A high-throughput nucleic acid sequence processing and clustering method and apparatus based on SELEX
By screening enriched sequences and extracting target random sequence regions, combined with adaptive parameter settings, the problems of versatility and accuracy in data processing in SELEX technology were solved, and efficient nucleic acid sequence clustering analysis was achieved.
Patent Information
- Application Number
- CN202611146498.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-30
- Publication Date
- 2026-11-06
AI Technical Summary
The existing SELEX technology lacks versatility and parameter adaptation capabilities in high-throughput sequencing data processing, leading to data loss and decreased analysis accuracy, especially when library tag design is complex or sequence variations exist.
By screening out sequences with enrichment trends, target random sequence regions are extracted, and sequence similarity analysis is performed before clustering. Adaptive parameter settings are used for clustering analysis.
It improves the accuracy of data processing and the sensitivity of cluster analysis, reduces false positive results, and enhances the signal-to-noise ratio and clustering efficiency.
Smart Images
Figure CN123053261A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the interdisciplinary field of bioinformatics and molecular biology, and specifically relates to a high-throughput nucleic acid sequence processing and clustering method and apparatus based on SELEX. Background Technology
[0002] SELEX technology is a common method for screening nucleic acid aptamers that specifically bind to target molecules (such as proteins, small molecules, etc.). With the development of high-throughput sequencing technology, SELEX experiments typically generate millions of nucleic acid sequences. How to efficiently and accurately identify enriched nucleic acid sequences from these massive amounts of data and analyze their family similarity has become a key step in aptamer screening.
[0003] Existing data processing workflows typically include steps such as removing sequencing adapters, merging paired-end sequences, splitting library tags, extracting random regions, and calculating abundance. Finally, high-abundance sequences are subjected to cluster analysis. Commonly used cluster analysis tools include BLAST (Basic Local Alignment Search) and MCL (Markov Clustering), but existing methods are often customized for specific experimental designs, lacking versatility and parameter adaptability. Furthermore, when library tag designs are complex or sequence variations exist, simple splitting methods may lead to significant data loss, affecting the accuracy of subsequent analyses.
[0004] Therefore, there is a need for a high-throughput nucleic acid sequence data processing and clustering method that can adapt to various library designs, has an automatic fault-tolerance mechanism, and has adjustable parameters. Summary of the Invention
[0005] This invention addresses the aforementioned problems by proposing a high-throughput nucleic acid sequence processing and clustering method and apparatus based on SELEX. The concept lies in screening sequences with enrichment trends and extracting target random sequence regions as core binding regions before clustering, thereby improving the method's versatility and parameter adaptability.
[0006] In a first aspect, this invention proposes a high-throughput nucleic acid sequence processing and clustering method based on SELEX, the steps of which include: S1. Obtain raw sequencing data in FASTQ format from nucleic acid samples that have undergone multiple rounds of screening using SELEX technology through a high-throughput sequencing platform; S2. Perform quality control on the raw sequencing data, remove adapters and low-quality sequences, and obtain quality control sequence data; S3. Perform library splitting and merging on the quality control sequence data, extract the target random sequence region, and obtain the extraction results; S4. Statistically analyze the extraction results obtained in step S3, perform sequence self-alignment, and then see the sequence similarity score matrix; S5. Perform cluster analysis based on the sequence similarity score matrix obtained in step S4.
[0007] Preferably, in S1, the original sequencing data is the paired-end data of a nucleic acid library after SELEX screening for the parp1 protein, containing two sequencing files, read1 and read2, with a sequencing read length of 150 bp.
[0008] More specifically, the process of step S2 is as follows: S2.1 Remove adapter sequences from the original sequencing data. The adapter sequence for read1 file is ACATCGGAAGAGCACACGTCTGAACTCCAGTCAC, and the adapter sequence for read2 file is AGATCGGAAGAGCGTCGGTAGGGAAAGAGTGT. S2.2 Perform low-quality filtering on the de-connected sequences, removing sequences with base quality below 30, and retaining all sequences with Q value ≥ 30; S2.3 Remove the polymerase chain reaction (PCR) tag, wherein the polymerase chain reaction tag is CAACAGTCCGTTATTCGGTG; S2.4 performs end-to-end merging of the read1 and read2 files (with PCR tags removed) to obtain quality control sequence data.
[0009] As a preferred method, S2.1, S2.2 and S2.3 are implemented using the cutadapt tool, and S2.4 is implemented using the flash tool.
[0010] More specifically, the specific process of step S3 is as follows: S3.1 Classify the quality control sequence data according to the preset library index sequence set, wherein the library index sequence set contains 5 different nucleotide sequence tags, which correspond to 5 different rounds of SELEX technology screening in S1. S3.2 The classified sequence data is subjected to target region truncation, specifically including identifying the upstream primer sequence and downstream primer sequence in the sequence; extracting the target random sequence region located between the upstream primer sequence and the downstream primer sequence, retaining the target random sequence region with a preset length, and removing sequences that cannot match the upstream primer sequence or downstream primer sequence, or whose length is not the preset length, to obtain the extraction result.
[0011] More specifically, the upstream primer sequence is ATTGCCTGGAGGTTGTCGAA, and the downstream primer sequence is GGCGTGCGTAAATGACTGAA.
[0012] More specifically, the specific process of step S4 is as follows: S4.1 Count the number of times each unique random sequence appears in the extraction results as the sequence abundance, sort them from high to low abundance, and select sequences with enrichment trend as high abundance candidate sequences for parp1 combination. S4.2 The high-abundance candidate sequences are constructed into a nucleic acid sequence database. The BLAST algorithm is used for sequence self-alignment. To adapt to the short sequence feature, the task type is set to blastn-short, the threshold of E-value is set to a preset value, and the filtering of low-complexity sequences is turned off to avoid losing functional motifs rich in specific bases, thereby obtaining a sequence similarity score. As a preferred choice, the sequence with enrichment tendency is the only random sequence among the top 500 in sequence abundance; the threshold for E-value is 0.05.
[0013] More specifically, the specific process of step S5 is as follows: S5.1 Based on the sequence similarity score, construct a similarity matrix suitable for graph clustering analysis, and normalize the similarity matrix; S5.2 The similarity matrix is iteratively calculated using the Markov clustering algorithm, and the expansion coefficient is set to a preset value to obtain several parp1 binding DNA sequence clusters with conserved binding motifs; S5.3 performs result parsing on parp1 binding DNA sequence clusters with conserved binding motifs, generating an analysis result file containing sequence IDs, nucleic acid sequences, and clustering groups.
[0014] As a preferred embodiment, in S5.2, the expansion coefficient is set to 1.5.
[0015] Secondly, the present invention proposes a high-throughput nucleic acid sequence processing and clustering device based on SELEX, comprising a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to implement a high-throughput nucleic acid sequence data processing and clustering method based on SELEX according to any one of claims 1-8.
[0016] Thirdly, the present invention proposes a computer-readable storage medium, characterized in that it stores a program thereon, which, when executed by a processor, implements a high-throughput nucleic acid sequence processing and clustering method based on SELEX as described in any one of claims 1-8.
[0017] The innovation of this invention lies in: 1. Use SELEX technology to filter out sequences with enrichment trends, and then calculate sequence similarity.
[0018] 2. Extract the target random sequence region before clustering.
[0019] 3. Adjust the parameters of the clustering algorithm based on the sequence features obtained from Selex filtering.
[0020] The beneficial effects of this invention are as follows: 1. Screening for enriched sequences can avoid false positives, and calculating sequence similarity can accelerate clustering.
[0021] 2. Extracting the target random sequence region can improve the signal-to-noise ratio.
[0022] 3. Targeted parameter adjustments can more sensitively capture subtle differences and similarities between sequences. Attached Figure Description
[0023] Figure 1 This is a flowchart of the high-throughput nucleic acid sequence processing and clustering method based on SELEX of the present invention. Detailed Implementation
[0024] The technical solution of the present invention will be further described below with reference to the accompanying drawings.
[0025] Example 1
[0026] This embodiment uses SELEX screening data of the parp1 protein as an example to describe in detail the implementation process of the method of the present invention, including the following steps: Experimental data and parameter settings The raw data consisted of paired-end FASTQ files (Read1 and Read2), sequenced using an Illumina platform with read lengths of 150 bp. Relevant sequence information is as follows: Read1 connector sequence: ACATCGGAAGAGCACACGTCTGAACTCCAGTCAC Read2 connector sequence: AGATCGGAAGAGCGTCGGTAGGGAAAGAGTGT PCR tag sequence: CAACAGTCCGTTATTCGGTG Document recognition tags (5): CCGTA, TCATG, CGTCA, TAGAC, TACGG Primer start tag: ATTGCCTGGAGGTTGTCGAA Reading direction tags: GGCGTGCGTAAATGACTGAA Random region length: 45 bp Key parameters: Number of high-abundance sequences screened: 500 BLAST E-value threshold: 0.05 MCL expansion coefficient: 1.5 Step S1: Data Acquisition Obtain the raw high-throughput sequencing data of parp1 protein after SELEX screening, which are FASTQ format paired-end files, named sample_R1.fq.gz and sample_R2.fq.gz respectively.
[0027] Step S2: Quality Control
[0028] S2.1 Use the cutadapt tool to remove the connector sequence in Read1 and Read2. The command is as follows: cutadapt -a ACATCGGAAGAGCACACGTCTGAACTCCAGTCAC -o trim1_R1.fq sample_R1.fq.gz cutadapt -a AGATCGGAAGAGCGTCGGTAGGGAAAGAGTGT -o trim2_R2.fq sample_R2.fq.gz S2.2 Perform Q30 quality filtering on the de-connector sequences to remove sequences with a base quality below 30: cutadapt -q 30 -o trim1_R1_q30.fq trim1_R1.fq cutadapt -q 30 -o trim2_R2_q30.fq trim2_R2.fq S2.3 Use cutadapt to remove the PCR tag sequence CAACAGTCCGTTATTCGGTG from read 2 and the reverse complementary sequence CACCGAATAACGGACTGTTG from read 1. cutadapt -a CAACAGTCCGTTATTCGGTG -o final_trimmed_R1.fq trim1_R1_q30.fq cutadapt -g CACCGAATAACGGACTGTTG -o final_trimmed_R2.fq trim2_R2_q30.fq S2.4 uses the Flash tool to merge the read1 and read2 files from both ends: flash final_trimmed_R1.fq final_trimmed_R2.fq -o merged_output Step S3: Library splitting and merging, extracting the target random sequence region S3.1 The sequence is split according to five library identification tags (CCGTA, TCATG, CGTCA, TAGAC, TACGG), and specific regions at the beginning or end of the sequence are matched. The matching rule is: the tag appears within the first 10 bp or the last 10 bp of the sequence and meets the length requirement.
[0029] S3.2 Merge the split front and back libraries according to their corresponding relationships. The sequences in the front library need to be reversed and complementary to ensure that all sequences are in the same direction.
[0030] S3.3 Extracts the middle 45bp random region using the primer start tag ATTGCCTGGAGGTTGTCGAA and the read direction tag GGCGTGCGTAAATGACTGAA. The extraction strategy includes: Prioritize extracting sequences where both ends of the label exist and the interval length is equal to 45bp; If only the start label exists, then truncate the 45bp sequence after the label; If only the read direction label exists, then extract the first 45bp of the label sequence; If none of them exist, discard them.
[0031] The extraction results are saved as five files, including extracted_1.fq and extracted_2.fq (corresponding to five libraries).
[0032] Step S4: High-abundance sequence screening and similarity score matrix construction
[0033] S4.1 Statistics for each extracted_ The occurrence counts of sequences in the .fq file are sorted in descending order of abundance. The top 500 high-abundance sequences are then used to generate FASTA format files such as extracted_1_top500.fasta, extracted_2_top500.fasta, etc.
[0034] S4.2 Builds a BLAST database for each library's FASTA files: makeblastdb -dbtype nucl -in extracted_1_top500.fasta -out db_1 S4.3 uses blastn for self-alignment, sets the task type to blastn-short, the E-value threshold to 0.05, and disables low-complexity sequence filtering. blastn -task blastn-short -db db_1 -query extracted_1_top500.fasta -evalue 0.05 -outfmt 7 -out blast_out_1.txt -dust no -num_threads 4 Step S5: Cluster Analysis S5.1 parses the BLAST results, calculates the similarity scores between sequence pairs, and generates an ABC format file mcl_graph_1.txt after normalization. Each line is formatted as: "Sequence Index 1 Sequence Index 2 Normalized Score".
[0035] S5.2 uses the Markov MCL tool for clustering, setting the inflation factor to 1.5: mcl mcl_graph_1.txt --abc -I 1.5 -o mcl_clusters_1.txt -V all S5.3 parses the MCL output file, groups the sequences by cluster, and generates a CSV file cluster_results_1.csv containing sequence IDs, nucleic acid sequences, and cluster groups. The clustering results show the parp1-binding DNA sequence clusters with conserved binding motifs.
[0036] Example 2
[0037] A high-throughput nucleic acid sequence data processing and clustering device based on SELEX includes a memory and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the high-throughput nucleic acid sequence data processing and clustering method based on SELEX as described above.
[0038] Example 3
[0039] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements a SELEX-based high-throughput nucleic acid sequence data processing and clustering method as described in any of the preceding claims.
[0040] The embodiments described in this specification are merely examples of implementations of the inventive concept. The scope of protection of this invention should not be considered as limited to the specific forms stated in the embodiments. The scope of protection of this invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.
[0041] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
Claims
1. A high-throughput nucleic acid sequence processing and clustering method based on SELEX, comprising the following steps: S1. Obtain raw sequencing data in FASTQ format from nucleic acid samples that have undergone multiple rounds of screening using SELEX technology through a high-throughput sequencing platform; S2. Perform quality control on the raw sequencing data, remove adapters and low-quality sequences, and obtain quality control sequence data; S3. Perform library splitting and merging on the quality control sequence data, extract the target random sequence region, and obtain the extraction results; S4. Statistically analyze the extraction results obtained in step S3, perform sequence self-alignment, and obtain the sequence similarity score matrix; S5. Perform cluster analysis based on the sequence similarity score matrix obtained in step S4.
2. The high-throughput nucleic acid sequence processing and clustering method based on SELEX as described in claim 1, characterized in that: In S1, the raw sequencing data is the paired-end data of a nucleic acid library after SELEX screening for the parp1 protein, containing two sequencing files, read1 and read2, with a sequencing read length of 150bp.
3. The high-throughput nucleic acid sequence processing and clustering method based on SELEX as described in claim 2, characterized in that, The process of step S2 is as follows: S2.1 Remove adapter sequences from the original sequencing data. The adapter sequence for read1 file is ACATCGGAAGAGCACACGTCTGAACTCCAGTCAC, and the adapter sequence for read2 file is AGATCGGAAGAGCGTCGGTAGGGAAAGAGTGT. S2.2 Perform low-quality filtering on the de-connected sequences, remove sequences with base quality below 30, and retain all sequences with Q value ≥ 30; S2.3 Remove the polymerase chain reaction (PCR) tag, wherein the polymerase chain reaction tag is CAACAGTCCGTTATTCGGTG; S2.4 performs end-to-end merging of the read1 and read2 files (with PCR tags removed) to obtain quality control sequence data.
4. The high-throughput nucleic acid sequence processing and clustering method based on SELEX as described in claim 1, characterized in that, The specific process of step S3 is as follows: S3.1 Classify the quality control sequence data according to the preset library index sequence set, wherein the library index sequence set contains 5 different nucleotide sequence tags, which correspond to 5 different rounds of SELEX technology screening in S1. S3.2 The classified sequence data is subjected to target region truncation, specifically including identifying the upstream primer sequence and downstream primer sequence in the sequence; extracting the target random sequence region located between the upstream primer sequence and the downstream primer sequence, retaining the target random sequence region with a preset length, and removing sequences that cannot match the upstream primer sequence or downstream primer sequence, or whose length is not the preset length, to obtain the extraction result.
5. The high-throughput nucleic acid sequence processing and clustering method based on SELEX as described in claim 1, characterized in that, The specific process of step S4 is as follows: S4.1 Count the number of times each unique random sequence appears in the extraction results as the sequence abundance, sort them from high to low abundance, and select sequences with enrichment trend as high abundance candidate sequences for parp1 combination. S4.2 The high-abundance candidate sequences are constructed into a nucleic acid sequence database. The BLAST algorithm is used for sequence self-alignment. To adapt to the short sequence characteristics, the task type is set to blastn-short. The threshold of E-value is set to a preset value. The filtering of low-complexity sequences is turned off to avoid losing functional motifs rich in specific bases. The sequence similarity score is obtained.
6. The high-throughput nucleic acid sequence processing and clustering method based on SELEX according to claim 5, characterized in that: The sequences with enrichment tendency are the unique random sequences in the top 500 of sequence abundance; the threshold for E-value is 0.
05.
7. The high-throughput nucleic acid sequence processing and clustering method based on SELEX according to claim 1, characterized in that, The specific process of step S5 is as follows: S5.1 Based on the sequence similarity score, construct a similarity matrix suitable for graph clustering analysis, and normalize the similarity matrix; S5.2 The similarity matrix is iteratively calculated using the Markov clustering algorithm, and the expansion coefficient is set to a preset value to obtain several parp1 binding DNA sequence clusters with conserved binding motifs; S5.3 performs result parsing on parp1 binding DNA sequence clusters with conserved binding motifs, generating an analysis result file containing sequence IDs, nucleic acid sequences, and clustering groups.
8. The high-throughput nucleic acid sequence processing and clustering method based on SELEX according to claim 7, characterized in that: In S5.2, the expansion coefficient is set to 1.
5.
9. A high-throughput nucleic acid sequence processing and clustering device based on SELEX, characterized in that, The device includes a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to implement a high-throughput nucleic acid sequence data processing and clustering method based on SELEX according to any one of claims 1-8.
10. A computer-readable storage medium, characterized in that, It stores a program that, when executed by a processor, implements a high-throughput nucleic acid sequence processing and clustering method based on SELEX, as described in any one of claims 1-8.