A multi-species gc-ms endogenous metabolite database and its establishment method

Abstract

The invention discloses a method for establishing a multi-species GC-MS endogenous metabolite database. The method comprises the following steps: 1) searching GCMS data obtained after derivatization of multi-species samples based on an NIST library, and reserving substances with scores of 700 or above as screened high-score substances; 2) extracting mass spectrum information of the high-score substances screened in the step 1), and establishing a high-score NIST library; 3) translating names with derivatization groups in the high-score NIST library and replacing the names with names before derivatization to obtain a high-score library; and 4) combining the high-score library with an amplified background noise library and an amplified standard substance database to obtain a multi-species GC-MS endogenous metabolite database. The database provided by the invention can meet the retrieval requirements of multiple types of biological samples such as plants, animals and microorganisms, and can also realize more accurate qualitative determination so as to obtain more metabolites.

Description

technical field [0001] The invention belongs to the field of biological databases, in particular to a multi-species GC-MS endogenous metabolite database and its establishment method. Background technique [0002] As one of the most commonly used analysis methods in metabolomics research, GCMS technology is usually used to analyze some highly volatile small molecule metabolites, for some such as amino acids, sugar alcohols, organic acids, biogenic amines and organic phosphates Metabolites with smaller molecular weight and higher polarity need to be derivatized (silanized or esterified, etc.) to reduce their boiling point and increase their thermal stability before they can be analyzed by GCMS. In the GCMS analysis process, the most important step is the qualitative analysis of metabolites, and the accuracy and quantity of the qualitative depends on the database. [0003] Currently, GCMS commonly used databases include NIST library, Fiehn library, etc. The NIST standard mass...

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Problems solved by technology

Compared with the NIST library, the Fiehn library is small and precise, and the names of the substances identified are already the names before derivation, but it still has certain limitations. The Fiehn library is mainly based on animal source samples. Metabolite profiles of other samples such as plants and microorganisms, so it is not suitable for qualitative analysis of other samples such as plants or microorganisms

[0004] Therefore, the two most widely used databases, the NIST library and the Fiehn library, have certain limitations. The NIST library is very large and complicated. However, although the Fiehn library has higher accuracy than the NIST library, and the substance name is already the name after the backtracking, it also has certain limitations. If it is mainly aimed at animal endogenous substances, it involves fewer types of species and covers only a few hundred kinds of substances

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