Lossless compression method, device, equipment and medium for proteomic mass spectrometry data

CN117854603BActive Publication Date: 2026-10-16SHENZHEN UNIV
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Patent Information

Application Number
CN202311795584.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-12-22
Publication Date
2026-10-16
Estimated Expiration
2043-12-22

AI Technical Summary

Technical Problem

[0006]本发明提供一种蛋白质组学质谱数据的无损压缩方法、装置、设备及介质，其主要目的在于解决相关技术中所存在的蛋白质组学质谱数据的压缩效率较差的问题

Benefits of technology

[0012] This invention proposes a lossless compression method for proteomics mass spectrometry data. By dividing the proteomics file into a meta-dataset and a mass spectrometry dataset, the meta-dataset is compressed into a compressed data package. Furthermore, a target truncated dataset and a tail dataset are extracted from the mass spectrometry dataset, reducing data length and improving compression efficiency and accuracy. A mass spectrometry polynomial function corresponding to the target truncated dataset is fitted, allowing the mass spectrometry data in the proteomics data to be represented using this function, thus improving compression efficiency. Different data compression methods are selected based on different data types to reduce data redundancy and effectively improve compression efficiency, achieving highly efficient compression of proteomics files. Therefore, the lossless compression method, apparatus, device, and medium for proteomics mass spectrometry data proposed in this invention can solve the problem of poor efficiency in lossless compression of proteomics mass spectrometry data.

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Abstract

The present application relates to the technical field of data compression, and discloses a lossless compression method, device, equipment and medium for proteomics mass spectrum data, which comprises the following steps: classifying a proteomics file into a metadata set and a mass spectrum data set; intercepting a target intercept data set and a mantissa data set from the mass spectrum data set; fitting a mass spectrum polynomial function corresponding to the target intercept data set; calculating a data difference value between each target intercept data in the target intercept data set and the corresponding target predicted data by using the mass spectrum polynomial function; and respectively performing data encoding compression on the metadata set, the data difference value and the mantissa data set to obtain compressed mass spectrum data corresponding to the proteomics file. Through the implementation of the present application, the proteomics file is classified into the metadata set and the mass spectrum data set, and the corresponding data compression mode is found according to different data types, so that the data redundancy can be reduced, the efficiency of data compression can be effectively improved, and efficient compression of the proteomics file can be realized.
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