CN114639442B - Method and system for predicting open reading frame based on single nucleotide polymorphism - Google Patents

Abstract

The invention discloses a method for predicting an open reading frame based on single nucleotide polymorphism and a system for predicting the open reading frame. The invention utilizes the 3 base periodicity of nucleotide polymorphism in coding sequence in group genome variation data to test and screen open reading frames in the gene sequence to be tested, counts the using frequency of codons in the open reading frames, combines the 3 base periodicity of nucleotide polymorphism and the statistical result of codon using frequency, and comprehensively evaluates the prediction probability of small open reading frames by statistical analysis, thereby realizing accurate prediction of the small open reading frames in genome.

Description

A method and system for predicting open reading frames based on single nucleotide polymorphisms

Technical field

The invention belongs to the field of biotechnology, and specifically relates to a method for predicting an open reading frame based on single nucleotide polymorphisms, and a system for predicting an open reading frame.

Background technique

Open reading frames (ORFs) are sequences in DNA sequences that have the potential to encode proteins. Their annotation in the genome is one of the most important processes required for downstream analysis and use of reference genomes. Currently, various algorithms have been developed to predict ORFs in genomes, but these sequence-based methods cannot predict small open reading frames (small ORFs, sORFs). Recent studies have shown that sORF-encoded polypeptides shorter than 100 amino acids play an important role in plant responses to abiotic and biotic stresses, human carcinogenesis, and some biological processes related to cancer treatment. For a long time, the prediction of sORFs has been problematic due to their short length and use of non-standard start codons (CUG, GUG, UUG).

In the existing technology, ribosome imprint sequencing technology (Ribo-seq) can analyze ribosome protected mRNA imprints (RPFs) and can be used to accurately predict translation in many species including yeast, humans, animals and plants. sORF. However, most of these species are simple model organisms, usually with homozygous diploid genomes, and the application of Ribo-seq technology in complex genomes has rarely been reported. The imprint length of a typical eukaryotic ribosome is 28 bases, which is too short for precise sequence positioning, and this problem will be more prominent in polyploid complex genomes. Many plant genomes are highly repetitive and heterozygous polyploid complex genomes, which greatly limits the application of Ribo-seq technology in these plants. Since many important crops, such as wheat (hexaploid) and cotton (tetraploid), are polyploid, it is necessary to develop new methods and tools to solve the problem of identification of small coding frames in polyploid complex genomes.

Contents of the invention

The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method and system for predicting an open reading frame based on single nucleotide polymorphisms. This invention utilizes the 3-base periodicity of the nucleotide polymorphism in the coding sequence in the population genome variation data, introduces the usage frequency of codons, comprehensively evaluates the predicted probability value of the coding frame through statistical analysis, and then predicts the coding frame in the complex genome. .

The object of the present invention is achieved through the following technical solutions: a method for predicting an open reading frame based on single nucleotide polymorphisms, including the following steps:

S1. Obtain transcript information to be predicted and extract candidate long open reading frames;

S2. Evaluate the changing patterns of single nucleotide polymorphisms in the candidate long open reading frames to be predicted, and screen the real long open reading frames according to the preset first screening conditions;

S3. Statistics of the usage frequency of each codon in the real long open reading frame;

S4. Extract candidate open reading frames from the transcript information, evaluate the changing patterns of single nucleotide polymorphisms and codon usage frequencies in the candidate open reading frames to be predicted, and select those that meet the preset second screening conditions. Candidate open reading frames as prediction results.

Further, the basis for extracting the candidate long open reading frame and the candidate open reading frame is: starting with the start codon AUG, ending with the stop codon UAG, UAA or UGA, and the candidate long open reading frame and the candidate open reading frame The sequence length is an integer multiple of 3.

Further, the length of the candidate long open reading frame is greater than 900 bp, and the length of the candidate open reading frame is greater than 100 bp.

Further, evaluating the change patterns of single nucleotide polymorphisms in the candidate long open reading frame to be predicted includes:

S21. Obtain the population variation data of the sample to be predicted, and calculate the nucleotide diversity value of each site in the candidate long open reading frame to be predicted;

S22. Test whether the nucleotide diversity value of the 3n base in the candidate long open reading frame is greater than the nucleotide diversity value of the 3n-2 base and the 3n-1 base, where 1≤n≤ L/3, L is the length of the candidate long open reading frame, get P ₁ and P ₂ , and calculate the combined P value.

Further, the first screening condition is that the P value is less than 0.0001.

Further, evaluating the change pattern and codon usage frequency of single nucleotide polymorphisms in the candidate open reading frame to be predicted includes:

S41. Obtain the population variation data of the sample to be predicted, and calculate the nucleotide diversity value of each site in the candidate open reading frame to be predicted;

S42. Test whether the nucleotide diversity value of the 3nth base in the candidate open reading frame is greater than the nucleotide diversity value of the 3n-2th base and the 3n-1th base, where 1≤n≤L '/3, L' is the length of the candidate open reading frame, and P ₁ ' and P ₂ ' are obtained; respectively test the usage frequency of the triplet starting from the 3n-2 base as a codon in the candidate open reading frame. Is it higher than the usage frequency of the triplet starting from the 3n-1th base and the 3nth base as a codon? Get P ₃ ' and P ₄ ', calculate P ₁ ', P ₂ ', P ₃ and P' value after combining the four values of P ₄ .

Further, the second filtering condition is to control the false discovery rate FDR of the P' value that meets the preset requirements, and control FDR≤0.0001.

Further, the preset requirement is that the P' value is less than 0.05.

Another object of the present invention is to provide a system for predicting open reading frames based on single nucleotide polymorphisms, including a processor and a storage medium, the storage medium storing machine-readable instructions executable by the processor, When the machine-readable instructions are executed, the above-mentioned method of predicting an open reading frame is executed.

The beneficial effects of the present invention are:

1) The present invention utilizes the 3-base periodicity of the nucleotide polymorphism in the coding sequence of the gene coding sequence in the population genome variation data. Based on the fact that the third base of the codon in the gene coding sequence is usually a degenerate base, it is easier to Mutations occur without natural selection, so the third base of the codon shows higher polymorphism in natural populations. By analyzing the sequence fragments with significant 3-base periodicity in the genome variation polymorphism data of the search population, we can determine the translation phase of the open reading frame, and then determine its start and end sites to complete the prediction of the open reading frame. By introducing the frequency of codon usage, statistical analysis comprehensively evaluates the predicted probability value of the open reading frame, thereby achieving accurate prediction of the open reading frame in the genome. The method of the present invention is also suitable for predicting and identifying small open reading frames in polyploid complex genomes, and is beneficial to promoting the research and development of polyploid complex genomes.

2) The present invention also provides a system for predicting open reading frames by applying the method of the present invention. The process of the method steps of the present invention is applied to the computer in the form of a computer program. The user inputs the population variation data, transcripts, etc. of the sample to be predicted as necessary. After receiving the information, the computer program outputs the prediction results, which is beneficial to improving the efficiency of the method of the present invention and promoting the application of the method of the present invention in the research of polyploid complex genomes.

Description of the drawings

Figure 1 is a schematic diagram of the technical route of the present invention.

Figure 2 is a flow chart of the method of the present invention.

Figure 3 is an example of two open reading frames predicted in Example 1 of the present invention.

Figure 4 is the prediction effect evaluation result of Example 1 of the present invention, and the performance of the open reading frame identified from cotton SNPs by the method of the present invention.

Figure 5 shows the prediction results of small open reading frames in Example 1 of the present invention.

Figure 6 is supporting evidence of protein mass spectrometry data in Example 1 of the present invention.

Figure 7 is an example of two open reading frames predicted in Example 2 of the present invention.

Figure 8 is the prediction effect evaluation result of Example 2 of the present invention, and the performance of the open reading frame identified from wheat SNPs by the method of the present invention.

Figure 9 shows the prediction results of small open reading frames in Example 2 of the present invention.

Figure 10 is supporting evidence of protein mass spectrometry data in Example 2 of the present invention.

Detailed ways

The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without any creative efforts fall within the scope of protection of the present invention.

As shown in Figures 1 and 2, the present invention provides a method for predicting an open reading frame based on single nucleotide polymorphisms, which includes the following steps:

S1. Obtain transcript information to be predicted and extract candidate long open reading frames.

By obtaining transcript sequence information from the genome sequence to be predicted, and extracting candidate long open reading frames with a length greater than 900 bp. The basis for extracting candidate long open reading frames here is: starting with the start codon AUG and ending with the stop codon UAG, UAA or UGA, and the length of the candidate long open reading frame sequence is an integer multiple of 3 and greater than 900 bp.

S2. Evaluate the changing patterns of single nucleotide polymorphisms in the candidate long open reading frames to be predicted, and screen the real long open reading frames according to the preset first screening conditions.

Among them, evaluating the change patterns of single nucleotide polymorphisms in candidate long open reading frames to be predicted mainly includes the following steps:

S21. Obtain the population variation data of the genome to be predicted, calculate the nucleotide diversity value of each site in the candidate long open reading frame to be predicted, and use the nucleotide diversity value of each site in the candidate long open reading frame to be predicted as The basis for filtering criteria.

S22. Test whether the nucleotide diversity value of the 3n base in the candidate long open reading frame is greater than the nucleotide diversity value of the 3n-2 base and 3n-1, that is, test each candidate long open reading frame. Whether the nucleotide diversity value of the third nucleotide of each codon in the reading frame is greater than the nucleotide diversity value of the first and second nucleotide of the codon. Among them, 1≤n≤L/3, L is the length of the candidate long open reading frame, and the test results P ₁ and P ₂ are obtained. Use the "combine_pvalues" function in the "scipy.stats" module of the python language to combine P1 and P2. Calculate the combined P value. The specific calculation method is:

P=scipy.stats.combine_pvalues([P1,P2]).

The first screening condition is that the P value is less than 0.0001. When the P value meets the first screening condition, the candidate long open reading frame is evaluated as a true long open reading frame.

S3. Count the usage frequency of each codon in the real long open reading frame, count the number of times each codon appears in the real long open reading frame, and calculate the proportion of each codon to the number of occurrences of all codons, which is each codon The usage frequency is used to represent the usage frequency of each codon in the entire gene to be predicted.

S4. Extract candidate open reading frames from transcript information. The basis for extracting candidate open reading frames here is: starting with the start codon AUG and ending with the stop codon UAG, UAA or UGA, and the sequence length of the candidate open reading frame is An integer multiple of 3 is 100bp longer. Verify whether each extracted candidate open reading frame conforms to the characteristics of the open reading frame through inspection and screening, and obtain the prediction result.

Among them, the inspection and screening process is mainly to evaluate the change pattern and codon usage frequency of single nucleotide polymorphisms in the candidate open reading frames to be predicted, which mainly includes the following steps:

S41. Obtain the population variation data of the genome to be predicted, calculate the nucleotide diversity value of each site in the candidate open reading frame to be predicted, and use the nucleotide diversity value of each site in the candidate open reading frame to be predicted as the screening condition. Base.

S42. Check whether the nucleotide diversity value of base 3n in the candidate open reading frame is greater than the nucleotide diversity value of base 3n-2 and base 3n-1, that is, check each candidate open reading frame. Whether the nucleotide diversity value of the third nucleotide of each codon is greater than the nucleotide diversity value of the first and second nucleotide of the codon, where 1≤n≤L'/ 3. L' is the length of the candidate open reading frame, and the test results P ₁ ' and P ₂ ' are obtained.

Test whether the triplet starting from the 3n-2 base in the candidate open reading frame is used as a codon more frequently than the triplet starting from the 3n-1 base and the 3n base as the codon. The usage frequency of codons is obtained, and the test results P ₃ ' and P ₄ ' are obtained. P ₃ ' and P ₄ ' can reflect the consistency of the usage frequency of triplets as codons in the candidate open reading frame with the statistical results in S3, thus reflecting the Reliability of candidate ORFs as ORFs. Use the "combine_pvalues" function in the "scipy.stats" module of the python language to combine P ₁ ', P ₂ ', P ₃ ' and P ₄ ', and calculate P ₁ ', P ₂ ', P ₃ ' and P ₄ ' The P' value after combining the four values. The specific calculation method is:

P=scipy.stats.combine_pvalues([P ₁ ',P ₂ ',P ₃ ',P ₄ ']).

The second filtering condition is to control the false discovery rate FDR of the P' value that meets the preset requirements. The preset requirement here is that the P' value is less than 0.05, and the control FDR ≤ 0.0001. Those that meet the second filtering condition are the prediction results.

It should be noted that the method of predicting open reading frames based on single nucleotide polymorphisms of the present invention is not suitable for population genome data with too low polymorphisms. For example, when the number of populations is less than 400, the method of the present invention is not suitable for open reading frame data. Reading frame prediction.

The present invention also provides a system for predicting open reading frames based on single nucleotide polymorphisms, including a processor and a storage medium. The storage medium can be in the form of a disk, ROM or RAM, and the storage medium stores executable files of the processor. The machine-readable instructions are mainly embodied as a computer program that can be executed on a computer processor. When the program is executed, the above-mentioned method of predicting an open reading frame is executed to achieve prediction of the open reading frame.

Example 1: Analysis of cotton population data

The experimental data of this example was downloaded from figshare. The data was published in Genome Biology by Li JiangYing and others in 2021. The article is titled "Cotton pan-genome retrieves the lost sequences and genes during domestication and selection", generating a total of 1961 complete genomes of samples. Resequencing data.

S1. Extract the candidate long open reading frame from the transcript information. The candidate long open reading frame starts with the start codon AUG and ends with the stop codon UAG, UAA or UGA, and the candidate long open reading frame sequence length is an integer of 3. times, the length is greater than 900bp.

S2. Test based on the single nucleotide diversity value of each site in the candidate long open reading frame to be predicted, and obtain 4065 true long open reading frames based on the first screening condition.

S3. Statistics of the usage frequency of each codon in the real long open reading frame obtained in S2.

S4. Extract all candidate open reading frames from the transcript sequence, and conduct inspection and screening based on the second filtering conditions. As shown in Figure 3 and Figure 4, a total of 86,889 candidate open reading frames were predicted to be true open reading frames, and the recall rate 76% (accounting for the proportion of known open reading frames in the genome, that is, the ratio of the number of true positives to the total number of annotated ORFs multiplied by 100%), and the accuracy is as high as 94% (the predicted open reading frame is consistent with the known reading frame The ratio, that is, the ratio of the number of true positives to the total number of predicted ORFs multiplied by 100%), the comprehensive score is 84% [comprehensive score = 2×recall×precision/(recall+precision)].

As shown in Figure 5, it also includes 4704 small open reading frames, including 1182 uORFs, 316 ouORFs, 2110 dORFs, 557 odORFs, 477 internal ORFs, and 62 truncated ORFs. As shown in Figure 6, the dotted line in the figure represents the support degree of protein mass spectrometry data for known ORFs in the genome. Published protein mass spectrometry data analysis shows that these predicted small open reading frames are well supported.

Example 2: Analysis of wheat population data

The experimental data of this example were downloaded from NCBI (No. PRJNA476679) and CNCB (No. GVM000082). The first set of data was published by Cheng Hong et al. in Genome Biology in 2019. The article is titled "Frequentintra-and inter-species introgression shape the landscape of genetic variation in bread wheat”, whole genome resequencing of 93 wheat species was performed. The second set of data was published in Nature Genetics by Zhou Yao et al. in 2020. The article is titled "Triticum sequencing population prov-ides insights into wheat adaptation". A total of 414 wheat varieties were resequenced across the whole genome. In this embodiment, two sets of data are combined and used to predict small open reading frames.

S1. Extract the candidate long open reading frame from the transcript information, starting with the start codon AUG and ending with the stop codon UAG, UAA or UGA, and the length of the candidate long open reading frame sequence is an integer multiple of 3, and the length is greater than 900bp .

S2. Test based on the nucleotide diversity value of each site in the candidate long open reading frame to be predicted, and obtain 13683 real long open reading frames based on the first screening condition.

S3. Statistics of the usage frequency of each codon in the real long open reading frame obtained in S2.

S4. Extract all candidate open reading frames from the transcript sequence, and conduct inspection and screening based on the second filtering conditions. As shown in Figure 7 and Figure 8, a total of 117,140 candidate open reading frames were predicted to be true open reading frames, accounting for the total The prediction is 87%, the accuracy is as high as 95%, and the overall score is 91%.

As shown in Figure 9, 5025 small open reading frames were successfully predicted after inspection and screening, including 232 uORFs, 21 ouORFs, 234 dORFs, 129 odORFs, 3532 internal ORFs, 675 extend ORFs, and 202 truncated ORF. As shown in Figure 10, the dotted line in the figure represents the support degree of protein mass spectrometry data for known ORFs in the genome. Published protein mass spectrometry data analysis shows that these predicted small open reading frames are well supported.

The above are only preferred embodiments of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and Modifications can be made within the scope of the ideas described herein through the above teachings or technology or knowledge in related fields. Any modifications and changes made by those skilled in the art that do not depart from the spirit and scope of the present invention shall be within the protection scope of the appended claims of the present invention.

Claims (7)

1. A method for predicting open reading frames based on single nucleotide polymorphisms, characterized by: the method comprises the following steps:

s1, acquiring transcript information to be predicted, and extracting candidate long open reading frames;

s2, evaluating the change rule of single nucleotide polymorphism in the candidate long open reading frame to be predicted, and screening the true long open reading frame according to a preset first screening condition;

s3, counting the use frequency of each codon in the real long open reading frame;

s4, extracting candidate open reading frames from the transcript information, evaluating the change rule and the codon use frequency of single nucleotide polymorphism in the candidate open reading frames to be predicted, and taking the candidate open reading frames meeting the preset second screening conditions as prediction results;

the evaluation of the change rule of the single nucleotide polymorphism in the candidate long open reading frame to be predicted comprises:

s21, acquiring group variation data of a sample to be predicted, and calculating nucleotide diversity values of all sites in a candidate long open reading frame to be predicted;

s22, respectively checking whether the nucleotide diversity value of the 3 n-th base in the candidate long open reading frame is larger than the nucleotide diversity values of the 3n-2 nd base and the 3n-1 st base, wherein n is more than or equal to 1 and less than or equal to L/3, L is the length of the candidate long open reading frame, P1 and P2 are obtained, and calculating the combined P value;

the evaluating of the change rule and codon usage frequency of single nucleotide polymorphisms in the candidate open reading frames to be predicted comprises:

s41, acquiring group variation data of a sample to be predicted, and calculating nucleotide diversity values of all sites in a candidate open reading frame to be predicted;

s42, respectively checking whether the nucleotide diversity value of the 3 n-th base in the candidate open reading frame is larger than the nucleotide diversity values of the 3n-2 nd base and the 3n-1 st base, wherein n is more than or equal to 1 and less than or equal to L '/3, L' is the length of the candidate open reading frame, and P1 'and P2' are obtained; and respectively checking whether the use frequency of the triplet taking the 3n-2 th base as the starting point in the candidate open reading frame is higher than that of the triplet taking the 3n-1 st base and the 3 n-th base as the starting point in the candidate open reading frame, obtaining P3' and P4', and calculating the P ' value after combining the four values of P1', P2', P3 and P4.

2. The method for predicting open reading frames based on single nucleotide polymorphisms according to claim 1, wherein: the basis for extracting the candidate long open reading frames and the candidate open reading frames is as follows: beginning with the start codon AUG and ending with the stop codon UAG, UAA or UGA, and the sequence lengths of the candidate long open reading frame and the candidate open reading frame are integer multiples of 3.

3. The method for predicting open reading frames based on single nucleotide polymorphisms according to claim 1, wherein: the length of the candidate long open reading frame is greater than 900bp, and the length of the candidate open reading frame is greater than 100bp.

4. The method for predicting open reading frames based on single nucleotide polymorphisms according to claim 1, wherein: the first screening condition is that the P value is less than 0.0001.

5. The method for predicting open reading frames based on single nucleotide polymorphisms according to claim 1, wherein: the second screening condition is to control the error discovery rate FDR of the P' value meeting the preset requirement, and the FDR is controlled to be less than or equal to 0.0001.

6. The method for predicting open reading frames based on single nucleotide polymorphisms as recited in claim 5 wherein: the preset requirement is that the P' value is less than 0.05.

7. A system for predicting open reading frames based on single nucleotide polymorphisms, characterized by: comprising a processor and a storage medium storing machine-readable instructions executable by the processor, which when executed perform the method of predicting an open reading frame of any one of claims 1-6.