Method, apparatus, device and medium for 5'utr sequence alternative splicing analysis
By employing multiple screening of reference genome and transcriptome data and RNA secondary structure analysis, the accuracy and cost issues of alternative splicing of 5'UTR sequences in non-model species were resolved, achieving efficient and low-cost alternative splicing analysis of 5'UTR sequences.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHAN POLYTECHNIC UNIVERSITY
- Filing Date
- 2023-02-03
- Publication Date
- 2026-06-05
AI Technical Summary
Existing technologies are difficult to use quickly and effectively for alternative splicing analysis of 5'UTR sequences in non-model species, and conventional methods are costly and have low accuracy, making them unsuitable for widespread application in biological research.
By acquiring reference genome data and conventional transcriptome data, the 5'UTR sequence coordinates were located and compared, multiple screening and RNA secondary structure analysis were performed, false positive sequences were eliminated, and the minimum free energy was calculated to predict the 5'UTR structure and function of alternative splicing.
This method achieves high precision and accuracy in the analysis of alternative splicing of 5'UTR sequences, expands its applicability to both model and non-model species, and reduces experimental costs.
Smart Images
Figure CN116230083B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of biotechnology, and in particular to a method, apparatus, device, and medium for the analysis of 5'UTR sequence alternative splicing. Background Technology
[0002] The 5' untranslated region (5'UTR) is located at the 5' end of a coding gene and is defined as the sequence between the start position of the messenger RNA (mRNA) transcribed from the gene and the start codon (usually ATG) of the mRNA. The 5'UTR is crucial in mRNA transcription, post-transcriptional processes, and translation, influencing mRNA stability, transport, and translation efficiency. However, a single gene can transcribe into multiple different mRNAs, and the same mRNA or different mRNAs can choose different transcription start positions, resulting in various 5'UTR sequences of varying lengths. These 5'UTR sequences are known to contain multiple regulatory sequences (including upstream open reading frames (uORFs), RNA-protein binding sites (RBPs), and other regulatory elements). Alternative splicing of the 5'UTR sequence enriches gene expression regulation; in different species, tissues, organs, or developmental stages, the length and sequence of the 5'UTR are subject to gene regulation, ultimately affecting gene expression regulation and the structure and function of related proteins through alternative splicing. Therefore, developing high-throughput methods for detecting the length and sequence alternative splicing of the 5'UTR is essential for both basic and applied biological research.
[0003] Currently, high-throughput identification and analysis of 5' UTR mainly rely on specialized library construction methods, corresponding dedicated sequencing technologies, and data analysis software and algorithms, such as CAGE-seq (Cap Analysis of Gene Expression by deep sequencing). The principle of CAGE-seq is to sequence the 5' end sequences of mRNA enriched through various methods using high-throughput sequencing, thereby obtaining different start sites of the mRNA and the usage of these start sites. Unlike conventional RNA-seq sequencing, it is technically more challenging and expensive. Related methods and analysis algorithms have primarily been developed and optimized in model organisms for biological research (mainly humans, mice, fruit flies, nematodes, and Arabidopsis thaliana), making it difficult to apply to other species. Therefore, it is difficult to rapidly and effectively apply it to biological research and its widespread use is limited.
[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0005] The main objective of this invention is to provide a method for alternative splicing analysis of 5'UTR sequences, which aims to enable the analysis of alternative splicing of 5'UTR in non-5'UTR sequencing data (conventional transcriptome data), improve the accuracy and precision of the analysis method, and expand the applicability of the analysis method to both model species and non-model species.
[0006] To achieve the above objectives, this invention provides a method for alternative splicing analysis of 5'UTR sequences, the method comprising the following steps:
[0007] Obtain reference genome data and routine transcriptome data to be analyzed;
[0008] Locate the coordinates of the 5'UTR sequence in the conventional transcriptome data, and obtain the 5'UTR sequence based on the coordinates of the 5'UTR sequence;
[0009] The 5'UTR sequence was compared with reference genome data. Based on the comparison results, multiple screening was performed on the 5'UTR sequences that underwent alternative splicing in the conventional transcriptome data to obtain the screened alternative spliced 5'UTR sequences.
[0010] RNA secondary structure analysis was performed on the screened alternative splicing 5'UTR sequences to obtain the RNA secondary structure of the screened alternative splicing 5'UTR sequences.
[0011] Optionally, locating the 5'UTR sequence coordinates in the conventional transcriptome data and obtaining the 5'UTR sequence based on the 5'UTR sequence coordinates includes:
[0012] All gene data in the conventional transcriptome data are obtained, and the all gene data are compared with the annotated genes in the reference genome data. Based on the comparison results, all annotated genes in the conventional transcriptome data are obtained.
[0013] Based on the 5'UTR coordinates annotated in the reference genome, locate the 5'UTR coordinates of the conventional transcriptome data with the same gene ID to obtain the sequence position of the true 5'UTR in the conventional transcriptome data;
[0014] The 5'UTR sequence of the conventional transcriptome data is obtained based on the actual 5'UTR sequence position.
[0015] Optionally, the step of comparing the 5'UTR sequence with reference genome data, and performing multiple screening on the alternatively spliced 5'UTR sequences in the conventional transcriptome data based on the comparison results, to obtain the screened alternatively spliced 5'UTR sequences, includes:
[0016] Obtain the reference 5'UTR sequence annotated in the reference genome data, and match the first three introns after the start codon of the reference 5'UTR sequence with the first three introns of the 5'UTR sequence;
[0017] When the first three introns after the start codon of the reference 5'UTR sequence successfully match the first three introns of the 5'UTR, the successfully matched 5'UTR sequence is taken as the 5'UTR sequence to be screened, and multiple screenings are performed on the 5'UTR sequence to be screened to obtain the screened variable splice 5'UTR sequence.
[0018] Optionally, the step of performing multiple screenings on the 5'UTR sequence to be screened to obtain the screened variable-length 5'UTR sequence includes:
[0019] Determine whether the open reading frame of the 5'UTR sequence to be filtered matches that of the reference 5'UTR sequence, and delete the 5'UTR sequence to be filtered that does not match the open reading frame of the reference 5'UTR sequence;
[0020] Determine whether the gene expression level in the 5'UTR sequence to be screened is lower than the gene expression level threshold, and delete the 5'UTR sequence to be screened whose gene expression level is lower than the gene expression level threshold, wherein the gene expression level threshold is 1;
[0021] Determine whether the number of read segments in the 5'UTR sequence to be screened is less than the number of read segments threshold, and delete the 5'UTR sequence to be screened if the number of read segments is less than the number of read segments threshold, where the number of read segments threshold is 30;
[0022] Determine whether the base pair length of the 5'UTR sequence to be screened is less than the base pair length threshold, and delete the 5'UTR sequence to be screened whose base pair length is less than the base pair length threshold, where the base pair length threshold is 50;
[0023] After performing the above deletion steps, the filtered variable-cut 5'UTR sequence is obtained.
[0024] Optionally, the step of performing RNA secondary structure analysis on the screened alternative splicing 5'UTR sequence to obtain the secondary structure of the screened alternative splicing 5'UTR sequence includes:
[0025] Obtain the position, length, and gene name of the filtered alternative splicing 5'UTR sequence;
[0026] Calculate the minimum free energy of the alternative splicing 5'UTR sequence based on the location, length, and gene name;
[0027] The RNA secondary structure of the alternative splicing 5'UTR sequence is obtained based on the minimum free energy of the alternative splicing 5'UTR sequence.
[0028] Furthermore, to achieve the above objectives, the present invention also proposes a 5'UTR sequence variable splicing analysis device, the 5'UTR sequence variable splicing analysis device comprising:
[0029] The gene sequence acquisition module is used to acquire reference genome data and routine transcriptome data to be analyzed;
[0030] The gene calculation and screening module is used to locate the coordinates of the 5'UTR sequence in the conventional transcriptome data and obtain the 5'UTR sequence based on the coordinates of the 5'UTR sequence.
[0031] The gene calculation and screening module is used to compare the 5'UTR sequence with the reference genome data, and perform multiple screening on the 5'UTR sequences that have undergone alternative splicing in the conventional transcriptome data according to the comparison results, so as to obtain the screened alternative spliced 5'UTR sequences.
[0032] The gene sequence analysis module is used to perform RNA secondary structure analysis on the screened alternative splicing 5'UTR sequence to obtain the RNA secondary structure of the screened alternative splicing 5'UTR sequence.
[0033] Furthermore, to achieve the above objectives, the present invention also proposes a 5'UTR sequence alternative splicing analysis device, the 5'UTR sequence alternative splicing analysis device comprising: a memory, a processor, and a 5'UTR sequence alternative splicing analysis program stored in the memory and executable on the processor, the 5'UTR sequence alternative splicing analysis program being configured to implement the steps of the 5'UTR sequence alternative splicing analysis method as described above.
[0034] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a 5'UTR sequence alternative splicing analysis program, wherein the 5'UTR sequence alternative splicing analysis program, when executed by a processor, implements the steps of the 5'UTR sequence alternative splicing analysis method as described above.
[0035] This invention identifies and cuts annotated 5'UTR fragments from the reference transcriptome into the transcriptome to be analyzed, compares them with 5'UTRs in the reference genome to obtain all 5'UTRs that have undergone alternative splicing, and obtains alternative spliced 5'UTRs with higher accuracy and precision through analysis and multiple screening of alternative spliced 5'UTRs. Based on the calculation of the minimum free energy of alternative spliced 5'UTR sequences, it enables the prediction of structural and functional changes of alternative spliced 5'UTRs. Attached Figure Description
[0036] Figure 1 This is a schematic diagram of the structure of the 5'UTR sequence variable shearing analysis device in the hardware operating environment involved in the embodiments of the present invention;
[0037] Figure 2 This is a flowchart illustrating the first embodiment of the 5'UTR sequence variable splicing analysis method of the present invention;
[0038] Figure 3 This is a schematic diagram illustrating the calculation principle of the 5'UTR sequence in an embodiment of the 5'UTR sequence variable splicing analysis method of the present invention.
[0039] Figure 4 This is a schematic diagram of the analysis process of an embodiment of the 5'UTR sequence variable splicing analysis method of the present invention;
[0040] Figure 5 This is a flowchart illustrating the second embodiment of the 5'UTR sequence variable splicing analysis method of the present invention;
[0041] Figure 6 This is a schematic diagram illustrating the filtering of false positive 5'UTR sequences in an embodiment of the 5'UTR sequence alternative splicing analysis method of the present invention;
[0042] Figure 7 This is a structural block diagram of the first embodiment of the 5'UTR sequence variable shearing analysis device of the present invention.
[0043] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0044] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.
[0045] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of the 5'UTR sequence variable shearing analysis device in the hardware operating environment involved in the embodiments of the present invention.
[0046] like Figure 1As shown, the 5'UTR sequence variable shearing analysis device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.
[0047] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the 5'UTR sequence variable shearing analysis device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0048] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a 5'UTR sequence variable shearing analysis program.
[0049] exist Figure 1 In the 5'UTR sequence variable splicing analysis device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the 5'UTR sequence variable splicing analysis device of the present invention can be set in the 5'UTR sequence variable splicing analysis device, and the 5'UTR sequence variable splicing analysis device calls the 5'UTR sequence variable splicing analysis program stored in the memory 1005 through the processor 1001 and executes the 5'UTR sequence variable splicing analysis method provided in the embodiment of the present invention.
[0050] This invention provides a method for alternative splicing analysis of 5'UTR sequences, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a 5'UTR sequence variable splicing analysis method according to the present invention.
[0051] In this embodiment, the 5'UTR sequence variable shearing analysis method includes the following steps:
[0052] Step S10: Obtain reference genome data and routine transcriptome data to be analyzed.
[0053] Understandably, reference genome data includes a reference genome in FASTA (The Fasta Format, or Fasta for short) format and genome annotation files in GTF / GFF3 format; FASTA is a text-based format used to represent nucleic acid or peptide sequences. Nucleic acids or amino acids are represented by single letters, and this format has become a standard in the field of bioinformatics.
[0054] The routine transcriptome data to be analyzed includes the results of Tophat-Cufflinks assembly and alignment of the transcriptome sequencing data. These include BAM (Binary Alignment Map, a binary storage file that, when converted to text, corresponds to SAM (The Sequencing Alignment Map), GTF (gene transfer format, primarily used for gene annotation), and FPKM_tracking files. Tophat is a fast program for rapidly splicing and mapping RNA-Seq data. It uses an ultra-fast, high-throughput short-read alignment program to align RNA-Seq information to a mammalian genome, then analyzes the mapping results to identify splice sites between exons. Cufflinks uses the Tophat alignment results to assemble transcripts, estimate their abundance, and detect differential expression and alternative splicing between samples.
[0055] It should be understood that each version of the reference genome has a corresponding genome annotation file. The gene annotation file is a detailed labeling and annotation of the genes in the reference genome, and can usually be downloaded from public databases such as NCBI and Ensemble.
[0056] Understandably, the transcriptome, broadly speaking, refers to the collection of all transcription products within a cell under a given physiological condition, including messenger RNA, ribosomal RNA, transfer RNA, and non-coding RNA; narrowly speaking, it refers to the collection of all mRNAs. Transcriptome sequencing generally involves high-throughput sequencing of mature mRNA and ncRNA transcribed from RNA polymerase II, which has been affinity-purified using oligo-dT. Compared to traditional microarray hybridization platforms, transcriptome sequencing does not require pre-designed probes for known sequences, allowing for the detection of overall transcriptional activity in any species. It provides more accurate digital signals, higher detection throughput, and a wider detection range, making it a powerful tool for in-depth research into the complexity of the transcriptome. The core function of transcriptome data is to identify transcripts and quantify their expression. Conventional raw transcriptome sequencing data analysis typically requires assembly and alignment to identify and quantify transcripts. This method uses input data assembled and aligned using Tophat-Cufflinks. Based on conventional transcriptome transcription identification and expression quantification, 5'UTR alternative splicing analysis is performed on the test samples.
[0057] It is important to emphasize that the genome annotation files (gtf / gff3 format) in the reference genome data annotate the genes in the transcripts to be analyzed. Based on the annotations, the corresponding gene IDs and detailed annotations can be found from the transcriptome to be analyzed. For example, in the transcriptome data, some genes are annotated as cold-resistant genes, and some genes are annotated as drought-resistant genes. Based on the annotations, all stress-resistant gene information in the gene data can be found.
[0058] Step S20: Locate the coordinates of the 5'UTR sequence in the conventional transcriptome data, and obtain the 5'UTR sequence based on the coordinates of the 5'UTR sequence.
[0059] In specific implementation, the algorithm principle for locating the 5'UTR sequence coordinates in the conventional transcriptome data and obtaining the 5'UTR sequence based on these coordinates is as follows: Figure 3 As shown.
[0060] Understandably, the 5'UTR coordinates of all annotated genes can be located from the gene annotations of the reference genome; based on the 5'UTR coordinate information, the 5'UTR fragments can be cut out from the genes, retaining only the information of the 5'UTR fragments and removing the gene fragment information of other parts.
[0061] It should be understood that if the reference genome for this species does not fully annotate the UTR regions, it will affect the alignment and quantification results of the transcriptome data, and will also affect the analysis results of the 5'UTR using this method. Therefore, it is recommended to choose a reference genome with good annotation for this species.
[0062] It should be noted that during the actual splicing process, the information of the transcriptome to be analyzed is stored in the Tophat-Cufflinks assembly results, including BAM and GTF format files; the gene information and gene annotation information of the reference genome are stored in reference genome files in FASTA format and genome annotation files in GTF / GFF3 format. The pre-defined program `genome.sh` locates all genes containing 5'UTR annotations in the reference genome and gene annotation files, and compares the gene IDs and coordinates of the annotated genes with those of all genes in the transcriptome to ensure they match. The matching principle is as follows: Obtain the 5'UTR sequence information of the reference genome annotation, and match the positions of the first three introns in the reference 5'UTR sequence with the first three introns in the 5'UTR sequence of the transcriptome to be analyzed; when the first three introns of the start codon in the reference 5'UTR sequence successfully match the position and length of the first three introns in the 5'UTR sequence information of the transcriptome to be analyzed, then according to the 5'UTR coordinates of the corresponding gene in the reference genome, the 5'UTR sequence of the corresponding gene in the transcriptome to be analyzed is extracted to obtain all the 5'UTR sequences in the transcriptome to be analyzed.
[0063] It should be noted that locating the 5'UTR sequence coordinates in the conventional transcriptome data and obtaining the 5'UTR sequence information based on the 5'UTR sequence coordinates includes:
[0064] Obtain all gene data from the conventional transcriptome data to be analyzed, compare the all gene data with the annotated genes in the reference genome, and obtain all annotated genes in the conventional transcriptome data to be analyzed based on the comparison results.
[0065] Based on the 5'UTR coordinates annotated in the reference genome, locate the 5'UTR coordinates of the conventional transcriptome data to be analyzed that have the same gene ID, and obtain the true 5'UTR sequence position in the conventional transcriptome data to be analyzed;
[0066] The 5'UTR sequence of the conventional transcriptome data to be analyzed is obtained based on the position information of the actual 5'UTR sequence.
[0067] Step S30: Compare the 5'UTR sequence with the reference genome data, and perform multiple screening on the 5'UTR sequences that have undergone alternative splicing in the conventional transcriptome data according to the comparison results to obtain the screened alternative spliced 5'UTR sequences.
[0068] In specific implementation, the 5'UTR sequence is compared with the reference genome data. Based on the comparison results, 5'UTR sequences that may undergo alternative splicing in the conventional transcriptome data are obtained. Multiple screening is then performed on these potentially alternatively spliced 5'UTR sequences to obtain the screening results. The algorithm principle is as follows: Figure 6 As shown.
[0069] Understandably, the transcriptome 5'UTR sequences that were successfully matched with the reference genome and extracted contained some alternative splices and some that were not, maintaining the same 5'UTR sequence length as the reference genome data. Furthermore, even among the alternatively spliced 5'UTR sequences, there is still a possibility of false positives. Therefore, this invention employs analysis and multiple filtering methods to eliminate alternatively spliced 5'UTR sequences with low accuracy and precision.
[0070] It can be noted that the analysis and multiple screening of all 5'UTR sequences in the transcriptome to be analyzed yields 5'UTR samples with alternative splicing, including: when the alternative splicing 5'UTR sequence does not match the open reading frame annotated in the matching gene of the reference genome, the gene annotation or the matching of the transcriptome gene with the reference genome may be incorrect, and the alternative splicing 5'UTR sequence is deleted; when a certain alternative splicing 5'UTR belongs to multiple different transcripts in the transcriptome to be analyzed, the accuracy of the alternative splicing 5'UTR cannot be determined, or when the alternative splicing 5'UTR sequence is located at the overlapping position of two different genes annotated in the reference genome, it is impossible to determine which gene the 5'UTR belongs to, and the above two types of alternative splicing 5'UTR sequences are deleted.
[0071] It is important to emphasize that 5'UTR sequences with excessively low expression levels or excessively short read lengths are unlikely to produce biologically functional proteins. Therefore, the following deletions are made: 5'UTR sequences containing alternatively spliced 5'UTR sequences with gene expression levels below a threshold are deleted; sequences containing alternatively spliced 5'UTR sequences with an RPKM value less than 1 are deleted; sequences containing alternatively spliced 5'UTR sequences with a read count less than a threshold are deleted, i.e., sequences containing alternatively spliced 5'UTR sequences with a read count less than 30 are deleted; and sequences containing alternatively spliced 5'UTR sequences with a base pair length less than a threshold are deleted, i.e., sequences with a base pair length less than 50 bp are deleted.
[0072] It should be noted that the step of comparing the 5'UTR sequence information with the reference genome data, and then cutting the conventional transcriptome data according to the comparison results to obtain the alternatively cut 5'UTR sequence includes:
[0073] Obtain the reference 5'UTR sequence annotated in the reference genome, and match the first three introns after the start codon of the reference 5'UTR sequence with the first three introns of the 5'UTR sequence;
[0074] When the first three introns after the start codon of the 5'UTR sequence in the reference genome match the first three introns of the 5'UTR sequence, the 5'UTR sequence is spliced to obtain an alternatively spliced 5'UTR sequence.
[0075] Step S40: Perform RNA secondary structure analysis on the cleavage results of the alternatively cleaved 5'UTR sequence to obtain the RNA secondary structure of the alternatively cleaved 5'UTR sequence.
[0076] The analysis based on the transcriptome to be analyzed, to obtain the alternative splicing 5'UTR sequence results in the transcriptome to be analyzed, includes:
[0077] Obtain the position, length, and gene name of the filtered alternative splicing 5'UTR sequence;
[0078] Calculate the minimum free energy of the alternative splicing 5'UTR sequence based on the location, length, and gene name;
[0079] The RNA secondary structure of the alternative splicing 5'UTR sequence is obtained based on the minimum free energy of the alternative splicing 5'UTR sequence.
[0080] Understandably, steps S10–S30 have yielded the following results: a list of alternative splicing 5'UTR genes in the transcriptome to be analyzed, including gene names, lengths, sequences, and locations. To predict the impact of 5'UTR alternative splicing on the biological function of the protein expressed by this gene, based on the 5'UTR sequence information and using the minimum free energy algorithm (where the RNA molecule reaches its minimum free energy through conformational adjustments to achieve thermodynamic equilibrium and thus the most stable state), the minimum free energy of the alternative splicing 5'UTR sequence is calculated to obtain the possible RNA secondary structure of the alternative splicing 5'UTR sequence.
[0081] Understandably, the Minimum Free Energy (MFE) algorithm was proposed by Zuker, who incorporated experimentally obtained energy data into the calculation of RNA secondary structure prediction. Base pairs and nucleotides in RNA molecules are linked by hydrogen bonds. By determining the energy required to break these hydrogen bonds in different substructures within the RNA secondary structure, we can obtain the energy consumed when these substructures recombine; this energy is called the free energy. The stability of the RNA molecule is due to the energy reduction caused by base pairing. Therefore, the MFE algorithm considers the minimum free energy to represent the true secondary structure of RNA. This is because when the free energy approaches its minimum, the RNA molecule will undergo conformational adjustments to reach a thermodynamic equilibrium, resulting in the most stable state of the RNA molecule. The central idea of the MFE algorithm is that, in the optimal recombination of RNA secondary structures, the total energy of various stem-loop structures obtained through molecular dynamics methods is minimized, resulting in the most stable structure. Based on this, we calculate the RNA secondary structure of the alternatively spliced 5'UTR sequence under the minimum free energy, using information such as the sequence of the alternatively spliced 5'UTR in the transcriptome to be analyzed.
[0082] In its specific implementation, the workflow is as follows: Figure 4 As shown, it includes the following modules:
[0083] Module 1: genome.sh extracts 5'UTR information containing gene annotations from the reference genome; Module 2: qsub.sh specifies the sample path and name of the transcriptome data to be analyzed; Module 3: 3r.sh contains gene sequence calculation, gene sequence screening, and gene sequence analysis modules. Its function is to perform 5'UTR alternative splicing on the transcriptome data to be analyzed in Module 2 based on the 5'UTR gene annotation information from Module 1, calculate and analyze the results, and output the analysis results.
[0084] As a preferred embodiment, a 5'UTR variable shear analysis is performed, including:
[0085] (1) Place the reference genome data and the transcriptome data to be analyzed into... Figure 1 In the user interface 1003 shown, the data includes:
[0086] A. Reference genome file genome.gff3 or genome.gtf, and genome annotation file genome.fasta;
[0087] B. Transcriptome Tophat-Cufflinks assembly and alignment data to be analyzed (generally 3 biological replicates): sample-1.bam, sample-2.bam, sample-3.bam; sample.merged.gtf, and sample-isform.fpkm_tracking.
[0088] (2) Through Figure 1 After inputting the number and name of all samples to be analyzed in the user interface 1002 shown, the 5'UTR sequence analysis will proceed sequentially according to the input sample order. The analysis time depends on the operating environment, genome size, and number of samples. For example, for maize genomes with 2 samples and 3 biological replicates per sample: Scientific Linux release 6.10 Carbon Research Server takes 10 minutes; a standalone MacOS 11.3 16GB takes 30 minutes; and a standalone Windows 10 Ubuntu 20.04 5GB takes approximately 1 hour.
[0089] (3) After the operation is complete, the results will be stored in the results folder in the memory, such as Figure 4 The output results include: a list of alternatively spliced 5'UTR genes (txt format), alternatively spliced 5'UTR gene sequences (fasta format), coordinate information of alternatively spliced 5'UTR genes (txt format), and possible secondary structures of alternatively spliced 5'UTR genes (ps format).
[0090] It is important to emphasize that this invention does not require special non-coding region library construction techniques; it only requires conventional transcriptome assembly and alignment data as input files, thus saving experimental costs. Based on bioinformatics technology, this invention can obtain highly accurate and precise 5'UTR alternative splicing results from conventional RNA-seq gene expression results, and can predict structural and functional changes. Furthermore, this invention has broad applicability, suitable for various species with reference genomes; the species to which the test sample belongs can be a model species or a non-model species.
[0091] refer to Figure 5 , Figure 5 This is a flowchart illustrating a second embodiment of a 5'UTR sequence variable splicing analysis method according to the present invention.
[0092] Based on the first embodiment described above, the UTR sequence variable shearing analysis method of this embodiment 5' further includes, in step S30:
[0093] Step S31: Determine whether the open reading frame of the gene containing the 5'UTR sequence to be screened matches that of the gene containing the reference 5'UTR sequence, and delete the 5'UTR sequence to be screened that does not match the open reading frame of the gene containing the reference 5'UTR sequence.
[0094] Understandably, when the alternative splicing 5'UTR sequence does not match the open reading frame annotated in the matching gene of the reference genome, the alternative splicing 5'UTR may belong to multiple different transcripts in the transcriptome to be analyzed, and the accuracy of the alternative splicing 5'UTR cannot be determined; when the alternative splicing 5'UTR sequence is located at the overlapping position of two different genes annotated in the reference genome, it is impossible to determine which gene the 5'UTR belongs to, so the above two types of alternative splicing 5'UTR sequences are deleted.
[0095] Step S32: Determine whether the gene expression level in the 5'UTR sequence to be screened is lower than the gene expression level threshold, and delete the 5'UTR sequence to be screened whose gene expression level is lower than the gene expression level threshold, wherein the gene expression level threshold is 1.
[0096] Understandably, genes can encode proteins; however, not all genes are expressed. Gene expression to produce proteins involves two steps: transcription and translation. DNA is transcribed into RNA, and RNA is translated into protein. Therefore, the expression level (abundance) of transcripts (RNA) is very important, as the abundance of transcript expression indicates whether a gene is likely to produce a functional protein.
[0097] It should be noted that low abundance (low expression level) genes are removed by expression filters. Low abundance (low expression level) also means low reliability and low confidence. RPKM (Reads Per Kilobase Per Million Mapped Reads) is usually used to represent the number of reads per kilobase length from a gene per million reads. It was proposed by Ali Mortazavi et al. in 2008 and is used to estimate gene expression levels. In this step, the default gene expression level threshold of less than 1 is the preferred threshold for low abundance. The preferred threshold of 1 RPKM can be modified. In addition, if the gene containing the alternative splicing 5'UTR sequence has too few reads detected during sequencing, the alternative splicing of the gene may be an accidental phenomenon. That is, it is common for the gene not to have the alternative splicing 5'UTR detected. Therefore, sequences containing the alternative splicing 5'UTR with fewer reads than the read number threshold are deleted. In this step, the default gene read number threshold of 30 is the preferred threshold. The preferred threshold of 30 can be modified.
[0098] Step S33: Determine whether the number of read segments in the 5'UTR sequence to be screened is less than the number of read segments threshold, and delete the 5'UTR sequence to be screened whose number of read segments is less than the number of read segments threshold, wherein the number of read segments threshold is 30.
[0099] Understandably, the read count is the number of reads detected in the 5'UTR of a single gene.
[0100] Step S34: Determine whether the base pair length of the 5'UTR sequence to be screened is less than the base pair length threshold, and delete the 5'UTR sequence to be screened whose base pair length is less than the base pair length threshold, wherein the base pair length threshold is 50.
[0101] Understandably, sequences that are too short cannot form functional peptides. Therefore, this step deletes alternative splicing 5'UTR sequences shorter than 50 bp by default; the preferred threshold of 50 bp can be modified.
[0102] In specific implementation, the implementation methods of steps S31 to S33 can be referred to Figure 6 .
[0103] Step S35: After performing the above deletion steps, the filtered variable cut 5'UTR sequence is obtained.
[0104] Understandably, after the above five screenings, the 5'UTR sequence to be screened is left with an alternative splicing 5'UTR sequence, which eliminates false positive sequences mixed in with the alternative splicing 5'UTR sequence.
[0105] It is important to emphasize that this invention can detect 5'UTR alternative splicing in transcripts in conventional RNA-seq data without the need for special reagents or 5'UTR-specific library construction. By analyzing and using multiple filtering to remove false positive 5'UTR results, the accuracy and precision of the results are greatly improved. The output results not only provide detailed gene sequences of 5'UTR alternative splicing in each gene, the gene to which the alternative splicing occurred, and detailed coordinates, but also calculate the RNA secondary structure of the 5'UTR, providing a scientific basis for predicting changes in protein function after 5'UTR alternative splicing.
[0106] This embodiment analyzes and performs multiple filtering on alternatively spliced 5'UTR sequences obtained from the transcriptome to be analyzed. False positive 5'UTR sequences that do not match the open reading frame, have excessively low gene expression levels, or have too few reads are removed, as well as 5'UTR sequences whose base pairs are too short to form an effective protein. The RNA secondary structure of the alternatively spliced 5'UTR sequence is predicted by calculating its minimum free energy (MFE). This method requires no special reagents or 5'UTR-specific library construction, involves minimal computation, and has low computational costs, significantly saving researchers' experimental costs and time. It provides rapid and accurate detailed information on 5'UTR alternative splicing in the tested sample.
[0107] Furthermore, this embodiment of the invention also proposes a storage medium storing a 5'UTR sequence alternative splicing analysis program, which, when executed by a processor, implements the steps of the 5'UTR sequence alternative splicing analysis method as described above.
[0108] Reference Figure 7 , Figure 7 This is a structural block diagram of the first embodiment of the 5'UTR sequence variable shearing analysis device of the present invention.
[0109] like Figure 7 As shown, the 5'UTR sequence variable shearing analysis device proposed in this embodiment of the invention includes:
[0110] Gene sequence acquisition module 10 is used to acquire reference genome data and routine transcriptome data to be analyzed;
[0111] The gene calculation and screening module 20 is used to locate the coordinates of the 5'UTR sequence in the conventional transcriptome data and obtain the 5'UTR sequence based on the coordinates of the 5'UTR sequence.
[0112] The gene calculation and screening module 20 is also used to compare the 5'UTR sequence with the reference genome data, and perform multiple screening on the 5'UTR sequences that have undergone alternative splicing in the conventional transcriptome data according to the comparison results, so as to obtain the screened alternative spliced 5'UTR sequences.
[0113] The gene sequence analysis module 30 is used to perform RNA secondary structure analysis on the screened alternative splicing 5'UTR sequence to obtain the RNA secondary structure of the screened alternative splicing 5'UTR sequence.
[0114] This embodiment identifies and cuts out the annotated target 5'UTR fragment from the transcriptome to be analyzed. Then, the cut 5'UTR fragment is subjected to multiple screening. Based on multiple screening, the true 5'UTR fragment in the transcriptome can be found more accurately. The alternative splicing of the 5'UTR fragment is analyzed to obtain the alternative splicing of the 5'UTR fragment, thereby realizing the alternative splicing analysis of the 5'UTR fragment and effectively avoiding the influence of false positive 5'UTR fragments on the alternative splicing analysis.
[0115] In one embodiment, the gene calculation and screening module 20 is further configured to acquire all gene data in the conventional transcriptome data, compare the all gene data with the annotated genes in the reference genome data, and obtain all annotated genes in the conventional transcriptome data based on the comparison results.
[0116] Based on the 5'UTR coordinates annotated in the reference genome, locate the 5'UTR coordinates of the conventional transcriptome data with the same gene ID to obtain the sequence position of the true 5'UTR in the conventional transcriptome data;
[0117] The 5'UTR sequence of the conventional transcriptome data is obtained based on the actual 5'UTR sequence position.
[0118] In one embodiment, the gene calculation and screening module 20 is further configured to obtain the reference 5'UTR sequence annotated in the reference genome data, and match the first three introns after the start codon of the reference 5'UTR sequence with the first three introns of the 5'UTR sequence;
[0119] When the first three introns after the start codon of the reference 5'UTR sequence successfully match the first three introns of the 5'UTR, the successfully matched 5'UTR sequence is taken as the 5'UTR sequence to be screened, and multiple screenings are performed on the 5'UTR sequence to be screened to obtain the screened variable splice 5'UTR sequence.
[0120] In one embodiment, the gene calculation and screening module 20 is used to determine whether the open reading frame of the 5'UTR sequence to be screened matches that of the reference 5'UTR sequence, and to delete the 5'UTR sequence to be screened that does not match the open reading frame of the reference 5'UTR sequence;
[0121] Determine whether the gene expression level in the 5'UTR sequence to be screened is lower than the gene expression level threshold, and delete the 5'UTR sequence to be screened whose gene expression level is lower than the gene expression level threshold, wherein the gene expression level threshold is 1;
[0122] Determine whether the number of read segments in the 5'UTR sequence to be screened is less than the number of read segments threshold, and delete the 5'UTR sequence to be screened if the number of read segments is less than the number of read segments threshold, where the number of read segments threshold is 30;
[0123] Determine whether the base pair length of the 5'UTR sequence to be screened is less than the base pair length threshold, and delete the 5'UTR sequence to be screened whose base pair length is less than the base pair length threshold, where the base pair length threshold is 50;
[0124] After performing the above deletion steps, the filtered variable-cut 5'UTR sequence is obtained.
[0125] In one embodiment, the gene sequence analysis module 30 is further configured to obtain the position, length, and gene name of the screened alternative splicing 5'UTR sequence;
[0126] Calculate the minimum free energy of the alternative splicing 5'UTR sequence based on the location, length, and gene name;
[0127] The RNA secondary structure of the alternative splicing 5'UTR sequence is obtained based on the minimum free energy of the alternative splicing 5'UTR sequence.
[0128] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.
[0129] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.
[0130] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0131] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0132] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0133] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.
Claims
1. A method for alternative splicing analysis of 5'UTR sequences, characterized in that, The 5'UTR sequence variable splicing analysis method includes: Obtain reference genome data and routine transcriptome data to be analyzed; Locate the coordinates of the 5'UTR sequence in the conventional transcriptome data, and obtain the 5'UTR sequence based on the coordinates of the 5'UTR sequence; Obtain the reference 5'UTR sequence annotated in the reference genome data, and match the start codon after the reference 5'UTR sequence with the 5'UTR sequence; When the first three introns after the start codon of the reference 5'UTR sequence match the first three introns of the 5'UTR, the successfully matched 5'UTR sequence is taken as the 5'UTR sequence to be screened. It is then determined whether the open reading frame of the 5'UTR sequence to be screened matches that of the reference 5'UTR sequence. 5'UTR sequences to be screened that do not match the open reading frame of the reference 5'UTR sequence are deleted. Determine whether the gene expression level in the 5'UTR sequence to be screened is lower than the gene expression level threshold, and delete the 5'UTR sequence to be screened whose gene expression level is lower than the gene expression level threshold, wherein the gene expression level threshold is 1; Determine whether the number of read segments in the 5'UTR sequence to be screened is less than the number of read segments threshold, and delete the 5'UTR sequence to be screened if the number of read segments is less than the number of read segments threshold, where the number of read segments threshold is 30; Determine whether the base pair length of the 5'UTR sequence to be screened is less than the base pair length threshold, and delete the 5'UTR sequence to be screened whose base pair length is less than the base pair length threshold, where the base pair length threshold is 50; After performing the above deletion steps, the filtered variable-length cut 5'UTR sequence is obtained; RNA secondary structure analysis was performed on the screened alternative splicing 5'UTR sequences to obtain the RNA secondary structure of the screened alternative splicing 5'UTR sequences.
2. The method for variable splicing analysis of 5'UTR sequences as described in claim 1, characterized in that, The process of locating the 5'UTR sequence coordinates in the conventional transcriptome data and obtaining the 5'UTR sequence based on the 5'UTR sequence coordinates includes: All gene data in the conventional transcriptome data are obtained, and the all gene data are compared with the annotated genes in the reference genome data. Based on the comparison results, all annotated genes in the conventional transcriptome data are obtained. Based on the 5'UTR coordinates annotated in the reference genome, locate the 5'UTR coordinates of the conventional transcriptome data with the same gene ID to obtain the sequence position of the true 5'UTR in the conventional transcriptome data; The 5'UTR sequence of the conventional transcriptome data is obtained based on the actual 5'UTR sequence position.
3. The method for variable splicing analysis of 5' UTR sequences as described in any one of claims 1-2, characterized in that, The RNA secondary structure analysis of the screened alternative splicing 5'UTR sequences yields the secondary structure of the screened alternative splicing 5'UTR sequences, including: Obtain the position, length, and gene name of the filtered alternative splicing 5'UTR sequence; Calculate the minimum free energy of the alternative splicing 5'UTR sequence based on the location, length, and gene name; The RNA secondary structure of the alternative splicing 5'UTR sequence is obtained based on the minimum free energy of the alternative splicing 5'UTR sequence.
4. A 5'UTR sequence variable shearing analysis device, characterized in that, The 5'UTR sequence variable shearing analysis device includes: The gene sequence acquisition module is used to acquire reference genome data and routine transcriptome data to be analyzed; The gene calculation and screening module is used to locate the coordinates of the 5'UTR sequence in the conventional transcriptome data and obtain the 5'UTR sequence based on the coordinates of the 5'UTR sequence. The gene computation and screening module is further configured to obtain the reference 5'UTR sequence annotated in the reference genome data, and match the start codon following the reference 5'UTR sequence with the 5'UTR sequence; when the first three introns following the start codon of the reference 5'UTR sequence successfully match the first three introns of the 5'UTR, the successfully matched 5'UTR sequence is taken as the 5'UTR sequence to be screened, and it is determined whether the open reading frame of the 5'UTR sequence to be screened matches that of the reference 5'UTR sequence, and the 5'UTR sequences to be screened that do not match the open reading frame of the reference 5'UTR sequence are deleted; the 5'UTR sequence to be screened is determined. The following steps are performed: First, determine if the gene expression level in the sequence is below a gene expression level threshold (1). Then, delete any 5'UTR sequences with gene expression levels below the threshold. Next, determine if the number of reads in the 5'UTR sequence is less than a read count threshold (30). Finally, determine if the base pair length of the 5'UTR sequence is less than a base pair length threshold (50). After performing these deletion steps, the selected alternatively spliced 5'UTR sequences are obtained. The gene sequence analysis module is used to perform RNA secondary structure analysis on the screened alternative splicing 5'UTR sequence to obtain the RNA secondary structure of the screened alternative splicing 5'UTR sequence.
5. A 5'UTR sequence variable shearing analysis device, characterized in that, The device includes: a memory, a processor, and a 5'UTR sequence alternative splicing analysis program stored in the memory and executable on the processor, the 5'UTR sequence alternative splicing analysis program being configured to implement the 5'UTR sequence alternative splicing analysis method as described in any one of claims 1 to 3.
6. A storage medium, characterized in that, The storage medium stores a 5'UTR sequence variable splicing analysis program, which, when executed by a processor, implements the 5'UTR sequence variable splicing analysis method as described in any one of claims 1 to 3.