Method, equipment, medium and program product for evaluating protein binding efficiency at gene transcription level

Abstract

The invention provides a method, equipment, a medium and a program product for evaluating protein binding efficiency at a gene transcription level, and relates to the field of biological analysis. The method comprises the following steps: acquiring at least one to-be-detected SNP; ref and Alt sequence pairs are called based on SNP; extracting the number of regulatory proteins combined with the Ref and Alt sequences of the sequence pair; the number of proteins combined with the Ref sequence is calculated and recorded as NumRef, and the number of proteins combined with the Alt sequence is calculated and recorded as NumAlt; and evaluating the influence of the SNP on the protein binding efficiency according to the ratio of the NumRef to the NumAlt. According to the development process, systematic regulation between SNP and transcriptional genome regulation elements is studied, how to evaluate the protein binding efficiency by SNP on the gene transcription level is deeply excavated, life laws implied behind biological data are excavated, and related life science problems are solved.

Description

Technical Field

[0001] The present invention relates to the field of biological analysis, and more specifically, to a method, a device, a medium and a program product for evaluating protein binding efficiency at the gene transcription level. Background Art

[0002] In biology, the transcription process is a key step in regulating gene expression and is essential for controlling cell development and fate; abnormalities in the transcription process can lead to serious health problems, including genetic diseases and cancer. For example, errors in the transcription process may lead to incorrect protein synthesis, which in turn affects the structure and function of the protein. Transcription is the process of converting DNA into RNA, in which DNA fragments are copied into messenger RNA (mRNA). Transcription determines the genes expressed in the cell, thereby determining the type and function of the cell. For example, in muscle cells, genes related to muscle contraction will be transcribed, while in nerve cells, genes related to transmitting nerve signals may be expressed.

[0003] Genomic regulatory elements are key regulators in the processes of gene transcription, expression, and translation. At the transcriptional level, enhancers, promoters, promoter flanking regions (PFRs), open chromatin regions (OCRs), and CTCFBS (CCCTC binding factors) are able to bind to specific proteins, such as transcription factors (TFs) and CTCF, thereby affecting transcriptional activity and regulating gene function. Molecular dysregulation of genomic regulatory elements may lead to human diseases, however, the functions of these elements remain poorly understood. Previous studies have shown that genomic regulatory elements overlap with the vast majority of genetic variations, especially single nucleotide polymorphisms (SNPs), which are prone to abnormal regulation and are likely to modulate disease susceptibility. Therefore, it is crucial to dissect the systemic regulation between SNPs, target gene regulatory elements, and the original phenotype at the gene transcription level. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a method, device, medium and program product for evaluating protein binding efficiency at the gene transcription level; the method development process of the present invention studies the system regulation between SNP and transcriptional genome regulatory elements, thereby deeply exploring how SNP evaluates protein binding efficiency at the gene transcription level, exploring the life laws hidden behind biological data, and solving related life science problems.

[0005] The first aspect of the present application discloses a method for evaluating protein binding efficiency at the gene transcription level, the method comprising: 101, obtaining at least one SNP to be tested; 102, retrieving a Ref and Alt sequence pair based on the SNP; 103, extracting the number of regulatory proteins bound by the sequence pair and the Ref and Alt sequences; 104, calculating the number of proteins bound by the Ref sequence, recorded as Num Ref Calculate the number of proteins bound to the Alt sequence and record it as Num Alt ; 105, according to the Num Ref and Num Alt The ratio of was used to evaluate the effect of SNP on protein binding efficiency.

[0006] In some embodiments, the step 105 includes: if the ratio is not equal to a first threshold, outputting an evaluation result of the effect of the SNP to be tested on the protein binding efficiency; the SNP to be tested is recorded as the first SNP.

[0007] In some embodiments, the 105 includes: if the ratio is greater than the first threshold, outputting an evaluation result that the SNP to be tested affects the protein binding efficiency is low; the SNP to be tested is recorded as the second SNP; if the ratio is less than the first threshold, outputting an evaluation result that the SNP to be tested affects the protein binding efficiency is high; the SNP to be tested is recorded as the third SNP.

[0008] In some embodiments, the method further includes: 106, screening sequence pairs with binding motifs located on transcriptional gene regulatory elements from the several sequence pairs; 107, calculating a first ratio of the number a of binding motifs overlapping only with the Ref sequence to the number b of binding motifs not overlapping with the Ref sequence based on the position information of the binding motifs, and calculating a second ratio of the number c of binding motifs overlapping only with the Alt sequence to the number d of binding motifs not overlapping with the Alt sequence; calculating a change value of the protein binding motif based on the first ratio and the second ratio; screening a SNP whose change value is less than a second threshold as a fourth SNP, and outputting an evaluation result of the fourth SNP affecting the protein binding efficiency.

[0009] In some embodiments, the method further comprises: taking the intersection between the first SNP and the fourth SNP, and outputting an evaluation result of the SNP in the intersection affecting the protein binding efficiency.

[0010] In some embodiments, the method further includes: taking the intersection between the second SNP and the fourth SNP, and outputting a prediction result that the protein binding efficiency of the SNP in the intersection is low; taking the intersection between the third SNP and the fourth SNP, and outputting a prediction result that the protein binding efficiency of the SNP in the intersection is high.

[0011] In some embodiments, the gene regulatory elements include: enhancers, open chromatin regions, promoters, promoter flanking regions; the regulatory proteins include transcription factors and CTCFs.

[0012] The second aspect of the present application discloses a computer device, which includes: a memory and a processor; the memory is used to store a computer program; and the processor executes the computer program to implement the steps of the above method.

[0013] A third aspect of the present application discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0014] A fourth aspect of the present application discloses a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0015] The present application has the following beneficial effects: 1. The present application innovatively discloses a method for evaluating protein binding efficiency at the gene transcription level. The method retrieves Ref and Alt sequence pairs based on SNPs, and calculates the FC value based on the number of regulatory proteins that bind to the Ref and Alt sequence pairs in the sequence pairs, and gives the evaluation result of SNP on protein binding efficiency according to the FC value. Furthermore, the quantitative result of whether SNP has a high or low impact on protein binding efficiency is given according to the size of the FC value. Furthermore, the present application also calculates P according to the number of binding motifs that only overlap with or do not overlap with the Ref and Alt sequences. T value, according to FC value and P T The relationship between the values ​​can further screen the factors that affect the protein binding efficiency. This method can better analyze the relationship between SNP and protein binding at the transcriptional level and improve the understanding of molecular regulation at the gene level.

[0016] 2. This application innovatively develops a new genome positioning method to screen sequence pairs located on binding motifs on transcriptional gene regulatory elements, abandoning the macro positioning method in existing databases that only locates the genome. Compared with the positioning method in existing databases that only locates the genome but not the genome regulatory elements, the positioning method in this application is more microscopic, precise, and accurate, and the results of subsequent analysis and evaluation are also more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a schematic diagram of a method flow chart provided by the first aspect of an embodiment of the present invention;

[0019] Figure 2 is a schematic diagram of a system for evaluating protein binding efficiency at the gene transcription level provided by an embodiment of the present invention;

[0020] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present invention;

[0021] Figure 4 is a schematic diagram of the architecture of an exemplary computing device provided by an embodiment of the present invention;

[0022] Figure 5 is a schematic diagram of a storage medium provided by an embodiment of the present invention;

[0023] Figure 6 is a fine-scale mapping map of myopia-related SNPs on genome-wide regulatory elements provided by an embodiment of the present invention; wherein, Figure 6 A is the distribution of each genomic regulatory element in transcription and post-transcriptional processes; Figure 6 B is the percentage of one-to-one pairing during transcription and post-transcription; Figure 6 C is the length of the genomic regulatory elements covering the myopia-associated SNPs, and the black-marked horizontal line in each violin represents the median length of each element; Figure 6 D is the SNP density of each regulatory element at the transcriptional and post-transcriptional levels. The dotted line shows the average SNP density of the entire human genome;

[0024] Figure 7 is a schematic diagram of the heterogeneity of TF binding mediated by SNPs between four regulatory elements in the transcription process provided by an embodiment of the present invention; wherein, Figure 7 A is the heterogeneity of binding between TF and enhancer, Figure 7 B is the heterogeneity of binding between TF and OCR, Figure 7 C is the heterogeneity of binding between TF and promoter, Figure 7 D is the heterogeneity of the binding between TF and PFR; P T Values ​​were calculated using Fisher's exact test, and FC was calculated by fold change. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0026] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0028] Figure 1 1 is a flow chart of a method for evaluating protein binding efficiency at the gene transcription level provided by an embodiment of the present invention. Specifically, the method comprises the following steps: 101: obtaining at least one SNP to be tested;

[0029] The full name of SNP is Single Nucleotide Polymorphism, which refers to the variation of a single nucleotide (base pair) in a genomic DNA sequence. This variation may be a substitution (e.g., A becomes G), an insertion or a deletion. SNP is the most common form of genetic variation in the genome, and their distribution in the population is polymorphic, that is, different individuals may have different nucleotides at the same position. In some embodiments, the SNP to be tested comes from a subject, and the terms "subject" or "person to be tested" or "sample to be tested" used herein refer to any animal (e.g., mammal), including but not limited to humans, non-human primates, rodents, etc., which will become the recipient of a specific treatment. Generally, the terms "subject" and "patient" are used interchangeably herein when referring to human subjects. Preferably, the subject is a human.

[0030] 102: Retrieve Ref and Alt sequence pairs based on SNP;

[0031] In some embodiments, based on each SNP, the Ref and Alt sequence pairs corresponding to the SNP are retrieved. Specifically, the Ref and Alt sequences are retrieved Mbp upstream and downstream to obtain several sequence pairs, where M is a natural number greater than 1; the range of M is 10-35, preferably 20. Specifically, Ref is a wild type, and Alt is a mutant.

[0032] 103: extracting the number of regulatory proteins bound by the sequence pair with the Ref and Alt sequences;

[0033] 104: Calculate the number of proteins bound to the Ref sequence and record it as Num Ref Calculate the number of proteins bound to the Alt sequence and record it as Num Alt ;

[0034] 105: According to the Num Ref and Num Alt The ratio of was used to evaluate the effect of SNP on protein binding efficiency.

[0035] In some embodiments, the step 105 includes: if the ratio is not equal to the first threshold, outputting the evaluation result of the SNP to be tested affecting the protein binding efficiency; the SNP to be tested is recorded as the first SNP. In a more specific embodiment, the first threshold ranges from 0.8 to 1.5; preferably 1.

[0036] In some embodiments, the 105 includes: if the ratio is greater than the first threshold, outputting an evaluation result that the SNP to be tested affects the protein binding efficiency is low; the SNP to be tested is recorded as the second SNP; if the ratio is less than the first threshold, outputting an evaluation result that the SNP to be tested affects the protein binding efficiency is high; the SNP to be tested is recorded as the third SNP.

[0037] In this embodiment, the first threshold is obtained through training of training set samples, and it can be a specific threshold or an interval range. The specific form is not specifically limited in this embodiment.

[0038] In some embodiments, the method further includes: 106, selecting sequence pairs of binding motifs located on transcriptional gene regulatory elements from the plurality of sequence pairs; 107, calculating a first ratio of the number of binding motifs a overlapping only with the Ref sequence and the number of binding motifs b not overlapping with the Ref sequence according to the position information of the binding motifs, and calculating a second ratio of the number of binding motifs c overlapping only with the Alt sequence and the number of binding motifs d not overlapping with the Alt sequence; calculating a change value of the protein binding motif according to the first ratio and the second ratio; selecting a SNP with a change value less than the second threshold as the fourth SNP, and outputting an evaluation result of the fourth SNP affecting the protein binding efficiency. The number refers to a natural number greater than 1.

[0039] In some embodiments, the method further comprises: taking the intersection between the first SNP and the fourth SNP, and outputting an evaluation result of the SNP in the intersection affecting the protein binding efficiency.

[0040] In some embodiments, the method further includes: taking the intersection between the second SNP and the fourth SNP, and outputting a prediction result that the protein binding efficiency of the SNP in the intersection is low; taking the intersection between the third SNP and the fourth SNP, and outputting a prediction result that the protein binding efficiency of the SNP in the intersection is high.

[0041] In some embodiments, the gene regulatory elements include: enhancers, open chromatin regions, promoters, promoter flanking regions; the regulatory proteins include transcription factors and CTCFs.

[0042] In some embodiments, the evaluation results or prediction results include but are not limited to paper or electronic report forms. The results are only obtained by the intelligent machine based on the analysis of relevant data of the subjects, and are only used as a reference, not as the final diagnosis result.

[0043] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present invention, such as Figure 3 As shown, the device 2000 may include: one or more processors 2010, and one or more memories 2020; wherein the memories store computer-readable codes, and when the computer-readable codes are run by the one or more processors, the method described above may be executed.

[0044] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, operations and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can be an X86 architecture or an ARM architecture.

[0045] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0046] For example, the method or device according to the embodiment of the present disclosure may also be implemented by Figure 4 The architecture of the computing device 3000 shown in FIG. Figure 4 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage device in the computing device 3000, such as ROM 3030 or hard disk 3070, may store various data or files used for processing and / or communication of the method provided by the present disclosure and program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 4 One or more components of a computing device are shown.

[0047] The embodiment of the present invention also provides a computer-readable storage medium, such as Figure 5As shown, it is a schematic diagram of a storage medium 4000 provided in an embodiment of the present invention, and a computer readable instruction 4010 is stored on the computer storage medium 4020. When the computer readable instruction 4010 is executed by a processor, the method according to the embodiment of the present disclosure described with reference to the above figures can be executed. The computer readable storage medium in the embodiment of the present disclosure can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory. It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0048] The embodiments of the present disclosure further provide a computer program product or system, including a computer program, which implements the steps of the above method when executed by a processor.

[0049] In some embodiments, this embodiment also discloses a system for evaluating protein binding efficiency at the gene transcription level, such as Figure 2 As shown, the system comprises:

[0050] An acquisition module 201 is used or configured to acquire at least one SNP to be tested;

[0051] A sequence pair retrieval module 202, used for or configured to retrieve Ref and Alt sequence pairs based on SNPs;

[0052] A protein quantity extraction module 203 is used or configured to extract the quantity of regulatory proteins bound to the sequence pair and the Ref and Alt sequences;

[0053] The calculation module 204 is used or configured to calculate the number of proteins bound to the Ref sequence, denoted as Num Ref Calculate the number of proteins bound to the Alt sequence and record it as Num Alt ;

[0054] Evaluation result output module 205, used or configured to output the evaluation result according to the Num Ref and Num Alt The ratio of was used to evaluate the effect of SNP on protein binding efficiency.

[0055] In some embodiments, the evaluation result output module comprises: if the ratio is not equal to the first threshold, outputting the evaluation result of the SNP to be tested affecting the protein binding efficiency; the SNP to be tested is recorded as the first SNP.

[0056] In some embodiments, the evaluation result output module includes: if the ratio is greater than a first threshold, outputting an evaluation result that the SNP to be tested affects the protein binding efficiency poorly; the SNP to be tested is recorded as a second SNP; if the ratio is less than the first threshold, outputting an evaluation result that the SNP to be tested affects the protein binding efficiency highly; the SNP to be tested is recorded as a third SNP.

[0057] In some embodiments, the system further includes: a sequence pair screening module, used for or configured to screen sequence pairs of binding motifs located on transcriptional gene regulatory elements from the several sequence pairs; an evaluation result calculation module, used for or configured to calculate a first ratio of the number a of binding motifs that only overlap with the Ref sequence to the number b of binding motifs that do not overlap with the Ref sequence based on the position information of the binding motifs, and calculate a second ratio of the number c of binding motifs that only overlap with the Alt sequence to the number d of binding motifs that do not overlap with the Alt sequence; calculate a change value of the protein binding motif based on the first ratio and the second ratio; screen the SNP whose change value is less than the second threshold as the fourth SNP, and output an evaluation result of the fourth SNP affecting the protein binding efficiency.

[0058] In some embodiments, the system further includes: a first result prediction module, used for or configured to take the intersection between the first SNP and the fourth SNP, and output an evaluation result of the SNP in the intersection affecting the protein binding efficiency; a second result prediction module, used for or configured to take the intersection between the second SNP and the fourth SNP, and output a prediction result that the protein binding efficiency of the SNP in the intersection is low; and take the intersection between the third SNP and the fourth SNP, and output a prediction result that the protein binding efficiency of the SNP in the intersection is high. Specific embodiments

[0059] 1 Materials and Methods

[0060] 1.1 Data collection: Myopia-related SNPs were collected from dbGap, GWAS catalogs, and published literature. Based on the population distribution of myopia-related SNPs in the 1000 Genomes Project (GRCh38), we performed quality control (QC) on samples and genotypes. SNPs were filtered according to the following selection criteria: East Asian, two alleles in NCBI, minor allele frequency (MAF)>5%, Hardy-Weinberg equilibrium P<0.01, recall rate>75%, and genotyping rate>75%. 343 SNPs were obtained for the following analysis. The start and end positions of eight genomic regulatory elements, including open chromatin regions (OCRs), CTCF binding sites (CTCFBS), enhancers, promoters, promoter flanking regions (PFRs), exons, introns, and noncoding RNA (ncRNA) transcripts, were derived from ENSEMBL (v102), and their sequences were extracted according to Refseq from NCBI. The sequences of 3'UTR and 5'UTR were obtained using the BioMart tool from ENSEMBL.

[0061] 1.2 Fine positioning of myopia-related SNPs on 10 genomic regulatory elements:

[0062] The classic sequence alignment tool Bowtie 2 was used to perform precise positioning. The primary sequences of genomic regulatory elements were considered as long reference reads. The 30bp flanking regions upstream and downstream of these SNPs were considered as short alignment sequences. Then, strict parameters were set using "--n-ceil C,3--np 0 --end-to-end -a --score -min C,0" to avoid seed sequence mismatches. After precise positioning, we constructed paired reference (Ref) and alternative (Alt) sequences based on the alleles of the SNP. The alignment software includes Bowtie1, Bowtie2, BLAST, preferably Bowtie2.

[0063] 1.3 Assessing changes in molecular binding at the transcriptional level:

[0064] We extracted 20 bp upstream and downstream of the paired Ref and Alt sequences induced by the SNP, and obtained the binding motifs of enhancers, open chromatin regions, promoters, and promoter flanking regions of these sequences from the HumanTFDB database, while CTCF was from the CTCFBSDB database. For protein binding, we obtained the number of regulatory proteins (TF and CTCF), protein identifiers, and the start and end positions of the binding motifs, respectively. First, we used fold change (FC) to obtain the change in the number of protein binding sites mediated by myopia-associated SNPs:

[0065]

[0066] and Represents the number of proteins bound by the Ref and Alt sequences, respectively. We defined FC>1 as a loss of protein binding, and conversely, FC<1 as a gain of protein binding. We then applied Fisher's exact test to determine changes in protein binding motifs as follows:

[0067]

[0068] a is the number of binding motifs that overlap only with the Ref sequence, b is the number of binding motifs that do not overlap with the Ref sequence, c is the number of binding motifs that overlap only with the Alt sequence, and d is the number of binding motifs that do not overlap with the Alt sequence. Here, when FC is not equal to 1 and P T When the score was < 0.05, it indicated that the myopia-associated SNPs significantly affected protein binding, and these SNPs were identified as cfSNPs. In addition, we defined the proteins with the highest scores as “leader” proteins.

[0069] 2 Results:

[0070] 2.1 Myopia-related SNPs are widely distributed at the transcriptional and post-transcriptional levels:

[0071] We obtained myopia-associated SNPs from public resources and, after quality control, used 343 SNPs for subsequent analysis (see Methods). To reveal the complete map of SNPs at the transcriptional and post-transcriptional levels, we used precise alignment and identified 636 pairs formed by 263 SNPs, 10 genomic regulatory elements, and myopia ( Figure 6 A). In addition, these SNPs were associated with five phenotypes, namely refractive error (RE), common myopia (CM), high myopia (HM), pathological myopia (PM) and visual impairment (VD). In the transcriptional process, 84 SNPs were located in enhancers, open chromatin regions (OCRs), CTCF binding sites (CTCFBS), promoters and promoter flanking regions (PFRs), forming 90 pairs. A total of 244 SNPs were located on five genomic regulatory elements, namely 5'UTR, exons, introns, 3'UTR and LncRNA, forming 546 pairs of relationships at the post-transcriptional level. Next, we found that 14.15% (90 / 636) and 85.85% (546 / 636) of the pairs were enriched in the transcriptional and post-transcriptional processes, respectively ( Figure 6 B).

[0072] To further evaluate the average distribution of SNPs in all genomic regulatory elements, we analyzed the transcript lengths of these elements and the density of SNPs in each genomic regulatory element. The results showed that the median length of the longest transcript LncRNA was 95.53 times the median length of the shortest transcript CTCFBS ( Figure 6 C). We then further evaluated the enrichment of SNPs at each regulatory element within every 1000 bp. Compared with the average of 1 SNP per 1000 bp in the human genome, we observed that myopia-associated SNPs were highly distributed in open chromatin, CTCF binding sites, 5' untranslated regions, and exons ( Figure 6 D). By analyzing myopia, 81.76% (520 / 636) of the one-to-ones were found to be associated with CM. In addition, little is known about HM, PM, RE, and VD, which are approximately 12.11% (77 / 636), 5.19% (33 / 636), 0.63% (4 / 636), and 0.31% (2 / 636), respectively. These data suggest that the distribution of myopia-associated SNPs differs at the transcriptional and post-transcriptional levels and between genomic regulatory elements. These differences may be related to the severity of myopia and potential molecular regulation.

[0073] 2.2 Scoring SNP-induced molecular binding heterogeneity during transcription:

[0074] To investigate SNP-mediated molecular binding heterogeneity at the transcriptional level, we developed a computational pipeline that used fold change (FC) to assess changes in the number of bound proteins and applied Fisher’s exact test to assess changes in the number of bound proteins. T <0.05, FC not equal to 1, 38.46% (5 / 13), 80% (4 / 5), 57.89% (11 / 19), and 44.73% (17 / 38) of the SNPs were found to be likely to disrupt transcription factor binding at enhancers, open chromatin regions, promoters, and promoter flanking regions, respectively ( Figure 7 ). For CTCF protein, we found that approximately 13.33% (2 / 15) of SNPs could disrupt the interaction of CTCF binding sites. In total, 43.33% (39 / 90) of the relationship pairs were able to disrupt the binding affinity, and 46.43% (39 / 84) SNPs were identified as "cfSNPs" during transcription. In summary, open chromatin was significantly enriched with myopia-associated SNPs and cfSNPs, showing high-density distribution and molecular binding disruption.

[0075] To determine the possible effects of regulatory proteins associated with cfSNPs that are associated or potentially associated with myopia, we explored the molecular functions of the “leader” proteins of each relationship pair in published studies. Interestingly, most of the “leader” proteins are able to regulate eye structures. For example, about 7.69% (3 / 39) of cfSNPs cause changes in the REST binding motif, thereby affecting the fate of RGC retinal ganglion cells (RGCs) in the developing retina. About 7.69% (3 / 39) of cfSNPs cause changes in IRF1 binding, which is known to be expressed in retinal microglia and plays a key role in microglial activation and retinal inflammation. In addition, 15.28% (6 / 39) of cfSNPs change the binding motif site of SPI1, which has been reported to regulate microglia in the retina. In summary, we found that there are abundant “leader” proteins in retinal inflammation, which have been confirmed to be associated with the occurrence and development of myopia. In addition, it can help us reveal potential protein regulators and understand how SNPs are involved in the molecular regulation of myopia. Finally, we evaluated the distribution of myopia types among the significantly changed relationship pairs at the transcriptional level. More than 75% (3 / 4) of them were related to PM, while approximately 44.44% (4 / 9) and 41.56% (32 / 77) were related to HM and CM ( Figure 7 AD). The results also showed that the effect of SNP on myopia increased with the severity of myopia.

[0076] At the transcriptional level, computational analysis revealed that myopia-associated SNPs may gain or lose T binding sites. In open chromatin, rs8110889 located on ENSR00001023878 can weaken the interaction between FOXA1 and ENSR00001023878 (P T =6.28e-14, FC=1.38, Figure 7 B). Previous studies have shown that FoxA1 is closely related to signaling pathways in the vertebrate retina, suggesting that rs8110889 may disrupt retina-related signaling pathways and lead to myopia susceptibility by altering molecular binding. In the promoter region, the Alt allele of rs7550232 in ENSR00000020131 (C) can increase the number of binding motifs on FLI1 (P T =1.86e-26, FC value = 0.75, Figure 7C). Another previous study reported that fli1 can drive endothelial gene expression and control eye development in zebrafish. These results suggest that SNP-induced gain and loss of regulatory proteins in the transcriptome may affect gene function and contribute to the pathogenesis of myopia. Recent studies have also reported similar relationship pairs. For example, the T allele of rs17079281 in the DCBLD1 promoter can create a YY1 binding site to inhibit the expression level of the DCBLD1 gene and reduce the risk of lung cancer in the Chinese population. In addition, rs3101339 may disrupt the binding between transcription factors and the NEGR1 gene, causing dysregulation of gene expression and leading to major depressive disorder. The discovery of SNP-induced loss or gain of protein motifs can reveal molecular interactions and provide potential intervention targets.

[0077] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0078] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0079] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0080] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0081] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0083] The exemplary embodiments of the present disclosure described in detail above are merely illustrative and not restrictive. It should be understood by those skilled in the art that various modifications and combinations may be made to these embodiments or their features without departing from the principles and spirit of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A method for evaluating protein binding efficiency at the gene transcription level, characterized in that: The method comprises: 101, obtaining at least one SNP to be tested; 102, retrieve Ref and Alt sequence pairs based on SNP; 103, extracting the number of regulatory proteins that the sequence pair binds to the Ref and Alt sequences; 104, calculate the number of proteins bound to the Ref sequence and record it as Num Ref Calculate the number of proteins bound to the Alt sequence and record it as Num Alt ; 105, according to the Num Ref and Num Alt The ratio of was used to evaluate the effect of SNP on protein binding efficiency.

2. The method for evaluating protein binding efficiency at the gene transcription level according to claim 1, characterized in that: The step 105 includes: if the ratio is not equal to the first threshold, outputting the evaluation result of the effect of the SNP to be tested on the protein binding efficiency; the SNP to be tested is recorded as the first SNP.

3. The method for evaluating protein binding efficiency at the gene transcription level according to claim 2, characterized in that: The 105 includes: if the ratio is greater than the first threshold, outputting an evaluation result that the SNP to be tested affects the protein binding efficiency is low; the SNP to be tested is recorded as the second SNP; if the ratio is less than the first threshold, outputting an evaluation result that the SNP to be tested affects the protein binding efficiency is high; the SNP to be tested is recorded as the third SNP.

4. The method for evaluating protein binding efficiency at the gene transcription level according to claim 3, characterized in that: The method also includes: 106, screening sequence pairs with binding motifs located on transcriptional gene regulatory elements from the several sequence pairs; 107, calculating a first ratio of the number a of binding motifs overlapping only with the Ref sequence to the number b of binding motifs not overlapping with the Ref sequence based on the position information of the binding motifs, and calculating a second ratio of the number c of binding motifs overlapping only with the Alt sequence to the number d of binding motifs not overlapping with the Alt sequence; calculating a change value of the protein binding motif based on the first ratio and the second ratio; screening a SNP with a change value less than a second threshold as a fourth SNP, and outputting an evaluation result of the fourth SNP affecting the protein binding efficiency.

5. The method for evaluating protein binding efficiency at the gene transcription level according to claim 4, characterized in that: The method further includes: taking the intersection of the first SNP and the fourth SNP, and outputting an evaluation result of the SNP in the intersection affecting the protein binding efficiency.

6. The method for evaluating protein binding efficiency at the gene transcription level according to claim 4, characterized in that: The method further includes: taking the intersection between the second SNP and the fourth SNP, and outputting a prediction result that the protein binding efficiency of the SNP in the intersection is low; taking the intersection between the third SNP and the fourth SNP, and outputting a prediction result that the protein binding efficiency of the SNP in the intersection is high.

7. The method for evaluating protein binding efficiency at the gene transcription level according to claim 1, characterized in that: The gene regulatory elements include: enhancers, open chromatin regions, promoters, and promoter flanking regions; the regulatory proteins include transcription factors and CTCFs.

8. A computer device, characterized in that: The device comprises: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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