Gene editing efficiency detection method, device and electronic equipment

Abstract

The application relates to a gene editing efficiency detection method, a gene editing efficiency detection device and electronic equipment. The gene editing efficiency detection method comprises the following steps: performing data splitting on the to-be-detected gene editing data based on matching of primers and amplification spacers; performing double-end read merging on the split data based on a dynamic minimum overlap lower limit; performing repeat marking on the merged data; performing local alignment on the repeat-marked data and data that has not undergone double-end read merging, and performing global realignment on the local alignment result; performing editing event identification and sequence filtering based on the local alignment result and the global realignment result; and calculating the gene editing efficiency based on the filtered qualified sequences. In this way, the detection efficiency of the gene editing efficiency can be improved while the accuracy of the gene editing efficiency detection is maintained.

Description

Technical Field

[0001] This application relates to the field of gene editing technology, and more specifically, to a gene editing efficiency detection method, a gene editing efficiency detection device, and an electronic device. Background Technology

[0002] With the widespread use of gene editing technology, especially in the increasingly in-depth research on drug development using gene editing as a technical means, the rapid and accurate detection of editing events at target sites and potential off-target sites has become increasingly important. Specific aspects of gene editing detection mainly include editing efficiency, edited product form, off-target effects, and consistency of editing results.

[0003] Currently, there are several main methods for detecting gene editing efficiency.

[0004] Traditional wet assays can assess changes in edited sites before and after editing by performing first-generation sequencing or other enzymatic reactions on the edited region. However, this method suffers from low sensitivity and low throughput.

[0005] Next-generation sequencing (NGS) methods, by using high-depth NGS sequencing, can significantly improve detection sensitivity and throughput. However, they also have a series of problems, such as PCR errors and sequencing errors. Here, DNA sequencing refers to the determination of the nucleotide sequence of a DNA molecule, specifically the sequence of A, T, G, and C. Furthermore, NGS technology is massively parallel sequencing, its core idea being to sequence DNA molecules using hundreds of millions of short reads simultaneously.

[0006] Currently, mainstream gene editing efficiency detection technologies are based on amplicon sequencing. Based on amplicon sequencing results, some computational methods and workflows have been published; however, these methods often only focus on performance optimization in a specific aspect, and the results are not ideal. Major implementations of these methods include: the R package Amplican (Labun et al., 2019), which provides good support for large indels and has relatively strict data quality control, but suffers from significant data loss and high resource consumption; the Python workflow packages CRISPResso / 2 (Clement et al., 2019; Pinello et al., 2016), which uses enzyme activity site-guided alignment, but has significant shortcomings in data splitting and merging; and the Python workflow package CrispRvariant (Lindsay et al., 2016), which for the first time analyzed allele-level editing results, using methods based on the cascade of open-source tools. The above methods for detecting gene editing efficiency are limited by factors such as process design, module algorithms, and overall compatibility. They suffer from problems such as random or compositional deviations in detection results, poor data compatibility, isolation between conversion processes, and chaotic conversion results, which pose challenges to data analysis and consistency management.

[0007] Therefore, it is desirable to provide an improved method for detecting gene editing efficiency. Summary of the Invention

[0008] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a gene editing efficiency detection method, a gene editing efficiency detection device, and an electronic device. By optimizing data splitting, data merging, duplication labeling, alignment methods, sequence filtering, and calculation methods in the gene editing efficiency detection process, it improves the detection efficiency of gene editing efficiency while maintaining the accuracy of gene editing efficiency detection.

[0009] According to one aspect of this application, a method for detecting gene editing efficiency is provided, comprising: splitting the gene editing data to be detected by primer and amplicon matching; merging paired-end reads of the split data based on a dynamic minimum overlap limit; marking the merged data for duplication; comparing the duplication-marked data with the data without paired-end read merging based on local alignment, and performing a global re-alignment on the local alignment results; identifying editing events and filtering sequences based on the local alignment results and the global re-alignment results; and calculating gene editing efficiency based on the filtered qualified sequences.

[0010] In the above-mentioned gene editing efficiency detection method, the data splitting of the gene editing data to be detected based on primer and amplicon matching includes: constructing an index based on primers; using a hash table to query a reference library that matches each read of the gene editing data with the amplicon at a predetermined precision; in response to a predetermined read of the gene editing data failing to match the reference library of the amplicon at a predetermined precision, performing a query matching based on the index using a portion of the predetermined read; and splitting the gene editing data to be detected into multiple first sequences based on the matching results.

[0011] In the above gene editing efficiency detection method, merging the split data based on the dynamic minimum overlap lower limit includes: using amplicon sequence specificity to pre-estimate the dynamic minimum overlap lower limit for merging the paired sequences, wherein the length of the dynamic minimum overlap lower limit is greater than the length of the repeat portion in the amplicon sequence; and merging the paired sequences of the plurality of first sequences based on the dynamic minimum overlap lower limit to obtain a plurality of second sequences.

[0012] In the above gene editing efficiency detection method, the duplicate labeling of the merged data includes: using a hash algorithm to repeatedly label the multiple second sequences to obtain multiple third sequences.

[0013] In the above-mentioned gene editing efficiency detection method, the use of a hash algorithm to repeatedly label the plurality of second sequences to obtain a plurality of third sequences includes: in response to the plurality of second sequences having directional sequences, marking reverse complementary sequences as repeats; and / or, in response to the plurality of second sequences containing single-cell labeled sequences, repeating the sequences at the single-cell level.

[0014] In the above-mentioned gene editing efficiency detection method, the global re-alignment of local comparison results includes: performing a global re-alignment of the local comparison results based on the sensitivity of the editing enzyme cleavage site to obtain multiple fourth sequences.

[0015] In the above-mentioned gene editing efficiency detection method, the editing event identification and sequence filtering based on local comparison results and global re-alignment results includes at least one of the following: determining unreliable sequences based on the number of base matches and the error rate of the matching portion in each sequence; determining unreliable sequences based on the insertions and deletions in each sequence and the intervals between matching sequences; clustering each sequence according to two dimensions: alignment score and number of mutations, and determining the sequence with the lowest quality and the largest number of mutations as unreliable sequences; determining sequences with a support number greater than or equal to a predetermined threshold as filtered sequences based on background samples; and determining sequences with a number of mutations and a support number lower than the user-defined mutation number threshold and support number threshold, respectively, as filtered sequences.

[0016] In the above-mentioned gene editing efficiency detection method, calculating gene editing efficiency based on filtered qualified sequences includes: identifying filtered qualified sequences that have no variation compared to a reference sequence as reference sequences; identifying sequences that have undergone a preset mutation type compared to the reference sequence within a preset detection range as editable sequences; identifying other sequences among the filtered qualified sequences as other types of sequences; and determining gene editing efficiency based on the ratio of the number of editable sequences to the total number of reference sequences, editable sequences, and other types of sequences.

[0017] In the above-mentioned gene editing efficiency detection method, the gene editing data to be detected is gene editing data within an arbitrary defined window centered on any region on both sides of sgRNA+PAM.

[0018] The above-mentioned gene editing efficiency detection method further includes: generating a report result of gene editing efficiency.

[0019] According to another aspect of this application, a gene editing efficiency detection device is provided, comprising: a data splitting unit for splitting the gene editing data to be detected by primer and amplicon matching; a paired-end merging unit for merging paired-end reads of the split data based on a dynamic minimum overlap limit; a repeat labeling unit for labeling the merged data with repeats; a data alignment unit for aligning the data with repeats and the data without paired-end read merging based on local alignment, and performing a global re-alignment on the local alignment results; an identification filtering unit for identifying editing events and filtering sequences based on the local alignment results and the global re-alignment results; and an efficiency calculation unit for calculating gene editing efficiency based on the filtered qualified sequences.

[0020] According to another aspect of this application, an electronic device is provided, comprising: a processor; and a memory storing computer program instructions that, when the processor is executed, cause the processor to perform the gene editing efficiency detection method as described above.

[0021] According to another aspect of this application, a computer-readable storage medium is provided, wherein computer program instructions are stored thereon, which, when executed by a computing device, are operable to perform the gene editing efficiency detection method as described above.

[0022] The gene editing efficiency detection method, gene editing efficiency detection device, and electronic device provided in this application can improve the detection efficiency of gene editing efficiency while maintaining the accuracy of gene editing efficiency detection by optimizing the data splitting, data merging, duplication labeling, comparison method, sequence filtering, and calculation method in the gene editing efficiency detection process. Attached Figure Description

[0023] Various other advantages and benefits of this application will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0024] Figure 1 The illustration shows a flowchart of a gene editing efficiency detection method according to an embodiment of this application;

[0025] Figure 2 The figure illustrates a consistency verification diagram of the gene editing efficiency detection method according to an embodiment of this application;

[0026] Figure 3 The figure illustrates a comparison of the analysis speeds of the gene editing efficiency detection method according to an embodiment of this application;

[0027] Figure 4 The figure illustrates a comparison of memory consumption for gene editing efficiency detection methods according to embodiments of this application;

[0028] Figure 5 The figure shows a block diagram of a gene editing efficiency detection device according to an embodiment of this application;

[0029] Figure 6 A block diagram of an electronic device according to an embodiment of this application is illustrated. Detailed Implementation

[0030] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0031] Exemplary methods

[0032] Figure 1 The illustration shows a flowchart of a gene editing efficiency detection method according to an embodiment of this application.

[0033] like Figure 1 As shown, the gene editing efficiency detection method according to the embodiments of this application includes the following steps.

[0034] Step S110 involves splitting the gene editing data to be detected based on primer and amplicon matching. Here, gene editing technology typically refers to techniques for performing operations such as knockout, replacement, and insertion of target DNA fragments. Commonly used methods include zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and regularly repeating short palindromic sequence clusters (CRISPR / Cas9). In this embodiment, gene editing primarily refers to techniques using regularly repeating short palindromic sequence clusters (CRISPR / Cas9) to perform operations such as knockout, replacement, and insertion of target DNA fragments.

[0035] As mentioned above, current mainstream gene editing detection technologies are all based on amplicon-based next-generation sequencing. Here, amplicon refers to the double-stranded nucleotide product obtained by polymerase chain reaction. Furthermore, when performing amplicon-based matching, primers are used as indexes. Here, primers mainly involve: (1) the RNA guide sequence for DNA replication synthesized by primase during DNA replication; and (2) single-stranded nucleotide fragments in in vitro artificially synthesized DNA that can bind to the template. In the embodiments of this application, primers mainly refer to single-stranded nucleotide fragments in in vitro artificially synthesized DNA that can bind to the template.

[0036] Furthermore, a template refers to a molecule whose structure serves as a model or basis for the synthesis of another molecule, such as template DNA upon which RNA is synthesized during transcription, template DNA complementary to the primer sequence during polymerase chain reaction amplification, and template RNA—messenger ribonucleic acid—that guides protein synthesis. In the embodiments of this application, the template mainly refers to template DNA complementary to the primer sequence during polymerase chain reaction amplification.

[0037] If the data splitting process only supports primer matching without misalignment, and does not support insertions and deletions, and / or read pairs in any direction, it may affect the amount of data obtained from the splitting. This is because during the sequencing or primer synthesis process, one or two base initiation misalignments may occur, as well as insertions and deletions; and read pairs are not necessarily strand-specific. Furthermore, if a simple one-to-one comparison method is used for matching, each read, i.e., the read gene sequence, must be matched with all possible primers, resulting in extremely high computational cost and slow speed.

[0038] Therefore, in this embodiment, a more flexible and efficient matching strategy is adopted for data splitting. First, an index is built based on primers, resulting in a smaller index. During matching, a hash table lookup for exact matching is performed first. Only if no exact match is found is an index comparison lookup performed, and only a portion of the read is used for matching. That is, a portion of each read is aligned to the index file built from all amplicon reads, thus providing good support for insertions and deletions, and also supporting read pairs in any direction. In other words, it allows for mismatches, insertions, deletions, and errors in the starting matching position.

[0039] Furthermore, since not all sequencing results have definitive amplicon sequences, such as those from single-end primer amplification library construction, the data splitting process described above in this embodiment provides good support for results with variable amplicon sequences or only single-end primers. Moreover, neglecting primer orientation information can easily lead to incorrect read allocation due to large insertions or deletions. Therefore, in this embodiment, the orientation of paired-end primers is fully considered in the matching results, allowing only correctly oriented products without mismatch amplification to be assigned to the corresponding amplicon.

[0040] Therefore, in the gene editing efficiency detection method according to the embodiments of this application, splitting the gene editing data to be detected by matching primers and amplicons includes: constructing an index based on primers; using a hash table to query a reference library that matches each read of the gene editing data with the amplicons to a predetermined precision; in response to a predetermined read of the gene editing data failing to match the reference library of the amplicons to a predetermined precision, performing a query matching based on the index using a portion of the predetermined read; and splitting the gene editing data to be detected into a plurality of first sequences based on the matching results.

[0041] The data splitting process described above improves the read and write speed of sequencing result files and the splitting speed by adopting an efficient read-write separation strategy. At the same time, it supports flexible single / double-end read pairing during the splitting process, allows customizable start position offsets, and allows mismatches and insertions / deletions.

[0042] Step S102 involves merging the split data into paired-end reads based on the dynamic minimum overlap limit. Here, for paired-end sequencing data, sequencing errors often occur at the ends of the reads, and these regions are generally close to the edit sites designed for the experiment. Effectively correcting these sequencing errors is crucial for evaluating the correct editing results.

[0043] In this embodiment, the paired-end merging process first estimates the amplicon sequence-specific dynamic minimum overlap limit required for merging. This is because if the dynamic minimum overlap limit is fixed for all amplicon, it is easy to favor partial perfect matches in order to achieve the minimum error rate in the terminal repeat regions. Point mutations at both ends of a perfect match will be compared as insertion mutations. If these insertion mutations are within the scope of gene editing events, they will introduce a large error into the results.

[0044] In addition, in the embodiments of this application, the dynamic minimum overlap limit is required to be longer than the repetitive sequence in the amplicon. This is to overcome the problem that some point mutations or sequencing errors in the repetitive region become false insertions under the excessively short overlap limit.

[0045] Therefore, in the gene editing efficiency detection method according to the embodiments of this application, merging the split data based on the dynamic minimum overlap lower limit includes: using amplicon sequence specificity to pre-estimate the dynamic minimum overlap lower limit for merging the paired sequences, wherein the length of the dynamic minimum overlap lower limit is greater than the length of the repeat portion in the amplicon sequence; and merging the paired sequences of the plurality of first sequences based on the dynamic minimum overlap lower limit to obtain a plurality of second sequences.

[0046] In other words, the above-mentioned paired-end read merging process adopts a merging method based on a set of fixed-length (e.g., length k) nucleotide strings (k-mer) and overlaps. At the same time, a priori calculation is performed on the dynamic minimum overlap lower limit length required for merging to prevent false positive insertions when merging repetitive regions with fixed overlap lengths.

[0047] Step S130 involves marking duplicates in the merged data. Specifically, this can be achieved by concatenating the two read segments and then sorting the strings to remove duplicates. However, this method doesn't consider direction, treating inversely complementary sequences as different amplicon sequences, and the sorting deduplication algorithm is computationally intensive and memory-intensive. On the other hand, a hash algorithm can be used for duplicate marking, which has lower computational and memory overhead. Furthermore, considering the directionality issue, inversely complementary sequences can all be marked as duplicates; and if there are single-cell labeled sequences, single-cell level duplicate marking will also be considered.

[0048] Therefore, in the gene editing efficiency detection method according to the embodiments of this application, the duplicate labeling of the merged data includes: using a hash algorithm to repeatedly label the plurality of second sequences to obtain a plurality of third sequences.

[0049] Furthermore, in the above-mentioned gene editing efficiency detection method, using a hash algorithm to repeatedly label the plurality of second sequences to obtain a plurality of third sequences includes: in response to the plurality of second sequences having directional sequences, marking reverse complementary sequences as repeats; and / or, in response to the plurality of second sequences containing single-cell labeled sequences, repeating the sequences at the single-cell level.

[0050] Thus, the repetition marking process according to the embodiments of this application, by employing a hash-based algorithm, can achieve faster speed. On the other hand, the repetition marking process according to the embodiments of this application also considers sequence directionality and other possible information, such as cell marker sequences, for single-cell level parsing. Therefore, after marking repetitions, only unique sequences are subsequently calculated, and the final result is calculated based on the number of repetitions, which can greatly improve computational efficiency and reduce memory usage.

[0051] Step S140 involves comparing the data after duplication labeling with the data without paired-end read merging based on local alignment, and then performing a global re-alignment on the local alignment results. Here, local alignment can support large insertions and deletions by optimizing the scoring matrix. For example, optimizing the scoring matrix can include changing the scores for matching, mismatch penalties, and misalignment penalties, changing the matrix's interpretation order and reading method, and setting matching and mismatch thresholds within the window. However, local optimization cannot optimize the alignment of insertions and deletions in duplicate regions from the perspective of the scoring matrix, nor can it filter out consecutive insertion and deletion alignments that do not conform to biological significance. Therefore, in this embodiment, a global re-alignment is further performed based on the local alignment. For example, a scoring matrix sensitive to the editing enzyme cleavage site is used, making insertions and deletions more likely to occur within the window range defined to the left and right of the cleavage site, that is, making insertions and deletions closer to the cleavage site throughout the entire sequence.

[0052] In addition, for data that was not merged into dual-end read segments in step S120 as described above, local comparison and global re-comparison are also performed here.

[0053] Therefore, in the gene editing efficiency detection method according to the embodiments of this application, global re-alignment of local comparison results includes: performing global re-alignment of the local comparison results based on the sensitivity of the editing enzyme cleavage site to obtain multiple fourth sequences.

[0054] In this way, by combining local and global alignment algorithms, a scoring strategy that supports large insertions and deletions is adopted on the one hand, and a re-alignment strategy that favors the activity of editing enzymes on a global scale is adopted on the other hand. This not only supports large insertions and deletions, but also ensures that all insertions and deletions reflect the activity of editing enzymes as much as possible.

[0055] Step S150 involves identifying editing events and filtering sequences based on local comparison results and global repetition comparison results. In this embodiment, to ensure the reliability of the evaluation result data, multiple possible filtering criteria are set, including:

[0056] 1) Filtering of mispriming alignment results (here, mispriming refers to the PCR primer binding to the template at the wrong position in the polymerase chain reaction, causing the DNA polymerase to produce incorrect DNA amplification products): The alignment results are graded. If there is no perfect match of more than 60 bp, or if there is no perfect match of at least 30 bp and the error rate of the matching part is less than 20%, then strict end matching detection is performed. Specifically, the primer end must be able to match at least 30 bases. If it is less than 30 bases, it is considered an unreliable sequence and discarded.

[0057] 2) Alignment results with consecutive insertions and deletions: If there are insertions and deletions with a short interval between matching sequences, it is considered a non-editing event caused by mispriming. There are two specific criteria: First, if the length of both the insertion and deletion is greater than 9 bp, and the length of the interval between the aligned sequences is less than 6 bp or the error rate is higher than 20%; second, if either the insertion or deletion is greater than 29 bp, and the length of the interval between the aligned sequences is less than 6 bp or the error rate is higher than 20%. If either criterion is met, the sequence is discarded.

[0058] 3) Cluster all sequences according to two dimensions: alignment score and number of variants. If it is more appropriate to cluster into three categories, then filter out the sequence with the lower quality and the most variants as low-quality sequences caused by sequencing errors.

[0059] 4) Background Variant Filtering: If a control (background) sample result (generated BAM result) is provided, variants with at least 3 supports that also exist in the background will be filtered out as background (the threshold for the number of supports in the background can be customized). BAM is an equivalent of Sequence Alignment Map File Format (SAM), containing the same information, but BAM is in binary format, making it easier for computers to read and saving storage space for large-scale sequencing files.

[0060] 5) Hard filtering of mutation count and support count: Based on the user's pre-designed criteria, filters out mutations whose mutation count and support count are below the preset threshold.

[0061] Therefore, in the gene editing efficiency detection method according to the embodiments of this application, the editing event identification and sequence filtering based on local comparison results and global re-alignment results includes at least one of the following: determining unreliable sequences based on the number of base matches and the error rate of the matching portion in each sequence; determining unreliable sequences based on the insertions and deletions in each sequence and the intervals between matching sequences; clustering each sequence according to two dimensions: alignment score and number of mutations, and determining the sequence with the lowest quality and the most mutations as unreliable sequences; determining sequences with a support number greater than or equal to a predetermined threshold as filtered sequences based on background samples; and determining sequences with a mutation number and a support number lower than the mutation number threshold and the support number threshold, respectively, as filtered sequences based on user-defined mutation number threshold and support number threshold.

[0062] In this way, by using comparison scores and mutation counts for clustering, and by employing more efficient algorithms in implementation, such as data type conversion, replacing double-precision floating-point operations with integer operations, and reducing the number of data points actually used for calculation through classification and deduplication, more accurate filtering can be achieved. At the same time, by supporting hard filtering indicators for sequence count and mutation count, it is possible to filter out those sequences with generally poor sequencing quality that cannot be separated into poor quality sequences through clustering.

[0063] Step S160: Calculate gene editing efficiency based on the filtered qualified sequences. Here, the editing event type can include insertions and deletions, point mutations, and substitution mutations. Therefore, in this embodiment, not only are mutations within the editing range considered, but sequences are also categorized into editable, reference, and other types, making the results clearer.

[0064] Specifically, the filtered qualified sequences are divided into three categories: the first category consists of sequences without any variation compared to the provided reference sequence, denoted as the reference type; the second category consists of sequences that have undergone a preset mutation type (default is insertion or deletion) within a preset detection range (default is the sgRNA_PAM region and 5 bp to its left and right), denoted as the edit type; and the third category is other types. Correspondingly, the editing efficiency is calculated using the formula: Editing efficiency = (#edit type) / (#reference type + #edit type + #other types)

[0065] Therefore, in the gene editing efficiency detection method according to the embodiments of this application, calculating the gene editing efficiency based on the filtered qualified sequences includes: determining the filtered qualified sequences that have no variation compared to the reference sequence as reference sequences; determining the sequences that have undergone a preset mutation type compared to the reference sequence within a preset detection range as editable sequences; determining the other sequences among the filtered qualified sequences as other types of sequences; and determining the gene editing efficiency based on the ratio of the number of editable sequences to the total number of reference sequences, editable sequences, and other types of sequences.

[0066] In addition, to support a wider editing range, the gene editing efficiency detection method according to the embodiments of this application supports calculations within any defined window centered on any region on both sides of sgRNA+PAM. That is, the gene editing data to be detected is gene editing data within any defined window centered on any region on both sides of sgRNA+PAM.

[0067] Furthermore, after calculating gene editing efficiency, a report on gene editing efficiency can be generated. For example, industrial-grade output results (BAM and JSON) can be provided for easy adaptation by upstream and downstream software. Alternatively, interactive dynamic web pages can be provided for users to intuitively view the results. For instance, the dynamic web page can provide complete QC information for each step, detailed statistical charts of the editing results by location, size, and type, and statistical charts of specific editing formats.

[0068] Effect verification

[0069] Figure 2 The diagram illustrates the consistency verification of the gene editing efficiency detection method according to an embodiment of this application.

[0070] like Figure 2 As shown, the gene editing efficiency detection method according to the embodiments of this application was analyzed with the existing Amplican and CRISPRESSO / 2 methods for a certain amplicon, and the results were compared, showing good consistency.

[0071] Figure 3 The illustration shows a comparison of the analysis speeds of gene editing efficiency detection methods according to embodiments of this application.

[0072] like Figure 3 As shown, when the gene editing efficiency detection method according to the embodiments of this application is implemented in C++ software, the test results of 60 samples show that the gene editing efficiency detection method according to the embodiments of this application is 20 times faster than CRISPRESSO / 2 and 100 times faster than Amplican on average.

[0073] Figure 4The illustration shows a comparison of memory consumption for gene editing efficiency detection methods according to embodiments of this application.

[0074] like Figure 4 As shown, when the gene editing efficiency detection method according to the embodiments of this application is implemented in C++ software, the test results of 60 samples show that the memory consumption of the gene editing efficiency detection method according to the embodiments of this application is on average 1 / 2 of CRISPRESSO / 2 and 1 / 3 of Amplican.

[0075] Exemplary device

[0076] Figure 5 The illustration shows a block diagram of a gene editing efficiency detection device according to an embodiment of this application.

[0077] like Figure 5 As shown, the gene editing efficiency detection device 200 according to an embodiment of this application includes: a data splitting unit 210, used to split the gene editing data to be detected by primer and amplicon matching; a paired-end merging unit 220, used to merge paired-end reads of the split data based on a dynamic minimum overlap limit; a repeat labeling unit 230, used to label the merged data with repeats; a data alignment unit 240, used to compare the data after repeat labeling with the data without paired-end read merging based on local alignment, and to perform a global re-alignment on the local alignment results; an identification filtering unit 250, used to identify editing events and filter sequences based on local alignment results and global re-alignment results; and an efficiency calculation unit 260, used to calculate gene editing efficiency based on the filtered qualified sequences.

[0078] In one example, in the gene editing efficiency detection device 200 described above, the data splitting unit 210 is configured to: construct an index based on primers; use a hash table to query a reference library that matches each read of the gene editing data with an amplicon to a predetermined precision; in response to a predetermined read of the gene editing data failing to match the reference library of the amplicon to a predetermined precision, perform a query matching based on the index using a portion of the predetermined read; and split the gene editing data to be detected into a plurality of first sequences based on the matching results.

[0079] In one example, in the gene editing efficiency detection device 200 described above, the paired-end merging unit 220 is configured to: pre-estimate a dynamic minimum overlap lower limit for merging the paired-end sequences using amplicon sequence specificity, wherein the length of the dynamic minimum overlap lower limit is greater than the length of the repeat portion in the amplicon sequence; and to merge the paired-end sequences of the plurality of first sequences based on the dynamic minimum overlap lower limit to obtain a plurality of second sequences.

[0080] In one example, in the gene editing efficiency detection device 200 described above, the repeat labeling unit 230 is used to: use a hash algorithm to repeat the plurality of second sequences to obtain a plurality of third sequences.

[0081] In one example, in the gene editing efficiency detection device 200 described above, the repeat labeling unit 230 uses a hash algorithm to repeat the plurality of second sequences to obtain a plurality of third sequences, including: in response to the plurality of second sequences having sequences containing directionality, marking reverse complementary sequences as repeats; and / or, in response to the plurality of second sequences containing single-cell labeled sequences, repeating the sequences at the single-cell level.

[0082] In one example, in the gene editing efficiency detection device 200 described above, the data alignment unit 240 performs a global re-alignment of the local alignment results, which includes performing a global re-alignment of the local alignment results based on the sensitivity of the editing enzyme cleavage site to obtain multiple fourth sequences.

[0083] In one example, in the gene editing efficiency detection device 200 described above, the identification and filtering unit 250 performs editing event identification and sequence filtering based on local comparison results and global re-alignment results, including at least one of the following: determining unreliable sequences based on the number of base matches and the error rate of the matching portion in each sequence; determining unreliable sequences based on insertions and deletions in each sequence and the intervals between matching sequences; clustering each sequence according to two dimensions: alignment score and number of mutations, and identifying the sequence with the lowest quality and the most mutations as unreliable sequences; identifying sequences with a support number greater than or equal to a predetermined threshold as filtered sequences based on background samples; and identifying sequences with a mutation number and a support number lower than the user-defined mutation number threshold and support number threshold, respectively, as filtered sequences.

[0084] In one example, in the gene editing efficiency detection device 200 described above, the efficiency calculation unit 260 is used to: identify qualified sequences after filtering that have no variation compared to a reference sequence as reference sequences; identify sequences that have undergone a preset mutation type compared to the reference sequence within a preset detection range as editable sequences; identify other sequences among the qualified sequences after filtering as other types of sequences; and determine the gene editing efficiency based on the ratio of the number of editable sequences to the total number of reference sequences, editable sequences, and other types of sequences.

[0085] In one example, in the gene editing efficiency detection device 200 described above, the gene editing data to be detected is gene editing data within an arbitrary defined window centered on any region on either side of sgRNA+PAM.

[0086] In one example, the gene editing efficiency detection device 200 further includes a report generation unit for generating a report result on gene editing efficiency.

[0087] Here, those skilled in the art will understand that the specific functions and operations of each unit and module in the gene editing efficiency detection device 200 described above have been referenced above. Figures 1 to 4 The gene editing efficiency testing method is described in detail here, and therefore, its repeated description will be omitted.

[0088] As described above, the gene editing efficiency detection device 200 according to embodiments of this application can be implemented in various terminal devices, such as servers for gene sequencing data processing. In one example, the gene editing efficiency detection device 200 according to embodiments of this application can be integrated into the terminal device as a software module and / or a hardware module. For example, the gene editing efficiency detection device 200 can be a software module in the operating system of the terminal device, or it can be an application developed for the terminal device; of course, the gene editing efficiency detection device 200 can also be one of many hardware modules of the terminal device.

[0089] Alternatively, in another example, the gene editing efficiency detection device 200 and the terminal device can also be separate devices, and the gene editing efficiency detection device 200 can be connected to the terminal device via wired and / or wireless networks, and transmit interactive information in accordance with an agreed data format.

[0090] Exemplary electronic devices

[0091] Below, for reference Figure 6 This describes an electronic device according to embodiments of the present application.

[0092] Figure 6 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0093] like Figure 6 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0094] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0095] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the gene editing efficiency detection methods of the various embodiments of this application described above, and / or other desired functions. Various contents such as gene sequencing data and amplicon reference libraries may also be stored in the computer-readable storage medium.

[0096] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0097] For example, the input device 13 can be, for example, a keyboard, a mouse, etc.

[0098] The output device 14 can output various information to the outside, such as the calculation results of gene editing efficiency. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0099] Of course, for the sake of simplicity, Figure 6 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0100] Exemplary computer program products and computer-readable storage media

[0101] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the gene editing efficiency detection methods according to various embodiments of this application as described in the "Exemplary Methods" section of this specification.

[0102] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0103] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the gene editing efficiency detection methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0104] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0105] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0106] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0107] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0108] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0109] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for detecting gene editing efficiency, characterized in that, include: Acquire gene editing data to be tested; The gene editing data to be detected is split into multiple sequences based on primer and amplicon matching. The splitting includes: constructing an index based on primers; using a hash table to query a reference library that matches each read of the gene editing data with an amplicon to a predetermined precision; in response to a predetermined read of the gene editing data failing to match the reference library of the amplicon to a predetermined precision, performing a query matching based on the index using a portion of the predetermined read; and splitting the gene editing data to be detected into multiple first sequences based on the matching results. The split first sequences are combined with paired-end reads, the paired-end read merging comprising: pre-estimating a dynamic minimum overlap lower limit for the paired-end read merging using amplicon sequence specificity, wherein the length of the dynamic minimum overlap lower limit is greater than the length of the repeat portion in the amplicon sequence; and merging the paired-end sequences of the multiple first sequences based on the dynamic minimum overlap lower limit to obtain multiple second sequences. Repeated marking is applied to the merged second sequences; The local comparison is used to compare the data after duplicate marking with the data without double-ended segment merging, and the local comparison results are then re-compared globally. Editing event identification and sequence filtering are performed based on local and global re-alignment results; and Gene editing efficiency is calculated based on the filtered qualified sequences.

2. The gene editing efficiency detection method as described in claim 1, characterized in that, Repeat marking for multiple merged second sequences includes: A hash algorithm is used to repeatedly mark the multiple second sequences to obtain multiple third sequences.

3. The gene editing efficiency detection method as described in claim 2, characterized in that, Using a hash algorithm to repeatedly mark the plurality of second sequences to obtain a plurality of third sequences includes: In response to the plurality of second sequences having a directional sequence, the reverse complementary sequence is marked as a repeat; and / or In response to the plurality of second sequences containing single-cell marker sequences, the sequences are repeatedly labeled at the single-cell level.

4. The gene editing efficiency detection method as described in claim 1, characterized in that, Performing a global re-alignment of the local alignment results includes: The local alignment results are then subjected to global realignment based on the sensitivity of the editing enzyme cleavage site to obtain multiple fourth sequences.

5. The gene editing efficiency detection method as described in claim 1, characterized in that, Editing event identification and sequence filtering based on local alignment results and global re-alignment results include at least one of the following: Unreliable sequences are identified based on the number of base matches and the error rate of the matching portion in each sequence. Unreliable sequences are identified based on the insertions and deletions in each sequence and the intervals between matching sequences. For each sequence, clustering is performed according to two dimensions: alignment score and number of mutations, and the sequence with the lowest quality and the largest number of mutations is identified as an unreliable sequence. Sequences with a support number greater than or equal to a predetermined threshold are identified as filter sequences based on background samples; and Sequences with a mutation count and a support count lower than the user-defined mutation count threshold and support count threshold, respectively, are identified as filtered sequences.

6. The gene editing efficiency detection method as described in claim 1, characterized in that, Gene editing efficiency is calculated based on the filtered qualified sequences, including: The qualified sequences that show no variation compared to the reference sequence are identified as reference sequences; Sequences that have undergone a preset mutation type compared to the reference sequence within a preset detection range are identified as edited sequences; Other sequences in the filtered qualified sequences are identified as other types of sequences; and Gene editing efficiency is determined by the ratio of the number of edited sequences to the total number of reference sequences, edited sequences, and other types of sequences.

7. The gene editing efficiency detection method as described in claim 1, characterized in that, The gene editing data to be detected is gene editing data within any defined window centered on any region on either side of sgRNA+PAM.

8. The gene editing efficiency detection method as described in claim 1, characterized in that, Further includes: Generate a report on gene editing efficiency.

9. A gene editing efficiency detection device, characterized in that, include: A data splitting unit is used to split the gene-edited data to be detected based on primer and amplicon matching. The data splitting includes: constructing an index based on primers; using a hash table to query a reference library that matches each read of the gene-edited data with an amplicon to a predetermined precision; in response to a predetermined read of the gene-edited data failing to match the reference library of the amplicon to a predetermined precision, performing a query matching based on the index using a portion of the predetermined read; and splitting the gene-edited data to be detected into multiple first sequences based on the matching results. A pairwise merging unit is used to merge pairwise reads of multiple split first sequences, wherein the pairwise read merging includes: pre-estimating a dynamic minimum overlap lower limit for the pairwise read merging using amplicon sequence specificity, wherein the length of the dynamic minimum overlap lower limit is greater than the length of the repeat portion in the amplicon sequence; and merging the multiple first sequences based on the dynamic minimum overlap lower limit to obtain multiple second sequences. A repeating labeling unit is used to repeat multiple merged second sequences; The data comparison unit is used to compare the data after repeated marking and the data without double-ended segment merging based on local comparison, and to perform global re-comparison on the local comparison results; The identification and filtering unit is used for editing event identification and sequence filtering based on local alignment results and global re-alignment results; and The efficiency calculation unit is used to calculate gene editing efficiency based on the filtered qualified sequences.

10. An electronic device, characterized in that, include: processor; as well as A memory storing computer program instructions that, when the processor is run, cause the processor to execute the gene editing efficiency detection method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a computing device, are operable to perform the gene editing efficiency detection method as described in any one of claims 1-8.

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