识别生物标志物(5),最后阐明网络和途径(6)。新软件可用于数据转换,预处理,对齐算法,存储,规范化,潜在挑战以及与国际数据库的比较。最后,提出了通过非靶向代谢组学技术(主成分分析-PCA),聚类分析-CA或有监督的(偏最小二乘判别分析(PLS-DA))评估的单个分子作为生物标记物的准确鉴定方法。完善的数据库(HMDB,LIPID MAPS,KEGG等)可以验证代谢物及其在代谢网络和途径中的作用,以验证所有实验数据。生物信息学是一种正弦准非工具,可以使用和评估通过非靶向或靶向代谢组学,作为系统生物学中的集成技术。

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