Metabonomics data processing method and device based on stable isotope labeling

Abstract

The invention discloses a metabonomics data processing method and device based on stable isotope labeling. The method specifically comprises the following steps: acquiring isotope information of a first annotation metabolite in a non-labeled sample; based on the isotope information, determining a reference isotope peak and a to-be-adjusted isotope peak in the isotope peaks of the to-be-analyzed sample according to a preset division condition; according to the chromatographic retention time and the peak boundary corresponding to the peak top point of the reference isotope, adjusting the chromatographic retention time and the peak boundary corresponding to the peak top point of the isotope to be adjusted to obtain a target isotope peak; respectively calculating a first isotope peak abundance value of the reference isotope peak and a second isotope peak abundance value of the target isotope peak; and correcting the first isotope peak abundance value and the second isotope peak abundance value through a natural isotope correction algorithm to obtain an isotope labeling result of the to-be-analyzed sample. Therefore, the marked condition of each metabolite can be accurately obtained, so that comprehensive dynamic metabolism analysis on the metabolome in the organism is realized.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a method and device for processing metabolomics data based on stable isotope labeling. Background technique [0002] In related technologies, the methods of processing stable isotope-labeled metabolomics data mainly include targeted extraction and non-targeted traversal search. The former targeted extraction strategy is usually aimed at specific metabolites, with relatively small coverage and low extraction efficiency. The latter method of non-targeted traversal search, although it can find all the mass spectrum peaks that have significant changes in the unlabeled sample and the labeled sample, and then infer the monoisotopic peak according to the theoretical mass number, but this usually leads to higher False positive rate, that is, the discovery of labeled peaks and the judgment of their corresponding monoisotopic peaks is often inaccurate, resulting in the i...

AI Technical Summary

Problems solved by technology

The latter method of non-targeted traversal search, although it can find all the mass spectrum peaks that have significant changes in the unlabeled sample and the labeled sample, and then infer the monoisotopic peak according to the theoretical mass number, but this usually leads to higher False positive rate, that is, the discovery of labeled peaks and the judgment of their corresponding monoisotopic peaks are often inaccurate, resulting in the inability to accurately obtain the labeling of each metabolite, which affects the comprehensive labeling analysis of the metabolome of biological samples

Images