Method for analyzing data of prokaryotic proteogenomics rapidly and automatically

Abstract

The invention provides a method for analyzing data of prokaryotic proteogenomics rapidly and automatically. Users only need to provide mass spectral data and a corresponding database file and set a simple retrieval parameter; retrieval of data of proteogenomics can be completed; simultaneously, a user defined data retrieval result can also be compatible; therefore, the identification coverage rate of the data of proteogenomics is increased; in the method disclosed by the invention, library search engines of different algorithms are integrated in advance; disadvantages of a single retrieval method are made up; a user defined library search result can also be compatible; the method has good compatibility; the peptide fragment identification coverage rate is increased to the most extent; in the method disclosed by the invention, functional annotation of new genes is automatically completed; large-scale identification of protein post-translational modification and analysis of non-labelled quantitative proteomics are realized for the first time; and automatic and rapid deep analysis of the data of proteogenomics is really realized.

Description

technical field [0001] The invention relates to a method for analyzing protein genome data, in particular to a method for automatically and rapidly analyzing prokaryotic protein genome data. Background technique [0002] With the rapid development of high-throughput DNA sequencing technology, humans have sequenced the genomes of more and more species. The purpose of genome sequencing is to gain a better understanding of the composition and function of genes involved in biological functions. Therefore, the basic task of genome annotation is to determine the location and structure of genes and other elements, and to determine the specific biological functions of these genes and elements. At present, this genome annotation method mainly relies on DNA and RNA sequence information; compared with genome or transcriptome annotation, proteomics can directly study the protein products translated from coding genes, so proteomics is more important than genome or transcriptome annotati...

AI Technical Summary

Problems solved by technology

Although the current research on proteomics is developing rapidly, there are still problems in the following aspects: a. In terms of database construction, integrating multi-omics databases can obtain protein sequence databases with wider coverage, but it will also cause problems in the database. Too large, so that the mass spectrometry identification search engine cannot cope; b. Most of the research data have quality control problems, such as directly obtaining the identified protein set only through the global FDR at the PSM level, and there is no precise FDR control for newly identified peptides; c. Multiple data integration and quality control tools are very lacking, and incremental genome annotation cannot be realized, which hinders the development of proteomics to a large extent; d. Mass spectrometry data also makes data sharing and transmission very difficult. Inconvenient, but also limits the promotion of proteomics

[0004] At present, the main software for protein genome data analysis includes Peppy, PPLine, PGTools and Genosuite, etc. However, the methods set by these software have relatively high limitations, and are only applicable to data generated by specific high-resolution mass spectrometry instruments and several common databases Moreover, the use settings of these software are relatively complicated, requiring users to have a deep background in proteomics research, and the scope of application is also greatly limited, and automatic and rapid analysis of data has not been realized; in addition, these The software is also unable to cope with the current mass spectrometry data collection, and the expansion of the search space due to the large database, which limits the research of proteomics

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