A. Lazar, Florina Romanciuc, M. Socaciu, C. Socaciu
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引用次数: 14
摘要
Â代谢组学是一项重要的 - œomicsâ -”技术,作为基因组学和蛋白质组学的补充,作为系统生物学的一部分,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最终产物参与细胞功能的控制和调节,它们的存在或水平对不同病理的早期诊断有用。生物信息学工具是未来 - œcomputationalâ代谢组学的必备工具,需要管理从生物样品(植物、动物或人体组织)中获得的大量实验数据。这篇综述介绍了不同高通量分析技术和数据acquisitionÂ软件(1-2)的最新信息,数据的预处理,转换为特定的矩阵,通过特定的归一化和alignmentÂ程序进一步处理(3),然后通过统计单变量和多变量化学计量学和/或统计技术进行分析(4),通过与数据库进行比较来识别生物标志物(5)。最后阐明网络和路径(6)。新的软件可用于数据转换,预处理,对齐算法,桶状,规范化,潜在的挑战和国际数据库的比较。最后,通过非靶向代谢组学技术(主成分分析- PCA),聚类分析- CA)或监督技术(偏最小二乘判别分析(PLS-DA)进行评估,准确识别单个分子作为生物标志物。通过完善的数据库(HMDB,脂质地图,KEGG等),可以准确识别代谢物及其参与代谢网络和途径,从而验证所有实验数据。生物信息学是一种必要的工具,作为系统生物学中的一项综合技术,可以被非靶向或靶向代谢组学使用和评价。Â Â Â
Bioinformatics tools for metabolomic data processing and analysis using untargeted liquid chromatography coupled with mass spectrometry.
 Metabolomics is an important “omics†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 “computational†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.   Â