代谢组
Introduction
If genomics and proteomics in systems biology are compared to the science of exploring the causes of biological phenomena, metabolomics can be understood as the science of exploring the results of biological phenomena, that is, the science of directly studying the change law and function of all metabolites regulated by genes and proteins. On the one hand, metabonomics research can explore some new molecular markers. On the other hand, through linkage analysis with proteomics and genomics data, it can analyze the internal changes of organisms from both causes and results, so as to push the research of system biology to a higher level.
1 technical characteristics
1.1 GC-MS (gas chromatography)
GC-MS is a classic technology in metabolomics research. It has the characteristics of mature and stable technology and high sensitivity. At the same time, due to its relatively perfect database, the qualitative analysis is more accurate and reliable. The disadvantages are mainly that the sample processing is relatively complex and it is difficult to determine the qualitative and quantitative analysis of substances that are not easy to be derived.
1.2 LC MS (liquid chromatography)
The advantages mainly lie in simple sample preparation and pretreatment, good experimental repeatability, high resolution, wide separation and analysis range, while the disadvantages mainly lie in insufficient database perfection and relatively difficult qualitative analysis.
1.3 NMR (nuclear magnetic resonance)
The biggest advantage is that it is non-destructive to the sample and non biased in the determination, that is, it is suitable for liquid samples such as blood, urine and body fluids, as well as solid samples such as tissues and organs. It has fast determination speed and can realize the dynamic monitoring of sample metabolome. The main disadvantage is low resolution.
2 Data analysis characteristics
2.1 PCA (principal component) statistical analysis
Principal component analysis is one of the commonly used analysis methods for metabolomic data. Through principal component analysis, we can understand the weight of different metabolites in distinguishing different samples, which is of great significance and value for the screening of different metabolites, especially important molecular markers.
2.2 PLSA Da (partial least squares discriminant analysis) statistical analysis
Partial least squares discriminant analysis (PLS DA) is an important method of pattern recognition. This method can extract the simplified information from complex data to the greatest extent for analysis.
2.3 Metabolite function analysis
Based on the screening of differential metabolites, the ultimate goal of metabolomics is to analyze the biological significance of metabolites. We can have a deeper understanding of the function and role of metabolites in organisms through statistical analysis such as PCA and clustering combined with pathway backtracking, bioinformatics analysis such as metabolites and protein / gene interaction.
企业名称
五山(广州)生物医药料技有限公司
企业信息已认证
企业类型
有限责任公司(自然人投资或控股)
信用代码
91440101MA9Y8PDK2J
成立日期
2021-12-20
注册资本
壹佰捌拾伍万陆仟叁佰元(人民币)
经营范围
专业技术服务业(具体经营项目请登录国家企业信用信息公示系统查询,网址:http://www.gsxl.gov.cn/,依法须经批准的项目,经相关部门批准后方可开展经营活动)
五山(广州)生物医药科技有限公司
公司地址
广州市越秀区农林下路81号新裕大厦18C
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