Three-generation sequencing read analysis method, application and device

Abstract

The invention provides a three-generation sequencing read analysis method, application and device. The method comprises the steps that a read segment is obtained, the read segment is obtained by sequencing a sequencing library through a three-generation sequencing platform, and molecules of the sequencing library comprise a sequencing primer recognition sequence, a tag sequence and a UMI sequence; based on the sequencing primer recognition sequence in the read, determining an identity marker area of the read; comparing the identity marking area with at least one standard tag sequence, and determining the tag sequence of the read section in the identity marking area based on the comparison result; and determining the UMI sequence of the read based on the tag sequence in the identity marking area. According to the method, the characteristic of high insertion and deletion error rate in a three-generation full-length transcriptome sequencing result is fully considered, the utilization rate of three-generation sequencing data is improved based on a sequence alignment method, and the accuracy of transcript quantification is improved based on K-mer UMI sequence correction.

Description

Technical Field

[0001] The present application relates to the field of biotechnology, specifically to the field of gene sequencing, and more specifically to a third-generation sequencing read analysis method, a single-cell sequencing method, a third-generation sequencing read analysis device, a single-cell sequencing system, a computer program product, a server, and a computer-readable storage medium. Background Art

[0002] Single cell RNA sequencing (scRNA-seq) has been widely used to analyze the heterogeneity of cell types in various biological tissues. Traditional single cell transcriptome sequencing based on second-generation sequencing technology mainly focuses on the differences in transcriptome expression levels between different cell types at the gene level, but cannot accurately measure the expression differences at the transcript level. The third-generation sequencing technology can overcome the above limitations and analyze the full-length transcript information of a single cell with high throughput. By cleverly combining the existing single cell technology and the third-generation sequencing platform, the full-length transcriptome sequencing of a single cell can be achieved with high throughput.

[0003] However, the high frequency of insertion and deletion errors in existing third-generation sequencing data poses challenges to the analysis of third-generation full-length single-cell transcriptome sequencing data.

[0004] Therefore, the current analysis methods of third-generation full-length single-cell transcriptome sequencing data still need to be improved. Summary of the invention

[0005] This application is completed by the inventor based on the following findings:

[0006] At present, there are some limitations in the analysis methods for third-generation full-length single-cell transcriptome sequencing data, mainly focusing on the challenges in processing cell barcode sequences and unique molecular identifier (UMI) sequences in third-generation sequencing data. Specifically, they are manifested in the following aspects:

[0007] 1) In the existing data processing process, fixed position information is usually used to determine the position of Cell barcode and UMI sequence in sequencing reads. However, insertion and deletion errors often exist in third-generation sequencing data, resulting in the length of Cell barcode and UMI sequence in the sequencing reads not being fixed.

[0008] 2) In order to determine the relationship between the cell barcode in the sequencing data and the standard label sequence (pre-established whitelist cell barcode), a fixed distance is often used as the judgment criterion. However, insertion and deletion errors also exist in the third-generation sequencing data, resulting in the distance between the cell barcode in the sequencing data and the corresponding standard label sequence exceeding the preset fixed distance.

[0009] 3) The standard for determining whether to merge sequencing reads with the same UMI sequence usually also uses a fixed distance. However, due to the influence of insertion and deletion errors in the third-generation sequencing data, the distance between the same UMI sequences may exceed the preset fixed distance.

[0010] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, one purpose of the present application is to propose a method for effectively analyzing third-generation sequencing reads.

[0011] In the first aspect of the present application, the present application proposes a method for analyzing third-generation sequencing reads. According to an embodiment of the present application, the method includes: obtaining a read, wherein the read is obtained by sequencing a sequencing library using a third-generation sequencing platform, and the molecules of the sequencing library include a sequencing primer recognition sequence, a label sequence, and a UMI sequence; based on the sequencing primer recognition sequence in the read, determining the identity tag region of the read; aligning the identity tag region with at least one standard label sequence, and based on the alignment result, determining the label sequence of the read in the identity tag region; and in the identity tag region, determining the UMI sequence of the read based on the label sequence.

[0012] According to the embodiments of the present application, the above method increases the utilization rate of sequencing data and can significantly improve the accuracy of identification of single-cell transcript expression levels.

[0013] In the second aspect of the present application, the present application proposes a single-cell sequencing method. According to an embodiment of the present application, the method includes: obtaining multiple single cells; constructing a sequencing library based on the multiple single cells; wherein the sequencing library molecule includes a sequencing primer recognition sequence, a label sequence and a UMI sequence, the label sequence includes at least one of a first label for distinguishing a single cell and a second label for distinguishing a sample source, and the UMI sequence is used to distinguish the inserted fragment of the sequencing library molecule; sequencing the sequencing library, and analyzing it using the method described in the first aspect to determine the read sequence of the single cell.

[0014] According to the embodiments of the present application, using this method for single-cell sequencing can solve the problem of inaccurate analysis of sequencing results caused by high-frequency insertion and deletion errors in the sequencing results, so as to obtain more accurate single-cell sequencing data analysis results.

[0015] In the third aspect of the present application, the present application proposes a third-generation sequencing read analysis device. According to an embodiment of the present application, the device includes: a sequencing unit, which is used to obtain a read segment, wherein the read segment is obtained by sequencing a sequencing library using a third-generation sequencing platform, and the molecules of the sequencing library include a sequencing primer recognition sequence, a label sequence, and a UMI sequence; an identity tag region determination unit, which is used to determine the identity tag region of the read segment based on the sequencing primer recognition sequence in the read segment; a comparison unit, which is used to compare the identity tag region with at least one standard label sequence, and determine the label sequence of the read segment in the identity tag region based on the comparison result; and a UMI sequence determination unit, which is used to determine the UMI sequence of the read segment in the identity tag region based on the label sequence.

[0016] According to the embodiments of the present application, the device is used to implement the third-generation sequencing read analysis method in any embodiment, significantly improving the analysis accuracy of the third-generation sequencing reads. Moreover, the device can automatically analyze the sequencing reads, improving the analysis efficiency of the reads.

[0017] In the fourth aspect of the present application, the present application proposes a single-cell sequencing system. According to an embodiment of the present application, the system includes: a sequencing library construction module, which is used to obtain multiple single cells and construct a sequencing library based on the multiple single cells; wherein the sequencing library molecule includes a sequencing primer recognition sequence, a label sequence and a UMI sequence, the label sequence includes at least one of a first label for distinguishing a single cell and a second label for distinguishing a sample source, and the UMI sequence is used to distinguish the inserted fragment of the sequencing library molecule; a sequencing module, which is used to sequence the sequencing library and analyze it using the device described in the third aspect of the present application to determine the read sequence of the single cell.

[0018] According to an embodiment of the present application, the device is used to perform any implementation of the single-cell sequencing method to achieve efficient and accurate analysis.

[0019] In a fifth aspect of the present application, the present application proposes a computer program product. According to an embodiment of the present application, the computer program product includes computer instructions, and when part or all of the computer instructions are executed on a computer, the method described in the first aspect or the second aspect of the present application is implemented.

[0020] In the sixth aspect of the present application, the present application proposes an electronic device. According to an embodiment of the present application, the electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first and second aspects of the present application.

[0021] In a seventh aspect of the present application, the present application proposes a computer-readable storage medium containing a computer program. According to an embodiment of the present application, when the computer program is executed by one or more processors, the method described in the first and second aspects of the present application is implemented.

[0022] It should be noted that in the present application, the features and advantages described for the methods, devices and systems in any of the above aspects, implementation modes or examples are also applicable to other aspects and will not be repeated here.

[0023] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0025] Figure 1 It is a schematic diagram of a flow chart of a method for analyzing third-generation sequencing reads according to one embodiment of the present application;

[0026] Figure 2 is a schematic diagram of a third-generation sequencing read analysis device according to an embodiment of the present application;

[0027] Figure 3 is a schematic diagram of a third-generation sequencing read analysis device according to an embodiment of the present application;

[0028] Figure 4 is a schematic diagram of a third-generation sequencing read analysis device (including a correction unit) according to another embodiment of the present application;

[0029] Figure 5 is a schematic diagram of a third-generation sequencing read analysis device (including a correction unit) according to another embodiment of the present application;

[0030] Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present application;

[0031] Figure 7 is a schematic diagram of verifying the UMI correction effect according to an embodiment of the present application;

[0032] Figure 8 It is a schematic diagram of positioning of a third-generation sequencing read analysis method according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0034] definition

[0035] As used herein, the singular forms "a", "an", etc. include plural referents (one or more). "A set" or "a plurality" refers to two or more.

[0036] In this document, "include" or "comprising" is an open expression, including the content or circumstances specified or exemplified thereafter, and also including content or circumstances that are applicable or consistent with the stated circumstances but are not specifically listed.

[0037] In this article, "sequencing" refers to determining the order of base arrangement of the primary structure of nucleic acid molecules, wherein the second generation sequencing can be implemented using the principles of synthetic sequencing (Sequencing by Synthesis, SBS) and / or ligation sequencing (Sequencing by ligation, SBL), and the third generation sequencing can be implemented using the principle of single-molecule real-time sequencing (SMRT). Sequencing may include DNA sequencing and / or RNA sequencing. It includes long fragment sequencing and / or short fragment sequencing, and the so-called long fragments and short fragments are relative, such as nucleic acid molecules longer than 1Kb, 2Kb, 5Kb or 10Kb can be called long fragments, and those shorter than 1Kb or 800bp can be called short fragments; it may include double-end sequencing, single-end sequencing and / or paired-end sequencing, etc., and the so-called double-end sequencing or paired-end sequencing may refer to the reading of any two segments or two parts of the same nucleic acid molecule that do not completely overlap.

[0038] Sequencing can be performed through a sequencing platform. According to the embodiments of the present application, the available sequencing platforms include but are not limited to PacBio's REVIO platform and Oxford Nanopore Technologies' PromethION platform. The sequencing method can be selected from single-end sequencing, double-end sequencing, or a sequencing method supported by the selected automated sequencing platform.

[0039] The sequence read out by sequencing is called sequencing sequence, also called read segment (reads), and the length of sequencing sequence or read segment is also called read length. "Sequencing read segment" and "read" or "read segment" can be used interchangeably, referring to a nucleic acid sequence obtained during sequencing. This sequence is referred to as "sequencing read segment" or "read" in this article. Usually, the length of sequencing read segments of third-generation sequencing is between several thousand and tens of thousands of bp.

[0040] In this article, "sequencing library" refers to a sample collection prepared in a specific form when conducting sequencing studies such as genome, transcriptome or proteome. Sequencing libraries generally contain the following sequences: adapter sequences, sequencing primer recognition sequences, cell label sequences, sample label sequences and UMI sequences, cDNA insert sequences, etc.

[0041] In this article, "read segment identity marker region" and "cell barcode sequence (cell barcode)" have the same meaning and are used to identify and distinguish RNA or DNA molecules from different cells. In single-cell transcriptome sequencing, the cellbarcode is generally located at the 5' end or 3' end of the RNA molecule to be tested. Each cell has a unique cell barcode sequence. By marking the RNA molecules in the same cell with the same cell barcode, the RNA molecules of different cells can be distinguished. In this way, during the sequencing process, the transcriptome data of different cells can be distinguished, thereby achieving transcriptome analysis at the single-cell level.

[0042] In this article, "standard tag sequence" is synonymous with "Cell barcode whitelist sequence", which means one or more known / predefined Cell barcode sequences. Unique identification sequences used to identify different cells. By using predefined whitelist sequences, it can be ensured that only pre-determined cell identifiers are identified and separated, thereby reducing the risk of misidentification and improving the quality and accuracy of sequencing data.

[0043] In this article, "tag sequence" refers to the cell barcode sequence of any sequencing read in the sequencing data.

[0044] In this article, the "UMI (Unique Molecular Identifier)" sequence is a molecular identifier used in single-cell transcriptome sequencing and other high-throughput sequencing experiments. The UMI sequence is a unique short DNA sequence attached to the end of each RNA molecule to identify the origin of each RNA molecule. The role of UMI is to eliminate the bias and errors introduced by PCR amplification, thereby improving the accuracy and reliability of sequencing data.

[0045] The so-called reference sequence (reference, ref) is the same as the reference chromosome sequence, which is a determined sequence. It can be a DNA and / or RNA sequence pre-determined and assembled by oneself, or a DNA and / or RNA sequence determined and disclosed by others. It can be any reference template in the biological category to which the sample source individual / target individual belongs, for example, all or at least a part of the disclosed genome assembly sequence of the same biological category. If the sample source individual or the target individual is human, its genome reference sequence (also called reference genome or reference chromosome group) can select the human reference genome provided by UCSC, NCBI or ENSEMBL database, such as HG19, HG38, GRCh36, GRCh37, GRCh38, etc., and those skilled in the art can understand the corresponding relationship of the above reference genome versions through the description of the database and select the version to be used. Furthermore, a resource library containing more reference sequences can also be pre-configured. For example, before comparison, a sequence that is closer or more characteristic in a certain aspect is selected or determined and assembled according to factors such as the gender, race, and region of the target individual as a reference sequence, which helps to obtain more accurate sequence analysis results later.

[0046] The so-called reference sequence can be constructed when testing the target sample, or it can be pre-constructed and stored and called when the prepared sample is tested. In some embodiments, the sample to be tested is from the human body, and the reference sequence is a human reference genome or a human autosome group.

[0047] In this article, "adjacency matrix" is a matrix structure used to represent the relationship between nodes in a graph. In graph theory, a graph is a data structure consisting of a set of nodes and edges connecting these nodes. The adjacency matrix provides a convenient way to represent the connection between nodes in a graph. In the adjacency matrix, rows and columns represent nodes in the graph, and the elements of the matrix represent the connection relationship between nodes. If there is a connection between node i and node j, the element in the i-th row and j-th column of the adjacency matrix will be set to 1 (or other non-zero values, depending on the situation). If there is no connection between node i and node j, the corresponding matrix element is usually 0.

[0048] Prior art

[0049] PacBio and Oxford Nanopore Technologies have launched some solutions for the analysis of third-generation full-length single-cell transcriptome sequencing data, including the following aspects:

[0050] 1) A fixed position determination strategy is used to determine the exact position of the cell barcode sequence and UMI (Unique Molecular Identifier) ​​sequence of a single cell in the sequencing reads;

[0051] 2) When determining the relationship between the cell barcode sequence in the sequencing data and the predefined cell barcode whitelist sequence, a specific fixed distance was used as a criterion;

[0052] 3) Based on the fixed distance, determine when to merge adjacent UMI sequences for further data processing.

[0053] First, the positions of cell barcode sequences and UMI sequences in sequencing reads are determined based on fixed position information. This rigid positioning strategy may lead to misjudgment in the face of sequencing errors, insertions and deletions, and possible sequence variations, thus affecting the correct extraction of cell identification and UMI.

[0054] Secondly, using a fixed distance as a criterion for judging the relationship between a cell barcode sequence and a preset whitelist sequence may not be able to adequately address sample-specific differences, especially when there is an offset or variation between cell barcodes. This determination method may lead to incorrect cell identification, which in turn affects subsequent cell classification and expression analysis.

[0055] Finally, using a fixed distance to decide whether to merge adjacent UMI sequences may not capture the true diversity of transcripts, especially in genes with abundant splicing isoforms. This approach may lead to underestimation or overestimation of transcript expression, affecting the accurate understanding of gene expression dynamics.

[0056] Third-generation sequencing read analysis method

[0057] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0058] In the first aspect of the present application, the present application proposes a third-generation sequencing read analysis method, referring to Figure 1 According to an embodiment of the present application, the method includes:

[0059] S101: Based on the sequencing results, determine the identity marker region of the read segment

[0060] The sequencing library is sequenced, and multiple sequencing reads are obtained based on the sequencing results. According to a specific embodiment of the present application, the sequencing library molecule includes multiple sequencing primer recognition sequences; and may also include multiple tag sequences. The multiple sequencing reads are obtained based on a third-generation sequencing platform, and the acquisition method is not particularly limited.

[0061] The sequencing primer recognition sequence is used to locate the cell barcode sequence in the multiple sequencing reads, and the identity marker region (cell barcode) in the reads is obtained. According to a specific embodiment of the present application, the sequencing primer recognition sequence includes but is not limited to the P3 primer. The cell barcode sequence region in the sequencing read is located based on the sequencing primer recognition sequence.

[0062] According to the specific embodiments of the present application, Figure 8 As shown, positioning is performed through the following steps: the P3 primer sequence (green) adjacent to the Cell barcode sequence is located in each third-generation sequencing read segment by sequence similarity alignment method; the m bases upstream of the P3 primer and the n bases downstream of the P3 primer in each third-generation sequencing read are used as the Cell barcode sequence recognition region with m+n bases. Among them, the length of m is generally selected as 10, and the length of n is greater than the length of the Cell barcode (blue), and generally does not exceed 1.5 times the length of the Cell barcode. In this way, the sequence of m+n bases can completely contain the Cell barcode sequence while reducing the content of non-Cell barcode sequences, thereby reducing the interference caused by non-Cell barcode sequences in subsequent analysis.

[0063] The so-called sequence similarity comparison method includes but is not limited to all existing or future developed algorithms or software that can solve the problem of pairwise sequence similarity comparison, such as Smith-Waterman algorithm, Blast, BWA or Needleman-Wunsch algorithm, etc.

[0064] The sequence of the so-called P3 amplification primer is known and provided by the selected single-cell experimental technology platform. The single-cell experimental technology platform determines the sequence structure of the sequencing library. The sequence structure of the sequencing library determines that the P3 amplification primer fixed sequence is directly adjacent to the Cell barcode sequence. Locating the position of the P3 amplification primer sequence in the third-generation sequencing read segment means locating the position of the Cell barcode sequence in the third-generation sequencing read segment.

[0065] S102: Compare the identity tag region with at least one standard tag sequence to determine the tag sequence of the read segment

[0066] Based on the identity tag region of the read determined in step S101, the identity tag region is similarly compared with at least one standard tag sequence (Cell barcode whitelist sequence) by a sequence similarity comparison method, and based on the comparison result, the tag sequence of the read is determined in the identity tag region.

[0067] The so-called sequence similarity comparison method includes but is not limited to all existing or future developed algorithms or software that can solve the problem of pairwise sequence similarity comparison.

[0068] The so-called Cell barcode whitelist sequences come from a known list of all possible Cell barcode sequences that may appear in the sequencing data provided by the selected single-cell experimental technology platform.

[0069] According to an embodiment of the present application, the identity mark region is compared with at least one standard label sequence to obtain multiple candidate label sequences; the similarity between the identity mark region and each of the candidate labels is determined; and the candidate label with the highest similarity is selected as the label sequence of the read segment.

[0070] The so-called similarity is determined by determining the edit distance, similarity score or similarity P value. These similarity evaluation indicators are determined based on the selected sequence similarity comparison method. In some specific embodiments, the edit distance is preferably selected as the evaluation indicator.

[0071] According to an embodiment of the present application, the candidate tag with the highest similarity and the similarity exceeding a predetermined threshold is further selected as the tag sequence of the read segment.

[0072] According to an embodiment of the present application, the comparison results are filtered based on a predetermined threshold. When the edit distance is used as the evaluation index, the so-called predetermined threshold does not exceed 3, preferably does not exceed 2. In some examples, the so-called predetermined threshold is no more than 5% of the corresponding sequence length.

[0073] According to an embodiment of the present application, when the similarity P value is used as an evaluation indicator, the so-called predetermined threshold is P<0.05.

[0074] According to an embodiment of the present application, the threshold of the similarity score is generally related to the algorithm of the similarity score and the length of the sequence. In some examples, the so-called predetermined threshold is greater than 80% of the similarity score obtained when two sequences of corresponding lengths are completely matched.

[0075] According to an embodiment of the present application, when there are multiple candidate tags with the highest similarity, it further includes: determining the frequency of occurrence of each candidate tag in the same batch of sequencing; and selecting the candidate tag with the highest frequency of occurrence as the tag sequence of the read segment.

[0076] The so-called occurrence frequency refers to the number of times the candidate tag appears in the same batch of sequencing data.

[0077] Among them, the method for determining the frequency of occurrence of each candidate tag in this sequencing includes but is not limited to using a statistical counting method, a frequency calculation method or a comparative frequency method. The so-called statistical counting method means that for each candidate tag sequence, the entire sequencing data set is traversed to calculate the number of times it appears in all read segments. The so-called frequency calculation method means dividing the number of occurrences of the candidate tag sequence in the entire data set by the total number of read segments to obtain the frequency of occurrence of the candidate tag. The so-called comparative frequency method means comparing the frequency of occurrence of all candidate tags. The higher the frequency of occurrence of a candidate tag, the more frequently the tag appears in the sequencing data, indicating that it is more reliable.

[0078] S103: Determine the UMI sequence of the read segment based on the tag sequence

[0079] Based on the tag sequence determined in step S102, check the Cell barcode comparison results corresponding to all third-generation sequencing reads. If the third-generation sequencing reads fail to match any whitelist Cell barcode sequence, the third-generation sequencing reads cannot be assigned a Cell barcode and should be removed. If the third-generation sequencing reads match exactly one whitelist Cell barcode sequence, the Cell barcode sequence is assigned as the Cell barcode of the third-generation sequencing reads. If the third-generation sequencing reads match multiple whitelist Cell barcode sequences, the Cell barcode with the highest occurrence frequency among these Cell barcodes is selected and assigned as the Cell barcode of the third-generation sequencing reads.

[0080] It should be noted that whenever a third-generation sequencing read is assigned a Cell barcode, the frequency of occurrence of the Cell barcode in the actual Cell barcode background distribution is updated. It can be understood that when a third-generation sequencing read is aligned, if it matches one or more sequences in the whitelist Cell barcode, it will be assigned a specific Cell barcode. Once this third-generation sequencing read is assigned a Cell barcode, the frequency of occurrence of the Cell barcode will be updated.

[0081] The so-called “actual Cell barcode background distribution” refers to the distribution of Cell barcode sequences that actually exist in the sample.

[0082] According to an embodiment of the present application, the Cell barcode whitelist comes from a known list of all possible Cell barcode sequences that may appear in the sequencing data provided by the selected single-cell experimental technology platform.

[0083] Based on the obtained Cell barcode sequence region and the comparison results with the Cell barcode whitelist sequence, the UMI sequence is separated from the third-generation sequencing reads. The single-cell transcriptome experimental technology platform determines the arrangement of the Cell barcode sequence and the UMI sequence adjacent to each other. The length of the UMI sequence extends from the end of the Cell barcode sequence to the position where the PolyA sequence first appears in the third-generation sequencing reads.

[0084] By using the sequence similarity alignment method, the P5 amplification primer sequence is located in the third-generation sequencing reads from which the UMI sequence is split, and all sequences including the P5 amplification primer sequence are truncated. Subsequently, in the third-generation sequencing reads after truncating the P5 amplification primer sequence, the PolyA sequence is also located using the sequence similarity alignment method, and the PolyA sequence and the subsequent sequence are removed to obtain the sequence of the cDNA insert fragment. The sequence of each cDNA insert fragment corresponds to a specific assigned Cell barcode sequence and the split UMI sequence. After obtaining the complete cDNA insert fragment sequence, it will be aligned with the reference sequence to determine the transcript corresponding to each cDNA insert fragment sequence.

[0085] It should be noted that the P5 amplification primer sequence referred to has been provided by the selected single-cell experimental technology platform, and its sequence is already known.

[0086] In some embodiments, a plurality of the UMI sequences of the plurality of the reads are obtained; a plurality of K-mer sequences are generated based on the plurality of the UMI sequences; the K-mer sequence corresponding to the given read is corrected based on the mutual similarity between the plurality of K-mer sequences; and the UMI sequence of the given read is corrected based on the corrected K-mer sequence.

[0087] Specifically, based on the mutual similarities between the multiple K-mer sequences, correcting the K-mer sequence corresponding to the given read segment further includes:

[0088] Calculate the edit distance between any two K-mer sequences; obtain an adjacency matrix of the UMI sequence based on the edit distance; obtain a UMI sequence set (connected components) based on the adjacency matrix; and select any sequence in the UMI set to replace the UMI sequence set.

[0089] In some examples, the edit distance is selected as an integer not greater than 2. For example, the edit distance is 1 or 2.

[0090] In some examples, all UMI sequences are converted into K-mer sequences of length 5. On this basis, the edit distance of the K-mer sequences between different UMIs is calculated, that is, the number of different characters between K-mers. In other embodiments, the K-mer sequence length may also be 3, 4, 6, 7, 8, 9, or 10.

[0091] Based on these edit distances, an adjacency matrix describing the relationship between all UMI sequences is established. In the adjacency matrix, if the edit distance between the K-mer sequences of two UMIs does not exceed 3, it indicates that the two UMIs are connected in the adjacency matrix. In other embodiments, the edit distance between the K-mer sequences of two UMIs does not exceed 2, indicating that the two UMIs are connected in the adjacency matrix.

[0092] Finally, the connected components in the adjacency matrix are relabeled so that the UMI sequences in the same connected component are unified into one identifier. This process helps to identify UMI sequences with common features and merge them.

[0093] The above-mentioned third-generation sequencing read analysis method fully considers the high insertion and deletion error rate in the third-generation full-length transcriptome sequencing results. The sequence alignment-based method improves the utilization rate of the third-generation sequencing data, and the K-mer-based UMI sequence correction improves the accuracy of transcript quantification.

[0094] In addition, the application scope of the above-mentioned third-generation sequencing read analysis method includes but is not limited to the field of single-cell transcriptome technology services or in the future, the fields of tumor and fertility diagnosis through single-cell transcriptome and single-cell genome technology.

[0095] Single-cell sequencing methods

[0096] In the second aspect of the present application, the present application proposes a single-cell sequencing method. The method includes: obtaining multiple single cells; constructing a sequencing library based on the multiple single cells; wherein the sequencing library molecule includes a sequencing primer recognition sequence, a label sequence and a UMI sequence, the label sequence includes at least one of a first label for distinguishing a single cell and a second label for distinguishing a sample source, and the UMI sequence is used to distinguish the inserted fragment of the sequencing library molecule; sequencing the sequencing library, and analyzing it using the third-generation sequencing read method described in any embodiment of the present application to determine the read sequence of the single cell.

[0097] In some examples, the so-called multiple single cells can originate from different biological samples.

[0098] In some examples, a sequencing library is constructed based on multiple collected single cells. The sequencing library contains molecules that need to be sequenced. Each sequencing library molecule includes a sequencing primer recognition sequence, a label sequence, and a UMI sequence. Among them, the label sequence may include at least one label, for example, a first label for distinguishing a single cell and a second label for distinguishing the source of the sample. The sequencing library also includes UMI sequences, which are used to distinguish inserts between different sequencing library molecules. The constructed sequencing library is sequenced, and then the data is analyzed using a third-generation sequencing read analysis method. The read sequence of a single cell is determined to obtain transcriptome information about the cell.

[0099] Third-generation sequencing read analysis device

[0100] In the third aspect of the present application, the present application proposes a third-generation sequencing read segment analysis device. Figure 2 ,include:

[0101] The read acquisition unit S200 is used to acquire reads, where the reads are obtained by sequencing a sequencing library using a third-generation sequencing platform, and the molecules of the sequencing library include a sequencing primer recognition sequence, a tag sequence, and a UMI sequence.

[0102] The identity tag region determining unit S300 is connected to the read segment acquiring unit S200 and is used to determine the identity tag region of the read segment based on the sequencing primer recognition sequence in the read segment.

[0103] The comparison unit S400 is connected to the identity mark region determination unit S300 and is used to compare the identity mark region with at least one standard label sequence and determine the label sequence of the read segment in the identity mark region based on the comparison result.

[0104] A UMI sequence determination unit S500 is connected to the comparison unit S400 and is used to determine the UMI sequence of the read segment based on the tag sequence in the identity tag region.

[0105] In some specific embodiments of the present application, Figure 3 As shown, the identity mark area determination unit S300 further includes:

[0106] The positioning component S301 is used to locate in multiple sequencing reads by using a sequencing primer recognition sequence to obtain an identity marker region (cell barcode) in the read.

[0107] The interception component S302 is connected to the positioning component S301 and is used to intercept m bases upstream of the sequencing primer (such as P3 primer) and n bases downstream of the sequencing primer (such as P3 primer) in each third-generation sequencing read, a total of m+n bases as the Cell barcode sequence region.

[0108] In some specific embodiments of the present application, Figure 3 As shown, the comparison unit S400 further includes:

[0109] The first comparison component S401 compares the sequence similarity with at least one standard tag sequence (cell barcode whitelist sequence) by using a sequence similarity comparison method.

[0110] The filtering component S402 is connected to the first comparison component S401 and is used to select the candidate tag with the highest similarity and the similarity exceeding a predetermined threshold as the tag sequence of the read segment based on the similarity comparison result.

[0111] The first calculation component S403 is connected to the filtering component S402. When there are multiple candidate tags with the highest similarity, the occurrence frequency of each candidate tag in the same batch of sequencing is calculated; and the candidate tag with the highest occurrence frequency is selected as the tag sequence of the read segment.

[0112] In some specific embodiments of the present application, Figure 3 As shown, the UMI sequence determination unit S500 further includes:

[0113] The inspection component S501 is used to check the Cell barcode comparison results corresponding to all third-generation sequencing reads. If the third-generation sequencing reads fail to match any white-list Cell barcode sequence, the third-generation sequencing reads cannot be assigned a Cell barcode and should be removed; if the third-generation sequencing reads just match one white-list Cell barcode sequence, the Cell barcode sequence is assigned as the Cell barcode of the third-generation sequencing reads; if the third-generation sequencing reads match multiple white-list Cell barcode sequences, the Cell barcode with the highest occurrence frequency among these Cell barcodes is selected and assigned as the Cell barcode of the third-generation sequencing reads.

[0114] The updating component S502 is connected to the checking component S501 and is used to update the occurrence frequency of the Cell barcode in the actual Cell barcode background distribution when the third-generation sequencing reads are assigned a Cell barcode.

[0115] Separation component S503, the separation component S503 is connected to the update component S502, and is used to separate the UMI sequence from the third-generation sequencing reads based on the obtained Cell barcode sequence region and the comparison result with the Cell barcode whitelist sequence. The length of the UMI sequence extends from the end of the Cell barcode sequence to the position where the PolyA sequence first appears in the third-generation sequencing reads.

[0116] The first truncation component S504 is connected to the separation component S503 and is used to locate the P5 amplification primer sequence in the third-generation sequencing reads from which the UMI sequence is separated by a sequence similarity comparison method, and to truncate all sequences including the P5 amplification primer sequence.

[0117] The second truncation component S505 is connected to the first truncation component S504 and is used to locate the PolyA sequence in the third-generation sequencing reads after truncating the P5 amplification primer sequence, and remove the PolyA sequence and the subsequent sequence to obtain the sequence of the cDNA insert. The sequence of each cDNA insert corresponds to a specific assigned Cell barcode sequence and the separated UMI sequence.

[0118] The second comparison component S506 is connected to the second truncation component S505 and is used to compare the complete cDNA insert sequence with the reference sequence after obtaining the complete cDNA insert sequence to determine the transcript corresponding to each cDNA insert sequence.

[0119] In some embodiments, Figure 4 As shown, the device may further include a correction unit S600, which is connected to the UMI sequence determination unit S500, and is used to obtain multiple UMI sequences of multiple read segments; generate multiple K-mer sequences based on the multiple UMI sequences; based on the mutual similarities between the multiple K-mer sequences, correct the K-mer sequence corresponding to the given read segment; based on the corrected K-mer sequence, correct the UMI sequence of the given read segment.

[0120] In some specific embodiments of the present application, Figure 5 As shown, the correction unit S600 further includes:

[0121] The conversion component S601 is used to convert all UMIs into K-mer sequences of length K; the value of K includes: 4, 5, 6, 7, 8, 9 or 10, etc.

[0122] The second calculation component S602 is connected to the conversion component S601 and is used to calculate the edit distance between two K-mers with different UMIs.

[0123] A construction component S603 is connected to the second calculation component S602 and is used to construct an adjacency matrix describing the relationship between all UMI sequences.

[0124] The judgment component S604 is connected to the construction component S603 and is used to judge whether the edit distance between two K-mers exceeds N; if it is less than N, it means that the two UMIs corresponding to the two K-mers are connected in the adjacency matrix; the value of N can be 3 or 2.

[0125] The labeling component S605 is connected to the judging component S604 and is used to relabel each connected component in the adjacency matrix into a unified UMI sequence.

[0126] It should be noted that in the present application, the features and advantages described for the methods, devices and systems in any of the above aspects, implementation modes or examples are also applicable to other aspects and will not be repeated here.

[0127] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0128] Computer program product, electronic device, and computer readable storage medium

[0129] According to an embodiment of the present application, the present application further provides a computer program product, an electronic device, and a computer-readable storage medium. This embodiment takes an electronic device as an example for detailed description.

[0130] The so-called electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Computing devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are intended to be examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0131] like Figure 6 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 to a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0132] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0133] The computing unit 501 can be various general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, CPU (Central Processing Unit), GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the construction method of the scRNA-Seq cell type annotation database. For example, in some embodiments, the construction method of the scRNA-Seq cell type annotation database can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the aforementioned method for constructing the scRNA-Seq cell type annotation database in any other appropriate manner (eg, by means of firmware).

[0134] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] The program code for implementing the method disclosed in the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0136] In the context disclosed in the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0137] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0138] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0139] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0140] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0141] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0142] It should be noted that the features and technical effects described in this article for different aspects can be used as reference for each other and will not be repeated here.

[0143] The present application is described below by way of examples, but this should not be construed as limiting the scope of the present application to the following examples. All technologies implemented based on the above content of the present application belong to the scope of the present application.

[0144] Embodiment 1:

[0145] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0146] The data samples in this embodiment are from two single-cell dissociation samples of fresh mouse kidney tissue, named b1_s1 and b1_s2. The data are obtained by first constructing a single-cell full-length transcriptome sequencing library and a single-cell second-generation transcriptome sequencing library through the commercialized MGIDNBelab C4 V2 single-cell platform technology. The former library is sequenced by the ONT PromethION sequencing platform for the third generation, and the latter library is sequenced by the BGISEQ2000 for the second generation. The second-generation sequencing library data is processed according to the existing second-generation single-cell sequencing data analysis strategy, and the results are used as a comparison benchmark for the quantitative expression of single cell genes; the third-generation sequencing library data is processed according to the scheme of the embodiment of the present invention, which is used to calculate the final Cell barcode splitting efficiency and compare the final third-generation single-cell gene expression quantitative results with the second-generation single-cell gene expression quantitative results.

[0147] The third-generation sequencing read analysis method of the present disclosure embodiment is described below with reference to the accompanying drawings.

[0148] like Figure 1 As shown, this embodiment includes the following steps:

[0149] Step S101: Determine the identity marker region of the read segment based on the sequencing results

[0150] First, the library structure contained in each read segment in the sequencing results is clarified. According to the single-cell technology platform used, the correct library structure in each read segment should include P3 primer sequence (22bp) + identity marker sequence (31bp) + UMI sequence (10bp) + cDNA insert fragment (the length is related to the specific RNA molecule measured) + P5 primer sequence (22bp). The relative positions of the above sequences should be connected in sequence, but the specific positions in each read segment cannot be determined in advance.

[0151] The Cell barcode sequence region in the sequencing read is located based on the known P3 primer sequence. The specific implementation method is to determine the position of the P3 primer sequence in each read segment based on the sequence alignment BLAST method, and take the position of the end of the P3 primer sequence as the standard, and take the 10bp base upstream of the position and the 45bp base downstream of the position, a total of 55bp as the Cell barcode sequence recognition region.

[0152] Step S102: Compare the identity tag region with at least one standard tag sequence to determine the tag sequence of the read segment

[0153] Based on step S101, a 55 bp Cell barcode region is extracted from each read segment in the raw data, and the Cell barcode region is aligned with the known Cell barcode whitelist sequence by using the sequence alignment BLAST method. The similarity score threshold is set to be higher than 37 according to the BLAST scoring algorithm and the length of the Cell barcode sequence, that is, for each Cell barcode region, the Cell barcode whitelist sequence with a similarity score higher than 37 is retained as the alignment result.

[0154] According to the BLAST scoring algorithm and the length of the Cell barcode sequence, the score of the two sequences that are completely matched is 43, so the frequency of occurrence of the Cell barcode is calculated based on the comparison result with a score of 43. The basis for this is that if a completely matched whitelist sequence appears in the sequencing result, it means that this whitelist sequence must be a valid Cell barcode sequence in the data.

[0155] Step S103: Determine the UMI sequence of the read segment based on the tag sequence

[0156] Based on the comparison results of step S102, all sequencing reads can be divided into the following three situations: 1. The sequencing reads fail to match any whitelist Cell barcode sequence, and these sequencing reads are directly removed from the original data; 2. The sequencing reads match exactly one whitelist Cell barcode, and the Cell barcode sequence is assigned as the Cell barcode of the third-generation sequencing read; 3. The sequencing read matches multiple whitelist Cell barcode sequences, and the Cell barcode with the highest frequency is selected and assigned as the Cell barcode of the sequencing read. Note that the Cell barcode frequency of step S102 needs to be updated in the latter two situations. For each read, the Cell barcode whitelist sequence corresponding to the read is now obtained.

[0157] The comparison result between the Cell barcode sequence region and the Cell barcode whitelist can determine the position of the Cell barcode whitelist sequence in the Cell barcode sequence region. The position of the Cell barcode sequence region in the original read segment is known, so the position of the Cell barcode whitelist sequence in the original read segment can be inferred.

[0158] Starting from the end of the Cell barcode whitelist sequence, find the first occurrence of the PolyA sequence and end it, and separate the sequence between the start and the end as the UMI sequence.

[0159] By using the sequence alignment BLAST method, the PolyA sequence and the P5 primer sequence were aligned and removed in turn in the remaining sequence of the read segment. Finally, a read segment only contained the cDNA insert sequence corresponding to the read segment.

[0160] The obtained cDNA insert fragment sequence is aligned to the reference genome of the mouse. For each read segment, the Cell barcode whitelist sequence corresponding to the read segment is now obtained, and the transcript molecule corresponding to the read segment is also obtained.

[0161] Finally, the UMI of the separated reads is corrected. All reads of a transcript molecule corresponding to a Cell barcode are taken as units. The K-mer sequence of length 5 of the UMI of these reads is extracted, and the edit distance of each pair of K-mers is calculated. If the edit distance of each pair of K-mers is less than or equal to 2, the adjacency matrix of the corresponding UMI is marked as 1; the connected components (UMI sequence set) in the adjacency matrix of the UMI are calculated, and a UMI is randomly selected from the connected components and all UMIs of the connected components are replaced with the randomly selected UMI sequence. For each read, the Cell barcode whitelist sequence corresponding to the read is now obtained, as well as the transcript molecule corresponding to the read and the corrected UMI sequence corresponding to the read.

[0162] Table 1: Cell barcode splitting efficiency results

[0163] Sample name Number of reads Valid barcode read count Effective barcode reading ratio (%) b1_S1 93451540 54074499 57.86 b2_S2 87414454 52380789 59.92

[0164] Among them, the first column is the sample name, the second column is the data volume of the original third-generation sequencing reads corresponding to each sample, the third column is the number of reads that can be assigned to the Cell barcode, complete UMI correction and split the cDNA insert fragments through the processing method of the present invention, and the fourth column is the ratio of the data in the third column to the data in the second column. The ratio reflects the final data utilization rate. The higher the ratio, the higher the data utilization rate.

[0165] The results are as follows Figure 7 As shown in the figure, the Pearson correlation coefficient of the second and third generation gene expression levels of each cell in each sample is displayed in the form of a violin plot. The distribution of this coefficient can be used to verify the final UMI correction effect. When the second generation single cell gene expression level is used as the comparison benchmark, the higher the correlation, the more consistent the third generation single cell gene expression level identified by this method is with the benchmark, thus reflecting that the UMI correction effect is better.

[0166] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0167] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for analyzing third-generation sequencing reads, characterized in that: include: Obtaining a read segment, wherein the read segment is obtained by sequencing a sequencing library using a third-generation sequencing platform, wherein the molecules of the sequencing library include a sequencing primer recognition sequence, a tag sequence, and a UMI sequence; Determining an identity tag region of the read segment based on the sequencing primer recognition sequence in the read segment; Comparing the identity tag region with at least one standard tag sequence, and determining the tag sequence of the read segment in the identity tag region based on the comparison result; and In the identity tag region, a UMI sequence of the read segment is determined based on the tag sequence.

2. The method according to claim 1, characterized in that Comparing the identity tag region with at least one standard tag sequence, and determining the tag sequence of the read segment in the identity tag region based on the comparison result further comprises: Comparing the identity marker region with at least one standard tag sequence to obtain a plurality of candidate tag sequences; Determining the similarity between the identity mark region and each of the candidate labels; and The candidate tag with the highest similarity is selected as the tag sequence of the read segment.

3. The method according to claim 2, characterized in that The candidate tag with the highest similarity and the similarity exceeding a predetermined threshold is selected as the tag sequence of the read segment.

4. The method according to claim 3, characterized in that When there are multiple candidate tags with the highest similarity, the method further includes: Determining the frequency of occurrence of each of the candidate tags in the same batch of sequencing; and The candidate tag with the highest occurrence frequency is selected as the tag sequence of the read segment.

5. The method according to claim 2, characterized in that: The similarity is determined by determining an edit distance, a similarity score or a similarity P value; Preferably, the similarity is determined by edit distance.

6. The method according to claim 3, characterized in that The predetermined threshold does not exceed 3; preferably does not exceed 2.

7. The method according to claim 1, characterized in that Further including: Obtaining a plurality of the UMI sequences of the plurality of the read segments; Based on the plurality of UMI sequences, generating a plurality of K-mer sequences; Based on the mutual similarities between the multiple K-mer sequences, correcting the K-mer sequence corresponding to the given read segment; The UMI sequence of the given read is corrected based on the corrected K-mer sequence.

8. The method according to claim 7, characterized in that Based on the mutual similarities between the multiple K-mer sequences, correcting the K-mer sequence corresponding to the given read segment further comprises: Calculate the edit distance between any two K-mer sequences; Obtaining an adjacency matrix of the UMI sequence based on the edit distance; Obtaining a UMI sequence set based on the adjacency matrix; Any sequence in the UMI set is selected to replace the UMI sequence set.

9. The method according to claim 7, characterized in that: The K-mer sequence length is 5.

10. The method according to claim 1, characterized in that The sequencing library molecule includes a plurality of sequencing primer recognition sequences; Optionally, the sequencing library molecule comprises a plurality of tag sequences.

11. A single-cell sequencing method, characterized in that: include: Acquire multiple single cells; Based on the multiple single cells, construct a sequencing library; wherein the sequencing library molecule includes a sequencing primer recognition sequence, a label sequence and a UMI sequence, the label sequence includes at least one of a first label for distinguishing a single cell and a second label for distinguishing a source of a sample, and the UMI sequence is used to distinguish an inserted fragment of the sequencing library molecule; The sequencing library is sequenced and analyzed using the method described in any one of claims 1 to 10 to determine the read sequence of the single cell.

12. A third-generation sequencing read analysis device, characterized in that: include: A read acquisition unit, used to acquire a read segment, wherein the read segment is obtained by sequencing a sequencing library using a third-generation sequencing platform, and the molecules of the sequencing library include a sequencing primer recognition sequence, a tag sequence, and a UMI sequence; an identity marker region determining unit, configured to determine the identity marker region of the read segment based on the sequencing primer recognition sequence in the read segment; a comparison unit, configured to compare the identity tag region with at least one standard tag sequence, and determine the tag sequence of the read segment in the identity tag region based on the comparison result; and A UMI sequence determining unit is configured to determine, in the identity tag region, a UMI sequence of the read segment based on the tag sequence.

13. The device according to claim 12, characterized in that Further including: A correction unit, configured to obtain a plurality of the UMI sequences of the plurality of read segments; and generate a plurality of K-mer sequences based on the plurality of the UMI sequences; Based on the mutual similarities between the multiple K-mer sequences, the K-mer sequence corresponding to the given read segment is corrected; based on the corrected K-mer sequence, the UMI sequence of the given read segment is corrected.

14. A single-cell sequencing system, characterized in that: include: A sequencing library construction module, used to obtain a plurality of single cells, and construct a sequencing library based on the plurality of single cells; wherein the sequencing library molecule comprises a sequencing primer recognition sequence, a label sequence and a UMI sequence, the label sequence comprises at least one of a first label for distinguishing a single cell and a second label for distinguishing a sample source, and the UMI sequence is used to distinguish an insert fragment of the sequencing library molecule; A sequencing module is used to sequence the sequencing library and analyze it using the device described in claim 12 or 13 to determine the read sequence of the single cell.

15. A computer program product, characterized in that The computer program product comprises computer instructions, and when part or all of the computer instructions are executed on a computer, the method according to any one of claims 1 to 11 is implemented.

16. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 11.

17. A computer-readable storage medium containing a computer program, characterized in that: When the computer program is executed by one or more processors, the method according to any one of claims 1 to 11 is implemented.

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