Gene sequencing data processing method and system

Abstract

The invention relates to the technical field of gene sequencing, in particular to a gene sequencing data processing method and system. The method comprises the following steps: acquiring a crop sample data set, and performing sample cell disruption and gene sequencing to obtain cell gene sequence data of crops; performing variation analysis on the gene sequence data, dividing the gene sequence data into a gene mutation sequence and gene sequence deletion data, further performing gene expression disorder analysis, and predicting disease resistance attenuation and character change; the analysis result is stored in a gene storage database, so that storage and query of gene expression character change data are realized; differential expression analysis is carried out on a crop character change storage database, gene abnormality of crops is detected, and finally improved crop gene data is obtained through a crop gene quality improvement method. According to the invention, through sequencing optimization of crop genes, prediction of gene mutation is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of gene sequencing technology, and in particular to a gene sequencing data processing method and system. Background Art

[0002] Crop genomics research has become one of the core areas for increasing crop yield, improving quality, and enhancing disease resistance and stress tolerance. The rapid development of crop gene sequencing technology, especially high-throughput genome sequencing technology, has greatly promoted the association study between crop genomes and phenotypic characteristics. Through deep sequencing of crop genomes, we can fully understand the genetic information, gene function, variation pattern, and the relationship between crops and traits such as environmental adaptability and disease resistance. Therefore, efficient processing, analysis, and interpretation of gene sequencing data are key links in crop genomics research. Crop genome data is usually very large and contains a variety of complex information, such as gene mutations, structural variations, transcriptional regulation, etc. In addition, crop genomes usually have a high degree of gene repetitiveness, complex gene families, non-standard genome structures, and diversity among species. However, a gene sequencing data processing method and system have the problem of inaccurate prediction of gene mutation direction and inaccurate prediction of changes in trait effects. Summary of the invention

[0003] Based on this, it is necessary to provide a gene sequencing data processing method and system to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a gene sequencing data processing method and system comprises the following steps:

[0005] Step S1: obtaining a crop sample data set; grinding crop sample cells according to the crop sample data set to obtain crop sample cell fragmentation data; performing cell gene sequencing on the crop sample cell fragmentation data to obtain crop cell gene sequence data;

[0006] Step S2: performing gene sequence variation analysis on the crop gene sequence data to obtain the crop gene sequence variation data; performing gene sequence variation classification based on the crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data; performing gene expression disorder analysis based on the gene mutation sequence data and the gene sequence deletion data to obtain gene expression disorder data;

[0007] Step S3: predicting crop disease resistance attenuation based on the abnormal gene expression data, thereby obtaining crop disease resistance attenuation data; performing trait change analysis on the crop disease resistance attenuation data and the abnormal gene expression data, thereby obtaining gene expression trait change data; and storing the gene expression trait change data in a database using a preset gene storage database, thereby obtaining a crop trait change storage database;

[0008] Step S4: Perform differential expression analysis based on the crop trait change storage database to obtain crop differential expression data; perform crop gene anomaly detection based on the crop differential expression data to obtain crop gene anomaly data; perform crop gene quality improvement based on the crop gene anomaly data to obtain crop gene quality improvement data.

[0009] The present invention obtains a crop sample data set and performs cell disruption, and combined with cell gene sequencing technology, the gene sequence data of crops can be fully obtained, providing accurate basic data for subsequent gene variation analysis, functional research, etc. Cell gene sequencing can reveal the diversity and variation of genes by performing efficient full sequence analysis on the crop genome, laying the foundation for further gene function analysis and trait research. Through the variation analysis of gene sequence data, mutations and deletion regions in the genome can be identified, and these variations directly affect the resistance, stress tolerance, growth and development and other traits of crops. Through the division of gene sequence variation, the mutation and deletion types are distinguished, which helps to determine which gene variations are related to the target trait. In addition, based on these variation data, gene expression disorder analysis can be performed, and the affected genes can be further screened out and the abnormalities generated in the biological process can be predicted, helping to identify key pathogenic or harmful gene variations. Through the analysis of gene expression disorder data, the attenuation of disease resistance of crops can be predicted, which can not only provide a basis for the health assessment of crops, but also can find which genes or variations cause the resistance of crops to be weakened under the infection of specific environments or pathogens, thereby providing data support for the improvement direction of enhanced disease resistance. At the same time, combining the data of abnormal gene expression with the analysis of trait changes can help understand the relationship between genes and crop trait changes, discover key trait genes, and promote precision breeding. Storing these gene expression trait change data in the gene storage database can not only manage data efficiently, but also provide a systematic data reference for future crop trait improvement. The establishment of this database can facilitate continuous updating, provide a basis for genomic research on different crops, and support the implementation of gene-phenotype association analysis under different environments. Further differential expression analysis can identify gene expression differences under different conditions at the whole genome level, which helps to understand the response and regulatory mechanism of crop genes under different environments, treatments or mutation conditions. Crop gene anomaly detection based on differential expression data can reveal abnormalities in gene expression and help discover potential mutations or variation regions, especially those key genes that affect important crop traits (such as yield, stress resistance, nutrients, etc.). Combined with the results of gene anomaly detection, the improvement of crop gene quality can be achieved through precise gene editing or breeding technology to optimize key genes, thereby improving the overall quality and adaptability of crops and improving agricultural production efficiency. Therefore, the present invention optimizes a traditional gene sequencing data processing method, solves the problem of inaccurate prediction of gene mutation direction and inaccurate prediction of gene influence on trait changes in the traditional gene sequencing data processing method, and improves the accuracy of predicting crop gene mutation direction and the accuracy of predicting gene influence on trait changes.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire a crop sample data set;

[0012] Step S12: grinding the crop sample cells according to the crop sample data set to obtain crop sample cell crushing data;

[0013] Step S13: performing centrifugal impurity filtration according to the crop sample cell disruption data, thereby obtaining sample cell impurity filtration data;

[0014] Step S14: performing cell gene sequencing on the sample cell impurity filtered data, thereby obtaining crop cell gene sequence data.

[0015] The present invention can effectively extract the genetic material in the cell from the crop sample by obtaining the crop sample data set and performing cell disruption, thereby providing the first step of raw materials for subsequent genomic analysis. Cell disruption can not only ensure the effective release of DNA or RNA in the cell nucleus, but also improve the efficiency and accuracy of subsequent analysis. The broken cell sample provides high-quality raw materials for genome analysis, ensuring the reliability of subsequent sequencing results. After cell disruption, impurity filtration is performed by centrifugation to remove unnecessary impurities in the cell sample, such as cell debris, proteins, lipids and other interfering substances. Impurity filtration not only improves the purity of the sample, but also reduces the impact of interfering substances on genome sequencing data, thereby ensuring that the final gene sequence data has higher quality and accuracy. This step helps to improve the signal-to-noise ratio of the data, making the subsequent genome analysis more accurate. Gene sequencing of samples after cell impurity filtration is a key step in obtaining crop genome information. Gene sequencing can capture the genetic information of crops in the whole genome range, providing rich data support for subsequent gene function analysis, variation detection and phenotypic association studies. By reading the gene sequence in the sample, sequencing technology can identify the presence and expression of genes, thereby revealing the genetic background, variation characteristics, and pathogenic or dominant genes of the crop.

[0016] Preferably, step S14 comprises the following steps:

[0017] Step S141: extracting sample cell nucleic acid from the sample cell impurity filtering data to obtain a crop sample cell nucleic acid dataset;

[0018] Step S142: performing DNA enzyme digestion according to the crop sample cell nucleic acid data set to obtain crop cell DNA fragment data;

[0019] Step S143: performing DNA fragment amplification on the crop cell DNA fragment data, thereby obtaining crop cell DNA fragment amplification data;

[0020] Step S144: performing gene sequencing according to the crop cell DNA fragment amplification data to obtain crop cell gene sequence data.

[0021] The present invention extracts cell nucleic acid from sample cell impurity filtering data, which can ensure that high-purity nucleic acid components are extracted from the sample, providing high-quality raw materials for subsequent genome analysis. This step is the key to ensure the success of the entire genomics study, because pure nucleic acid samples can avoid interference from impurities in the sequencing process and improve the accuracy of downstream analysis. High-quality cell nucleic acid data provides a stable and reliable basis for subsequent DNA digestion, amplification and gene sequencing. After extracting the cell nucleic acid, DNA digestion treatment is performed to cut the macromolecular DNA into smaller fragments. This process is a common technical step in genome analysis, which is intended to facilitate subsequent genome sequencing and fragment splicing. Through digestion, it can not only ensure the generation of DNA fragments suitable for sequencing, but also help researchers to conduct targeted analysis for specific genes or regions, and identify the characteristics of structural variations, mutations and the like of target fragments. This process provides important data for constructing genome data sets and gene annotations. After digestion of DNA fragments, amplification is performed, and replication can be performed on the basis of existing DNA fragments to obtain a sufficient number of fragments for subsequent analysis. The effect of DNA fragment amplification is to ensure that the required gene sequence has sufficient abundance in the experiment so that gene sequencing can be performed more accurately. This step is particularly important for research that requires high-throughput sequencing, as it ensures the quality and reliability of sequencing data and avoids sequencing bias or data loss due to too small a sample size. Ultimately, gene sequencing based on the amplified DNA fragments can fully obtain crop genome information and provide core data for subsequent gene variation analysis, gene function research, and phenotypic association research. Gene sequencing is the basis for revealing the genetic background, gene structure, and function of crops. It can efficiently identify important characteristics such as mutations, variations, duplications, and deletions in crop genes, thereby providing an accurate basis for crop genetic improvement, resistance cultivation, and precision breeding.

[0022] Preferably, step S2 comprises the following steps:

[0023] Step S21: performing gene sequence variation analysis on the crop gene sequence data to obtain crop gene sequence variation data;

[0024] Step S22: performing gene sequence variation classification according to the crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data;

[0025] Step S23: performing genome stability decay prediction based on the gene mutation sequence data to obtain genome stability decay data;

[0026] Step S24: performing gene deletion anomaly analysis based on the gene sequence deletion data to obtain crop gene deletion anomaly data;

[0027] Step S25: Perform gene expression disorder analysis on the genome stability decay data and crop gene deletion abnormality data to obtain gene expression disorder data.

[0028] The present invention can deeply understand various types of variation in crop genomes, including mutations, insertions, deletions, etc., by performing gene sequence variation analysis on crop gene sequence data, thereby revealing genetic changes that affect crop traits. This step provides a basis for identifying key gene variations, and can provide an important basis for subsequent trait improvement, disease resistance enhancement, and breeding strategies. Variation analysis can provide strong support for studying the genetic diversity, adaptability, and performance of crops under different environmental conditions. The division of gene sequence variation further refines the variation types and distinguishes the specific characteristics of gene mutations and gene sequence deletions. By clarifying the types of these variations, scientists can better understand which variations affect crop traits, especially those that cause adverse gene deletions or important gene mutations. Identifying mutations and deletion regions is of great significance for precision breeding and crop genome repair, and helps to improve the disease resistance, stress tolerance, and production performance of crops in a targeted manner. Gene mutation sequence data is further used to predict genome stability attenuation, which can reveal the potential impact of mutation accumulation and genome instability on crops. Genome stability is the core of crop genetic quality. Predicting genome stability decay can provide scientific guidance for crop genetic improvement, healthy breeding and disease prevention, especially predicting risk areas with excessively high mutation frequencies, thereby providing early warning for improvement programs. When analyzing gene sequence deletion data, gene deletion abnormality analysis can help identify those missing gene regions and evaluate the potential impact on crops. Gene deletion leads to the loss of specific functions, thereby affecting the growth, development or disease resistance of crops. Understanding these missing functional defects is crucial for accurately improving crop performance. By identifying abnormal deletion regions, scientists can further analyze the role of missing genes and provide data support for improvement programs. Finally, combining genome stability decay data with crop gene deletion abnormality data for gene expression disorder analysis can help identify gene expression disorders caused by gene mutations or deletions, and then understand how these gene variations affect the overall gene regulatory network of crops. This analysis provides a new perspective for understanding the molecular mechanism of crop trait changes, and can provide a theoretical basis and technical support for crop genetic improvement, trait optimization and gene function restoration.

[0029] Preferably, step S23 includes the following steps:

[0030] Step S231: performing gene mutation accumulation detection according to the gene mutation sequence data to obtain gene mutation accumulation data;

[0031] Step S232: performing chromosome structure break detection on the gene mutation accumulation data, thereby obtaining chromosome structure break data;

[0032] Step S233: Predicting the probability of chromosome rearrangement according to the accumulated gene mutation data, thereby obtaining chromosome rearrangement probability data;

[0033] Step S234: Calculating the gene duplication probability according to the chromosome rearrangement probability data, thereby obtaining the gene duplication probability data;

[0034] Step S235: Predict genome stability decay based on the chromosome structure breakage data and gene duplication probability data to obtain genome stability decay data.

[0035] The present invention detects the accumulation of gene mutations through gene mutation sequence data, which can help scientists gain a deep understanding of the accumulation of mutations in crop genomes and identify regions with higher mutation frequencies. The accumulation of these mutations is often related to changes in crop adaptability, disease resistance, production performance and other traits. Timely detection of mutation accumulation helps to discover potential genome instability, evaluate the impact of mutations on long-term crop adaptability, and provide a basis for further breeding strategies. On the basis of gene mutation accumulation detection, chromosome structure break detection can reveal chromosome structure changes caused by mutation accumulation or external environmental factors. Chromosome structure breakage can cause genome instability, thereby affecting the normal expression and regulation of genes, leading to disorder or adverse changes in crop traits. By detecting chromosome structure breakage, the integrity of crop genomes can be effectively monitored, providing necessary information for preventing and repairing genome damage. Further, the probability of chromosome rearrangement is predicted based on gene mutation accumulation data, which can reveal the rearrangement events that may occur in chromosomes during evolution. These rearrangements lead to duplication, deletion or rearrangement of genes, thereby affecting the function and expression of genes. Understanding the probability of chromosome rearrangement helps predict future changes in crop genomes and helps researchers to perform more accurate genome design and optimization during breeding. In the context of chromosome rearrangement, calculating the probability of gene duplication can help analyze which genes are repeated, leading to overexpression or dysfunction of genes. Gene duplication not only leads to abnormal gene expression, but also affects crop metabolism, resistance and other traits. By calculating the probability of gene duplication, potential "hotspot" areas can be identified, providing targets for subsequent gene improvement and mutation repair. Finally, predicting the genome stability decay of chromosome structure break data and gene duplication probability data can comprehensively evaluate the stability of the genome and provide early warnings for crop genetic improvement. Genome stability is directly related to the genetic diversity and breeding potential of crops. By predicting the decay of genome stability, researchers can take corresponding measures to ensure the stability of the crop genome, avoid trait degradation due to genome instability, and ensure the long-term productivity and adaptability of crops.

[0036] Preferably, step S24 comprises the following steps:

[0037] Step S241: predicting protein function loss according to gene sequence deletion data to obtain protein function loss data;

[0038] Step S242: collecting protease gene fragment deletion data for the gene sequence deletion data to obtain protease gene fragment deletion data;

[0039] Step S243: performing protease inactivation detection according to the white enzyme gene fragment deletion data to obtain crop protease inactivation data;

[0040] Step S244: performing a crop resistance reduction test on the crop protease inactivation data and the protein function loss data to obtain crop resistance reduction data;

[0041] Step S245: predicting the attenuation of crop environmental adaptability according to the crop resistance weakening data and the crop protease inactivation data to obtain crop environmental adaptability attenuation data;

[0042] Step S246: Perform gene deletion anomaly analysis based on the crop environmental adaptability attenuation data and the crop resistance weakening data to obtain crop gene deletion anomaly data.

[0043] The present invention predicts protein function loss through gene sequence deletion data, and can accurately identify protein function loss caused by gene deletion. This process provides core data for studying the impact of gene deletion on crop physiological and biochemical functions, and helps scientists predict which protein function loss in crops will have a negative impact on traits such as disease resistance and stress tolerance. Protein function loss prediction can provide important clues for functional annotation and genetic improvement of crop genes, thereby avoiding the introduction of mutations that will cause function loss when improving crops. On the basis of gene sequence deletion, by collecting protease gene fragment deletion data, specific fragment deletions that affect protease function can be further accurately identified. These protease gene fragment deletions are closely related to crop growth, development, disease resistance, etc. By collecting these deletion fragments, researchers can understand which protease deletions will affect crop growth and its resistance, and provide targeted basis for subsequent functional repair and gene improvement. Further protease inactivation detection based on protease gene fragment deletion data can help identify protease function loss caused by gene deletion or mutation in crops. This test can clearly identify which protease inactivation will affect crop metabolism, disease resistance and physiological functions, especially in the defense mechanism of crops, protease usually plays an important role. After detecting protease inactivation, researchers can give early warnings for crop resistance and adjust breeding strategies. By testing crop resistance reduction on protease inactivation data and protein function loss data, it can help evaluate the decline in crop disease resistance when subjected to environmental stress. This test can reveal the specific impact of gene loss or protein function loss on crop resistance, and predict whether crop resistance will be significantly reduced with gene loss or protein function loss, thereby providing a basis for breeding strategies to improve resistance. Further prediction of environmental adaptability reduction based on resistance reduction data and protease inactivation data can reveal the adaptive changes of crops under changing environments. Environmental adaptability is a key factor for long-term survival and high yield of crops. By predicting adaptive reduction, potential environmental stress risks can be identified in advance, helping to formulate precise breeding strategies to enhance crop environmental adaptability, especially for drought, high temperature, salinity and other adverse conditions. Finally, by performing gene deletion anomaly analysis on environmental adaptability reduction data and resistance reduction data, it can help discover key gene deletions related to environmental adaptability reduction and resistance reduction. This analysis provides the necessary information for the precise repair of crop genomes, allowing researchers to further identify and repair gene deletions that lead to poor crop adaptability or weak disease resistance, and provide theoretical support and technical means for efficient breeding and genome improvement.

[0044] Preferably, step S25 comprises the following steps:

[0045] Step S251: predicting the mutation frequency growth according to the genome stability decay data to obtain mutation frequency growth data;

[0046] Step S252: performing gene structure variation analysis according to the mutation frequency growth data to obtain gene structure variation data;

[0047] Step S253: performing transcription factor abnormality prediction according to the gene structure variation data and the mutation frequency growth data to obtain gene transcription factor abnormality data;

[0048] Step S254: performing genome integrity attenuation detection on the crop gene deletion abnormal data and gene structure variation data to obtain genome integrity attenuation data;

[0049] Step S255: performing gene regulatory network destruction detection according to the genome integrity decay data and the gene transcription factor abnormality data to obtain gene regulatory network destruction data;

[0050] Step S256: Perform gene expression disorder analysis on the gene regulatory network destruction data and the genome integrity attenuation data to obtain gene expression disorder data.

[0051] The present invention can effectively identify the trend and rate of mutation accumulation in the genome by predicting the growth of mutation frequency according to the genome stability decay data, and then evaluate the long-term stability of the genome. The growth of mutation frequency is often closely related to the changes in crop traits such as genetic diversity, adaptability, and stress resistance. By accurately predicting the growth of mutation frequency, researchers can warn of future genetic trends, provide data support for improving the stability and breeding efficiency of crops, and provide a basis for early intervention to prevent the negative effects of mutation accumulation (such as decreased disease resistance, weakened adaptability, etc.). Gene structure variation analysis based on mutation frequency growth data can reveal large-scale structural changes in the genome, such as gene rearrangement, insertion, deletion, etc. Gene structure variation has an important impact on the phenotype, development process, and resistance of crops. By analyzing these variations, researchers can gain an in-depth understanding of how the structural characteristics of genes and their changes lead to the expression of crop traits, providing basic data for gene function research, crop improvement, and precision breeding. Then, combining gene structure variation data and mutation frequency growth data to predict transcription factor abnormalities can help researchers identify which transcription factors have functional abnormalities due to variations in gene structure. Transcription factors play a key role in gene expression regulation. Abnormal transcription factor function leads to disordered gene expression, thus affecting the growth, development and stress resistance of crops. By predicting transcription factor abnormalities, researchers can effectively discover gene regulation disorders that lead to loss of crop function or poor traits, providing a basis for the repair and optimization of crop gene regulatory networks. By performing genome integrity attenuation detection on crop gene deletion abnormality data and gene structure variation data, it is possible to assess whether there are large deletions or damages in the crop genome, which directly affect the growth, reproduction and stress resistance of crops. Genome integrity is the basis for healthy crop growth, and any structural damage leads to decreased crop productivity. By detecting these data, regions in the genome that cause functional loss can be discovered in a timely manner, thereby providing support for improving the stability of crop genomes. Furthermore, combining genome integrity attenuation data with gene transcription factor abnormality data for gene regulatory network damage detection can reveal potential disordered regions in the gene regulatory network. The gene regulatory network is the core of gene expression and metabolic regulation, and any damage leads to abnormal development or decreased stress resistance of crops. By detecting the damage to this network, researchers can locate the key regions in the genome that affect crop performance, providing a theoretical basis for improving the gene expression regulatory network of crops. Finally, by analyzing the gene expression disorders of the gene regulatory network damage data and the genome integrity attenuation data, it is possible to comprehensively evaluate the gene expression problems caused by structural damage or regulatory disorders in the genome. Abnormal gene expression not only affects the development and adaptability of crops, but also leads to the weakening of important traits such as disease resistance and stress tolerance.

[0052] Preferably, step S3 comprises the following steps:

[0053] Step S31: predicting crop disease resistance attenuation based on the abnormal gene expression data, thereby obtaining crop disease resistance attenuation data;

[0054] Step S32: performing crop growth rate attenuation calculation according to the abnormal gene expression data to obtain crop growth rate attenuation data;

[0055] Step S33: performing trait change analysis based on the crop growth rate attenuation data and the crop disease resistance attenuation data to obtain gene expression trait change data;

[0056] Step S34: using a preset gene storage database to store the gene expression trait change data in a database to obtain a crop trait change storage database.

[0057] The present invention predicts the attenuation of crop disease resistance based on abnormal gene expression data, and can accurately evaluate the degree of reduction in crop disease resistance caused by disordered gene expression. This step can provide important data support for crop disease resistance improvement by quantifying the impact of abnormal gene expression on the crop immune system, and help researchers identify and repair key genes that cause resistance attenuation. Through this prediction, researchers can identify the risk of reduced disease resistance in advance, so as to take appropriate intervention measures to ensure the survival and reproduction ability of crops in future environments. Further, the calculation of crop growth rate attenuation based on abnormal gene expression data helps to understand how abnormal gene expression affects the growth and development of crops. Crop growth rate is one of the core indicators for measuring crop productivity, and its attenuation is related to factors such as abnormal gene expression and limited nutrient absorption. Through this calculation, it can effectively reveal which abnormal gene expression affects the growth rate of crops, thereby providing a theoretical basis for improving crop growth performance and improving growth rate. Trait change analysis based on crop growth rate attenuation data and disease resistance attenuation data can comprehensively evaluate the trait changes caused by abnormal gene expression in crops. This analysis can reveal the comprehensive trait changes of crops in terms of resistance, adaptability, yield, etc., and provide powerful guidance for the genetic improvement of crops. Through a comprehensive analysis of trait changes, researchers can identify which gene variants or abnormal expressions affect the overall performance of crops, thereby providing a scientific basis for precision breeding and performance improvement. Finally, using a gene storage database to store gene expression trait change data can ensure efficient archiving and convenient query of these data. The establishment of a database not only helps with the long-term storage and management of existing data, but also provides a convenient infrastructure for future data sharing and genotype-phenotype association analysis. Through the centralized storage of trait change data, researchers can quickly access, integrate and analyze data from different crop types or under different environmental conditions.

[0058] Preferably, step S4 comprises the following steps:

[0059] Step S41: performing differential expression analysis according to the crop trait change storage database to obtain crop differential expression data;

[0060] Step S42: performing crop gene anomaly detection according to the crop differential expression data to obtain crop gene anomaly data;

[0061] Step S43: annotating the crop change storage database according to the differential expression data and the crop gene abnormality data to obtain crop change annotation data;

[0062] Step S44: Improve crop gene quality according to the crop variation annotation data to obtain crop gene quality improvement data.

[0063] The present invention can identify gene expression differences between different samples, different environmental conditions or different crop varieties by performing differential expression analysis on the crop trait change storage database. This analysis not only helps researchers find expression changes of crops at specific growth stages or when subjected to specific stresses, but also reveals which genes play a key role in different phenotypic performances. Based on the crop differential expression data, crop gene anomaly detection can effectively screen and identify gene anomalies that cause crop performance to decline. This process helps to find gene mutations or functional deficiencies related to problems such as crop disease susceptibility, low yield, and weakened resistance, and provides key information for subsequent gene repair, gene function analysis, and crop improvement. By annotating the crop change storage database in combination with differential expression data and crop gene anomaly data, these data can be more systematically associated with known gene functions and traits. This annotation process can not only help researchers understand how gene anomalies affect crop traits, but also provide more background information for the data, so as to facilitate better understanding and utilization of these data. Crop gene quality improvement can be carried out according to crop change annotation data, and crop gene improvement can be carried out in a targeted manner based on the results of gene anomalies and differential expression analysis. The core goal of this step is to improve important traits such as crop resistance, adaptability, growth rate and ultimate yield by identifying key gene regulatory networks.

[0064] The present invention also provides a gene sequencing data processing system for executing the gene sequencing data processing method as described above, the gene sequencing data processing system comprising:

[0065] Gene sequencing module: obtain crop sample data set; grind crop sample cells according to the crop sample data set to obtain crop sample cell crushing data; perform cell gene sequencing on the crop sample cell crushing data to obtain crop cell gene sequence data;

[0066] Gene expression disorder analysis module: perform gene sequence variation analysis on crop gene sequence data to obtain crop gene sequence variation data; perform gene sequence variation classification based on crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data; perform gene expression disorder analysis based on gene mutation sequence data and gene sequence deletion data to obtain gene expression disorder data;

[0067] Database storage module: predict crop disease resistance attenuation based on abnormal gene expression data, thereby obtaining crop disease resistance attenuation data; perform trait change analysis on crop disease resistance attenuation data and abnormal gene expression data, thereby obtaining gene expression trait change data; use a preset gene storage database to store the gene expression trait change data in a database, thereby obtaining a crop trait change storage database;

[0068] Gene quality improvement module: perform differential expression analysis based on the crop trait change storage database to obtain crop differential expression data; perform crop gene anomaly detection based on the crop differential expression data to obtain crop gene anomaly data; perform crop gene quality improvement based on the crop gene anomaly data to obtain crop gene quality improvement data.

[0069] The present invention has beneficial effects. The present invention obtains crop sample data sets and performs cell disruption. Combined with cell gene sequencing technology, the gene sequence data of crops can be fully obtained, providing accurate basic data for subsequent gene variation analysis, functional research, etc. Cell gene sequencing can reveal the diversity and variation of genes by performing efficient full sequence analysis of crop genomes, laying the foundation for further gene function analysis and trait research. Through the variation analysis of gene sequence data, mutations and deletion regions in the genome can be identified, and these variations directly affect the resistance, stress tolerance, growth and development and other traits of crops. By dividing the gene sequence variation, distinguishing the mutation and deletion types, it is helpful to determine which gene variations are related to the target traits. In addition, based on these variation data, abnormal gene expression analysis can be performed, and the affected genes can be further screened out and the abnormalities produced in the biological process can be predicted, helping to identify key pathogenic or harmful gene variations. By analyzing abnormal gene expression data, predicting the attenuation of crop disease resistance can not only provide a basis for crop health assessment, but also find out which genes or mutations cause the crop resistance to weaken under specific environments or pathogens, thereby providing data support for the improvement direction of enhanced disease resistance. At the same time, combining abnormal gene expression data for trait change analysis helps to understand the relationship between genes and crop trait changes, discover key trait genes, and promote precision breeding. Storing these gene expression trait change data in a gene storage database can not only manage data efficiently, but also provide a systematic data reference for future crop trait improvement. The establishment of this database can facilitate continuous updating, provide a basis for genomic research on different crops, and support the implementation of gene-phenotype association analysis under different environments. Further differential expression analysis can identify differences in gene expression under different conditions at the whole genome level, which helps to understand the response and regulatory mechanism of crop genes under different environments, treatments or mutation conditions. Crop gene anomaly detection based on differential expression data can reveal abnormalities in gene expression and help discover potential mutations or variant regions, especially those key genes that affect important traits of crops (such as yield, stress resistance, nutrients, etc.). Combined with the results of gene anomaly detection, for the improvement of crop gene quality, key genes can be optimized through precise gene editing or breeding technology, thereby improving the overall quality and adaptability of crops and improving agricultural production efficiency. Therefore, the present invention is an optimization process made to a traditional gene sequencing data processing method, which solves the problem of inaccurate prediction of gene mutation direction and inaccurate prediction of gene effect changes on traits in a traditional gene sequencing data processing method, and improves the accuracy of crop gene mutation direction prediction and gene effect changes on traits prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1A schematic diagram of the steps of a gene sequencing data processing method;

[0071] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0072] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0073] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0074] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0075] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0076] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0077] To achieve this, please refer to Figures 1 to 3 , a gene sequencing data processing method, comprising the following steps:

[0078] Step S1: obtaining a crop sample data set; grinding crop sample cells according to the crop sample data set to obtain crop sample cell fragmentation data; performing cell gene sequencing on the crop sample cell fragmentation data to obtain crop cell gene sequence data;

[0079] Step S2: performing gene sequence variation analysis on the crop gene sequence data to obtain the crop gene sequence variation data; performing gene sequence variation classification based on the crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data; performing gene expression disorder analysis based on the gene mutation sequence data and the gene sequence deletion data to obtain gene expression disorder data;

[0080] Step S3: predicting crop disease resistance attenuation based on the abnormal gene expression data, thereby obtaining crop disease resistance attenuation data; performing trait change analysis on the crop disease resistance attenuation data and the abnormal gene expression data, thereby obtaining gene expression trait change data; and storing the gene expression trait change data in a database using a preset gene storage database, thereby obtaining a crop trait change storage database;

[0081] Step S4: Perform differential expression analysis based on the crop trait change storage database to obtain crop differential expression data; perform crop gene anomaly detection based on the crop differential expression data to obtain crop gene anomaly data; perform crop gene quality improvement based on the crop gene anomaly data to obtain crop gene quality improvement data.

[0082] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a gene sequencing data processing method of the present invention. In this example, the gene sequencing data processing method includes the following steps:

[0083] Step S1: obtaining a crop sample data set; grinding crop sample cells according to the crop sample data set to obtain crop sample cell fragmentation data; performing cell gene sequencing on the crop sample cell fragmentation data to obtain crop cell gene sequence data;

[0084] In an embodiment of the present invention, a crop sample data set is collected, including crop samples at different growth stages and different varieties. The collected samples must be strictly screened to ensure the representativeness and pollution-free nature of the samples. Subsequently, mechanical or chemical methods are used to grind and crush the cells, usually using liquid nitrogen grinding or an ultrasonic cell disruptor to break the cell membrane in the crop sample, thereby releasing nucleic acids, proteins and cell components in the cell. The crushed sample is centrifuged to remove unnecessary impurities to obtain cell crushing data. Next, high-throughput gene sequencing technology, such as the Illumina platform or the PacBio platform, is used to perform gene sequencing on the cell crushing sample to obtain the cell gene sequence data of the crop. Gene sequencing constructs a library using the extracted DNA, and determines the gene sequence through a DNA synthesis reaction, thereby obtaining high-quality gene sequence data.

[0085] Step S2: performing gene sequence variation analysis on the crop gene sequence data to obtain the crop gene sequence variation data; performing gene sequence variation classification based on the crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data; performing gene expression disorder analysis based on the gene mutation sequence data and the gene sequence deletion data to obtain gene expression disorder data;

[0086] In an embodiment of the present invention, after the acquisition of the gene sequence data, the original sequence is aligned with the reference genome by an alignment tool such as BWA (Burrows-WheelerAligner) or Bowtie to identify the variation of the gene sequence. For each gene position, GATK (Genome Analysis Toolkit) is used to perform SNP (single nucleotide polymorphism) and InDel (insertion / deletion) detection, and then the gene sequence variation data of the crop is obtained. According to these variation information, the gene sequence variation is divided into two categories: gene mutation sequence and gene sequence missing data. The gene mutation sequence is further confirmed by comparing the variation detected to determine whether it affects the functional gene. For missing data, the unaligned gene fragments are reassembled using a de novo assembly method (such as SPAdes or Trinity) to identify the gene missing region. Finally, in combination with the mutant sequence and missing data, differential analysis tools such as DESeq2 are used to analyze the changes in gene expression to obtain abnormal gene expression data.

[0087] Step S3: predicting crop disease resistance attenuation based on the abnormal gene expression data, thereby obtaining crop disease resistance attenuation data; performing trait change analysis on the crop disease resistance attenuation data and the abnormal gene expression data, thereby obtaining gene expression trait change data; and storing the gene expression trait change data in a database using a preset gene storage database, thereby obtaining a crop trait change storage database;

[0088] In an embodiment of the present invention, based on the abnormal gene expression data, a data analysis platform such as "edgeR" or "DESeq2" in the R language is used to predict the attenuation of disease resistance in combination with the expression level, mutation and missing information of the gene. By comparing the known gene regions related to resistance in the crop genome, bioinformatics methods such as functional annotation and pathway enrichment analysis are used to predict which gene mutations lead to reduced disease resistance. For example, if a gene involved in functions such as insect resistance and disease resistance mutates or its expression level decreases, resistance will be attenuated. Then, based on the disease resistance attenuation data and the abnormal gene expression data, crop trait change analysis is performed. Through multi-dimensional statistical analysis (such as principal component analysis PCA or cluster analysis), crop trait change data under the conditions of resistance attenuation and abnormal gene expression are obtained. Finally, these data are stored in a gene storage database, such as MySQL or MongoDB, for unified management and storage, to construct a crop trait change storage database.

[0089] Step S4: Perform differential expression analysis based on the crop trait change storage database to obtain crop differential expression data; perform crop gene anomaly detection based on the crop differential expression data to obtain crop gene anomaly data; perform crop gene quality improvement based on the crop gene anomaly data to obtain crop gene quality improvement data.

[0090] In an embodiment of the present invention, by querying the crop trait change storage database, data mining techniques (such as differential analysis, regression analysis, etc.) are used to perform differential expression analysis on the stored trait change data. Differential analysis tools such as "limma" are used to compare the expression differences of genes between different crop samples to find genes that show significant differences under different traits. According to these differential expression data, gene abnormality detection methods such as genome scanning based on SNP and InDel or gene function annotation are used to identify whether genes are abnormal or mutated. This process helps to detect pathogenic gene mutations that cause differences in crop performance. Finally, combining differential expression data with gene abnormality data, systems biology tools (such as Cytoscape) are used to annotate gene-trait relationships to determine which gene mutations or deletions will cause changes in crop traits. Finally, through gene improvement algorithms, such as gene editing technology (CRISPR-Cas9) or traditional breeding methods, the obtained crop gene quality improvement data is used to select suitable candidate genes for crop genetic improvement to optimize crop disease resistance, yield and quality.

[0091] Preferably, step S1 comprises the following steps:

[0092] Step S11: Acquire a crop sample data set;

[0093] Step S12: grinding the crop sample cells according to the crop sample data set to obtain crop sample cell crushing data;

[0094] Step S13: performing centrifugal impurity filtration according to the crop sample cell disruption data, thereby obtaining sample cell impurity filtration data;

[0095] Step S14: performing cell gene sequencing on the sample cell impurity filtered data, thereby obtaining crop cell gene sequence data.

[0096] In the embodiment of the present invention, in the process of obtaining the crop sample data set, it is necessary to collect crop samples from different regions, different growth stages and different environmental conditions to ensure the diversity and representativeness of the samples. Use appropriate sampling tools, such as surgical scissors, sampling forceps, etc., to extract the leaves, stems or roots of crops from the farmland. Then the collected samples are numbered and grouped according to the set conditions to ensure that each group of samples comes from crops under the same environment and growth conditions. According to the obtained crop sample data set, the cell components in the sample are extracted using cell disruption technology. Liquid nitrogen grinding is usually used to disrupt the cells of the collected crop samples. The liquid nitrogen grinding method freezes the crop samples at extremely low temperatures to make the cell walls brittle, thereby breaking the cell membrane by grinding and releasing nucleic acids, proteins and other components in the cells. The ultrasonic disruption method uses the mechanical vibration of high-frequency sound waves to destroy the cell walls and release the cell contents into the solution. Regardless of which method is used, the broken samples should be centrifuged to remove larger particulate matter and obtain cell disruption data. During the cell disruption process, the temperature and time need to be controlled to avoid degradation or denaturation of nucleic acids, proteins, etc. in the sample. After cell disruption, the sample contains various impurities, such as cell membrane fragments, incompletely broken cells and organelles, etc. These impurities interfere with subsequent gene sequencing. Therefore, the sample needs to be centrifuged to filter impurities. By setting appropriate centrifugation conditions, such as a rotation speed of 3000rpm and a centrifugation time of 5 minutes, larger cell fragments and impurities are precipitated at the bottom of the tube, while smaller cell contents (including nucleic acids) are retained in the supernatant. Use an Eppendorf centrifuge for operation, use a 15mL centrifuge tube to ensure that the sample is not contaminated during the operation, and obtain the sample cell impurity filtration data by aspirating the supernatant after the operation. The sample liquid after centrifugal impurity filtration enters the cell gene sequencing stage, and high-quality DNA is extracted from the filtered sample. Common DNA extraction kits (such as Qiagen DNeasy or Thermo Fisher GeneJET) can be used to extract DNA from cells. During the extraction process, the kit operation manual must be strictly followed to ensure the integrity and purity of the DNA. After the extraction is completed, the DNA is quantified and quality tested, and its concentration and purity are measured using NanoDrop. Next, the extracted DNA is sequenced using high-throughput gene sequencing technology. Commonly used sequencing platforms include Illumina NovaSeq or HiSeq series, which can provide extremely high sequencing depth and accuracy and are suitable for high-precision crop genome analysis. Through DNA sequencing, crop cell gene sequence data is obtained.

[0097] Preferably, step S14 comprises the following steps:

[0098] Step S141: extracting sample cell nucleic acid from the sample cell impurity filtering data to obtain a crop sample cell nucleic acid dataset;

[0099] Step S142: performing DNA enzyme digestion according to the crop sample cell nucleic acid data set to obtain crop cell DNA fragment data;

[0100] Step S143: performing DNA fragment amplification on the crop cell DNA fragment data, thereby obtaining crop cell DNA fragment amplification data;

[0101] Step S144: performing gene sequencing according to the crop cell DNA fragment amplification data to obtain crop cell gene sequence data.

[0102] In an embodiment of the present invention, in the process of extracting sample cell nucleic acid from sample cell impurity filtering data, cell nucleic acid is extracted from the filtered sample to ensure that a pure DNA or RNA sample is obtained. This process uses a standardized cell nucleic acid extraction method, and commonly used extraction kits such as Qiagen DNeasy or Thermo Fisher GeneJET can effectively extract high-quality DNA. According to specific needs, the corresponding extraction method is selected (such as for samples with thicker plant cell walls, CTAB or PVP buffer is selected to improve the efficiency of nucleic acid extraction). During the extraction process, the temperature and time of the sample need to be controlled to prevent DNA degradation. The extracted nucleic acid is measured by Nanodrop to measure its concentration and purity to ensure that the sample does not contain impurities and confirm that the DNA quality meets the requirements of gene sequencing. After obtaining the crop sample cell nucleic acid data set, a DNA enzyme digestion operation is performed. Select a suitable restriction endonuclease (such as EcoRI, BamHI, HindIII, etc.) to digest DNA according to the characteristics of the crop genome. These enzymes can accurately identify specific DNA sequences and cut out DNA fragments with the same restriction sequence. During the DNA enzyme digestion process, the enzyme digestion reaction system needs to include a suitable buffer and reaction temperature to ensure that the enzyme activity is maximized. Common reaction conditions include incubation at 37°C for 30 minutes to 1 hour. After enzyme digestion, the fragment size is separated by agarose gel electrophoresis to further verify the enzyme digestion effect and the integrity of the DNA fragment. The integrity and size range of the DNA fragment are confirmed by electrophoresis detection, and the crop cell DNA fragment data is finally obtained. The crop cell DNA fragment data after enzyme digestion is amplified. The polymerase chain reaction (PCR) technology is used to amplify the target DNA fragment. The PCR reaction system includes high-fidelity DNA polymerase, primers (forward primers and reverse primers), dNTPs, buffer and sample DNA. Specific primers are designed according to the characteristics of the DNA fragment to be amplified, and the PCR conditions are optimized to ensure the best amplification efficiency. The amplification procedure usually includes pre-denaturation steps (95°C for 5 minutes), denaturation (95°C for 30 seconds), annealing (depending on the melting temperature of the primer, generally 55-65°C for 30 seconds), extension (72°C for 1 minute) and other steps, and end extension (72°C for 10 minutes) is performed after 30-40 cycles. After amplification, agarose gel electrophoresis is used to detect the size and concentration of the PCR product. If the amplified DNA fragment meets the requirements, the crop cell DNA fragment amplification data is obtained. After the DNA fragment amplification is completed, the gene sequencing stage is entered. The amplified DNA fragments are sequenced using high-throughput gene sequencing platforms such as Illumina NovaSeq or PacBio. Before sequencing, the PCR product needs to be purified to remove impurities such as primers and dNTPs that are not involved in the reaction to ensure the sequencing quality.The sequencing process uses a suitable library construction method to connect the DNA fragments to adapters, and perform library amplification and purification. After the library is constructed, it is loaded into the gene sequencing platform for high-throughput sequencing, usually using short read length (Illumina) or long read length (PacBio) technology to obtain complete gene sequence data. During the sequencing process, ensure that the sequencing depth of each DNA fragment is sufficient to ensure the coverage and accuracy of the genome. After the sequencing data is quality controlled (such as removing low-quality reads, connector contamination, etc.), the crop cell gene sequence data is obtained.

[0103] Preferably, step S1 comprises the following steps:

[0104] Step S21: performing gene sequence variation analysis on the crop gene sequence data to obtain crop gene sequence variation data;

[0105] Step S22: performing gene sequence variation classification according to the crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data;

[0106] Step S23: performing genome stability decay prediction based on the gene mutation sequence data to obtain genome stability decay data;

[0107] Step S24: performing gene deletion anomaly analysis based on the gene sequence deletion data to obtain crop gene deletion anomaly data;

[0108] Step S25: Perform gene expression disorder analysis on the genome stability decay data and crop gene deletion abnormality data to obtain gene expression disorder data.

[0109] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0110] Step S21: performing gene sequence variation analysis on the crop gene sequence data to obtain crop gene sequence variation data;

[0111] In an embodiment of the present invention, the genome sequence is aligned and calibrated, and standard gene alignment tools, such as BWA or Bowtie2, are used to ensure accurate alignment of sequencing data. Gene sequence variation analysis uses software tools such as GATK (Genome Analysis Toolkit) or Samtools to perform variation detection on the aligned data to identify single nucleotide polymorphisms (SNPs) and small insertion / deletion (InDels) variations. During variation detection, appropriate quality thresholds and filtering criteria, such as the minimum variation quality value and the minimum supported number of reads, are set to ensure the reliability and accuracy of variation detection. Through statistical analysis of variation information, crop gene sequence variation data is finally obtained.

[0112] Step S22: performing gene sequence variation classification according to the crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data;

[0113] In an embodiment of the present invention, gene sequence variation information is classified, and SNPs variations are annotated using existing databases such as dbSNP to distinguish common variations from potential pathogenic mutations. For each mutation site, it is further classified according to the gene region in which it is located, and is divided into non-coding region variations and coding region variations. For mutations in the coding region, they are further divided into synonymous mutations, missense mutations, and nonsense mutations. In addition, according to the missing parts of the gene sequence, tools such as BEDTools or VCFtools are used to analyze the variation data, mark the missing regions in the gene sequence, and determine whether these regions affect the coding of functional genes. Finally, gene mutation sequence data and gene sequence missing data are obtained through the above classification and analysis.

[0114] Step S23: performing genome stability decay prediction based on the gene mutation sequence data to obtain genome stability decay data;

[0115] In an embodiment of the present invention, a special genome stability analysis tool, such as StabilitySeq or Genomic Instability Scanner (GIS), is used. These tools can combine known genome stability indicators, such as mutation accumulation rate, gene rearrangement frequency, and chromosome number, to conduct in-depth analysis of mutation sequence data. In the prediction process, the effect of mutation on gene function is evaluated according to the location and type of mutation, and then whether the mutation causes structural instability of the genome is analyzed. By comparing normal and mutant genome data, the trend of genome stability decay is calculated in combination with bioinformatics algorithms, and finally the genome stability decay data is obtained.

[0116] Step S24: performing gene deletion anomaly analysis based on the gene sequence deletion data to obtain crop gene deletion anomaly data;

[0117] In an embodiment of the present invention, the missing gene region is compared with the reference genome using data in VCF format to identify whether the missing region affects the presence of specific functional genes. For each missing region of the crop genome, a functional analysis is further performed in combination with a gene annotation database (such as Ensembl, NCBI, etc.) to determine whether the missing gene is related to important physiological characteristics of the crop (such as disease resistance, stress resistance, nutrient accumulation, etc.). On this basis, a genome browser (such as IGV) is used to visualize the missing region to check whether there is a large-scale chromosome deletion or loss of important genes. Through these analyses, abnormal crop gene deletion data is finally obtained.

[0118] Step S25: Perform gene expression disorder analysis on the genome stability decay data and the crop gene deletion abnormality data to obtain gene expression disorder data.

[0119] In an embodiment of the present invention, the genome stability decay data is combined with the gene deletion abnormal data, and the potential effects of mutation and deletion on gene expression are evaluated by bioinformatics analysis methods. The gene expression patterns of different treatment groups are compared using transcriptome data (RNA-Seq), and statistical analysis methods such as DESeq2 or EdgeR are applied to identify genes whose gene expression levels change significantly when the genome stability decays or the gene deletion is abnormal. For genes with abnormal expression, gene function enrichment analysis is further performed, and the functions of these genes in plant resistance, growth, development, etc. are analyzed using GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases. Finally, abnormal gene expression data are obtained through these analyses.

[0120] Preferably, step S23 includes the following steps:

[0121] Step S231: performing gene mutation accumulation detection according to the gene mutation sequence data to obtain gene mutation accumulation data;

[0122] Step S232: performing chromosome structure break detection on the gene mutation accumulation data, thereby obtaining chromosome structure break data;

[0123] Step S233: Predicting the probability of chromosome rearrangement according to the accumulated gene mutation data, thereby obtaining chromosome rearrangement probability data;

[0124] Step S234: Calculating the gene duplication probability according to the chromosome rearrangement probability data, thereby obtaining the gene duplication probability data;

[0125] Step S235: Predict genome stability decay based on the chromosome structure breakage data and gene duplication probability data to obtain genome stability decay data.

[0126] In an embodiment of the present invention, the gene sequence data is compared with the reference genome, using genome comparison tools such as BWA or Bowtie2. The mutation sites occurring in the sample are identified by comparison, including single nucleotide mutations (SNPs), insertions and deletions (InDels) and other types of mutations. Next, the mutation frequency and the type of mutation in each gene region are counted, with particular attention paid to the high-frequency mutation region. For the accumulation of mutation frequency, software such as VCFtools is used to perform statistical analysis on the mutation data in VCF format, and the increasing trend of mutation frequency over time or condition changes in different samples is identified by calculating the cumulative density of mutations. Gene mutation accumulation data is obtained by such analysis, and the gene mutation accumulation data is analyzed within the whole genome in combination with chromosome map information. By software such as BreakDancer or Delly, chromosome structure rearrangement detection is performed for mutation accumulation regions to identify chromosome breakage or rearrangement phenomena present in the genome. Specifically, these tools can detect structural variations such as chromosome deletions, inversions, and translocations present in the genome according to the mutation accumulation situation. By comparing the positions of genome breaks and mutations, it is further determined which mutations cause the structural instability of the genome, thereby obtaining chromosome structure breakage data. Statistical models and algorithms are used to predict the probability of chromosome rearrangement by comparing gene mutation accumulation data with known chromosome rearrangement patterns. Tools such as MosaicPlot or CIRCOS are used to visualize chromosome structure in order to observe whether there is a higher risk of rearrangement in regions with high mutation frequencies in the genome. Based on the existing genome database and mutation data, the model algorithm is combined to predict chromosome rearrangement events in the genome, and the prediction accuracy is further optimized using machine learning algorithms or probability analysis methods. Through this step, chromosome rearrangement probability data is obtained to identify chromosome regions that are prone to structural rearrangement during mutation accumulation. Using the previously generated chromosome rearrangement data, genes involved in the rearrangement region are screened, with special attention paid to genes located in the rearrangement hotspot region. These genes have copy number changes due to chromosome rearrangement. Next, Copy Number Variation (CNV) analysis tools such as GISTIC or Control-FREEC are used to analyze the duplication of each gene in different individuals through genome data and calculate the duplication probability of each gene. By analyzing the frequency of gene duplication in different samples, it is determined which genes have a higher probability of duplication in the chromosome rearrangement region. The analysis results of the first two steps are combined to evaluate the relationship between gene mutation accumulation, chromosome breakage, and gene duplication. A genome stability decay model is established using a specialized stability decay prediction algorithm, combined with the correlation between chromosome breakage location, gene duplication frequency, and mutation location. Bioinformatics tools, such as StabilitySeq or Instability-SCAN, are used for data integration and prediction.These tools calculate the mutation frequency, structural rearrangement and duplication probability of each region of the genome, and comprehensively derive the trend of genome stability decay. Finally, statistical analysis is performed based on these data to predict the probability of genome stability decay under different environments or selection pressures, and obtain the final genome stability decay data.

[0127] Preferably, step S24 comprises the following steps:

[0128] Step S241: predicting protein function loss according to gene sequence deletion data to obtain protein function loss data;

[0129] Step S242: collecting protease gene fragment deletion data for the gene sequence deletion data to obtain protease gene fragment deletion data;

[0130] Step S243: performing protease inactivation detection according to the white enzyme gene fragment deletion data to obtain crop protease inactivation data;

[0131] Step S244: performing a crop resistance reduction test on the crop protease inactivation data and the protein function loss data to obtain crop resistance reduction data;

[0132] Step S245: predicting the attenuation of crop environmental adaptability according to the crop resistance weakening data and the crop protease inactivation data to obtain crop environmental adaptability attenuation data;

[0133] Step S246: Perform gene deletion anomaly analysis based on the crop environmental adaptability attenuation data and the crop resistance weakening data to obtain crop gene deletion anomaly data.

[0134] In the embodiment of the present invention, the region missing in the gene is identified by gene deletion data, and special attention is paid to the missing sites that affect the protein function. Protein structure prediction software, such as SWISS-MODEL or Phyre2, is used to analyze the influence of the missing sequence on the protein structure and function in combination with the reference genome and functional gene information. By comparing the relationship between the sequence missing in the genome and the known functional region, it is identified which gene deletions lead to the loss of protein function. Then, by comparing databases such as InterPro or Pfam, the influence of gene deletion on the protein functional domain is further predicted, thereby obtaining the predicted protein function loss data. Genes containing protease function are screened out, and which protease gene regions are missing according to the gene deletion data. By comparing the known protease gene database (such as MEROPS) and the specific crop genome information, it is determined which protease genes or gene fragments are missing. Use genome analysis tools, such as BEDTools or GATK, to extract the missing data of specific genes or gene fragments. These missing data are mapped to the specific protease gene region to generate the protease gene fragment missing data. Utilize the known functional structure of gene deletion data and protease, combined with the predicted protease structure changes, analyze whether the missing fragment leads to the loss of protease activity. Use molecular dynamics simulation tools such as GROMACS or AutoDock to simulate the functional residues of proteases and evaluate the impact of missing fragments on their active sites. By comparing with functional verification data, such as enzyme activity assays or protease inhibitor experiments, determine which proteases lose activity due to the loss of gene fragments. Combine experimental data and calculation results to finally obtain crop protease inactivation data. Combine protease inactivation data and protein function loss data to analyze the impact of these changes on crop disease resistance, pest resistance and other abilities. Use databases of plant immune response-related genes, such as PlantCyc or PANTHER, to identify genes related to immune response and their functions, and evaluate the relationship between protease inactivation and immune response ability. By comparing genotype and phenotype data, combined with biostatistical methods such as analysis of variance or regression analysis, evaluate the impact of protein function loss and protease inactivation on reduced crop resistance. Verify the prediction results through experimental data, such as disease resistance experiments or pest tests, and finally obtain crop resistance reduction data. Perform correlation analysis on reduced resistance data and genotype-phenotype data related to environmental adaptability. Environmental adaptability models (such as environmental adaptability index or crop growth model) and resistance reduction data are used to analyze the adaptability of crops under different environmental conditions.Combine crop genome information and ecological and environmental factors, and apply simulation algorithms, such as LUMO (Land Use and Modeling Optimization) or GIS (Geographic Information System) tools, to predict the adaptability decline trend of crops under adverse environmental conditions. By combining climate change models, the impact of reduced resistance on environmental adaptability is predicted, so as to obtain crop environmental adaptability decline data. Combine environmental adaptability decline data with resistance reduction data to analyze which genes are related to resistance reduction and environmental adaptability decline. Through gene function annotation databases such as Gene Ontology (GO) and KEGG, identify key genes related to environmental adaptability and resistance. Combine genomic data and use bioinformatics methods such as gene enrichment analysis and pathway analysis to identify which genes are missing and cause crop adaptability to decline. Use gene mutation detection tools such as GATK or Samtools to further verify the accuracy of missing data, and finally determine the crop gene deletion abnormality data related to gene deletion abnormality.

[0135] Preferably, step S25 comprises the following steps:

[0136] Step S251: predicting the mutation frequency growth according to the genome stability decay data to obtain mutation frequency growth data;

[0137] Step S252: performing gene structure variation analysis according to the mutation frequency growth data to obtain gene structure variation data;

[0138] Step S253: performing transcription factor abnormality prediction according to the gene structure variation data and the mutation frequency growth data to obtain gene transcription factor abnormality data;

[0139] Step S254: performing genome integrity attenuation detection on the crop gene deletion abnormal data and gene structure variation data to obtain genome integrity attenuation data;

[0140] Step S255: performing gene regulatory network destruction detection according to the genome integrity decay data and the gene transcription factor abnormality data to obtain gene regulatory network destruction data;

[0141] Step S256: Perform gene expression disorder analysis on the gene regulatory network destruction data and the genome integrity attenuation data to obtain gene expression disorder data.

[0142] In an embodiment of the present invention, genome stability decay data is analyzed to identify which gene regions have undergone more frequent mutations. The frequency information of mutation events is extracted by genome variation detection tools (such as GATK, Samtools, etc.), and the cumulative frequency of mutations under different time or environmental conditions is calculated. Statistical analysis methods (such as linear regression, time series analysis) are used to predict the growth trend of mutation frequency. By evaluating the type of mutation site (such as single nucleotide polymorphism (SNP), insertion / deletion (Indel), etc.) and the frequency change of mutation, the predicted mutation frequency growth data is obtained. The mutation frequency growth data is associated with the change of gene structure. Through genome annotation information, it is determined which genes or gene regions are affected in the process of mutation frequency growth. Structural variation detection tools (such as Manta, Delly, etc.) are used to perform in-depth analysis of mutation data to identify large-scale structural variations (such as gene rearrangement, inversion, copy number variation, etc.) in the genome. Combined with the frequency growth data of mutations, it is analyzed whether these structural variations are related to changes in gene expression or loss of function. The specific location of gene structure variation is presented by visualization tools (such as IGV), and gene structure variation data is finally obtained. Identify transcription factor genes affected in the process of mutation frequency growth. Transcription factor databases (such as TFDB and JASPAR) combined with gene structural variation data are used to analyze whether the mutation region covers the transcription factor binding site or regulatory region. Gene expression prediction tools (such as CAGE and RNA-Seq analysis) are used to analyze whether these variations lead to abnormal expression or functional changes of transcription factors. By comparing the transcription factor action mechanisms in existing literature and databases, the impact of increased mutation frequency on the normal function of transcription factors is predicted. Integrate gene deletion abnormal data with gene structural variation data to analyze which gene deletions and structural variations will cause damage to the overall integrity of the genome. Use genome integrity detection tools (such as CheckM and QUAST) to assess the quality of genome data and determine which regions in the genome where deletions or structural variations lead to genome integrity attenuation. Combined with existing genome annotation information, further analyze the potential impact of deletions and variant regions on gene expression. Quantitative analysis methods (such as gene copy number variation (CNV) analysis and genome alignment) are used to measure the degree of attenuation, and finally obtain genome integrity attenuation data. The genome integrity attenuation data is combined with gene transcription factor abnormality data to identify which genes lose normal regulation during integrity attenuation. Gene regulatory network analysis tools (such as Cytoscape and GeneMANIA) are used to construct gene regulatory networks, and genes with impaired regulatory functions are identified based on abnormal data of gene transcription factors. Combined with gene expression data (such as RNA-Seq data), the relationship between damaged transcription factors and their downstream target genes is analyzed to evaluate their impact on the entire gene regulatory network. By comparing normal and abnormal gene regulatory networks, gene regulatory network disruption data is obtained.Combined with the data on disruption of the gene regulatory network, evaluate whether abnormal gene regulation leads to abnormal gene expression levels. Gene expression analysis tools (such as DESeq2 and EdgeR) are used to analyze gene expression data to identify which genes have abnormal expression in the context of genome integrity attenuation and gene regulatory network disruption. Heat maps, principal component analysis (PCA) and other methods are used to intuitively display changes in gene expression patterns and further screen out key genes with abnormal expression levels. Combined with experimental verification (such as qPCR, Western blot, etc.), changes in gene expression are confirmed, and finally data on abnormal gene expression are obtained.

[0143] Preferably, step S3 comprises the following steps:

[0144] Step S31: predicting crop disease resistance attenuation based on the abnormal gene expression data, thereby obtaining crop disease resistance attenuation data;

[0145] Step S32: performing crop growth rate attenuation calculation according to the abnormal gene expression data to obtain crop growth rate attenuation data;

[0146] Step S33: performing trait change analysis based on the crop growth rate attenuation data and the crop disease resistance attenuation data to obtain gene expression trait change data;

[0147] Step S34: using a preset gene storage database to store the gene expression trait change data in a database to obtain a crop trait change storage database.

[0148] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0149] Step S31: predicting crop disease resistance attenuation based on the abnormal gene expression data, thereby obtaining crop disease resistance attenuation data;

[0150] In an embodiment of the present invention, the abnormal gene expression data is deeply analyzed to screen out key genes that affect the disease resistance of crops. These genes are functionally annotated using bioinformatics analysis tools (such as Gene Ontology, KEGG pathway) to determine whether they are closely related to biological processes or pathways related to disease resistance (such as immune response, pathogen recognition, etc.). Combined with the disease resistance phenotypic data of crops (such as disease resistance test results), the relationship between gene expression and disease resistance is established through statistical models (such as multiple regression analysis, decision trees, etc.). The model predicts the potential impact of different gene mutations or abnormal expression on crop disease resistance based on changes in gene expression levels, and ultimately obtains crop disease resistance attenuation data.

[0151] Step S32: performing crop growth rate attenuation calculation according to the abnormal gene expression data to obtain crop growth rate attenuation data;

[0152] In an embodiment of the present invention, the abnormal expression data of genes is analyzed to identify which abnormal expression of genes is related to the change of growth rate. By consulting existing literature and databases (such as plant genome database, PANTHER, etc.), the abnormally expressed genes are compared with the genes known to affect growth, and a list of growth-related genes is constructed. Then, in combination with the growth data of crops (such as growth curve, root length, leaf area, etc.), the specific impact of abnormal gene expression on growth rate is calculated by mathematical models (such as linear regression model, Logistic regression model). By matching the growth rate data at different time points with the gene expression data, the growth rate attenuation data is obtained.

[0153] Step S33: performing trait change analysis based on the crop growth rate attenuation data and the crop disease resistance attenuation data to obtain gene expression trait change data;

[0154] In an embodiment of the present invention, the growth rate attenuation data and the disease resistance attenuation data are integrated to determine whether the two attenuation factors are correlated with each other and their common influence on the trait change. Statistical analysis methods (such as variance analysis, principal component analysis, etc.) are used to evaluate the correlation between gene expression, disease resistance and growth rate, and identify key genes or genetic variations that affect crop trait changes. By comparing trait data (such as phenotypic data such as plant height, yield, disease resistance, etc.) with attenuation data, the change pattern of crop traits under different growth and disease resistance attenuation backgrounds is further determined. Finally, through comprehensive analysis, gene expression trait change data is obtained.

[0155] Step S34: using a preset gene storage database to store the gene expression trait change data in a database to obtain a crop trait change storage database.

[0156] In an embodiment of the present invention, all generated trait change data are collated, including mutations of related genes, abnormal expression information, and corresponding phenotypic changes. According to data storage requirements, a suitable database format (such as SQL, NoSQL, etc.) is selected to organize and store the data. A database management system (such as MySQL, MongoDB, etc.) is used to build a special crop gene trait change database. All data are classified in the database according to key features such as crop varieties, genes, trait types, phenotypes, etc., to ensure that the data can be easily retrieved and analyzed. Data access and extraction are performed through the data query interface in the database (such as SQL query, API, etc.) to provide efficient data support for subsequent genotype-phenotype association analysis, crop breeding research, and genetic improvement. Finally, a complete crop trait change storage database is obtained.

[0157] Preferably, step S4 comprises the following steps:

[0158] Step S41: performing differential expression analysis according to the crop trait change storage database to obtain crop differential expression data;

[0159] Step S42: performing crop gene anomaly detection according to the crop differential expression data to obtain crop gene anomaly data;

[0160] Step S43: annotating the crop change storage database according to the differential expression data and the crop gene abnormality data to obtain crop change annotation data;

[0161] Step S44: Improve crop gene quality according to the crop variation annotation data to obtain crop gene quality improvement data.

[0162] In an embodiment of the present invention, all trait change data, including gene expression levels, phenotypic changes, and experimental results under different environmental conditions, are extracted from the database. Differential expression analysis tools (such as DESeq2, edgeR, etc.) are used to perform statistical tests on gene expression in the data to identify genes with significant expression differences under different conditions. During the analysis, the trait change data is combined with the gene expression data, and genes closely related to specific trait changes (such as disease resistance, growth rate, etc.) are selected. The expression level of each gene is standardized to further determine which genes show significant expression differences in different treatment groups or at different time points. The false positive rate is controlled by multiple test correction (such as the Benjamini-Hochberg method), and the differential expression data of crops are finally obtained. Genes with abnormal performance are screened out from the differential expression data, especially those with significant expression changes under multiple experimental conditions. Gene mutations or deletions are analyzed using gene abnormality detection methods (such as SNP calling, Indel detection) to check whether these genes have functional or structural variations. Genome data analysis tools (such as GATK, Samtools) are further applied to compare the genome sequence to identify whether there are abnormalities such as mutations, copy number variations (CNVs), or deletions compared with the normal genotype. The detection results of abnormal genes will be compared with known gene function data to identify genes that affect crop phenotypes or traits. Finally, crop gene abnormality data is obtained, and the crop gene abnormality data is combined with differential expression data to perform detailed functional annotations for each gene. Gene function annotation tools (such as BLAST, InterProScan, etc.) are used to identify genes related to known functions by comparing genome sequences or protein sequences, especially genes related to crop traits such as resistance, nutrient accumulation, and disease resistance. Annotation information (such as gene name, function, relationship with traits, etc.) is attached to each relevant gene in the database to form detailed crop gene annotation data. By establishing gene function annotation mapping rules, the changes in different traits are associated with gene expression and mutation status, and complete crop change annotation data is finally obtained. Genes that are significantly associated with target traits (such as disease resistance, stress resistance, yield, etc.) are screened out from the variation annotation data. Combined with modern gene editing technologies (such as CRISPR-Cas9, TALENs, etc.), these key genes are targeted for editing. Through gene editing technology, gene variants with advantageous traits are introduced into the crop genome, or the loss of gene function caused by mutation is repaired. In actual operation, suitable crop varieties are selected to ensure the stable transmission and expression of the edited genes. Through plant transformation technology, the edited genes are passed on to the offspring, and finally the gene improvement effect is verified through field trials.If the improved variety shows the expected traits (such as improved disease resistance, increased yield, etc.), the genetically improved variety can be further promoted. Ultimately, the genetic quality improvement data of crops can be obtained.

[0163] The present invention also provides a gene sequencing data processing system for executing the gene sequencing data processing method as described above, the gene sequencing data processing system comprising:

[0164] Gene sequencing module: obtain crop sample data set; grind crop sample cells according to the crop sample data set to obtain crop sample cell crushing data; perform cell gene sequencing on the crop sample cell crushing data to obtain crop cell gene sequence data;

[0165] Gene expression disorder analysis module: perform gene sequence variation analysis on crop gene sequence data to obtain crop gene sequence variation data; perform gene sequence variation classification based on crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data; perform gene expression disorder analysis based on gene mutation sequence data and gene sequence deletion data to obtain gene expression disorder data;

[0166] Database storage module: predict crop disease resistance attenuation based on abnormal gene expression data, thereby obtaining crop disease resistance attenuation data; perform trait change analysis on crop disease resistance attenuation data and abnormal gene expression data, thereby obtaining gene expression trait change data; use a preset gene storage database to store the gene expression trait change data in a database, thereby obtaining a crop trait change storage database;

[0167] Gene quality improvement module: perform differential expression analysis based on the crop trait change storage database to obtain crop differential expression data; perform crop gene anomaly detection based on the crop differential expression data to obtain crop gene anomaly data; perform crop gene quality improvement based on the crop gene anomaly data to obtain crop gene quality improvement data.

[0168] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for processing gene sequencing data, characterized in that: The following steps are involved: Step S1: obtaining a crop sample data set; grinding the crop sample cells according to the crop sample data set to obtain crop sample cell crushing data; Perform cell gene sequencing on the cell fragmentation data of crop samples to obtain the crop cell gene sequence data; Step S2: performing gene sequence variation analysis on the crop gene sequence data to obtain the crop gene sequence variation data; performing gene sequence variation classification based on the crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data; performing gene expression disorder analysis based on the gene mutation sequence data and the gene sequence deletion data to obtain gene expression disorder data; Step S3: predicting crop disease resistance attenuation based on the abnormal gene expression data, thereby obtaining crop disease resistance attenuation data; performing trait change analysis on the crop disease resistance attenuation data and the abnormal gene expression data, thereby obtaining gene expression trait change data; and storing the gene expression trait change data in a database using a preset gene storage database, thereby obtaining a crop trait change storage database; Step S4: performing differential expression analysis according to the crop trait change storage database to obtain crop differential expression data; performing crop gene anomaly detection according to the crop differential expression data to obtain crop gene anomaly data; Crop gene quality improvement is performed based on crop gene abnormality data to obtain crop gene quality improvement data.

2. The method for processing gene sequencing data according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire a crop sample data set; Step S12: grinding the crop sample cells according to the crop sample data set to obtain crop sample cell crushing data; Step S13: performing centrifugal impurity filtration according to the crop sample cell disruption data, thereby obtaining sample cell impurity filtration data; Step S14: performing cell gene sequencing on the sample cell impurity filtered data, thereby obtaining crop cell gene sequence data.

3. The method for processing gene sequencing data according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: extracting sample cell nucleic acid from the sample cell impurity filtering data to obtain a crop sample cell nucleic acid dataset; Step S142: performing DNA enzyme digestion according to the crop sample cell nucleic acid data set to obtain crop cell DNA fragment data; Step S143: performing DNA fragment amplification on the crop cell DNA fragment data, thereby obtaining crop cell DNA fragment amplification data; Step S144: performing gene sequencing according to the crop cell DNA fragment amplification data to obtain crop cell gene sequence data.

4. The method for processing gene sequencing data according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing gene sequence variation analysis on the crop gene sequence data to obtain crop gene sequence variation data; Step S22: performing gene sequence variation classification according to the crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data; Step S23: performing genome stability decay prediction based on the gene mutation sequence data to obtain genome stability decay data; Step S24: performing gene deletion anomaly analysis based on the gene sequence deletion data to obtain crop gene deletion anomaly data; Step S25: Perform gene expression disorder analysis on the genome stability decay data and crop gene deletion abnormality data to obtain gene expression disorder data.

5. The method for processing gene sequencing data according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: performing gene mutation accumulation detection according to the gene mutation sequence data to obtain gene mutation accumulation data; Step S232: performing chromosome structure break detection on the gene mutation accumulation data, thereby obtaining chromosome structure break data; Step S233: Predicting the probability of chromosome rearrangement according to the accumulated gene mutation data, thereby obtaining chromosome rearrangement probability data; Step S234: Calculating the gene duplication probability according to the chromosome rearrangement probability data, thereby obtaining the gene duplication probability data; Step S235: Predict genome stability decay based on the chromosome structure breakage data and gene duplication probability data to obtain genome stability decay data.

6. The method for processing gene sequencing data according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: predicting protein function loss according to gene sequence deletion data to obtain protein function loss data; Step S242: collecting protease gene fragment deletion data for the gene sequence deletion data to obtain protease gene fragment deletion data; Step S243: performing protease inactivation detection according to the white enzyme gene fragment deletion data to obtain crop protease inactivation data; Step S244: performing a crop resistance reduction test on the crop protease inactivation data and the protein function loss data to obtain crop resistance reduction data; Step S245: predicting the attenuation of crop environmental adaptability according to the crop resistance weakening data and the crop protease inactivation data to obtain crop environmental adaptability attenuation data; Step S246: Perform gene deletion anomaly analysis based on the crop environmental adaptability attenuation data and the crop resistance weakening data to obtain crop gene deletion anomaly data.

7. The method for processing gene sequencing data according to claim 4, characterized in that: Step S25 includes the following steps: Step S251: predicting the mutation frequency growth according to the genome stability decay data to obtain mutation frequency growth data; Step S252: performing gene structure variation analysis according to the mutation frequency growth data to obtain gene structure variation data; Step S253: performing transcription factor abnormality prediction according to the gene structure variation data and the mutation frequency growth data to obtain gene transcription factor abnormality data; Step S254: performing genome integrity attenuation detection on the crop gene deletion abnormal data and gene structure variation data to obtain genome integrity attenuation data; Step S255: performing gene regulatory network destruction detection according to the genome integrity decay data and the gene transcription factor abnormality data to obtain gene regulatory network destruction data; Step S256: Perform gene expression disorder analysis on the gene regulatory network destruction data and the genome integrity attenuation data to obtain gene expression disorder data.

8. The method for processing gene sequencing data according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: predicting crop disease resistance attenuation based on the abnormal gene expression data, thereby obtaining crop disease resistance attenuation data; Step S32: performing crop growth rate attenuation calculation according to the abnormal gene expression data to obtain crop growth rate attenuation data; Step S33: performing trait change analysis based on the crop growth rate attenuation data and the crop disease resistance attenuation data to obtain gene expression trait change data; Step S34: using a preset gene storage database to store the gene expression trait change data in a database to obtain a crop trait change storage database.

9. The gene sequencing data processing method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing differential expression analysis according to the crop trait change storage database to obtain crop differential expression data; Step S42: performing crop gene anomaly detection according to the crop differential expression data to obtain crop gene anomaly data; Step S43: annotating the crop change storage database according to the differential expression data and the crop gene abnormality data to obtain crop change annotation data; Step S44: Improve crop gene quality according to the crop variation annotation data to obtain crop gene quality improvement data.

10. A gene sequencing data processing system, characterized in that: Used to execute the gene sequencing data processing method according to claim 1, the gene sequencing data processing system comprises: Gene sequencing module: obtain crop sample data set; grind crop sample cells according to the crop sample data set to obtain crop sample cell crushing data; perform cell gene sequencing on the crop sample cell crushing data to obtain crop cell gene sequence data; Gene expression disorder analysis module: perform gene sequence variation analysis on crop gene sequence data to obtain crop gene sequence variation data; perform gene sequence variation classification based on crop gene sequence variation data to obtain gene mutation sequence data and gene sequence deletion data; perform gene expression disorder analysis based on gene mutation sequence data and gene sequence deletion data to obtain gene expression disorder data; Database storage module: predict crop disease resistance attenuation based on abnormal gene expression data, thereby obtaining crop disease resistance attenuation data; perform trait change analysis on crop disease resistance attenuation data and abnormal gene expression data, thereby obtaining gene expression trait change data; use a preset gene storage database to store the gene expression trait change data in a database, thereby obtaining a crop trait change storage database; Gene quality improvement module: perform differential expression analysis based on the crop trait change storage database to obtain crop differential expression data; perform crop gene anomaly detection based on the crop differential expression data to obtain crop gene anomaly data; perform crop gene quality improvement based on the crop gene anomaly data to obtain crop gene quality improvement data.

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