A GPU-accelerated metagenomic species abundance determination method

By adopting GPU parallel computing method in Dingfeng analysis of metagenomic species, the problem of low computing efficiency in the existing technology is solved, and the metagenomic analysis efficiency is significantly improved and the results are accurate.

CN115756827BActive Publication Date: 2025-03-21NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Application Number
CN202211312534.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-10-25
Publication Date
2025-03-21
Estimated Expiration
2042-10-25

AI Technical Summary

Technical Problem

The existing metagenomic species Dingfeng analysis method has low computational efficiency when processing large-scale data, resulting in a long analysis time and affecting the efficiency of the field of biotechnology and information.

Method used

The parallel computing method based on GPU is adopted to improve the computing efficiency by decomposing a large number of operations into multiple small operations and using parallel operations of a large number of threads. Specific steps include initialization, data preparation, GPU accelerated calculation and output.

Benefits of technology

The overall efficiency of metagenomic species fixed abundance analysis has been significantly improved, and the efficiency in calculation abundance has been increased by 6-7 times, with good accuracy and strong practical significance.

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Abstract

The present invention discloses a method for determining the abundance of metagenomic species based on GPU acceleration, first analyzing the needs of user input, and determining the mode of calculation according to the input parameters of the user; then initializing, checking the integrity and version of the file, to ensure the stable operation of the present invention; after ensuring the stability, data preparation is performed, and preparation is made for subsequent GPU calculations, and all data are stored in an array of numpy format; after the current data preparation is completed, the GPU will be called by the numba package of python, and the data will be first transferred to the GPU, and then the GPU call calculation will be performed twice, the former as an intermediate result, and the latter outputs the relative abundance of each final clade; finally, the present invention will select the file format of the output according to the parameters output by the user, and save it in the saved file input by the user. The present invention greatly improves the overall efficiency of metagenomic analysis, and the accuracy is also well performed in the same type of metagenomic analysis software, with strong practical significance.
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