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Identification method of key modules in Parkinson's disease evolution based on miRNA sequencing data

A technology for sequencing data and key modules, applied in the field of biological information, can solve problems such as not very good results and difficult to use heuristic algorithms

Active Publication Date: 2022-04-19
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

Use supervised learning or unsupervised learning methods to identify disease modules in the network, but heuristic algorithms often require a large amount of sample data, and samples in the biological field are often very rare, which makes it difficult for heuristic algorithms in this case use, the effect is not very good

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  • Identification method of key modules in Parkinson's disease evolution based on miRNA sequencing data
  • Identification method of key modules in Parkinson's disease evolution based on miRNA sequencing data
  • Identification method of key modules in Parkinson's disease evolution based on miRNA sequencing data

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, and the present invention includes but not limited to the following embodiments.

[0043] Such as figure 1 As shown, the present invention provides a method for identifying key modules of Parkinson's disease evolution based on miRNA sequencing data, and its specific implementation process is as follows:

[0044] 1. High-throughput sequencing data preprocessing

[0045] First, use fastp and fastxtoolkits software to perform quality control on high-throughput sequencing data (TCGA data), including removing N-base sequences, filtering sequences with low Q20 ratios, and performing length filtering. The data obtained after quality control is recorded as clean-data, and then in order to improve the next comparison task, deduplicate and count the repeated sequences in clean-data, and record the result as uniq-data. The data format of uniq-data is fasta, mainly ...

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Abstract

The invention provides a method for identifying key modules of Parkinson's disease based on miRNA sequencing data. First, preprocess the high-throughput sequencing data; then, group the samples according to different stages of PD disease, and then perform differential expression analysis; then, perform hierarchical clustering according to the correlation coefficient between differentially expressed miRNAs to construct co-expression Network and modules; finally, the module network is constructed and the key modules of PD are identified. The method of the invention can identify the key modules of the PD evolution process, judge the PD stage of the current patient according to the key modules, and provide help for doctors to find early PD patients.

Description

technical field [0001] The invention belongs to the field of biological information technology, and specifically relates to a method for identifying key modules of Parkinson's disease evolution based on miRNA sequencing data. By studying the differences in miRNA expression data and constructing a co-expression network and a module network, the key modules in the PD evolution process are identified. identify. Background technique [0002] Parkinson's disease (PD) is a degenerative disease of the nervous system, which is common in middle-aged and elderly patients. The early symptoms of Parkinson's disease are not obvious. Exosomes and their miRNAs not only play an early warning role in neurodegenerative diseases such as PD, but may also play a role in targeted therapy based on the regulation of miRNAs on genes. The current methods and technologies for studying PD based on miRNA data are mainly aimed at the static process of the disease, that is, the stage of some diseases in ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B30/10G16B40/00G16B40/30G16B25/10
CPCG16B30/10G16B40/00G16B40/30G16B25/10
Inventor 陈伯林邵慈王腾苗立珺尚学群
Owner NORTHWESTERN POLYTECHNICAL UNIV
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