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Multi-omics data combined analysis method

A technology for omics data and joint analysis, applied in the field of bioinformatics, which can solve problems such as poor versatility, high method limitations, and difficulty in meeting scientific research needs.

Active Publication Date: 2019-09-06
BGI TECH SOLUTIONS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The existing analysis methods have poor versatility, and different projects may need to adjust the methods and parameters for analysis; the method has relatively high limitations, and it is difficult to meet the scientific research needs of more than two omics data

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] Embodiment 1, joint analysis of rat mRNA and small RNA data

[0057] 1. Experimental samples

[0058] Normal tissue samples from rats at 6 different developmental time stages.

[0059] 2. Data collection

[0060] Using conventional RNA-Seq and small RNA standard information analysis, the input data of mRNA and small RNA were obtained from normal tissue samples of 6 different developmental time stages of rats.

[0061] Finally, the mRNA data includes the expression levels of 25,289 genes, and the small RNA data includes the expression levels of 631 miRNAs. The targeting relationship between miRNA and mRNA is based on the prediction results of TargetScan and miRanda target gene prediction software, including 76069 pairs of targeting relationships.

[0062] 3. Data preprocessing

[0063] Delete data with a missing rate higher than the set threshold (the default threshold is 0.2, that is, the missing rate is 20%) (delete the corresponding row).

[0064] 4. Co-expressio...

Embodiment 2

[0097] Example 2, joint analysis of apple methylation, mRNA and small RNA data

[0098] 1. Experimental samples

[0099] Samples of 4 different treatments of apples.

[0100] 2. Data collection

[0101] The input data of mRNA, methylation and small RNA were obtained from 4 different apple samples by routine RNA-Seq, genome-wide Bisulfite methylation and Small RNA standard information analysis.

[0102] Finally, the data of mRNA include the expression of 31,964 genes, the data of small RNA are the expression of 167 miRNAs, and the methylation is the result of the methylation rate of 34,889 methylated regions. The targeting relationship between miRNA and mRNA is based on the prediction results of psRobot and targetfinder target gene prediction software, including 9033 pairs of targeting relationships.

[0103] 3. Data preprocessing

[0104] Delete data with a missing rate higher than the set threshold (the default threshold is 0.2, that is, the missing rate is 20%) (delete t...

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Abstract

The invention discloses a multi-omics data combined analysis method. The multi-omics data combined analysis method comprises the following steps of (A), performing coexpression network analysis on each single-omics index data in to-be-analyzed multi-omics data, and finding a respective expression model; and (b), according to the overlapping relation between respective expression modules of different omics data, screening interaction modules which are remarkably correlated in the to-be-analyzed multi-omics data. The multi-omics data combined analysis method according to the invention is not restricted by the number of omics data, and random multiple groups can be utilized. Furthermore the method does not depend on a data source. The index data (such as gene expression magnitude, apparent methylation degree, SNP mutation rate) which can measure the corresponding omics can be used as input data.

Description

technical field [0001] The invention relates to the field of bioinformatics, in particular to a method for joint analysis of multi-omics data. Background technique [0002] With the continuous advancement of science and technology, high-throughput omics data has become easy to obtain, and they provide a comprehensive description of almost all members and interactions in cells. Joyce et al. divided these data into 3 categories: membership, interaction and functional status data. Member data describes the properties of cell molecules; interaction data records the relationship between molecular members; functional state data refers to the overall cell phenotype, revealing the overall performance of all omics data. Existing genome data describe the flow of biological signals from the genome to the metabolome. First, DNA (genome) is transcribed into mRNA (transcriptome), which is then translated into proteins (proteome), which catalyze reactions to generate metabolites, glycopr...

Claims

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Application Information

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IPC IPC(8): G16B25/00G16B40/00G16B50/00
CPCG16B25/00G16B40/00G16B50/00
Inventor 朱欠华高强杨林峰唐冲刘赤川何长寿
Owner BGI TECH SOLUTIONS
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