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A method for biomolecular network analysis based on functional modules

A biological network and functional module technology, applied in the field of biomolecular networks such as protein-protein interaction networks or gene expression regulatory networks, can solve the problem of not considering the functional similarity of adjacent nodes, and achieve the effect of high functional correlation

Active Publication Date: 2017-02-15
艾吉泰康(嘉兴)生物科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The common problem of these methods is that when mining network modules, they are based on the topological properties of nodes in the network (degree, clustering coefficient, betweenness, etc.) without considering the functional similarity between adjacent nodes.

Method used

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  • A method for biomolecular network analysis based on functional modules
  • A method for biomolecular network analysis based on functional modules
  • A method for biomolecular network analysis based on functional modules

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Experimental program
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Embodiment 1

[0042] Embodiment 1. Taking the gene and protein interaction network composed of a group of gene chip expression data after administration and treatment of nifedipine in the hypertensive mouse model as an example, the method of the present invention is specifically described:

[0043] Integrating protein interaction data in protein interaction databases such as BIOGRID, INTACT, MINT, and NIA Mouse Protein-Protein InteractionDatabase, after removing duplicate data and self-interactions, a global mouse gene and protein interaction network was obtained. Due to the limited data of this kind, the interaction data of orthologous and paralogous proteins of mouse proteins in other model organisms were used to predict the interaction of these proteins in mice, and a total of 65,850 small Mouse protein interaction data.

[0044] By performing mean centered normalization on the gene chip data, the genes whose normalized expression value was greater than 1 were regarded as expressed genes...

Embodiment 2

[0058] Embodiment 2. Based on the 39,240 protein interactions verified by experiments provided in the human Human Protein Reference Database (HPRD) database, 3000 protein interaction relationships were randomly screened, and protein self-interactions were removed to obtain 1,478 protein interactions from 2,095 edges. A network G of protein nodes.

[0059] Step 1: Calculate the adjacency matrix M of the network G adj ,M adj It is a matrix of 1,478 rows and 1,478 columns, each row and each column represent a unique gene, if there is an interaction edge between two proteins in the random network G, the corresponding element in the matrix is ​​1, otherwise it is 0.

[0060] Step 2: Using R's GOSemSim software package, based on the biological process in Gene Ontology (GO), calculate the semantic similarity score M between 1,478 proteins sim ,M sim The median value of 0.307.

[0061] Step 3, calculate the function weight matrix M of the edge of the network G E , , " "Repres...

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Abstract

The invention belongs to the field of a biotechnology, and provides a comparison method based on a function module and used for biomolecular networks such as a genetic expression regulatory network or protein-protein interaction and the like. The method mainly comprises steps as follows: an adjacency matrix Madj of a biological network is built, a function similarity matrix Msim between network nodes is calculated, a function weight matrix ME of a network side is calculated according to the formula as follows: ME=Madj.*Msim, a network module is mined with a minimum graph entropy algorithm, and finally, function enrichment analysis is performed on the network module, wherein the symbols are defined in the instruction.

Description

technical field [0001] The invention belongs to the technical field of biological information. More specifically, the present invention relates to biomolecular networks such as protein-protein interaction networks or gene expression regulatory networks. Background technique [0002] In the past few decades, the research object of molecular biology is mainly a single tissue, cell or gene inside the organism, using the idea of ​​classical reductionism, so the research is inevitably localized. In fact, organisms are a complex system, and there are hierarchical correlations and interactions among biomolecules, and the phenomenon of life is not a static but a dynamic evolution process. Network analysis has become an important method in the study of modern biology and systems biology because of its systematic nature and the correlation among internal elements. [0003] With the rapid development of molecular biology experimental technologies such as biochips, high-throughput seq...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/12
Inventor 不公告发明人
Owner 艾吉泰康(嘉兴)生物科技有限公司
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