Inbred analysis methods, systems, and electronic devices for high-throughput sequencing
By extracting RNA from pig samples and performing multidimensional quality assessment and verification, combined with high-throughput sequencing from PacBio Iso-seq and Illumina RNA-seq platforms, the problem of poor sequencing data reliability in pig inbreeding analysis was solved, improving sequencing accuracy and the credibility of analysis results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YUNNAN AGRICULTURAL UNIVERSITY
- Filing Date
- 2025-11-04
- Publication Date
- 2026-07-14
AI Technical Summary
In inbreeding analysis of pig breeds, existing technologies suffer from poor reliability of sequencing data, resulting in insufficient accuracy and reliability of high-throughput sequencing results.
RNA was extracted from adult inbred boars and Yunnan boars. A multidimensional RNA quality assessment weight set was used for comprehensive sample quality verification to ensure that the quality of the RNA samples input for sequencing was controllable. High-throughput sequencing was performed using PacBio Iso-seq and Illumina RNA-seq platforms, and inbreeding analysis was conducted.
This improved the accuracy of high-throughput sequencing and the reliability of inbreeding analysis results, ensuring the reliability of sequencing data and the accuracy of analysis results.
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Figure CN121046523B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of animal molecular detection technology, specifically to inbreeding analysis methods, systems, and electronic devices for high-throughput sequencing. Background Technology
[0002] Inbred animal strains, due to prolonged inbreeding, exhibit highly homozygous genomes and are frequently used in genetic research and biomedical model construction. However, the molecular mechanisms underlying phenotypic changes caused by inbreeding, particularly decreased reproductive capacity, remain unclear. To explore genetic differences between inbred and non-inbred strains, high-throughput RNA sequencing has become an effective method. However, prior to conducting high-throughput transcriptome analysis, RNA sample acquisition and quality control are crucial factors affecting the reliability of sequencing data and the results of subsequent differential analysis.
[0003] Currently, some studies still suffer from problems such as non-standard sample collection, limited methods for evaluating the purity and integrity of extracted RNA, and a lack of systematic multidimensional quality assessment mechanisms. These issues lead to significant deviations in sequencing input data, affecting the accurate identification of transcriptional expression characteristics among different pig breeds and restricting the accuracy of inbreeding analysis. Summary of the Invention
[0004] This application provides a high-throughput sequencing inbreeding analysis method, system, and electronic equipment to address the technical problem of poor sequencing data reliability in existing technologies for porcine inbreeding analysis.
[0005] In view of the above problems, this application provides a method, system and electronic equipment for inbreeding analysis of high-throughput sequencing.
[0006] The first aspect of this application provides a method for inbreeding analysis in high-throughput sequencing, the method comprising:
[0007] Samples obtained from adult inbred boars and Yunnan boars of a predetermined number were compiled to obtain adult inbred boar sample sets and Yunnan boar sample sets, respectively. RNA was extracted from the adult inbred boar sample sets and Yunnan boar sample sets using common reagents to obtain adult inbred boar sample RNA sets and Yunnan boar sample RNA sets, respectively. The sample quality of the adult inbred boar sample RNA sets and Yunnan boar sample RNA sets was comprehensively certified according to a predetermined multidimensional sample RNA quality assessment weight set to obtain certification results. When the certification result was passed, high-throughput sequencing was performed on the adult inbred boar sample RNA sets and Yunnan boar sample RNA sets, and inbreeding analysis was performed based on the sequencing results.
[0008] A second aspect of this application provides a high-throughput sequencing inbreeding analysis system, the system comprising:
[0009] The module comprises the following components: a summary module, used to summarize a preset number of samples obtained from adult inbred boars and Yunnan boars, to obtain adult inbred boar sample sets and Yunnan boar sample sets; an RNA extraction module, used to extract RNA from the adult inbred boar sample sets and Yunnan boar sample sets using common reagents, to obtain adult inbred boar sample RNA sets and Yunnan boar sample RNA sets; a comprehensive certification module, used to perform comprehensive quality certification of the adult inbred boar sample RNA sets and Yunnan boar sample RNA sets according to a preset multidimensional sample RNA quality assessment weight set, to obtain certification results; and an inbreeding analysis module, used to perform high-throughput sequencing based on the adult inbred boar sample RNA sets and Yunnan boar sample RNA sets when the certification result is successful, and to perform inbreeding analysis based on the sequencing results.
[0010] A third aspect of this application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the high-throughput sequencing inbreeding analysis method provided in this application.
[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0012] This application summarizes a predetermined number of samples obtained from adult inbred boars and Yunnan boars to obtain adult inbred boar sample sets and Yunnan boar sample sets, respectively. RNA is extracted from these sample sets using general reagents to obtain adult inbred boar RNA sets and Yunnan boar RNA sets, respectively. The RNA quality of these sample sets is comprehensively certified according to a predetermined multidimensional RNA quality assessment weight set to obtain certification results. When the certification result is successful, high-throughput sequencing is performed on the adult inbred boar RNA sets and Yunnan boar RNA sets, and inbreeding analysis is conducted based on the sequencing results. This invention solves the technical problem of poor sequencing data reliability in pig inbreeding analysis in existing technologies. By extracting RNA from adult inbred boars and Yunnan boars and performing comprehensive sample quality certification based on a multidimensional RNA quality assessment weight set, the quality of the input RNA samples for sequencing is ensured to be controllable, thereby improving the accuracy of high-throughput sequencing and the reliability of inbreeding analysis results. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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 effort.
[0014] Figure 1 This is a schematic flowchart of a high-throughput sequencing inbreeding analysis method provided in an embodiment of this application;
[0015] Figure 2 This is a schematic diagram of the structure of a high-throughput sequencing inbreeding analysis system provided in an embodiment of this application;
[0016] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application;
[0017] Figure 4 The diagram illustrates the analysis of inbred boars and Yunnan boars using high-throughput sequencing, as exemplified in this application.
[0018] Explanation of reference numerals in the attached diagram: Bus 300, Receiver 301, Processor 302, Transmitter 303, Memory 304, Bus Interface 305, Summary Module 11, RNA Extraction Module 12, Comprehensive Authentication Module 13, Inbreeding Analysis Module 14. Detailed Implementation
[0019] This application provides a high-throughput sequencing inbreeding analysis method, system, and electronic equipment to address the technical problem of poor sequencing data reliability in pig inbreeding analysis using existing technologies. By extracting RNA from samples of adult inbred boars and Yunnan boars, and performing comprehensive sample quality certification based on a multidimensional RNA quality assessment weight set, the quality of the RNA samples input for sequencing is ensured to be controllable, thereby achieving the technical effect of improving the accuracy of high-throughput sequencing and the reliability of inbreeding analysis results.
[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0021] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.
[0022] Example 1, as Figure 1 As shown, this application provides a method for inbreeding analysis in high-throughput sequencing, the method comprising:
[0023] Step S100: Summarize the samples obtained from the preset number of adult inbred boars and Yunnan boars to obtain the sample set of adult inbred boars and the sample set of Yunnan boars.
[0024] In this embodiment, a predetermined number of obtained tissue samples from adult inbred boars and Yunnan boars are first classified, organized, and numbered to construct adult inbred boar sample sets and Yunnan boar sample sets, respectively. These sets include multiple key reproductive tissue samples such as testes, epididymis, seminal vesicles, prostate, and bulbourethral glands.
[0025] Step S200: RNA was extracted from the adult inbred boar sample set and the Yunnan boar sample set using general reagents to obtain the RNA sets of the adult inbred boar sample set and the Yunnan boar sample set.
[0026] In this embodiment, TRNzol universal reagent was used to extract RNA from a collection of adult inbred boar samples and a collection of Yunnan boar samples. TRNzol is a phenol / guanidine isothiocyanate-based RNA extraction reagent that can effectively lyse cells and release total RNA while inhibiting RNase activity, thereby protecting RNA from degradation. Specifically, each tissue sample was ground in liquid nitrogen and then TRNzol reagent was added. After thorough lysis, phase separation was performed to extract RNA from the aqueous phase. RNA was then precipitated with isopropanol, and the precipitate was washed with ethanol and finally dissolved in an appropriate amount of enzyme-free water. After extraction, two independent RNA datasets were obtained: one for adult inbred boar samples and one for Yunnan boar samples.
[0027] Step S300: Perform comprehensive quality assessment on the RNA sets of adult inbred boars and Yunnan boars according to the preset multidimensional sample RNA quality assessment weight set, and obtain the assessment results.
[0028] In this embodiment, based on a preset multidimensional sample RNA quality assessment weight set, comprehensive sample quality certification is performed on RNA samples from adult inbred boars and Yunnan boars. The preset multidimensional sample RNA quality assessment weight set includes RNA purity weight values, RNA quantification weight values, and RIN value weight values, corresponding to the purity, quantitative concentration, and integrity of the RNA samples, respectively. The aforementioned indicators are detected using a NanoDrop One spectrophotometer, a Qubit 3.0 fluorometer, and an Agilent 2100 Bioanalyzer, respectively, and corresponding assessment result sets are generated.
[0029] Subsequently, the obtained RNA purity assessment results, RNA quantification assessment results, and RIN value sets were centrifuged and cleaned to extract the central assessment results for each type of data, eliminating outlier data that deviated from the principal values. Based on this, the central assessment values of the adult inbred boar samples and the Yunnan boar samples were weighted and integrated according to multidimensional assessment weights to calculate the RNA quality assessment results separately. Finally, the above assessment results were compared with the preset quality certification threshold to determine whether the certification standards were met, thus obtaining the certification result.
[0030] Furthermore, the method provided in the application embodiments also includes:
[0031] The preset multidimensional sample RNA quality assessment weight set includes RNA purity weight value, RNA quantification weight value, and RIN value weight value.
[0032] In this embodiment, the preset multidimensional sample RNA quality assessment weight set includes RNA purity weight value, RNA quantification weight value, and RIN value weight value, corresponding to RNA purity, concentration, and integrity, respectively. The RNA purity weight value, RNA quantification weight value, and RIN value weight value are preset by technical experts.
[0033] Furthermore, the method provided in the application embodiment, which performs comprehensive sample quality certification on the adult inbred boar sample RNA set and the Yunnan boar sample RNA set according to a preset multidimensional sample RNA quality assessment weight set to obtain certification results, also includes:
[0034] RNA purity and quantification were assessed using a spectrophotometer and a fluorometer, respectively, for the RNA sets of adult inbred boar samples and the RNA sets of Yunnan boar samples. Sets of RNA purity assessment results for adult inbred boar samples, RNA purity assessment results for Yunnan boar samples, and RNA quantification assessment results for adult inbred boar samples and Yunnan boar samples were obtained. The RIN values of the RNA sets of adult inbred boar samples and the RNA sets of Yunnan boar samples were measured, resulting in sets of RIN values for adult inbred boar samples and Yunnan boar samples. The RNA purity assessment results for adult inbred boar samples were centrifuged and washed to determine the center assessment results for RNA purity. The RNA purity assessment results for Yunnan boar samples, the RNA quantification results for adult inbred boar samples, and the RNA quantification results for Yunnan boar samples were then analyzed. The evaluation results were centrifuged and washed to determine the center evaluation results for RNA purity, quantification, and RIN values of Yunnan boar samples. A pre-set multidimensional RNA quality evaluation weight set was used to weight these results for each of the following samples: RNA purity, quantification, and RIN values of adult inbred boar samples, and the RNA quality evaluation results for Yunnan boar samples. The results were then weighted to obtain the RNA quality evaluation results for adult inbred boar samples and Yunnan boar samples. Finally, the RNA quality evaluation results for adult inbred boar samples and Yunnan boar samples were certified according to a pre-set quality certification threshold to obtain the certification results.
[0035] In this embodiment, a spectrophotometer (NanoDrop One) was first used to comprehensively measure the RNA purity of all samples in the adult inbred boar sample set and the Yunnan boar sample set. The measured values of each sample were recorded, forming the adult inbred boar sample RNA purity assessment result set and the Yunnan boar sample RNA purity assessment result set, respectively. Subsequently, a fluorometer (Qubit3.0) was used to quantitatively detect RNA in the above samples. This device uses an RNA-specific dye to bind to the target molecule and output fluorescence intensity to achieve precise concentration assessment. After measurement, the adult inbred boar sample RNA quantitative assessment result set and the Yunnan boar sample RNA quantitative assessment result set were formed. After completing the purity and concentration determination, the integrity of all RNA samples was further analyzed using an Agilent 2100 Bioanalyzer. This device, based on the microfluidic chip capillary electrophoresis principle, outputs a standardized score characterizing RNA integrity, namely the RNA integrity value (RIN value), forming the adult inbred boar sample RIN value set and the Yunnan boar sample RIN value set.
[0036] After collecting the original assessment data, the various assessment result sets were centrifuged and cleaned to remove outliers and marginal data, and to extract representative central indicators. Specifically, the RNA purity assessment result set of adult inbred boar samples was first cleaned to extract its most representative statistical center, namely the assessment result of RNA purity center of adult inbred boar samples. Subsequently, the same operation was performed on the RNA purity assessment result set of Yunnan boar samples, the RNA quantitative assessment result set of adult inbred boar samples, the RNA quantitative assessment result set of Yunnan boar samples, the RIN value set of adult inbred boar samples, and the RIN value set of Yunnan boar samples, respectively, to obtain the assessment results of RNA purity center of Yunnan boar samples, the assessment results of RNA quantitative center of adult inbred boar samples, the assessment results of RNA quantitative center of Yunnan boar samples, the assessment results of RIN value center of adult inbred boar samples, and the assessment results of RIN value center of Yunnan boar samples.
[0037] To further unify the numerical scales of different indicators and make them additive, all the above-mentioned center evaluation results were normalized and mapped to the standardized interval [0, 1]. The normalized indicators were then weighted according to the multidimensional sample RNA quality assessment weight set set in the system. This weight set includes RNA purity weight value, RNA quantification weight value, and RIN value weight value, for example, set to 0.3, 0.3, and 0.4, reflecting the actual impact of each indicator on the stability of subsequent sequencing data. After weighted calculation, the RNA quality assessment results of adult inbred boar samples and Yunnan boar samples were obtained, respectively, and used as representative indicators to measure the overall sample quality level.
[0038] Finally, in the certification judgment stage, a unified quality certification threshold is set, such as a normalized score of no less than 0.75. The certification mechanism here follows the principle of simultaneous pass on both sides; that is, only when the RNA quality assessment results of adult inbred boar samples and Yunnan boar samples both simultaneously reach or exceed the certification threshold will the overall result be output as "certification passed"; if either falls below the threshold, the overall result is judged as "certification failed". The certification result is obtained through this process.
[0039] Furthermore, the method provided in the application embodiments, which involves centrifuging and washing the set of RNA purity assessment results from adult inbred boar samples to determine the center assessment results of RNA purity from adult inbred boar samples, also includes:
[0040] Extract the first representative center from the set of RNA purity assessment results of adult inbred boar samples, where the first representative center is the mode of the set of RNA purity assessment results of adult inbred boar samples; extract the two extreme values of the set of RNA purity assessment results of adult inbred boar samples, determine the limiting interval, and determine the center nearest neighbor bandwidth based on the limiting interval; construct the first nearest neighbor region of the first representative center based on the center nearest neighbor bandwidth; determine the neighborhood to be cleaned from the set of RNA purity assessment results of adult inbred boar samples; determine whether the neighborhood density of the first nearest neighbor region is greater than or equal to the neighborhood density of the neighborhood to be cleaned; if so, stop centrifugation and cleaning, and calculate the mean of the first nearest neighbor region to obtain the center assessment result of RNA purity of adult inbred boar samples.
[0041] In this embodiment, the set of RNA purity assessment results of adult inbred boar samples is first analyzed. The mode extraction method is used, that is, the frequency of occurrence of each purity value is counted, and the value with the highest frequency is selected as the first representative center.
[0042] Next, boundary identification was performed on the RNA purity assessment result set to extract the maximum and minimum values in the data to determine the limiting interval of the data. This limiting interval is used to describe the complete range of fluctuations in the set. Based on this, a scaling factor (e.g., 10%) was set according to the span of the limiting interval to calculate the center nearest neighbor bandwidth used for neighborhood construction.
[0043] Subsequently, based on the obtained first representative center and its nearest neighbor bandwidth, a neighborhood set with the interval [first representative center − bandwidth, first representative center + bandwidth] is constructed, called the first nearest neighbor neighborhood. At the same time, the entire set of RNA purity assessment results of adult inbred boar samples is defined as the neighborhood to be cleaned, serving as a reference set for evaluating and comparing neighborhood density.
[0044] Next, the density of the first nearest neighbor and the density of the neighborhood to be cleaned are calculated. During the calculation, the neighborhood density = number of sample points in the neighborhood / (2 × central nearest neighbor bandwidth). After the calculation, the neighborhood density of the first nearest neighbor is compared with the neighborhood density of the neighborhood to be cleaned. If the neighborhood density of the first nearest neighbor is greater than or equal to the neighborhood density of the neighborhood to be cleaned, it indicates that the local neighborhood can cover the main distribution area of the samples. Under this condition, the centrifugation cleaning process is terminated, and no further expansion operation is performed.
[0045] Finally, the arithmetic mean of all RNA purity values in the first nearest neighbor region is calculated to obtain the average value of the region, which is used as the final output of the RNA purity center assessment result for adult inbred boar samples.
[0046] Furthermore, in the method provided in the application embodiments, determining whether the neighborhood density of the first nearest neighbor is greater than or equal to the neighborhood density of the neighborhood to be cleaned further includes:
[0047] If the neighborhood density of the first nearest neighbor is less than the neighborhood density of the neighborhood to be cleaned, then the first nearest neighbor is expanded by 1 / 10 of the central nearest neighbor bandwidth to obtain the first expanded nearest neighbor. Next, it is determined whether the neighborhood density of the first expanded nearest neighbor is greater than or equal to the neighborhood density of the neighborhood to be cleaned. If not, the first expanded nearest neighbor is expanded by 1 / 10 of the central nearest neighbor bandwidth until a preset expansion stop condition is met to obtain the target expanded neighborhood. The mean of the target expanded neighborhood is calculated to obtain the center evaluation result of RNA purity of the adult inbred boar sample.
[0048] Furthermore, the method provided in the application embodiments also includes:
[0049] The preset expansion stopping condition is that the neighborhood density of the two nearest neighboring neighborhoods in two adjacent expansions is less than or equal to the preset neighborhood density difference.
[0050] Furthermore, the method provided in the application embodiments also includes:
[0051] The neighborhood density of the first nearest neighbor is the ratio of the total number of RNA purity assessment results of adult inbred boar samples within the first nearest neighbor to twice the bandwidth of the central nearest neighbor.
[0052] In this embodiment, the neighborhood density of the first nearest neighbor is first calculated, defined as the total number of RNA purity assessment samples contained in the first nearest neighbor, divided by the interval width of that neighborhood. Specifically, the central nearest neighbor bandwidth is used as the standard, and the neighborhood width is set to twice the bandwidth. Therefore, the neighborhood density is equal to the number of samples in the first nearest neighbor divided by twice the central nearest neighbor bandwidth. This density is then compared with the neighborhood density of the neighborhood to be cleaned.
[0053] When the neighborhood density of the first nearest neighbor is less than that of the neighborhood to be cleaned, it is determined that the local area has not fully covered the main distribution area of the sample and is not representative enough. At this time, the edge of the first nearest neighbor is symmetrically expanded in units of 1 / 10 of the central nearest neighbor bandwidth, expanding the same interval to both sides to form a new neighborhood, namely the first expanded nearest neighbor.
[0054] After expansion is complete, the neighborhood density of the expanded nearest neighbor is recalculated and compared again with the density of the neighborhood to be cleaned. If the density is still insufficient, expansion continues in steps of 1 / 10 of the bandwidth. Each expansion generates a new expanded nearest neighbor and updates its neighborhood density.
[0055] To prevent indefinite expansion, a preset expansion stop condition is set. This condition is defined as follows: if the neighborhood density difference between two adjacent nearest neighbor regions formed by two consecutive expansions is less than or equal to a preset neighborhood density difference, then the expansion is considered to have stabilized, meaning the neighborhood density no longer changes significantly, and the expansion process ends. At this point, the current expansion neighborhood is identified as the target expansion neighborhood.
[0056] Finally, the arithmetic mean of all RNA purity assessment results within the target expansion neighborhood is calculated, and the resulting mean is the center assessment result of RNA purity in adult inbred boar samples.
[0057] Step S400: When the authentication result is successful, high-throughput sequencing is performed based on the RNA set of adult inbred boar samples and the RNA set of Yunnan boar samples, and inbreeding analysis is performed based on the sequencing results.
[0058] In this embodiment, when the authentication result is successful, high-throughput sequencing is performed on RNA sets from adult inbred boar samples and Yunnan boar samples, and inbreeding analysis is performed based on the sequencing results. Long-read sequencing and paired-end sequencing are performed on the RNA sets from adult inbred boar samples and Yunnan boar samples using the PacBio Iso-seq platform and the Illumina RNA-seq platform, respectively, to obtain complete transcriptome structure and expression data. Long-read sequencing using the PacBio Iso-seq platform obtains full-length transcriptome structure information. Simultaneously, short-read data obtained using the Illumina RNA-seq platform provides high-depth gene expression data. These data, combined with the sequencing results, generate data such as... Figure 4 The high-throughput sequencing diagram shown.
[0059] By performing inbreeding analysis on sequencing results (including high-throughput sequencing maps), we identified differences in transcript expression and splicing structures between the two pig breeds, revealing molecular-level differences in reproductive capacity and other aspects of inbred pig breeds, and providing data support for inbreeding effect research.
[0060] Furthermore, the method provided in the application embodiments, which involves high-throughput sequencing based on the RNA sets of adult inbred boar samples and Yunnan boar samples, and inbreeding analysis based on the sequencing results, further includes:
[0061] The RNA sets of adult inbred boar samples and Yunnan boar samples were subjected to long-read sequencing and paired-end RNA sequencing using the PacBio Iso-seq platform and the Illumina RNA-seq platform, respectively. Sequencing results were obtained, and inbreeding analysis was performed based on the sequencing results.
[0062] In this embodiment of the application, after the RNA sets of adult inbred boar samples and the RNA sets of Yunnan boar samples passed the comprehensive RNA quality certification, high-throughput sequencing of the two types of samples was performed using the PacBio Iso-seq platform and the Illumina RNA-seq platform, respectively.
[0063] In the PacBio Iso-seq sequencing pipeline, certified RNA samples are first reverse transcribed and amplified to obtain full-length cDNA using the SMARTer PCR cDNA synthesis kit, preserving the 5' and 3' end structural information. Subsequently, the BluePippin system is used for fragment length screening, selecting cDNA within the target length range. Then, through end repair and hairpin adapter ligation, a closed-loop SMRTbell library is constructed. The library is loaded onto the PacBio Sequel II platform, and high-fidelity (HiFi) long-read data is generated using single-molecule real-time sequencing technology. After sequencing, data analysis is performed using SMRT Link software and the IsoSeq3 pipeline to obtain a non-redundant full-length transcript set. The cDNA_Cupcake and SQANTI3 tools are then used for isoform annotation and functional classification of the transcripts.
[0064] In the Illumina RNA-seq sequencing pipeline, the RNA sample is first enriched with polyA+, mRNA is extracted and fragmented, then double-stranded cDNA is synthesized, and library construction is completed using the NEBNext kit. Next, paired-end sequencing is performed on the NovaSeq 6000 platform to obtain a large number of short read data. After quality control using Cutadapt or Fastp, clean reads are aligned to the reference pig genome using the STAR alignment tool, and expression levels of each gene or transcript are statistically analyzed using tools such as FeatureCounts or StringTie.
[0065] After sequencing, the transcript structure information output from the PacBio platform and the expression data obtained from the Illumina platform were comprehensively analyzed to perform inbreeding analysis on adult inbred boar samples and Yunnan boar samples. This analysis included comparing the full-length transcript structure in different tissues, identifying isoform differences and new transcript expression differences between the two pig breeds; using differential expression analysis tools (such as DESeq2) to identify functionally relevant genes that were significantly upregulated or downregulated in inbred lines; and using tools such as SUPPA2 or rMATS to analyze the frequency and type changes of alternative splicing events.
[0066] Ultimately, through the above sequencing and analysis process, significant differences in transcript expression patterns, splicing structures, and expression levels between inbred pig breeds (inbred boars) and non-inbred pig breeds (Yunnan boars) in multiple reproductive tissues were revealed, providing molecular-level mechanistic support for understanding the phenotypic effects of inbreeding on reproductive capacity, reproductive system development, and other aspects.
[0067] In summary, the embodiments of this application have at least the following technical effects:
[0068] This application summarizes a predetermined number of samples obtained from adult inbred boars and Yunnan boars to obtain adult inbred boar sample sets and Yunnan boar sample sets, respectively. RNA is extracted from these sample sets using general reagents to obtain adult inbred boar RNA sets and Yunnan boar RNA sets, respectively. The RNA quality of these sample sets is comprehensively certified according to a predetermined multidimensional RNA quality assessment weight set to obtain certification results. When the certification result is successful, high-throughput sequencing is performed on the adult inbred boar RNA sets and Yunnan boar RNA sets, and inbreeding analysis is conducted based on the sequencing results. This invention solves the technical problem of poor sequencing data reliability in pig inbreeding analysis in existing technologies. By extracting RNA from adult inbred boars and Yunnan boars and performing comprehensive sample quality certification based on a multidimensional RNA quality assessment weight set, the quality of the input RNA samples for sequencing is ensured to be controllable, thereby improving the accuracy of high-throughput sequencing and the reliability of inbreeding analysis results.
[0069] Example 2, based on the same inventive concept as the high-throughput sequencing inbreeding analysis method in the foregoing examples, such as... Figure 2 As shown, this application provides a high-throughput sequencing inbreeding analysis system. The system and method embodiments in this application are based on the same inventive concept. The system includes:
[0070] The module 11 is used to summarize the samples obtained from adult inbred boars and Yunnan boars, respectively, to obtain adult inbred boar sample sets and Yunnan boar sample sets; the RNA extraction module 12 is used to extract RNA from the adult inbred boar sample sets and Yunnan boar sample sets using general reagents, respectively, to obtain adult inbred boar sample RNA sets and Yunnan boar sample RNA sets; the comprehensive certification module 13 is used to perform comprehensive certification of the sample quality of the adult inbred boar sample RNA sets and Yunnan boar sample RNA sets according to a preset multidimensional sample RNA quality assessment weight set, and obtain certification results; the inbreeding analysis module 14 is used to perform high-throughput sequencing based on the adult inbred boar sample RNA sets and Yunnan boar sample RNA sets when the certification result is successful, and perform inbreeding analysis based on the sequencing results.
[0071] Furthermore, the system is also used to implement the following functions:
[0072] The preset multidimensional sample RNA quality assessment weight set includes RNA purity weight value, RNA quantification weight value, and RIN value weight value.
[0073] Furthermore, the system is also used to implement the following functions:
[0074] RNA purity and quantification were assessed using a spectrophotometer and a fluorometer, respectively, for the RNA sets of adult inbred boar samples and the RNA sets of Yunnan boar samples. Sets of RNA purity assessment results for adult inbred boar samples, RNA purity assessment results for Yunnan boar samples, and RNA quantification assessment results for adult inbred boar samples and Yunnan boar samples were obtained. The RIN values of the RNA sets of adult inbred boar samples and the RNA sets of Yunnan boar samples were measured, resulting in sets of RIN values for adult inbred boar samples and Yunnan boar samples. The RNA purity assessment results for adult inbred boar samples were centrifuged and washed to determine the center assessment results for RNA purity. The RNA purity assessment results for Yunnan boar samples, the RNA quantification results for adult inbred boar samples, and the RNA quantification results for Yunnan boar samples were then analyzed. The evaluation results were centrifuged and washed to determine the center evaluation results for RNA purity, quantification, and RIN values of Yunnan boar samples. A pre-set multidimensional RNA quality evaluation weight set was used to weight these results for each of the following samples: RNA purity, quantification, and RIN values of adult inbred boar samples, and the RNA quality evaluation results for Yunnan boar samples. The results were then weighted to obtain the RNA quality evaluation results for adult inbred boar samples and Yunnan boar samples. Finally, the RNA quality evaluation results for adult inbred boar samples and Yunnan boar samples were certified according to a pre-set quality certification threshold to obtain the certification results.
[0075] Furthermore, the system is also used to implement the following functions:
[0076] Extract the first representative center from the set of RNA purity assessment results of adult inbred boar samples, where the first representative center is the mode of the set of RNA purity assessment results of adult inbred boar samples; extract the two extreme values of the set of RNA purity assessment results of adult inbred boar samples, determine the limiting interval, and determine the center nearest neighbor bandwidth based on the limiting interval; construct the first nearest neighbor region of the first representative center based on the center nearest neighbor bandwidth; determine the neighborhood to be cleaned from the set of RNA purity assessment results of adult inbred boar samples; determine whether the neighborhood density of the first nearest neighbor region is greater than or equal to the neighborhood density of the neighborhood to be cleaned; if so, stop centrifugation and cleaning, and calculate the mean of the first nearest neighbor region to obtain the center assessment result of RNA purity of adult inbred boar samples.
[0077] Furthermore, the system is also used to implement the following functions:
[0078] If the neighborhood density of the first nearest neighbor is less than the neighborhood density of the neighborhood to be cleaned, then the first nearest neighbor is expanded by 1 / 10 of the central nearest neighbor bandwidth to obtain the first expanded nearest neighbor. Next, it is determined whether the neighborhood density of the first expanded nearest neighbor is greater than or equal to the neighborhood density of the neighborhood to be cleaned. If not, the first expanded nearest neighbor is expanded by 1 / 10 of the central nearest neighbor bandwidth until a preset expansion stop condition is met to obtain the target expanded neighborhood. The mean of the target expanded neighborhood is calculated to obtain the center evaluation result of RNA purity of the adult inbred boar sample.
[0079] Furthermore, the system is also used to implement the following functions:
[0080] The preset expansion stopping condition is that the neighborhood density of the two nearest neighboring neighborhoods in two adjacent expansions is less than or equal to the preset neighborhood density difference.
[0081] Furthermore, the system is also used to implement the following functions:
[0082] The neighborhood density of the first nearest neighbor is the ratio of the total number of RNA purity assessment results of adult inbred boar samples within the first nearest neighbor to twice the bandwidth of the central nearest neighbor.
[0083] Furthermore, the system is also used to implement the following functions:
[0084] The RNA sets of adult inbred boar samples and Yunnan boar samples were subjected to long-read sequencing and paired-end RNA sequencing using the PacBio Iso-seq platform and the Illumina RNA-seq platform, respectively. Sequencing results were obtained, and inbreeding analysis was performed based on the sequencing results.
[0085] Example 3: Based on the inventive concept of the inbreeding analysis method for high-throughput sequencing in the foregoing examples, this application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of any of the methods described in Example 1 above.
[0086] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3 In this document, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.
[0087] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0088] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0089] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.
Claims
1. A method for inbreeding analysis in high-throughput sequencing, characterized in that, The method includes: The samples of adult inbred boars and Yunnan boars that have been obtained are summarized according to the preset number of samples to obtain the sample set of adult inbred boars and the sample set of Yunnan boars. RNA was extracted from adult inbred boar samples and Yunnan boar samples using general reagents to obtain RNA sets from adult inbred boar samples and Yunnan boar samples, respectively. The RNA samples of adult inbred boars and Yunnan boars were comprehensively certified according to a preset multidimensional sample RNA quality assessment weight set to obtain the certification results. When the authentication result is successful, high-throughput sequencing is performed based on the RNA set of adult inbred boar samples and the RNA set of Yunnan boar samples, and inbreeding analysis is performed based on the sequencing results; The RNA samples from the adult inbred boars and the Yunnan boars were comprehensively evaluated for quality according to a preset multidimensional RNA quality assessment weight set. If the evaluation was successful, the following criteria were met: RNA purity and quantification were assessed by traversing the RNA sets of adult inbred boar samples and the RNA sets of Yunnan boar samples using a spectrophotometer and a fluorometer, respectively, to obtain sets of RNA purity assessment results for adult inbred boar samples, sets of RNA purity assessment results for Yunnan boar samples, sets of RNA quantification assessment results for adult inbred boar samples, and sets of RNA quantification assessment results for Yunnan boar samples. The RIN values of the RNA sets of the adult inbred boar samples and the RNA sets of the Yunnan boar samples were determined to obtain the RIN value sets of the adult inbred boar samples and the RIN value sets of the Yunnan boar samples. The RNA purity assessment results of adult inbred boar samples were centrifuged and washed to determine the center assessment results of RNA purity in adult inbred boar samples; The RNA purity assessment results set, the RNA quantitative assessment results set, the RNA quantitative assessment results set, the RNA quantitative assessment results set, the RNA quantitative assessment results set, and the RNA quantitative assessment results set of Yunnan boar samples were centrifuged and washed to determine the center assessment results of RNA purity, RNA quantitative assessment results, RIN value, and RIN value of Yunnan boar samples. A pre-defined set of multidimensional sample RNA quality assessment weights was used to weight the results of the center assessment of RNA purity, the center assessment of RNA quantification, and the center assessment of RIN value in adult inbred boar samples, as well as the results of the center assessment of RNA purity, the center assessment of RNA quantification, and the center assessment of RIN value in Yunnan boar samples, to obtain the RNA quality assessment results for adult inbred boar samples and Yunnan boar samples. The RNA quality assessment results of the adult inbred boar samples and the RNA quality assessment results of the Yunnan boar samples were certified according to a preset quality certification threshold to obtain the certification results. The RNA purity assessment results from adult inbred boar samples were centrifuged and washed to determine the center assessment results for RNA purity in adult inbred boar samples, including: Extract the first representative center of the set of RNA purity assessment results from adult inbred boar samples, wherein the first representative center is the mode of the set of RNA purity assessment results from adult inbred boar samples; Extract the two extreme values of the set of RNA purity assessment results from the adult inbred boar samples, determine the limiting interval, and determine the central nearest neighbor bandwidth based on the limiting interval; The first nearest neighbor domain representing the center is constructed based on the center's nearest neighbor bandwidth; The set of RNA purity assessment results from the adult inbred boar samples was used to determine the neighborhood to be cleaned; Determine whether the neighborhood density of the first nearest neighbor is greater than or equal to the neighborhood density of the neighborhood to be cleaned. If so, stop centrifugation and cleaning, and calculate the mean of the first nearest neighbor to obtain the center evaluation result of RNA purity of the adult inbred boar sample. The preset multidimensional sample RNA quality assessment weight set includes RNA purity weight value, RNA quantification weight value, and RIN value weight value; Determining whether the neighborhood density of the first nearest neighbor is greater than or equal to the neighborhood density of the neighborhood to be cleaned further includes: If the neighborhood density of the first nearest neighbor is less than the neighborhood density of the neighborhood to be cleaned, then the first nearest neighbor is expanded at 1 / 10 of the central nearest neighbor bandwidth to obtain the first expanded nearest neighbor. The neighborhood density of the first expanded nearest neighbor is determined again to be greater than or equal to the neighborhood density of the neighborhood to be cleaned. If not, the first expanded nearest neighbor is expanded at the edge according to 1 / 10 of the central nearest neighbor bandwidth until the preset expansion stop condition is met, and the target expanded neighborhood is obtained. Calculate the mean of the target expanded neighborhood to obtain the RNA purity center assessment result of the adult inbred boar sample; The preset expansion stopping condition is that the neighborhood density of the two nearest neighboring neighborhoods in two adjacent expansions is less than or equal to the preset neighborhood density difference; The neighborhood density of the first nearest neighbor is the ratio of the total number of RNA purity assessment results of adult inbred boar samples within the first nearest neighbor to twice the bandwidth of the central nearest neighbor.
2. The inbreeding analysis method for high-throughput sequencing as described in claim 1, characterized in that, High-throughput sequencing was performed on the RNA sets of the aforementioned adult inbred boar samples and the RNA sets of Yunnan boar samples, and inbreeding analysis was performed based on the sequencing results, including: The RNA sets of adult inbred boar samples and Yunnan boar samples were subjected to long-read sequencing and paired-end RNA sequencing using the PacBio Iso-seq platform and the Illumina RNA-seq platform, respectively. Sequencing results were obtained, and inbreeding analysis was performed based on the sequencing results.
3. A high-throughput sequencing inbreeding analysis system, characterized in that, The system is used to perform the inbreeding analysis method for high-throughput sequencing according to any one of claims 1-2, comprising: The summary module is used to summarize the samples of adult inbred boars and Yunnan boars that have been obtained, respectively, to obtain the sample set of adult inbred boars and the sample set of Yunnan boars; The RNA extraction module is used to extract RNA from adult inbred boar sample sets and Yunnan boar sample sets using general reagents, respectively, to obtain the RNA sets of adult inbred boar samples and Yunnan boar samples. The comprehensive certification module is used to perform comprehensive certification of the sample quality of the adult inbred boar sample RNA set and the Yunnan boar sample RNA set according to a preset multidimensional sample RNA quality assessment weight set, and obtain the certification result. The inbreeding analysis module is used to perform high-throughput sequencing based on the RNA sets of adult inbred boar samples and Yunnan boar samples when the authentication result is successful, and to perform inbreeding analysis based on the sequencing results.
4. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; A processor, when executing executable instructions stored in the memory, implements the inbreeding analysis method for high-throughput sequencing according to any one of claims 1-2.