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Method for mining relevance of species in micro-organisms

A technology of microbiota and association, applied in the field of bioinformatics, can solve the problems of complex microbial network, large amount of metagenomics data, insufficient to restore the true relationship of bacterial communities, etc., to achieve the effect of deepening understanding and ensuring comprehensiveness

Pending Publication Date: 2019-08-16
EZHOU INST OF IND TECH HUAZHONG UNIV OF SCI & TECH +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the current lack of understanding of community structure and the increasing amount of metagenomics data, traditional network analysis is not enough to recover the true relationship in bacterial communities (Weiss, S., et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. The ISME journal 2016; 10(7):1669-1681.)
In microbial communities, the relationship between two species is influenced in a variety of ways (e.g. overlapping ecological niches, two species are simultaneously influenced by a third species), resulting in an unusually complex microbial network
Therefore, identifying complex and important connections (for example, exploring the cycling of substances or elements in bacterial communities) is increasingly challenging for traditional methods

Method used

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  • Method for mining relevance of species in micro-organisms

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] Embodiment 1 FS-Weight method

[0034] (1) Sampling and sequencing

[0035] In the MG-Rast database, a healthy Chinese youth human gut data set numbered MGP 15838 was obtained (this data set contains 16s rRNA samples of intestinal flora of 314 healthy people, covering 8 ethnic groups in 7 provinces of China samples), use the species annotation software QIIME (version 1.91) for species annotation and relative abundance calculation, count the obtained species to the genus level, and take the genera with an average abundance greater than 0.01% at the genus level for network construction.

[0036] DNA was extracted, and 5,102,015 high-quality metagenomic sequences were generated by high-throughput sequencing. The quality control software Mothur was used to control the quality of the sequences, and 24,125 microbial OTUs were obtained, and the species annotation and relative abundance were performed using the species annotation software QIIME (version 1.91). degree calculati...

Embodiment 2

[0054] Embodiment 2PCA-PMI method

[0055] (1) Sampling and sequencing

[0056] Step is with embodiment 1;

[0057] (2) Loose Definition algorithm to process data

[0058] Step is with embodiment 1

[0059] (3) Relevance Mining

[0060] Using the concept of entropy in information theory, the partial mutual information (PCA-PMI) algorithm adjusted by the road consistency algorithm is used to calculate the linear and nonlinear correlation of the network.

[0061] The steps to mine linear and nonlinear relationships are divided into 3 steps:

[0062] 1) Calculate the entropy between any two species using mutual information;

[0063] 2) On the basis of mutual information, for any two species, consider other species connected to the two species, and calculate the partial mutual information of the two species;

[0064] 3) On the basis of the mutual information threshold (80%) given by the user, the road consistency algorithm is used to screen the mutual information. The road ...

Embodiment 3

[0068] Embodiment 3 Loose Definition method

[0069] (1) Sampling and sequencing

[0070] Step is with embodiment 1;

[0071] (2) Loose Definition algorithm to process data

[0072] Step is with embodiment 1

[0073] (3) Relevance Mining

[0074] Co-occurrence networks were constructed using the traditional Pearson algorithm. The Pearson correlation coefficient is a linear correlation coefficient, which is used to reflect the statistics of the degree of linear correlation between two variables. The larger the absolute value of the Pearson correlation coefficient, the stronger the correlation.

[0075] (4) Calculation and detection

[0076] For the constructed network, the cytoscape software was used for visualization, and the MCODE clustering algorithm was used to detect potential clusters; cluster members were aligned according to taxonomic or functional annotation databases; the most connected nodes were calculated as candidates for hub nodes, and these nodes were sele...

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Abstract

The invention provides a method for mining relevance of species in micro-organisms, wherein the method comprises the steps of firstly, performing high-throughput sequencing on the DNA of the micro-organisms, calculating the sequencing data for obtaining species composition and abundance distribution; then, enlarging an inter-species relevance candidate range through a Loosen Definition method; andmining an indirect relation, a linear relation and a non-linear relation. Compared with the prior art, the method has advantages of well reproducing a micro-organism network for facilitating researching of a micro-organism co-occurrence network in a system level, and making a preparation for finding unknown relevance between key species.

Description

technical field [0001] The invention relates to the technical field of biological information, in particular to a method and device for mining the association of species in a microbial group. Background technique [0002] Microorganisms live in every corner of the biosphere, including soil, oceans, hot springs, and more. They are very adaptable and live together as a pack no matter where they are. Microorganisms are key to biogeochemical cycles in ecosystems and have important applications in biotechnology such as traditional food, beverage preparation, and modern technologies based on genetic engineering. Microbial research has undergone a transformation in the past decade. Metagenomics, the genomic analysis of a population of microbes, has emerged as a powerful approach. We no longer mainly focus on monoculture microorganisms, but pay more attention to microbial communities, focusing on the analysis of microbial species composition, functions, and interactions between s...

Claims

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Application Information

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IPC IPC(8): G16B40/00
CPCG16B40/00
Inventor 宁康杨朋硕余少俊韩毛振
Owner EZHOU INST OF IND TECH HUAZHONG UNIV OF SCI & TECH
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