Dynamic observation device based on time sequence metagenome data and detection method thereof

Abstract

The invention discloses a dynamic observation device based on time sequence metagenome data and a detection method of the dynamic observation device, comprising the following steps of: collecting human excrement samples of the same individual at different time, and performing gene extraction and sequencing on each excrement sample to obtain intestinal flora information; after pretreatment, calculating the strain similarity, the correlation and the usual time to obtain a flora interaction relationship; furthermore, carrying out clustering and dimensionality reduction on the flora, obtaining categories and dimensionality reduction positions, drawing a flora interaction network diagram in combination with the interaction relationship, and identifying key strains, so as to achieve intestinal flora interaction network detection. Help and reference are provided for personalized intervention on the intestinal flora, improvement of human health and disease intervention.

Description

technical field [0001] The invention relates to the technical field of biological detection, in particular to a dynamic observation device based on time-series metagenomic data and a detection method thereof. Background technique [0002] With the advancement of next-generation sequencing technology and lower prices, metagenomic research has also developed rapidly. More and more studies have found that gut flora is associated with a variety of human health conditions, including gastrointestinal diseases (colorectal cancer, irritable bowel syndrome, etc.), metabolic diseases (obesity, type 2 diabetes, etc.), mental diseases (depression disease, etc.) and so on. Although more and more evidences have found that the intestinal flora is related to human health, due to the many factors affecting the intestinal flora, the reproducibility of the research results on the intestinal flora is not good, and the level of evidence is low. Therefore, in order to improve this situation, mo...

AI Technical Summary

Problems solved by technology

However, there are still some deficiencies in the existing interaction network construction: 1. Most studies only consider the relationship between the flora at a certain time point, and even longitudinal studies only use the abundance information at a certain time point to construct the interaction The network does not make full use of all the information at multiple time points in the time series, and the abundance of the flora is prone to fluctuations. Only based on information at a certain time point, false associations are prone to occur

2. Although a small number of studies have used information from multiple time points, they lack consideration of sequence translation similarity and sequence context

3. Most of the existing studies focus on the visualization of the interaction network to show the interaction relationship, without considering the distance relationship between the overall bacterial population and the bacterial species, that is, the distance between the two bacterial species in the figure is for the convenience of display, not the two real distance

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