Method, system and server for protein data analysis processing

Abstract

The application provides a protein data analysis processing method, system and server, relates to the field of protein data analysis processing, and realizes a standardized and automated analysis process, that is, the whole process from original mass spectrum data import, library matching, quantitative analysis, annotation enrichment, visual display to report generation is automatically completed, an efficient closed-loop analysis system of 'delivery as soon as downloading' is realized, and the efficiency, depth and stability of blood protein data processing are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of protein data analysis and processing, and in particular to a protein data analysis and processing method, system, and server. Background Technology

[0002] With the development of proteomics technology, especially the widespread application of mass spectrometry platforms in biological samples, high-throughput detection of blood proteins has become an important means for early disease diagnosis, therapeutic target discovery, and biomarker screening. However, blood, as a special and complex sample type, is characterized by a wide dynamic range of proteins, difficulty in detecting low-abundance proteins, and strong background interference, posing significant challenges to the analysis and interpretation of blood proteomics data. Currently available protein annotation tools and analysis workflows mainly rely on general databases (such as uniprot, GO, KEGG, Pfam, etc.) for functional annotation. While these can meet the basic information identification of routine proteins, their annotation coverage and accuracy remain severely insufficient for proteins with specific expression or low abundance characteristics in blood samples. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a protein data analysis and processing method, system and server. This solution realizes a standardized and automated analysis process, which automatically completes the entire process from raw mass spectrometry data import, library search and matching, quantitative analysis, annotation and enrichment, visualization and report generation, realizing a highly efficient closed-loop analysis system that can be delivered immediately after the data is processed, and significantly improving the efficiency, depth and stability of blood protein data processing.

[0004] In a first aspect, embodiments of the present invention provide a protein data analysis and processing method, comprising:

[0005] Based on the Uniprot, GO, and KEGG databases, we introduce a human blood low-abundance protein database, a tissue-specific expression database, and a drug target database.

[0006] The tissue-specific expression database was divided into annotation information for different tissues according to expression levels, and corresponding protein annotation tables were generated for each tissue.

[0007] Protein name alignment is prioritized based on perfect match. If the match fails, sequence alignment is performed using BLAST software. Annotation information is retrieved from the corresponding database using the ID of the successful alignment.

[0008] The above-obtained annotation information is summarized with the protein annotation table to form a multidimensional annotation library for blood samples.

[0009] Protein data analysis based on multidimensional annotation libraries.

[0010] Optional steps for protein data analysis based on multidimensional annotation libraries include:

[0011] The WDL workflow engine is used to call the mass spectrometry analysis software to complete the format conversion, database matching, quantitative calculation and quality control assessment of the protein data to be processed.

[0012] Based on a Python multithreaded / multiprocess architecture, the system performs differential filtering, enrichment analysis, functional annotation, and report generation in parallel. Monitoring mechanisms are set up between modules to trigger subsequent steps, and finally, an analysis report is output.

[0013] Optionally, the mass spectrometry analysis software is DIANN, the format conversion is performed using msconvert software, and the database matching and quantitative calculations are performed by DIANN software.

[0014] Optional, quality control assessment includes the following steps:

[0015] The parameters are statistically analyzed to determine whether the proteins identified in each sample meet the preset standards.

[0016] The maximum peak area normalization method was implemented using Python to standardize the quantitative values.

[0017] Based on the standardized data, sample correlation calculations are performed to determine whether the delivery standards are met.

[0018] If it meets the requirements, it will be processed for further analysis; if it does not meet the requirements, production will resume.

[0019] Optionally, during the database matching process, the identified protein names are compared with the multidimensional annotation library with the priority of complete matching. If the matching fails, the sequence is compared using BLAST software, and the corresponding annotation information is retrieved from the multidimensional annotation library using the ID of the successful match.

[0020] During the functional annotation process, a multidimensional annotation library is called to provide Uniprot functional annotation, GO annotation, KEGG pathway annotation, blood low-abundance protein annotation, tissue-specific expression annotation and drug target annotation for proteins obtained by differential screening.

[0021] During the report generation process, the obtained quantitative results, difference screening results, and functional annotation results are summarized, and the final output is an analysis report containing multidimensional annotation information.

[0022] Optional, also includes:

[0023] Obtain the raw data file of the off-machine sample produced by the production platform;

[0024] After searching the database using DIANN with the raw sample files and standardized protein sequence database files, the protein data to be processed is obtained based on the raw peak area quantification results.

[0025] Protein data is input into the quantitative quality control analysis module, differential analysis module, annotation analysis module, and enrichment analysis module according to the corresponding process parameters to achieve the analysis of the protein data to be processed.

[0026] In a second aspect, the present invention provides a protein data analysis and processing system, the protein data analysis and processing system comprising:

[0027] The local database construction module is used to introduce human blood low-abundance protein databases, tissue-specific expression databases, and drug target databases on the basis of Uniprot, GO, and KEGG databases. The tissue-specific expression database is divided into annotation information for different tissues according to expression levels, and corresponding protein annotation tables are generated for each tissue. Protein name alignment is performed first with exact matches. If the match fails, sequence alignment is performed using BLAST software. The annotation information is retrieved from the corresponding database using the ID of the successful alignment. The obtained annotation information and protein annotation tables are summarized to form a multidimensional annotation library for blood samples.

[0028] The functional annotation execution module is used to perform functional annotation on proteins based on a multidimensional annotation library, obtain functional annotation results, and determine the corresponding functional annotation analysis results for the proteins based on the functional annotation results.

[0029] The parallel analysis execution module is used to summarize and process the functional annotation results through preset concurrent threads to obtain the corresponding annotation analysis results for proteins.

[0030] Optionally, the functional annotation execution module includes: a protein data acquisition module, a quantitative quality control analysis module, a differential analysis module, an annotation analysis module, an enrichment analysis module, a multi-threaded concurrency module, and a results summary module;

[0031] The quantitative quality control analysis module, differential analysis module, annotation analysis module, and enrichment analysis module are each housed within a multi-threaded concurrent module. The protein data acquisition module is connected to the corresponding input terminals of the quantitative quality control analysis module, differential analysis module, annotation analysis module, and enrichment analysis module via the multi-threaded concurrent module. The result summarization module is connected to the corresponding output terminals of the quantitative quality control analysis module, differential analysis module, annotation analysis module, and enrichment analysis module via the multi-threaded concurrent module.

[0032] The protein data acquisition module is used to acquire protein data to be processed through a preset DIANN search library;

[0033] The quantitative quality control analysis module is used to determine the mass spectrometry data contained in the protein data, obtain the quantitative value by using the maximum peak area verification result corresponding to the mass spectrometry data, and determine the quality control analysis result by using the number of protein identifications, peptide length distribution data, index retention time stability data, and correlation data of the measured samples corresponding to the mass spectrometry data.

[0034] The differential analysis module is used to determine the protein quantification data contained in the mass spectrometry data based on the quality control analysis results, and to determine the differential analysis results of the protein based on the protein quantification data.

[0035] The annotation analysis module is used to obtain functional annotation results after functional annotation of proteins, and to determine the corresponding functional annotation analysis results of proteins based on the functional annotation results.

[0036] The enrichment analysis module is used to enrich proteins into corresponding functional modules using the results of functional annotation analysis, and then obtain the enrichment analysis results corresponding to the functional modules.

[0037] A multi-threaded concurrency module is used to perform asynchronous parallel processing on the quantitative quality control analysis module, the difference analysis module, the annotation analysis module, and the enrichment analysis module;

[0038] The results summary module is used to summarize and display the quality control analysis results, difference analysis results, annotation analysis results, and enrichment analysis results obtained after asynchronous parallel processing.

[0039] Optional, quantitative quality control analysis module, including:

[0040] The protein quantification module is used to determine the peak area data of each protein in each raw file of the project. After standardizing the protein data according to the maximum peak area normalization scheme, the quantitative results are obtained for subsequent differential analysis.

[0041] The verification result quality control module is used to obtain the visualization results after visualizing the quantitative results and protein data, and to determine the quality control analysis results based on the correlation, number of identifications and peptide length distribution corresponding to the visualization results.

[0042] The difference analysis module includes:

[0043] The protein quantification data acquisition module is used to acquire the protein quantification table corresponding to the quality control analysis results, and to determine the protein quantification data based on the standardized results of the protein quantification table;

[0044] The differential analysis execution module is used to standardize protein quantification data based on the protein quantification table, and then calculate the differential results according to the sample affiliation of the protein quantification data.

[0045] The enrichment analysis module includes:

[0046] The functional enrichment execution module is used to control the functional enrichment of proteins into corresponding functional modules using annotation analysis results and differential analysis results; wherein, functional modules include: functional pathways or biological functions;

[0047] The enrichment analysis acquisition module is used to determine the enrichment analysis results corresponding to the functional modules by using the functional enrichment results of proteins in functional pathways or biological functions.

[0048] The multi-threaded concurrency module includes:

[0049] A multi-threaded input module is used to receive input data from the quantitative quality control analysis module, the difference analysis module, the annotation analysis module, and the enrichment analysis module.

[0050] The multi-threaded output module is used to send the output data corresponding to the quantitative quality control analysis module, the difference analysis module, the annotation analysis module, and the enrichment analysis module.

[0051] The results summary module includes:

[0052] The asynchronous acquisition module is used to collect the quality control analysis results, difference analysis results, annotation analysis results, and enrichment analysis results after asynchronous parallel processing.

[0053] The summary execution module is used to summarize and display the results of quality control analysis, difference analysis, annotation analysis, and enrichment analysis.

[0054] Thirdly, embodiments of the present invention also provide a server, including a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the steps of the protein data analysis and processing method provided in the first aspect.

[0055] This invention provides a protein data analysis and processing method, system, and server. In the process of analyzing and processing protein data from a mass spectrometry platform, this method introduces a human blood low-abundance protein database, a tissue-specific expression database, and a drug target database on top of the Uniprot, GO, and KEGG databases. Then, the tissue-specific expression database is divided into annotation information for different tissues according to expression levels, and corresponding protein annotation tables are generated for each. Subsequently, protein name alignment is prioritized based on perfect matches; if a match fails, sequence alignment is performed using BLAST software, and annotation information is retrieved from the corresponding database using the ID of the successful alignment. The obtained annotation information and protein annotation tables are then summarized to form a multidimensional annotation library for blood samples. Finally, protein data analysis is performed based on this multidimensional annotation library. This solution achieves a standardized and automated analysis workflow, automatically completing the entire process from raw mass spectrometry data import, library search and matching, quantitative analysis, annotation enrichment, visualization, to report generation. It realizes a highly efficient closed-loop analysis system that is ready for immediate delivery upon arrival at the analyzer, significantly improving the efficiency, depth, and stability of blood protein data processing.

[0056] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0058] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0059] Figure 1 A flowchart of a protein data analysis and processing method provided in an embodiment of the present invention;

[0060] Figure 2 A flowchart of step S105 in a protein data analysis and processing method provided in an embodiment of the present invention;

[0061] Figure 3 This is a flowchart of quality control assessment in a protein data analysis and processing method provided in an embodiment of the present invention;

[0062] Figure 4 This is a flowchart illustrating the analysis of protein data to be processed in a protein data analysis and processing method provided in an embodiment of the present invention.

[0063] Figure 5 A flowchart of another protein data analysis and processing method provided in an embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of the structure of a protein data analysis and processing system provided in an embodiment of the present invention;

[0065] Figure 7 This is a schematic diagram of the structure of a functional annotation execution module in a protein data analysis and processing system provided in an embodiment of the present invention;

[0066] Figure 8 This is a schematic diagram of the structure of a quantitative quality control analysis module in a protein data analysis and processing system provided in an embodiment of the present invention;

[0067] Figure 9 This is a schematic diagram of the differential analysis module in a protein data analysis and processing system provided in an embodiment of the present invention;

[0068] Figure 10 This is a schematic diagram of the enrichment analysis module in a protein data analysis and processing system provided in an embodiment of the present invention;

[0069] Figure 11 This is a schematic diagram of the structure of a multi-threaded concurrent module in a protein data analysis and processing system provided in an embodiment of the present invention;

[0070] Figure 12 This is a schematic diagram of the structure of a result aggregation module in a protein data analysis and processing system provided in an embodiment of the present invention;

[0071] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0072] icon:

[0073] 100 - Local database construction module; 200 - Function annotation execution module; 300 - Parallel analysis execution module;

[0074] 110 - Protein Data Acquisition Module; 120 - Quantitative Quality Control Analysis Module; 130 - Differential Analysis Module; 140 - Annotation Analysis Module; 150 - Enrichment Analysis Module; 160 - Multi-threaded Concurrency Module; 170 - Result Summary Module;

[0075] 810 - Protein Quantification Module; 820 - Verification Result Quality Control Module;

[0076] 910 - Protein Quantification Data Acquisition Module; 920 - Differential Analysis Execution Module;

[0077] 1010 - Functional enrichment execution module; 1020 - Enrichment analysis acquisition module;

[0078] 1110 - Multi-threaded input module; 1120 - Multi-threaded output module;

[0079] 1210 - Asynchronous Acquisition Module; 1220 - Summary Execution Module;

[0080] 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed Implementation

[0081] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0082] With the development of proteomics technology, especially the widespread application of mass spectrometry platforms in biological samples, high-throughput detection of human blood proteins has become an important means for early disease diagnosis, therapeutic target discovery, and biomarker screening. However, blood, as a special and complex sample type, is characterized by a wide dynamic range of proteins, difficulty in detecting low-abundance proteins, and strong background interference, posing significant challenges to the analysis and interpretation of blood proteomics data. Currently available protein annotation tools and analysis workflows mainly rely on general databases (such as uniprot, GO, KEGG, Pfam, etc.) for functional annotation. While these can meet the basic information identification of routine proteins, their annotation coverage and accuracy remain severely insufficient for proteins with specific expression or low abundance characteristics in blood samples. The closest existing implementation to this invention uses existing software toolchains (such as MaxQuant for database searching, R / Python scripts for differential protein analysis, and DAVID / Metascape / R scripts for annotation and pathway enrichment). However, these solutions have the following limitations: they rely on external general databases and cannot provide blood sample-specific annotation information; the data processing workflow is fragmented, requiring cross-platform operation and lacking a unified standard; frequent manual intervention makes it difficult to scale up processing or automate results delivery; and the reproducibility of analysis results is poor and greatly affected by the operator's experience.

[0083] Based on this, the present invention provides a protein data analysis and processing method, system and server. This solution realizes a standardized and automated analysis process, which automatically completes the entire process from raw mass spectrometry data import, library search and matching, quantitative analysis, annotation and enrichment, visualization and report generation, realizing a highly efficient closed-loop analysis system that can be delivered immediately after the data is processed, and significantly improving the efficiency, depth and stability of blood protein data processing.

[0084] To facilitate understanding of this embodiment, a protein data analysis and processing method disclosed in this embodiment will first be described in detail, such as... Figure 1 As shown, the method includes:

[0085] Step S101: Based on the Uniprot, GO, and KEGG databases, introduce the human blood low-abundance protein database, tissue-specific expression database, and drug target database.

[0086] Based on the Uniprot, GO, and KEGG universal protein function annotation databases, we further introduce human blood low-abundance protein databases, tissue-specific expression databases (such as the Human Protein Atlas HPA database), and drug target databases to complete the integration of multiple authoritative databases. This provides a basic data source for building a blood sample-specific annotation system and makes up for the deficiencies of existing general databases in terms of coverage and accuracy of blood sample-specific and low-abundance protein annotation.

[0087] Step S102: Divide the tissue-specific expression database into annotation information for different tissues according to the expression level, and generate corresponding protein annotation tables for each tissue.

[0088] The introduced tissue-specific expression database was standardized and classified. Based on the established schemes of the literature and the database, the tissue-specific expression data in the database were divided into annotation information for different tissues according to the protein expression level. At the same time, the plasma protein content database was organized, the boundaries between high and low protein abundance were clarified, high-abundance interfering proteins were removed, and a detailed table of plasma protein-related data and corresponding protein annotation tables were compiled.

[0089] Step S103: Prioritize protein name alignment for perfect matches. If a match fails, perform sequence alignment using BLAST software and retrieve annotation information from the corresponding database using the ID of the successful alignment.

[0090] Establish annotation information matching rules for dual alignment of protein name and sequence. When performing annotation information matching, prioritize the method of complete matching of protein name to complete the alignment of target protein with database. If no matching result is found for protein name, perform homologous sequence alignment of protein using BLAST software to obtain the ID of the successfully aligned protein. Then, retrieve the matching protein annotation information from the corresponding database based on the ID. Simultaneously complete the unified ID mapping and annotation standard format specification to ensure the accuracy and consistency of annotation information.

[0091] Step S104: Summarize the above-obtained annotation information with the protein annotation table to form a multidimensional annotation library for blood samples.

[0092] The protein annotation information obtained from the above comparison and matching, as well as the protein annotation tables corresponding to the tissue-specific expression annotation information at each level, are integrated, summarized, and standardized to finally construct a dedicated multidimensional annotation library for blood samples, covering protein functions, expression characteristics, disease associations, drug targets, subcellular localization, and other multidimensional information.

[0093] Step S105: Perform protein data analysis based on multidimensional annotation libraries.

[0094] Using a dedicated multidimensional annotation library for blood samples as the core data support, this library is applied to the entire process of blood proteomics data analysis, covering core analytical steps such as protein function annotation, differential protein annotation analysis, and functional enrichment analysis. Combined with a multi-threaded asynchronous parallel processing mechanism, it automates and standardizes the entire process from importing raw mass spectrometry data, searching and matching the library, quantitative quality control, annotation enrichment, to summarizing and displaying the results, thereby improving the efficiency, annotation depth, and result stability of blood protein data processing.

[0095] Optionally, step S105, which involves protein data analysis based on a multidimensional annotation library, such as... Figure 2 As shown, it includes:

[0096] Step S201: Use the WDL workflow engine to call the mass spectrometry analysis software to complete the format conversion, database matching, quantitative calculation and quality control assessment of the protein data to be processed.

[0097] A standardized workflow engine is built based on the mainstream WDL workflow framework. It calls the DIANN mass spectrometry analysis software, which supports command-line operation, to automatically complete the entire process of raw data format conversion, database matching, quantitative calculation and quality control assessment for the protein data to be processed.

[0098] Optionally, the mass spectrometry analysis software is DIANN, format conversion is performed using msconvert software, and database matching and quantitative calculation are executed by DIANN software. Based on this, the format conversion of the raw mass spectrometry data is first completed using msconvert software. Then, DIANN software is called, combining the previously constructed multidimensional annotation library of blood samples and standardized protein sequence database, to complete the matching analysis of mass spectrometry signals and protein sequences. Simultaneously, protein quantification based on the raw peak area is performed and the quantitative results are output. A full-process quality control assessment is performed concurrently. First, it is determined whether the proteins identified in each sample meet the preset identification criteria. Then, the quantitative values ​​are standardized using the maximum peak area normalization scheme. Based on the standardized data, the correlation between samples is calculated to verify whether the data meets the delivery standards. At the same time, the input and output formats, execution order, and resource dependencies of each step of the process are uniformly defined and managed through the WDL framework, ensuring the traceability, reusability, and large-scale batch processing capabilities of the analysis process.

[0099] Step S202: Based on Python's multi-threaded / multi-process architecture, perform differential filtering, enrichment analysis, functional annotation, and report generation in parallel, and set up a monitoring mechanism between modules to trigger subsequent steps, finally outputting an analysis report.

[0100] Built on a multi-threaded and multi-process concurrent execution architecture using Python, this system modularizes core analytical tasks such as differential protein screening, functional annotation, functional enrichment analysis, visualization, and report generation. Through a multi-threaded asynchronous parallel processing mechanism, the system executes the analytical tasks of each module synchronously, significantly improving the efficiency of processing large-scale data. Simultaneously, an automatic result monitoring mechanism is built into each analytical module throughout the entire process. This mechanism tracks the processing progress of each module in real time, dynamically detects the status of each module's output results, automatically determines whether the analytical task has been successfully completed, and triggers subsequent analytical steps based on the judgment result, effectively reducing manual intervention and improving process stability. Finally, the results summary module performs multi-dimensional visualization integration and summarization of quality control analysis results, differential analysis results, annotation analysis results, and enrichment analysis results, generating standardized analysis reports with embedded key statistical data and interactive analysis charts. These reports support export in multiple formats such as PDF, Excel, and JSON, completing the fully automated analysis and result delivery of blood proteomics data.

[0101] Optional, quality control assessment such as Figure 3 As shown, it includes the following steps:

[0102] Step S301: Analyze whether the proteins identified in each sample meet the preset standards.

[0103] After completing the format conversion of the raw mass spectrometry data and DIANN database search, the protein-related indicators identified in each sample data are statistically analyzed one by one. The core indicators such as the number of identified proteins and peptide matching confidence are verified to meet the preset identification standards, thus completing the first round of quality control verification of the sample protein identification results.

[0104] Step S302: Use Python to implement the maximum peak area standardization method to standardize the quantitative values.

[0105] For samples that passed the first round of identification standard verification, based on the peak area data of each protein in the corresponding raw file of the machine, a standardization processing scheme for normalizing the maximum peak area was implemented using Python language to standardize the raw quantitative values ​​of the proteins and generate standardized quantitative results suitable for subsequent differential analysis.

[0106] Step S303: Calculate the sample correlation based on the standardized data to determine whether it meets the delivery standards.

[0107] Based on standardized protein quantification data, correlation analysis was conducted among the tested samples and correlation values ​​were calculated. At the same time, auxiliary quality control indicators such as peptide length distribution data and index retention time stability data were combined to comprehensively determine whether the overall quality control results of the samples meet the preset project delivery standards.

[0108] Step S304: If it meets the requirements, proceed to the next analysis; if it does not meet the requirements, start production again.

[0109] If the quality control analysis results of the sample meet the preset delivery standards, the standardized protein quantification data and quality control results corresponding to the sample will be automatically transferred to subsequent bioinformatics analysis stages such as differential analysis, functional annotation analysis, and enrichment analysis. If the quality control analysis results do not meet the delivery standards, the sample reprocessing process will be triggered, and mass spectrometry detection and corresponding data processing analysis will be carried out again.

[0110] Optionally, during the database matching process, the identified protein names are compared with the multidimensional annotation library with the priority of complete matching. If the matching fails, the sequence is aligned using BLAST software, and the corresponding annotation information is retrieved from the multidimensional annotation library using the ID of the successful alignment.

[0111] Specifically, after obtaining protein identification results from the DIANN database search, a two-layer precise matching mechanism is established, prioritizing complete protein name matching and using homologous sequence alignment as a fallback. First, the protein names identified in the database search are compared with a pre-constructed multidimensional annotation library specifically for blood samples to achieve a complete match and retrieve initial annotation information. If no valid match is found, homologous sequence alignment is performed using BLAST software to obtain the IDs of successfully matched proteins. Based on these IDs, the full annotation information for the corresponding protein is retrieved from the multidimensional annotation library. During the matching process, a unified ID mapping and annotation standard format are simultaneously implemented to ensure complete matching of annotation information for blood-specific and low-abundance proteins. This addresses the issue of insufficient coverage of general databases for specific proteins in blood samples, comprehensively improving the accuracy and completeness of the annotation results.

[0112] During the implementation of functional annotation, a multidimensional annotation library is invoked to provide Uniprot functional annotation, GO annotation, KEGG pathway annotation, blood low-abundance protein annotation, tissue-specific expression annotation, and drug target annotation for proteins obtained by differential screening.

[0113] In the functional annotation phase, for significantly differentially expressed proteins identified through the differential analysis module, the previously constructed multidimensional annotation library for blood samples was used to conduct multidimensional and comprehensive targeted functional annotation analysis. This included Uniprot basic functional annotation, GO functional classification annotation (covering three core levels: molecular function, biological process, and cellular components), and KEGG metabolic and signal transduction pathway annotation. Furthermore, tailored annotations for low-abundance blood proteins, tissue-specific expression grading annotations, and disease- and drug target association annotations were provided to comprehensively reveal the biological function, subcellular localization, tissue expression characteristics, disease regulatory effects, and drug potential of the target proteins. An intelligent annotation strategy was employed, prioritizing the use of highly reliable experimental validation annotation information. When experimental validation data was insufficient, the system automatically switched to homology sequence prediction annotation methods, ensuring the comprehensiveness of the annotation results while maximizing the reliability of the annotation information.

[0114] During the report generation process, the quantitative results, difference screening results, and functional annotation results obtained from the above implementation process are summarized, and the final output is an analysis report containing multi-dimensional annotation information.

[0115] In the report generation stage, the asynchronous acquisition mechanism of the results aggregation module collects and integrates the protein quantification results, quality control analysis results, differential protein screening results, multi-dimensional functional annotation results, and supporting functional enrichment analysis results obtained in the aforementioned process. This allows for structured integration and multi-dimensional visualization of the multi-source data. Specifically, core quality control indicators are presented using violin plots, correlation heatmaps, and peak area distribution line charts; differential analysis results are presented using volcano plots, expression heatmaps, and clustering tree diagrams; and functional annotation and enrichment results are presented using functional network diagrams, enrichment bar charts, bubble charts, and KEGG pathway highlighting diagrams. Finally, a standardized analysis report is automatically generated, embedding complete statistical data, interactive analysis charts, and full multi-dimensional annotation information. It supports export in multiple formats such as PDF, Excel, and JSON, and also includes a built-in result traceability function, allowing users to view the original analysis record by clicking on any data point. This achieves a closed-loop delivery process from raw mass spectrometry data to the analysis report.

[0116] Optional, such as Figure 4 As shown, the method also includes:

[0117] Step S401: Obtain the raw data file of the off-machine original sample produced by the production platform.

[0118] The RAW data acquisition module collects raw data files of the off-machine samples output by the mass spectrometry production platform, and simultaneously completes dual access verification of the raw data: on the one hand, the acquired raw data files are verified to be consistent with the order sample information to ensure that the sample information corresponds one-to-one with the detection data; on the other hand, the integrity and accuracy of the raw protein data are checked by verifying the MD5 value of the raw data files. After the verification is completed, the subsequent analysis process is started, ensuring the compliance and reliability of the system input data from the source.

[0119] Step S402: After searching the database using DIANN with the raw sample file and the standardized protein sequence database file, the protein data to be processed is obtained based on the raw peak area quantification results.

[0120] After verifying the raw data, a fully automated database search process is performed through the database search workflow execution module: First, the raw sample files are preprocessed by format conversion. Then, combined with the standardized protein sequence database file, DIANN software is called to complete the accurate database search and matching of the mass spectrometry data. The raw mass spectrometry signal is compared with the peptide spectrum simulated by the protein sequence in the database. Low-confidence peptide matching results are eliminated. Finally, protein quantification is performed based on the raw peak area quantification results, generating standardized protein data to be processed that can be directly called by downstream analysis modules.

[0121] Step S403: Input the protein data into the quantitative quality control analysis module, differential analysis module, annotation analysis module, and enrichment analysis module according to the corresponding process parameters to realize the analysis of the protein data to be processed.

[0122] By leveraging the asynchronous scheduling capabilities of the multi-threaded concurrency module through the delivery process intervention module, the generated protein data to be processed is adapted and distributed according to the corresponding process parameters of the quantitative quality control analysis module, differential analysis module, annotation analysis module, and enrichment analysis module. This includes adaptation processing such as data format standardization, key field screening, sample grouping information attachment, and batch processing batch division. For example, the full data containing complete peak area information is prioritized to be delivered to the quantitative quality control analysis module, while sample grouping information is synchronously attached to the differential analysis module. This ensures that the four core analysis modules can directly start analysis tasks synchronously based on the adapted target data, achieving full-dimensional, parallel, and efficient analysis and processing of the protein data to be processed.

[0123] In its implementation, the above method integrates multi-source information and enriches annotation dimensions. Building upon existing protein function annotation databases (such as uniprot, GO, and KEGG), it further introduces human blood low-abundance protein databases, tissue-specific expression databases (such as HPA), and drug target databases to establish a multi-dimensional annotation system for blood samples. (Data processing details: HPA data was manually downloaded. The downloaded data was then compiled into two categories: one is databases related to plasma protein content, where high-abundance proteins and the high / low abundance boundaries were removed based on available documentation, and finally, detailed data tables and corresponding protein sequence files were compiled for subsequent project analysis; the other category is tissue-specific expression data, where the entire database was divided into three levels of annotation information and corresponding protein sequence tables based on literature and website protocols).

[0124] Specifically, by using a unified ID mapping and annotation standard format, key information such as specific proteins, low-abundance proteins, and disease-related targets in the blood can be systematically presented (the implementation logic is: first, perform complete matching of protein names, then match annotation information; if no match is found, use the software BLAST for comparison, then use the matched ID, and then retrieve the corresponding annotation information from the corresponding table database), thereby improving the accuracy of annotation and clinical interpretability.

[0125] The database search process in this method can be automated, enabling mass spectrometry data to be processed without manual intervention. Specifically, a version of the mainstream mass spectrometry analysis software (DIANN) with command-line capabilities can be used, combined with the mature WDL workflow language, to build an automated database search process. The process automatically completes operations such as raw data preprocessing, database matching, quantitative calculation, and quality control evaluation. (Raw data preprocessing: The data from the spectrometer is converted to a different format using msconvert software, and then processed using the database search software DIANN. This software performs database matching and quantitative calculation, and outputs the quantitative results.)

[0126] The quality control assessment process first counts whether the proteins identified in each sample meet the identification criteria. Then, a scheme for standardizing the maximum peak area is written in Python to standardize the quantitative values. Next, the correlation of the samples is calculated based on the processed data to check whether the correlation meets the delivery criteria. If it does, it is directly transferred to bioinformatics analysis; otherwise, it is reprocessed. This avoids manual step-by-step execution, supports large-scale batch sample parallel processing, and ensures parameter consistency and process traceability.

[0127] The overall analysis workflow employs a novel Python architecture to achieve concurrent execution and intelligent monitoring. In the result processing and functional analysis phases after the database search, this method constructs a multi-threaded and multi-process concurrent execution architecture based on the Python language. This modularizes tasks such as differential protein screening, enrichment analysis, functional annotation, visualization, and report generation, supporting the parallel execution of multiple tasks and significantly improving analysis speed. Simultaneously, an automatic result monitoring mechanism is introduced into the workflow, which can detect the status of the output results of each module in real time, automatically determine the success of the analysis, and trigger subsequent steps, effectively reducing human intervention and improving workflow stability and data delivery efficiency.

[0128] By integrating the three core technologies mentioned above, this method enables fully automated processing from the initial processing of blood sample mass spectrometry data to protein function annotation, result analysis, and report output. This not only improves the depth and specificity of protein annotation but also significantly reduces the manpower and time costs required for analysis, demonstrating good potential for widespread application and clinical use. The specific process described above can be illustrated as follows: Figure 5 The flowchart of another protein data analysis and processing method is shown below, and will not be described in detail again.

[0129] Corresponding to the protein data analysis and processing method in the above embodiments, this invention also provides a protein data analysis and processing system, such as... Figure 6 As shown, the protein data analysis and processing system includes:

[0130] The local database construction module 100 is used to introduce a human blood low-abundance protein database, a tissue-specific expression database, and a drug target database on the basis of Uniprot, GO, and KEGG databases; it divides the tissue-specific expression database into annotation information for different tissues according to expression levels and generates corresponding protein annotation tables for each tissue; it prioritizes protein name alignment based on perfect matches, and if a match fails, it performs sequence alignment using BLAST software, retrieving annotation information from the corresponding database using the ID of the successful alignment; it summarizes the above-obtained annotation information with the protein annotation tables to form a multidimensional annotation library for blood samples;

[0131] The functional annotation execution module 200 is used to perform functional annotation on proteins based on a multidimensional annotation library to obtain functional annotation results, and to determine the corresponding functional annotation analysis results for the proteins based on the functional annotation results.

[0132] The parallel analysis execution module 300 is used to summarize the functional annotation results through preset concurrent threads to obtain the annotation analysis results corresponding to the protein.

[0133] Specifically, the function annotation execution module 200, such as... Figure 7 As shown, it includes: a protein data acquisition module 110, a quantitative quality control analysis module 120, a differential analysis module 130, an annotation analysis module 140, an enrichment analysis module 150, a multi-threaded concurrency module 160, and a result summary module 170.

[0134] The quantitative quality control analysis module 120, the differential analysis module 130, the annotation analysis module 140, and the enrichment analysis module 150 are respectively set in the multi-threaded concurrent module 160. The protein data acquisition module 110 is connected to the corresponding input terminals of the quantitative quality control analysis module 120, the differential analysis module 130, the annotation analysis module 140, and the enrichment analysis module 150 through the multi-threaded concurrent module 160. The result summary module 170 is connected to the corresponding output terminals of the quantitative quality control analysis module 120, the differential analysis module 130, the annotation analysis module 140, and the enrichment analysis module 150 through the multi-threaded concurrent module 160.

[0135] Specifically, the protein data acquisition module 110 is used to acquire protein data to be processed through the RAW data acquisition and DIANN library modules. As the system's data entry point, this module not only accurately extracts the protein data to be processed from the locally pre-set protein annotation database, but also performs preliminary standardization processing on the data format (such as unifying file formats and verifying data integrity), ensuring that subsequent analysis modules can directly call high-quality raw data. The local protein annotation database typically contains basic data such as protein sequences, known modified proteins, and species-specific protein information, providing core reference data for subsequent analysis.

[0136] The quantitative quality control analysis module 120 is used to determine the mass spectrometry data contained in the protein data and to use the quality control analysis results corresponding to the mass spectrometry data. Specifically, it uses the normalization of the maximum peak area corresponding to the mass spectrometry data to obtain the quantitative value, and determines the quality control analysis results through the number of protein identifications, peptide length distribution, IRT (indexed retention time) stability, and the correlation of the measured samples.

[0137] The differential analysis module 130 is used to determine the quantitative protein data corresponding to the proteins contained in the mass spectrometry data based on the quality control analysis results, and to determine the differential analysis results corresponding to the proteins based on the quantitative protein data. After identifying qualified mass spectrometry data based on the quantitative quality control analysis results, the module focuses on the proteins and extracts their corresponding quantitative protein data (including the expression level and fold change of modified proteins in different samples). Statistical methods (such as t-tests and analysis of variance) are used to perform significance analysis on the quantitative protein data, and the differential analysis results are finally output, clarifying which proteins show significant expression differences under different experimental conditions (such as disease groups and control groups).

[0138] The annotation analysis module 140 is used to perform functional annotation on proteins, obtain functional annotation results, and determine the corresponding annotation analysis results for each protein based on these results. For key proteins screened by differential analysis, the module performs functional annotation from multiple dimensions, including molecular function (e.g., catalytic activity, binding function), biological processes involved (e.g., cell metabolism, signal transduction), and subcellular localization (e.g., nucleus, cytoplasm). The functional annotation results can be linked to authoritative information in public databases (e.g., GO, KEGG, blood low-abundance protein database, tissue-specific expression database, drug target database), ultimately forming structured annotation analysis results that reveal the potential biological role of the protein.

[0139] The enrichment analysis module 150 utilizes annotation analysis results to functionally enrich proteins into corresponding functional modules and then obtains the enrichment analysis results for each module. Based on the annotation analysis results, the module uses enrichment algorithms (such as hypergeometric tests) to classify function-related proteins into specific functional modules (such as "apoptosis pathway" and "DNA repair complex"). The enrichment analysis results can visually demonstrate which biological functions or pathways are significantly activated or inhibited under experimental conditions, helping researchers extract core biological significance from massive protein data, such as discovering key signaling pathways enriched in a certain disease state.

[0140] A multi-threaded concurrency module 160 is used to asynchronously and parallelly process the quantitative quality control analysis module 120, the differential analysis module 130, the annotation analysis module 140, and the enrichment analysis module 150. As the system's "computational hub," this module employs an asynchronous parallel processing mechanism, enabling the four core modules—quantitative quality control analysis, differential analysis, annotation analysis, and enrichment analysis—to start independently, with each module's analysis points running in parallel. This design significantly improves the processing efficiency of large-scale protein data, making it particularly suitable for complex analysis scenarios involving hundreds of samples or tens of thousands of protein data points, and reducing overall analysis time.

[0141] The Results Summary Module 170 summarizes and displays the results of quality control analysis, difference analysis, annotation analysis, and enrichment analysis obtained after asynchronous parallel processing. After receiving the parallel analysis results from the four modules, the module integrates and visualizes the data: quality control analysis results are displayed as quality control charts (such as peak area distribution histograms), difference analysis results are presented as volcano plots and heatmaps, and annotation and enrichment analysis results are presented as functional network diagrams and bar charts. Finally, the results are summarized and displayed through a unified interface, allowing researchers to quickly compare multi-dimensional results and form complete data analysis conclusions.

[0142] In specific scenarios, the quantitative quality control analysis module 120, such as... Figure 8 As shown, it includes:

[0143] The protein quantification module 810 is used to determine the peak area data of each protein in each raw sample file of the corresponding project. After standardizing the protein data according to the maximum peak area normalization scheme, it obtains the quantitative results for subsequent differential analysis. The protein quantification module 810 is the core quantitative execution unit of the quantitative quality control analysis module 120. It receives the full protein data processed by the DIANN library from upstream, accurately analyzes and determines the raw chromatographic peak area data of each protein in each raw sample file of the target analysis project. Simultaneously, based on this peak area data, it uses the maximum peak area normalization standardization scheme to systematically correct the full protein data, eliminating systematic errors caused by differences in mass spectrometry response, ensuring the uniformity and comparability of protein quantification data between different samples, and ultimately generating standardized protein quantification results that meet data quality standards and can be directly used in downstream differential analysis.

[0144] The verification result quality control module 820 is used to obtain the visualization results after visualizing the quantitative results and protein data, and to determine the quality control analysis results based on the correlation, number of identifications and peptide length distribution corresponding to the visualization results. The verification result quality control module 820 is the core unit for quality judgment of the quantitative quality control analysis module 120. It receives the standardized quantitative results output by the protein quantification module 810, as well as the raw protein data and basic mass spectrometry detection data output from the upstream library search process. First, the above two types of core data undergo multi-dimensional visualization processing to generate visualization results including sample correlation heatmaps, statistical charts of protein identification numbers, line graphs of peptide length distribution, and scatter plots of retention time stability. Then, based on the visualization results, a systematic quality assessment is conducted around four core quality control dimensions: 1) Protein identification number, verifying whether the protein coverage of the sample detection meets the project's preset standards; 2) Peptide length distribution, verifying the sample enzymatic digestion efficiency and the stability of the mass spectrometry detection process; 3) Index retention time (IRT) stability data, checking the process stability of chromatographic separation and mass spectrometry acquisition; and 4) Correlation data between test samples, verifying the consistency of detection for biologically or technically replicated samples. Finally, based on the multi-dimensional verification results, a clear quality control analysis result is formed, providing a compliance judgment basis for data access in downstream analysis processes, and providing data support for the review of sample detection processes and anomaly tracing.

[0145] Difference analysis module 130, such as Figure 9 As shown, it includes:

[0146] The protein quantification data acquisition module 910 is used to acquire the protein quantification table corresponding to the quality control analysis results and determine the protein quantification data based on the standardized results of the protein quantification table. The protein quantification data acquisition module 910 is the upstream data preprocessing core unit of the difference analysis module 130. It receives the compliant quality control analysis results output by the quantitative quality control analysis module, parses and extracts the standardized protein quantification table matching the quality control results. This table fully covers the core quantitative information such as chromatographic peak area data and expression intensity values ​​of each protein in all samples to be tested within the project. Based on this, the module performs systematic standardized preprocessing on the protein quantification table: first, it performs data filtering to remove low-confidence proteins with a quantitative value missing rate exceeding a preset threshold, ensuring the reliability of the basic analysis data; second, it performs secondary normalization correction of the data based on the maximum peak area normalization scheme to eliminate systematic errors caused by differences in mass spectrometry response, ensuring the comparability of data between different sample groups. Finally, the module combines the sample classification information preset by the project to group and organize the preprocessed protein quantification data, and construct a multidimensional dataset that can be directly used for differential significance analysis. At the same time, it fully retains the sample group labels (such as disease group / control group, experimental group / blank group, etc.), providing standardized and structured input data for downstream differential analysis operations.

[0147] The differential analysis execution module 920 is used to standardize protein quantification data based on the protein quantification table, and then calculate the differential results according to the sample affiliation of the protein quantification data. The differential analysis execution module 920 is the core operation and result output unit of the differential analysis module 130. It receives the standardized grouped protein quantification data output by the protein quantification data acquisition module, first completing the final standardization verification of the protein quantification data based on the protein quantification table, and then, according to the sample affiliation of the protein quantification data, adapting a differential statistical analysis strategy to accurately calculate the differential results. Specifically, for samples with biological or technical replicates, the module uses a T-test scheme to calculate the significance p-value of the difference in protein expression between groups, combined with the standardized quantification results, and simultaneously uses a preset screening threshold for fold change in protein expression to accurately screen target proteins with significant expression differences between groups. For samples without replicates, the module uses a Significance A-test scheme to calculate the significance p-value, and simultaneously uses the fold change screening criterion to screen differentially expressed proteins. After completing the differential protein screening and statistical analysis, the module simultaneously performs supporting visualization analysis, automatically generating differential protein volcano plots, protein expression level clustering heatmaps, and sample clustering tree diagrams. Finally, it outputs complete differential analysis results, including a detailed list of differential proteins, significance statistics, and visualization charts, providing core research targets and data foundations for subsequent functional annotation and functional enrichment analysis.

[0148] Enrichment Analysis Module 150, such as Figure 10 As shown, it includes:

[0149] The functional enrichment execution module 1010 is used to control the protein to perform functional enrichment towards the corresponding functional module using annotation analysis results and differential analysis results. The functional modules include functional pathways or biological functions. The functional enrichment execution module 1010 is the core computational execution unit of the enrichment analysis module 150. It receives the full protein functional annotation analysis results output by the upstream annotation analysis module and the significantly differentially expressed protein analysis results screened by the differential analysis module. Based on this data, it drives the target protein to perform targeted functional enrichment analysis towards the preset functional modules, which are divided into two core categories: functional pathways and biological functions. This module employs a dual enrichment strategy for comprehensive analysis. For functional pathway modules, it performs KEGG and Reactome pathway enrichment analyses, calculating the enrichment level of proteins in various biological pathways based on differentially expressed protein datasets to accurately identify perturbed core signaling pathways under experimental conditions. For biological function modules, it performs GO functional enrichment, protein domain enrichment, and subcellular localization enrichment analyses, uncovering the functional aggregation characteristics of differentially expressed proteins from multiple dimensions, including molecular function, biological processes, and cellular components. The module incorporates various statistical analysis methods, such as hypergeometric distribution tests and Fisher's exact test, and uses the Benjamini-Hochberg method for multiple hypothesis testing and correction, fundamentally ensuring the statistical reliability of the functional enrichment results.

[0150] The enrichment analysis acquisition module 1020 is used to determine the enrichment analysis results corresponding to functional modules based on the functional enrichment results of proteins in functional pathways or biological functions. The enrichment analysis acquisition module 1020 is the result in-depth mining and standardized output unit of the enrichment analysis module 150. Its core function is to receive the full set of functional enrichment results for functional pathways and biological functions output by the functional enrichment execution module, perform systematic data mining, screening, verification, and visualization on these results, and ultimately determine the complete enrichment analysis results corresponding to each functional module. This module possesses four core processing capabilities: First, intelligent filtering of enrichment results, which can filter significantly enriched functional modules based on a preset p-value threshold (default p < 0.05) and eliminate statistically insignificant redundant results; second, hierarchical clustering analysis, which can perform semantic similarity clustering on enrichment results, merge redundant categories with overlapping functions, and extract core biological themes; third, protein-function association network construction, which visualizes the regulatory relationships between target proteins and enriched functional modules, accurately identifying core node proteins; and fourth, multi-dimensional result visualization, which can automatically generate various visualization charts such as enrichment result bar charts, bubble charts, protein-pathway chord diagrams, and KEGG pathway highlighting charts, fully presenting core information such as enrichment significance, number of differentially expressed proteins, and enrichment fold. Finally, the module outputs a structured enrichment analysis result report, including complete enrichment statistics, visualization charts, and accompanying functional annotation information, providing data support for researchers to identify disease-related core biological processes and screen potential drug targets.

[0151] Multi-threaded concurrency module 160, such as Figure 11 As shown, it includes:

[0152] The multi-threaded input module 1110 receives input data from the quantitative quality control analysis module, differential analysis module, annotation analysis module, and enrichment analysis module. As the core unit for input scheduling and preprocessing of the multi-threaded concurrent module 160, the multi-threaded input module 1110 is the central hub for system data flow distribution. It receives all input data from the four core analysis modules (quantitative quality control analysis, differential analysis, annotation analysis, and enrichment analysis) and also receives standardized protein data from the upstream protein data acquisition module, providing stable and compliant data flow support for the asynchronous, parallel, and independent operation of the four analysis modules. During data reception and distribution, the modules simultaneously perform multi-dimensional preprocessing operations: First, the input data is classified and labeled according to the module's functional type, clarifying the analysis stage and processing priority to which the data belongs, providing a basis for intelligent scheduling of multi-threaded parallel tasks; second, the format compatibility of the input data is verified, and data that does not conform to the target module's input specifications is automatically converted to a standardized format, ensuring that each analysis module can directly call the adapted input data without additional format processing; third, based on the volume of the input data and the complexity of the corresponding analysis task, matching memory resources are dynamically allocated to avoid task congestion and resource contention issues that occur during large-scale protein data processing, ensuring that the four analysis modules can start independently and operate in parallel from the data flow perspective, fully releasing the efficiency advantages of the asynchronous parallel processing architecture.

[0153] The multi-threaded output module 1120 is used to send the output data corresponding to the quantitative quality control analysis module, the difference analysis module, the annotation analysis module, and the enrichment analysis module. The multi-threaded output module 1120 is the core unit for output scheduling and transmission management of the multi-threaded concurrent module 160. It is a key node connecting the parallel analysis results with the downstream result aggregation module. Its core function is to receive the output data from the quantitative quality control analysis module, the difference analysis module, the annotation analysis module, and the enrichment analysis module after asynchronous parallel processing, and to accurately and efficiently send it to the downstream result aggregation module, thus completing the unified collection of the parallel analysis results from multiple modules. During data sending and transmission, the module synchronously performs refined end-to-end control operations: First, it adds corresponding timestamps and processing status markers to the output results of each analysis module, including status indicators such as "completed," "partially abnormal," and "processing failed," which not only achieves traceability of the entire analysis process but also provides accurate judgment criteria for the system's anomaly handling mechanism. Second, it performs fragmented compression processing on large-volume analysis result data, significantly reducing the resource consumption of data transmission, improving the efficiency of parallel transmission of results from multiple modules, and avoiding link congestion during large-scale data transmission. Third, it monitors the transmission status of the data output link in real time, verifies the integrity of data transmission, and ensures that the analysis results of each module are accurately delivered to the result aggregation module, eliminating data loss and omissions. Through end-to-end control of output data, the module effectively ensures the stability and reliability of asynchronous parallel processing of multiple modules, providing a complete and standardized data foundation for subsequent result aggregation, visualization, and analysis report generation.

[0154] Results summary module 170, such as Figure 12 As shown, it includes:

[0155] The asynchronous acquisition module 1210 is used to collect the quality control analysis results, difference analysis results, annotation analysis results, and enrichment analysis results after asynchronous parallel processing. The asynchronous acquisition module 1210 is the core data acquisition and scheduling front-end unit of the result aggregation module 170, and a key hub connecting the multi-threaded concurrent module and the result aggregation stage. Its core function is to perform full-process, intelligent acquisition and aggregation of the quality control analysis results, difference analysis results, annotation analysis results, and enrichment analysis results output by the quantitative quality control analysis module, difference analysis module, annotation analysis module, and enrichment analysis module after asynchronous parallel processing. This module possesses four core processing capabilities: First, real-time status monitoring throughout the entire process, dynamically tracking the task processing progress of the four analysis modules and providing real-time updates and visualizations of the analysis startup, running, completed, and abnormal interruption statuses of each module. Second, intelligent automatic data capture, with a built-in callback trigger mechanism, automatically identifies the output results of completed analysis tasks and instantly triggers the corresponding data collection process, achieving real-time aggregation of analysis results without manual intervention, ensuring the timeliness and completeness of data collection. Third, an anomaly tolerance mechanism, marking module results that fail to complete analysis on time, fail processing, or fail data integrity verification with dedicated anomaly markers, while providing optional solutions for retrying collection of abnormal results and skipping anomaly items, preventing single-module analysis anomalies from affecting the overall data collection progress. Fourth, large-scale data volume caching optimization, employing memory mapping technology to cache temporarily collected result data, effectively avoiding performance bottlenecks caused by large-scale proteomics data file read and write operations, ensuring the stability and efficiency of data collection in high-concurrency scenarios with hundreds of samples and tens of thousands of protein data points.

[0156] The summary execution module 1220 is used to summarize and display the results of quality control analysis, difference analysis, annotation analysis, and enrichment analysis. The summary execution module 1220 is the core data integration, visualization, and standardized delivery unit of the result summary module 170. It is primarily responsible for receiving all the quality control analysis results, difference analysis results, annotation analysis results, and enrichment analysis results collected by the asynchronous acquisition module 1210, completing multi-dimensional data integration, structured processing, visualization, and standardized report output. This module addresses the needs of blood proteomics data analysis by implementing three core functions: First, it provides comprehensive visualization of results, adapting customized visualization solutions for different types of analysis results. For quality control analysis results, it generates a quality control overview dashboard, visually displaying key quality control indicators such as sample repeatability, number of protein identifications, peptide length distribution, and index retention time stability in the form of violin plots, sample correlation heatmaps, and peak area distribution line graphs. For differential analysis results, it generates differential protein volcano plots, protein expression level clustering heatmaps, and sample clustering dendrograms, clearly presenting the protein expression differences between different groups. For annotation and enrichment analysis results, it generates functional enrichment bar charts, bubble charts, protein-pathway chord diagrams, KEGG pathway highlighting charts, and protein-function association network diagrams, comprehensively presenting multi-dimensional functional annotation information and core biological pathway enrichment characteristics of proteins. Second, interactive analysis reports are automatically generated, supporting user-defined report templates. Key statistical data, multi-dimensional visualization charts, and core analytical conclusions are automatically embedded into the report templates to generate standardized blood proteomics data analysis reports. Simultaneously, a built-in end-to-end result traceability function allows users to click on any data point or chart element in the report to view the corresponding original analysis records and raw test data, ensuring traceability throughout the entire analysis process. Reports can be exported in multiple formats such as PDF, Excel, and JSON, with exported files retaining complete data interactivity, adapting to delivery needs in different scenarios such as clinical biomarker screening, disease mechanism research, and drug target discovery. Third, adaptive layout intelligent optimization features a built-in adaptive layout algorithm that automatically adjusts the layout of the display interface and report based on the amount of collected data and the complexity of the analytical dimensions. This ensures that core analytical information and key biological conclusions are clearly highlighted, significantly improving the efficiency of interpreting large-scale blood proteomics data analysis results. Ultimately, this, combined with the entire system workflow, achieves a closed-loop process of "delivery immediately upon arrival" from raw mass spectrometry data to standardized analysis report output.

[0157] The protein data analysis and processing system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned protein data analysis and processing method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned protein data analysis and processing method embodiment.

[0158] This embodiment also provides a server, the structural diagram of which is shown below. Figure 13 As shown, the device includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer instructions, which are executed by the processor to implement the steps of the protein data analysis and processing method described above.

[0159] Figure 13 The server shown also includes a bus 103 and a communication interface 104. The processor 101, the communication interface 104, and the memory 102 are connected via the bus 103.

[0160] The memory 102 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 13 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0161] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the user terminal through the network interface.

[0162] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. The processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0163] This invention also provides a storage medium storing a computer program, which, when run by a processor, executes the steps of the protein data analysis and processing method described in the foregoing embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0167] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0168] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for protein data analysis and processing, characterized in that, include: Based on the Uniprot, GO, and KEGG databases, we introduce a human blood low-abundance protein database, a tissue-specific expression database, and a drug target database. The tissue-specific expression database is divided into annotation information for different tissues according to expression levels, and corresponding protein annotation tables are generated for each tissue. Protein name alignment is prioritized based on perfect match. If the match fails, sequence alignment is performed using BLAST software. Annotation information is retrieved from the corresponding database using the ID of the successful alignment. The above-obtained annotation information is summarized with the protein annotation table to form a multidimensional annotation library for blood samples. Protein data analysis was performed based on the aforementioned multidimensional annotation library; The steps for protein data analysis based on the multidimensional annotation library include: The WDL workflow engine is used to call the mass spectrometry analysis software to complete the format conversion, database matching, quantitative calculation and quality control assessment of the protein data to be processed. Based on a Python multithreaded / multiprocess architecture, the differential filtering, enrichment analysis, functional annotation and report generation are performed in parallel. A monitoring mechanism is set up between each module to trigger subsequent steps and finally output the analysis report. During the matching process in the database, the identified protein names are compared with the multidimensional annotation library with the priority of complete matching. If the matching fails, the sequence is compared using BLAST software, and the corresponding annotation information is retrieved from the multidimensional annotation library with the ID of the successful match. During the functional annotation process, a multidimensional annotation library is invoked to provide Uniprot functional annotation, GO annotation, KEGG pathway annotation, blood low-abundance protein annotation, tissue-specific expression annotation, and drug target annotation for proteins obtained by differential screening. During the report generation process, the obtained quantitative results, difference screening results, and functional annotation results are summarized, and the final output is an analysis report containing multidimensional annotation information.

2. The protein data analysis and processing method according to claim 1, characterized in that, The mass spectrometry analysis software is DIANN, the format conversion is performed using msconvert software, and the database matching and quantitative calculation are performed by DIANN software.

3. The protein data analysis and processing method according to claim 1, characterized in that, The quality control assessment includes the following steps: The parameters are statistically analyzed to determine whether the proteins identified in each sample meet the preset standards. The maximum peak area normalization method was implemented using Python to standardize the quantitative values. Based on the standardized data, sample correlation calculations are performed to determine whether the delivery standards are met. If it meets the requirements, it will be processed for further analysis; if it does not meet the requirements, production will resume.

4. The protein data analysis and processing method according to claim 1, characterized in that, Also includes: Obtain the raw data file of the off-machine sample produced by the production platform; After searching the database using the raw sample file and the standardized protein sequence database file via DIANN, the protein data to be processed is obtained based on the original peak area quantification results. The protein data is input into the quantitative quality control analysis module, the differential analysis module, the annotation analysis module, and the enrichment analysis module according to the corresponding process parameters, so as to realize the analysis of the protein data to be processed.

5. A protein data analysis and processing system, characterized in that, The protein data analysis and processing system includes: The local database construction module is used to introduce human blood low-abundance protein databases, tissue-specific expression databases, and drug target databases on the basis of Uniprot, GO, and KEGG databases. The tissue-specific expression database is divided into annotation information for different tissues according to expression levels, and corresponding protein annotation tables are generated for each tissue. Protein name alignment is performed first with exact matches. If the match fails, sequence alignment is performed using BLAST software. The annotation information is retrieved from the corresponding database using the ID of the successful alignment. The obtained annotation information and protein annotation tables are summarized to form a multidimensional annotation library for blood samples. The functional annotation execution module is used to perform functional annotation on proteins based on a multidimensional annotation library, obtain functional annotation results, and determine the corresponding functional annotation analysis results for the proteins based on the functional annotation results. The parallel analysis execution module is used to summarize the functional annotation results through preset concurrent threads to obtain the annotation analysis results corresponding to the protein. The functional annotation execution module includes: a protein data acquisition module, a quantitative quality control analysis module, a differential analysis module, an annotation analysis module, an enrichment analysis module, a multi-threaded concurrency module, and a result summary module; The quantitative quality control analysis module, the differential analysis module, the annotation analysis module, and the enrichment analysis module are respectively located in the multi-threaded concurrent module. The protein data acquisition module is connected to the corresponding input terminals of the quantitative quality control analysis module, the differential analysis module, the annotation analysis module, and the enrichment analysis module through the multi-threaded concurrent module. The result summarization module is connected to the corresponding output terminals of the quantitative quality control analysis module, the differential analysis module, the annotation analysis module, and the enrichment analysis module through the multi-threaded concurrent module. The protein data acquisition module is used to acquire the protein data to be processed through a preset DIANN search library; The quantitative quality control analysis module is used to determine the mass spectrometry data contained in the protein data, obtain the quantitative value by using the maximum peak area verification result corresponding to the mass spectrometry data, and determine the quality control analysis result by using the number of protein identifications, peptide length distribution data, index retention time stability data, and correlation data of the measured samples corresponding to the mass spectrometry data. The differential analysis module is used to determine the protein quantification data contained in the mass spectrometry data based on the quality control analysis results, and to determine the protein differential analysis results based on the protein quantification data. The annotation analysis module is used to perform functional annotation on the protein to obtain the functional annotation result, and to determine the functional annotation analysis result corresponding to the protein based on the functional annotation result. The enrichment analysis module is used to enrich proteins into corresponding functional modules using the results of functional annotation analysis, and then obtain the enrichment analysis results corresponding to the functional modules. A multi-threaded concurrency module is used to perform asynchronous parallel processing on the quantitative quality control analysis module, the difference analysis module, the annotation analysis module, and the enrichment analysis module; The results summary module is used to summarize and display the quality control analysis results, difference analysis results, annotation analysis results, and enrichment analysis results obtained after asynchronous parallel processing.

6. The protein data analysis and processing system according to claim 5, characterized in that, The quantitative quality control analysis module includes: The protein quantification module is used to determine the peak area data of each protein in each raw file of the project corresponding to the protein data, and to standardize the protein data according to the maximum peak area normalization scheme corresponding to the peak area data to obtain the quantitative results for the subsequent differential analysis. The verification result quality control module is used to obtain the visualization processing result after visualizing the quantitative result and the protein data, and to determine the quality control analysis result based on the correlation, number of identifications and peptide length distribution corresponding to the visualization processing result; The difference analysis module includes: The protein quantification data acquisition module is used to acquire the protein quantification table corresponding to the quality control analysis results, and to determine the protein quantification data based on the standardization results of the protein quantification table; The difference analysis execution module is used to standardize the protein quantification data based on the protein quantification table, and then calculate the difference results according to the sample affiliation of the protein quantification data. The enrichment analysis module includes: A functional enrichment execution module is used to control the protein to perform functional enrichment towards the corresponding functional module using the annotation analysis results and the differential analysis results; wherein, the functional module includes: functional pathway class or biological function class; The enrichment analysis acquisition module is used to determine the enrichment analysis result corresponding to the functional module through the functional enrichment result corresponding to the protein in the functional pathway class or the biological function class; The multi-threaded concurrency module includes: A multi-threaded input module is used to receive input data corresponding to the quantitative quality control analysis module, the difference analysis module, the annotation analysis module, and the enrichment analysis module; A multi-threaded output module is used to send the output data corresponding to the quantitative quality control analysis module, the difference analysis module, the annotation analysis module, and the enrichment analysis module; The result aggregation module includes: An asynchronous acquisition module is used to acquire the quality control analysis results, the difference analysis results, the annotation analysis results, and the enrichment analysis results after asynchronous parallel processing. The summary execution module is used to summarize and display the quality control analysis results, the difference analysis results, the annotation analysis results, and the enrichment analysis results.

7. A server, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of the method according to any one of claims 1-4.

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