Method for mining methylation pattern by whole genome data

Abstract

The invention belongs to the technical field of data processing of bioinformatics, and discloses a method for mining the methylation pattern by whole genome data. The method comprises the following steps that: using a SAM (Significance Analysis of Microarrays) method in various data sample sets to independently screen differential methylation sites on a whole genome; taking an intersection from the differential methylation sites of a plurality of sample sets to obtain a common difference site set; calculating a Pearson correlation coefficient between the methylation levels of the differential methylation sites and a corresponding gene expression level, and identifying a methylation regulation and control site; and carrying out AP (Affinity Propagation) clustering on the differential methylation site set to obtain a methylation cluster, independently carrying out pattern analysis on each methylation cluster, and carrying out demonstration through gene annotation and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. By use of the method, references can be provided by aiming at a demethylation medicine, different types of diseases have generality on a methylation pattern, and the method has a practical and clinic meaning for researching a relationship between the methylation pattern and the disease from a perspective of the whole genome.

Description

technical field [0001] The invention belongs to the technical field of data processing of bioinformatics, and in particular relates to a method for mining methylation patterns by using whole genome data. Background technique [0002] With the continuous development and progress of high-throughput sequencing technology and gene chip technology, efficient and massive genetic data can be obtained. Gene data contains many intricate life phenomena, making it possible to comprehensively explore the genetic and epigenetic basis of diseases. Modern life science research provides new directions and ideas. However, massive data cannot intuitively reveal life phenomena or reflect biological laws. Complex statistical methods and other means and techniques must be used to analyze and explore the biological phenomena contained in massive data. Thus, the discipline of bioinformatics was derived. Bioinformatics is an emerging discipline combining life science and computer science. It stud...

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Problems solved by technology

[0003] To sum up, the problem existing in the prior art is that traditional statistical methods have high requirements on the distribution of data, that is, the distribution of data is required to be definite, but the distribution of actual methylation data is not clear, so traditional statistical methods There are limitations; different omics data have different dimensions, so data integration is also a challenge for current research

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