Method for the quantitative analysis of RNA 5-methylcytosine modifications on splice isoforms

Abstract

The application discloses a data analysis method in the field of epigenetics: a quantitative analysis method of RNA 5-methylcytosine modification on splicing isomers. The method can solve the problem of how to quantitatively analyze RNA 5-methylcytosine modification on the level of gene splicing isomers, and the technical points include: converting the data set generated by the RNA-BSseq sequencing technology into a virtual RNA-Seq data set and marking the data in which RNA 5-methylcytosine modification occurs; estimating the expression amount of the splicing isomer in the virtual data set, and calculating the mean and variance thereof by using the bootstrap technology; estimating the RNA 5-methylcytosine modification level of the splicing isomer, and calculating the mean and variance of the RNA 5-methylcytosine modification level of the splicing isomer by using the Delta technology; and based on the normal distribution, testing whether there is a significant difference in the estimated RNA 5-methylcytosine modification level of the splicing isomer under different conditions. The application helps to reveal the regulation mechanism of RNA 5-methylcytosine modification in physiological processes and pathological changes.

Description

Technical Field

[0001] This invention relates to the field of epigenetic information analysis, specifically to a method for quantitative analysis of RNA 5-methylcytosine modification information. Background Technology

[0002] 5-Methylcytosine modification (m 5 C) is a modification mediated by a methanogenase, which adds a methyl group to the 5th carbon atom of cytosine. It is widely present in the transcriptomes of humans and other plants and animals, playing a crucial role in many important biological processes and is associated with various human diseases. Although research indicates that RNA m... 5 C is an important regulator of gene expression and is essential for normal development, but its exact function remains unclear. Therefore, in order to study RNAm... 5 The exact function of C, quantitative analysis of RNA m 5 C is essential, especially in the following three aspects: (1) RNA m 5 (2) Changes in C levels at different developmental stages of an organism; 5 C level variations in different tissues of an organism; (3) RNA m 5 Changes in C levels under different environmental and stress conditions.

[0003] RNA m 5 A key method for large-scale RNA sequencing is combining bisulfite nucleotide sequence analysis (BS-seq) with RNA sequencing. This technique (RNA-BSseq) can pinpoint the exact location of 5-methylcytosine, providing essential technical support for quantitative analysis of 5-methylcytosine modifications at the splice isoform level. However, the prevalence of alternative splicing makes quantitative analysis of RNA m... 5 The following challenges are faced when dealing with C data: <1> How to estimate the m of RNA 5 C-level? In eukaryotes, alternative splicing transcribes the same gene into different splice isoforms. When the modified site is located in a shared exon region of gene splice isoforms, convolution operations are needed on the fragment containing that site to determine the relative contribution of each splice isoform, and only then can the m-level of that site be estimated. 5 C level. However, how to determine the relative contribution of each isoform is a question for RNA m. 5 A challenging problem in C data analysis; <2> How to perform RNA m 5 Differential analysis of C-level? Under different experimental conditions, sequencing depth, biological noise, and splicing structures will affect the estimated RNA m5 The existence of differences in C levels makes it challenging to interpret the observed differences and determine whether they are significant. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, the present invention provides an RNA m 5 C quantitative analysis method to estimate RNA m at the splice isoform level. 5 C level, and test the estimated RNA m 5 Whether there is a significant difference in C levels.

[0005] This invention discloses a method for quantitative analysis of RNA 5-methylcytosine modification on splice isomers, comprising:

[0006] Convert RNA-BSseq data into virtual RNA-Seq data;

[0007] Based on virtual RNA-Seq data, the 5-methylcytosine modification level (m) of splice isoform RNA was estimated. 5 C) Level, and calculate the expected value and variance of the estimated value by combining bootstrap and Delta techniques;

[0008] Based on the normal distribution, we examine the splice isomers m among different samples. 5 Are there significant differences in C levels?

[0009] As a further improvement to this invention, the formulas for calculating the expected value and variance of the estimated value by combining bootstrap and Delta techniques are as follows:

[0010]

[0011]

[0012] In the formula, x represents the expression level of the splice isoform in the dataset composed of labeled data from the virtual RNA-Seq dataset, and y represents the expression level of the splice isoform in the virtual RNA-Seq dataset. The RNA 5-methylcytosine modification level of the splice isoform. This represents the mean level of 5-methylcytosine modification in the splice isoform RNA. The variance of the RNA 5-methylcytosine modification level of the splice isoform, μ x Let μ be the mean of x (calculated using the bootstrap technique). y Let y be the mean (calculated using the bootstrap technique). Let x be the variance (calculated using the bootstrap technique). The variance of y (calculated using the bootstrap technique).

[0013] As a further improvement to the present invention, it is assumed that the splice isomer m 5 The estimated C level values ​​follow a normal distribution. Based on the mean and variance of the estimated values ​​(calculated using a combination of bootstrap and Delta techniques), the splice isomer m among different samples is examined. 5 Are there significant differences in C levels?

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] This invention enables quantitative analysis of RNA 5-methylcytosine modification on splice isomers, allowing for the estimation of RNA m... 5 C level, and can be used for RNA m from different samples 5 Differential analysis of C levels will provide necessary technical support for research on the association between RNA 5-methylcytosine modification and human diseases. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for quantitative analysis of RNA 5-methylcytosine modification on splice isomers disclosed in one embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The present invention will now be described in further detail with reference to the accompanying drawings:

[0019] like Figure 1 As shown, this invention provides a method for quantitatively analyzing RNA 5-methylcytosine modification on splice isomers, comprising:

[0020] S1. Convert RNA-BSseq data into virtual RNA-Seq data and construct virtual methylated RNA-Seq data: Select an appropriate tool to align the data generated by RNA-BSseq technology to a reference genome. Based on the alignment results, convert the T corresponding to unmethylated cytosine to C and label fragments containing methylated cytosine, thereby converting the RNA-BSseq dataset into virtual RNA-Seq data. Furthermore, extract fragments containing methylation labels from the virtual RNA-Seq data to construct a virtual methylated RNA-Seq dataset.

[0021] S2. Estimating RNA Expression Levels and Performing Bootstrap Processing Based on Virtual RNA-Seq Datasets: Based on the virtual RNA-Seq dataset, an appropriate RNA expression analysis tool is selected to estimate the number of fragments on each transcript. Furthermore, the virtual RNA-Seq dataset is bootstrap processed to generate several virtual RNA-Seq datasets, and then the mean and variance of the estimated number of fragments on each transcript are calculated.

[0022] S3. Estimating RNA Expression Levels and Performing Bootstrap Processing Based on Virtual Methylated RNA-Seq Datasets: Based on the virtual methylated RNA-Seq dataset, an appropriate RNA expression analysis tool is selected to estimate the number of methylated fragments on each transcript. Furthermore, the virtual methylated RNA-Seq dataset is bootstrap processed to generate several virtual methylated RNA-Seq datasets, and then the mean and variance of the estimated number of methylated fragments on each transcript are calculated.

[0023] S4. Estimating RNA m 5 The level of C and the mean and variance of the estimates: RNA m for each transcript 5 The estimated value of level C is Where x represents the number of methylated fragments in the transcript from the virtual methylated RNA-Seq dataset, and y represents the number of fragments in the transcript from the virtual RNA-Seq dataset. Estimates were calculated using the Delta technique. The expected value and variance are given by the following formulas:

[0024]

[0025]

[0026] In the formula, For transcript RNA m 5 mean of C level For transcript RNA m 5 Variance at level C, μ xLet μ be the mean of x (calculated using the bootstrap technique). y Let y be the mean (calculated using the bootstrap technique). Let x be the variance (calculated using the bootstrap technique). The variance of y (calculated using the bootstrap technique).

[0027] S5. Analysis of transcript RNA m between different samples 5 Differential analysis was performed on the estimated C level values: assuming transcript RNA m 5 The estimated C level values ​​follow a normal distribution, with their mean and variance calculated using the formula in S4. If the transcript RNA m... 5 If the estimated C level is significant in its distribution, it indicates that the transcript RNA m 5 The estimated values ​​for the C level varied significantly across different samples.

[0028] The advantages of this invention are:

[0029] This invention achieves quantitative analysis of RNA 5-methylcytosine modification on splice isomers through the above steps, enabling more accurate estimation of RNA m... 5 C level, and in RNA m of different samples 5 Differential analysis using C levels exhibits good statistical performance, thus effectively overcoming the limitations of quantitative RNA m-level analysis. 5 Challenges faced when dealing with C data.

[0030] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for quantitative analysis of RNA 5-methylcytosine modification on splice isomers, characterized in that, include: Convert RNA-BSseq data into virtual RNA-Seq data; Based on virtual RNA-Seq data, the 5-methylcytosine modification of splice isoform RNA (m) was estimated. 5 C) Level, and calculate the expected value and variance of the estimated value by combining bootstrap and Delta techniques; Based on the normal distribution, we examine the splice isomers m among different samples. 5 Are there significant differences in C levels? 2. The quantitative analysis method as described in claim 1, characterized in that, The formulas for calculating the expected value and variance of the estimate by combining the bootstrap and Delta techniques are as follows: In the formula, x represents the expression level of the splice isoform in the set of virtual RNA-Seq datasets containing methylated cytosine data; y represents the expression level of the splice isoform in the virtual RNA-Seq dataset. The level of 5-methylcytosine modification in the splice isoform of RNA; The expected level of 5-methylcytosine modification for splice isoforms of RNA; The variance of the RNA 5-methylcytosine modification level for splice isoforms; μ x Let x be the mean, calculated using the bootstrap technique; μ y The mean of y is calculated using the bootstrap technique; The variance of x is calculated using the bootstrap technique; The variance of y is calculated using the bootstrap technique.

3. The quantitative analysis method as described in claim 1, characterized in that, Assuming the splice isomer m 5 The estimated values ​​of C level follow a normal distribution. Based on the expected value and variance of the estimated values ​​calculated using a combination of bootstrap and Delta techniques, the splice isomer m among different samples is examined. 5 Are there significant differences in C levels?

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