Virus identification method in NGS sequence based on multi-dimensional feature fusion

The NGS sequence identification method based on multidimensional feature fusion solves the problems of background noise and false positives in virus identification in waterfowl farming, and achieves high accuracy and stability in the identification of low-abundance viruses, supporting the accurate issuance of fully automated pathogens.

CN122637893APending Publication Date: 2026-08-25ZHONGKAI UNIV OF AGRI & ENG +1
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Patent Information

Application Number
CN202610782183.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-06-02
Publication Date
2026-08-25

AI Technical Summary

Technical Problem

Existing technologies for virus identification in waterfowl farming suffer from problems such as strong background noise, extremely low target abundance, and high false positive rates, making it difficult to accurately identify low-abundance viruses in complex samples.

Method used

An NGS sequence identification method based on multidimensional feature fusion is adopted, including nonparametric assembly analysis, sample-specific reference sequence library construction, parametric coverage analysis and machine learning model design. It integrates nonparametric features and parametric multidimensional features, and constructs a multidimensional quantization matrix for virus identification through supervised learning.

Benefits of technology

It has achieved highly reliable identification of low-abundance viruses in complex waterfowl samples, improved the accuracy and stability of virus identification, reduced false positives, and realized fully automated intelligent identification from sequencing data to accurate pathogen identification.

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Abstract

The present application relates to the technical field of virus identification, in particular to a virus identification method in NGS sequence based on multi-dimensional feature fusion, comprising the following steps: S1, non-parametric assembly analysis and non-parametric feature extraction; S2, sample-specific reference sequence library construction; S3, parametric coverage analysis and feature extraction; S4, machine learning model design and pathogen accurate determination; S5, virus identification in NGS sequence. The present application performs non-parametric assembly analysis on NGS sequence, constructs a reference sequence for parametric comparison according to the results obtained by non-parametric assembly, carries out three kinds of parametric coverage analysis based on the constructed reference sequence library, finally designs a machine learning algorithm, constructs a training label based on qPCR verification data, screens sequence features and trains a model, uses the trained model as the final determination basis, and realizes accurate virus identification.
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