Methods, systems, and storage media for removing propeptides from the amino acid sequences of bioinformatics-assembled products.

Abstract

This invention discloses a method, system, and storage medium for removing propeptides from amino acid sequences assembled using bioinformatics. The method relates to the field of bioinformatics, particularly to data mining in transcriptome sequencing. It involves acquiring peptide sequence information generated through a bioinformatics workflow, segmenting and aligning the peptide sequences to obtain protein files that meet preset requirements, cleaning the obtained protein files, selecting sequences with the highest homology based on the protein files, and integrating these sequences with the peptide sequence information; determining the portion of the peptide sequence information to retain based on the position of the target protein in the protein files; and storing the cleaned information. The method provided by this invention is easy to operate, allows for batch removal of propeptides from amino acid sequences, provides a standard analysis workflow, and achieves automated, batch processing of propeptides in peptide sequences. It solves the problems of difficulty and low efficiency in screening naturally active peptides in current transcriptome analysis, while also offering advantages such as ease of use and low learning costs.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, and in particular to a method, system, and storage medium for removing propeptides from the amino acid sequences of bioinformatics assemblies. Background Technology

[0002] High-throughput sequencing technology, with its ability to sequence hundreds of thousands to millions of nucleic acid molecules at once, has greatly reduced the cost of nucleic acid sequencing, leading to its widespread application in fields such as biology, medicine, and the environment. The advent of high-throughput sequencing technology has made comprehensive and detailed analysis of an entire species possible. However, high-throughput sequencing technology also generates massive amounts of data, thus posing a significant challenge to bioinformatics in the process of specifically and comprehensively mining this data.

[0003] Transcriptome sequencing is an important branch of high-throughput sequencing technology. It involves sequencing the cDNA of specific cells or tissues of a species to obtain information about gene expression in those cells or tissues. Transcriptome sequencing not only helps understand the similarities and differences between tumor cells and normal cells, drug-treated groups and placebo groups, and experimental groups and control groups, but it can also help uncover specific peptides expressed and synthesized in specific tissues by different species (for example, toxic peptides are found in the venom glands of venomous animals such as spiders).

[0004] These special peptides possess potential medicinal value, but the peptides obtained after a series of bioinformatics analyses, including transcriptome sequencing, assembly, and annotation, contain signal peptides, propeptides, and mature peptides. In cells, after sorting, processing, and modification by various organelles, signal peptides and propeptides are cleaved and discarded, leaving only the mature peptides with true biological activity. Currently, specialized software such as SignalP6 can predict, identify, and remove signal peptides from peptide sequences, but reliable and efficient software for removing propeptides from peptide sequences is still lacking. This makes it impossible to obtain large quantities of naturally active peptides from transcriptome data quickly and efficiently. Therefore, there is an urgent need for an accurate and efficient propeptide prediction tool to identify and remove propeptides from peptides in batches, thereby improving the efficiency of transcriptome data analysis and rapidly obtaining large quantities of potentially medicinally valuable active natural peptides.

[0005] With the development of technology and its wide application, bioinformatics software has covered a variety of functions, ranging from basic sequence analysis and alignment to advanced protein structure prediction and pathway analysis. However, many bioinformatics software need to be installed through the command line, which requires users to input a series of commands in sequence and handle some details during the installation process. This process is rather cumbersome and error-prone. Additionally, many bioinformatics software rely on specific libraries and software packages, and if the user's environment does not meet the requirements, the installation will fail. At this time, users need to manually solve the environmental dependency problem, which requires certain technical capabilities and a large amount of time cost. Therefore, software portability and operability need to be considered during software development. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a method, a system and a storage medium for removing propeptides from amino acid sequences assembled by bioinformatics, and this method utilizes data mining for transcriptome sequencing.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] The method for removing propeptides from amino acid sequences assembled by bioinformatics provided by the present invention includes the following steps:

[0009] Step 1) Obtain the polypeptide sequence information generated through the bioinformatics process, and perform segmented alignment on the polypeptide sequence information to obtain a protein file that meets the preset requirements;

[0010] Step 2) Perform data cleaning on the obtained protein file: screen the sequence with the highest homology according to the obtained protein file and integrate it with the polypeptide sequence information; determine the retained part in the polypeptide sequence information according to the position of the target protein in the protein file;

[0011] Step 3) Store the information after data cleaning.

[0012] Further, the protein file in the step 1) is obtained in the following manner:

[0013] 11) Input the fasta file of the polypeptide sequence generated through the bioinformatics process, and align the segmented sequences with the protein sequences in the alignment database according to the selected comparison database by the Blast algorithm to obtain the similarity information of each segment;

[0014] 12) Search for similar k-mer sequences in the alignment database, calculate the similarity score for each found sequence, and output the Evalue value of each result;

[0015] 13) Output the corresponding protein file according to the Evalue threshold, where the protein file includes any one or more combinations of sequence name, name of the matched target protein, position in the target protein, homology, and Evalue value.

[0016] Furthermore, the data cleaning in step 2) is performed in the following manner:

[0017] 21) Convert the fasta sequence file into a polypeptide sequence file in data frame format;

[0018] 22) Obtain the output result of the Blast algorithm alignment, sort it by homology, and retain the polypeptide sequences whose homology meets the preset conditions;

[0019] 23) Integrate the polypeptide sequence file converted into data frame format with the screened polypeptide sequences;

[0020] 24) According to the position of the input sequence in the target protein in the alignment result, cut off the rest by the sub function, calculate the sequence length of the retained part using the nchar function, and calculate the number of cysteines and the cysteine backbone in the retained part.

[0021] Furthermore, the information in step 3) is the calculation result information stored in the format of a text file.

[0022] Furthermore, the alignment database in step 11) uses the Swiss-Prot database.

[0023] Furthermore, the data cleaning in step 2) uses a program compiled in the R language.

[0024] Furthermore, the text file output in step 3) includes any one or more combinations of sequence ID, Sequence, Evalue, cysteine backbone, and number of cysteines.

[0025] The system for removing propeptides from the amino acid sequence of bioinformatics assembly provided by the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above method is implemented.

[0026] The storage medium provided by the present invention has a computer program stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above external storage medium detection method are implemented.

[0027] The beneficial effects of the present invention are as follows:

[0028] This invention provides a method, system, and storage medium for removing propeptides from amino acid sequences assembled via bioinformatics. This method relates to the field of bioinformatics, particularly to data mining in transcriptome sequencing, and is an easy-to-operate, batch-processable method for removing propeptides from amino acid sequences. Based on the R language, this method develops a procedure for removing propeptides from amino acid sequences assembled via bioinformatics, providing a standard analysis workflow and enabling automated, batch processing of propeptides in peptide sequences. It solves the problems of difficulty and low efficiency in screening natural bioactive peptides in current transcriptome analysis, while also offering advantages such as ease of use and low learning curve.

[0029] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0030] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0031] Figure 1 This is a flowchart of the method for removing propeptides provided in this embodiment.

[0032] Figure 2 Flowchart for removing propeptides from an amino acid sequence.

[0033] Figure 3 Screenshot of the test data running. Detailed Implementation

[0034] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0035] Example 1

[0036] like Figure 1 As shown, the method for removing propeptides from the amino acid sequence of bioinformatics assembly provided in this embodiment includes the following steps:

[0037] 1) The FASTA file of the polypeptide sequence generated by the bioinformatics process is compared with the Blast algorithm. Swiss-Prot is selected as the comparison database. The Blast algorithm uses the sliding window method to segment the protein sequence and compares the segmented sequence with the protein sequence in the database to find similarity and obtain multiple alignment results.

[0038] For example, suppose an amino acid sequence is "ENSFDRALMDLLVASDEQSECGTLFSGCSTHAD", such as Figure 2 As shown, the Blast algorithm takes the k neighboring amino acids of each amino acid in the sequence as the subsequence to be searched (if k is 4, the subsequence formed by the first amino acid is "ENSF").

[0039] The Blast algorithm then searches for similar k-mer sequences in a specified database based on the similarity comparison results (the database used in this embodiment is Swiss-Prot). For each sequence found, the Blast algorithm uses the Smith-Waterman algorithm to calculate a similarity score (the higher the score, the greater the similarity). It also outputs the Evalue of each result as an indicator to evaluate the randomness of the result (the lower the Evalue, the more reliable the result).

[0040] Finally, the Blast algorithm can output proteins that are not higher than the Evalue threshold (for example, an evalue of 1e-6 indicates that the Evalue of the alignment result must not be higher than 10-6). The output file of the Blast software includes information such as sequence name, name of the matched target protein, position in the target protein, homology, and Evalue. This file is a text file.

[0041] 2) Using the BLAST alignment output file and the FASTA file of the peptide sequence as the data to be processed, a series of data cleaning steps are performed using R language. The specific steps are as follows:

[0042] ① Use the readAAStringSet function in the Biostrings package to convert the peptide FASTA sequence file into a data frame format in R and read the Blast results;

[0043] ② Since the alignment results output by the Blast algorithm are based on the Evalue threshold, a single peptide sequence may contain multiple matching results. Therefore, it is necessary to sort the multiple results using functions such as arrange, group_by, rowwise, and filter, and sort them by homology, retaining peptide sequences whose homology meets the preset conditions. In this embodiment, only the sequence with the highest homology is ultimately retained.

[0044] ③ The peptide sequence file converted to data frame format and the Blast alignment results after screening are integrated together using the left_join function of the dplyr package. Subsequent data cleaning work will be based on the merged file.

[0045] ④According to the results output by Blast alignment, use the sub function to cut off the remaining part, which is the removal of the propeptide part of the prepeptide. Use the nchar function to calculate the sequence length of the remaining part, and calculate the number of cysteines and the cysteine backbone in the remaining part.

[0046] 3) Retain various calculation results in the data cleaning process in the form of text files on the computer, including information such as the similarity and Evalue of Blast alignment, cleavage sites, sequence lengths, cysteine numbers, cysteine backbones, etc.

[0047] As a preferred solution, in step 1), the Swiss-Prot database is used as data support. The Swiss-Prot database is a manually annotated protein sequence database that contains a large number of protein sequences from different species and integrates them together, providing researchers with very extensive biological information. At the same time, the annotation information in the Swiss-Prot database has been manually reviewed and sorted, with very high accuracy. These annotation information include important information such as protein names, functions, domains, signal peptides, propeptides, enzyme catalytic activities, subcellular localizations, etc.

[0048] As a preferred solution, in step 2), leveraging the advantages of the R language in data cleaning, integrate the output results of Blast alignment with the polypeptide sequence file, providing a clear and comprehensive display of the data before and after processing.

[0049] As a preferred solution, the text file output in step 3) contains content such as sequence ID, Sequence, Evalue, cysteine backbone, number of cysteines, etc., comprehensively and clearly displaying the relevant information before and after cleavage;

[0050] As a preferred solution, this invention is written in the R language, realizing the automated and batch processing of propeptides in sequences.

[0051] As a preferred solution, this method is finally packaged into a singularity container, reducing the deployment difficulty and the subsequent maintenance difficulty. At the same time, it has advantages such as simple dependencies and no need for root privileges. It only needs to be copied to the Linux system before the first use to start analyzing data, eliminating the complex and time-consuming software installation, configuration, etc.

[0052] Example 2

[0053] This example details the entire process of the method.

[0054] 1) Data Preparation: To test the effectiveness of this method in processing propeptides from peptide sequences generated by bioinformatics methods, a set of FASTA files containing 3583 peptide sequences was prepared. Statistical information about this sequence file (obtained using seqkit software) is shown in Table 1:

[0055] Table 1 Statistical information of test sequences

[0056]

[0057] 2) Data Processing: Use this method to process the data on a Linux server. The processing command is:

[0058] singularity exec --pwd / task / -B

[0059] / home / huangsen / processing / prop / fasta: / task-B

[0060] / home / huangsen / processing / prop: / database

[0061] The parameters for ` / home / huangsen / processing / prop / prop.sif prop.Rd / database / propdb-i / task / Sequence.fa-e 1e-6` are explained below:

[0062] The --pwd parameter toggles the working directory within the container to the / task directory;

[0063] The -B parameter is used to specify directory mounting. The first -B parameter is to map the external link of the input sequence FASTA file, and the second -B is to map the FASTA file of the database.

[0064] The -i parameter is the name of the input fasta file;

[0065] The -e parameter specifies the E-value threshold for the Blast algorithm.

[0066] The -d parameter specifies the path to the selected database file.

[0067] 3) Processing results: The total processing time for the input data containing 3583 entries was 1 minute 27.811 seconds. Figure 3 As shown, Figure 3 Screenshot of the test data running.

[0068] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for removing propeptides from the amino acid sequence of bioinformatics assembly, characterized in that: Includes the following steps: Step 1) Obtain the polypeptide sequence information generated by the bioinformatics process, and perform segmentation and comparison of the polypeptide sequence information to obtain a protein file that meets the preset requirements; The protein file in step 1) is obtained in the following manner: 11) The FASTA file of the polypeptide sequence generated by the bioinformatics process is compared with the protein sequences in the comparison database according to the selected comparison database using the Blast algorithm to obtain the similarity information of each segment. 12) Search for similar k-mer sequences in the alignment database. For each sequence found, calculate the similarity score and output the Evalue of each result. 13) Output the corresponding protein file based on the Evalue threshold. The protein file includes any one or more of the following: sequence name, matching target protein name, position in the target protein, homology, and Evalue value. Step 2) Clean the obtained protein files: Select the sequences with the highest homology from the obtained protein files and integrate them with the polypeptide sequence information; Based on the location of the target protein in the protein file, determine the portion of the polypeptide sequence information to be retained; The data cleaning in step 2) is performed in the following manner: 21) Convert the FASTA sequence file into a peptide sequence file in data frame format; 22) Obtain the output results of the Blast algorithm alignment, sort them according to homology, and retain the peptide sequences whose homology meets the preset conditions; 23) Integrate the peptide sequence file converted to data frame format with the screened peptide sequences; 24) Based on the position of the input sequence in the target protein in the alignment results, the remaining part is truncated using the sub function, the length of the retained part is calculated using the nchar function, and the number of cysteine ​​residues and the cysteine ​​backbone in the retained part are calculated. Step 3) Store the cleaned information; The text file output in step 3) includes any one or more of the following: sequence ID, sequence, Evalue, cysteine ​​backbone, and number of cysteine ​​residues.

2. The method for removing propeptides from the amino acid sequence of bioinformatics assembly as described in claim 1, characterized in that: The information in step 3) is the calculation result information stored in the format of a text file.

3. The method for removing propeptides from the amino acid sequence of bioinformatics assembly as described in claim 2, characterized in that: The comparison database used in step 11) is the Swiss-Prot database.

4. The method for removing propeptides from the amino acid sequence of bioinformatics assembly as described in claim 1, characterized in that: The data cleaning in step 2) is performed using a program written in R language.

5. A system for removing propeptides from the amino acid sequence of a bioinformatics assembly, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method described in any one of claims 1 to 4.

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