Methods, systems, devices, and media for assembling third-generation sequencing data based on adaptive sampling
Patent Information
- Application Number
- CN202610977019.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-02
- Publication Date
- 2026-09-22
- Estimated Expiration
- 2046-07-02
AI Technical Summary
[0004]然而，Flye软件进行组装也存在一些困难
本申请的方法、系统、设备和介质基于自适应的策略，既能组装小质粒，也能组装大质粒，大大提高了质粒的应用范围。
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Figure CN122493964B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of gene sequencing data processing technology, and in particular to a method, system, device and medium for assembling third-generation sequencing data based on adaptive sampling. Background Technology
[0002] With the rapid development of third-generation sequencing technology, it has been increasingly widely used in recent years. Due to its ultra-long read length, it is particularly suitable for the complete assembly of circular plasmids, which not only greatly improves the plasmid sequencing throughput of traditional first-generation sequencing, but also significantly reduces sequencing costs.
[0003] There are currently several software programs available for assembling long-read sequencing data, such as Canu, Flye, Miniasm / Minipolish, and NECAT. Among them, Flye is the most widely used, and its assembly accuracy and integrity are relatively high.
[0004] However, there are some difficulties in assembling with Flye software. First, Flye software has strict requirements on the depth of sample data, with the optimal range being approximately 30 to 100X. Too high or too low a depth will directly lead to assembly failure. Second, if the same plasmid sample is sampled multiple times and assembled using Flye, the assembly results may vary greatly: some may assemble into multiple plasmids, some may successfully assemble into one plasmid, and some may fail completely.
[0005] Therefore, it is not feasible to directly assemble long-read sequencing data of plasmids using Flye software, and a complete assembly process is urgently needed in this field. Summary of the Invention
[0006] To solve at least one of the above-mentioned technical problems, the technical solution adopted in this application is as follows.
[0007] The first aspect of this application provides a method for assembling third-generation sequencing data based on adaptive sampling, comprising the following steps: Based on the third-generation sequencing data, the average read length (LR) was obtained. First sampling assembly: The number of samples N1 for the first sampling assembly is determined based on the preset plasmid size LP, target depth D1 and average read length LR, and sampling is performed. The sampled data is then assembled using third-generation sequencing assembly software. No. i Second sampling assembly: Determine the second sampling assembly based on the results of the first sampling assembly. i Number of samples in the second sampling: If the first sampling assembly is successful, then based on the number of samples N1 in the first sampling assembly, the assembly depth D1' of the first sampling assembly, and the... iThe target depth D of the sub-sampling assembly i Determine the number N of samples to be assembled in the i-th sampling. i N i Calculate using the following formula and round up: N i =N1 / D1'*D i ; If the first sampling assembly fails, then based on the number of samples assembled in the first sampling assembly and the number of samples assembled in the second sampling assembly... i The target proportion R of the second sampling assembly i Determine the first i The number of sample items N in the sub-sampling assembly i N i Calculate using the following formula and round up: N i =N1*R i , Among them, 2≦ i ≦ j And it is an integer. j ≧2, D1~D j R is an integer value selected from 20 to 500. i Less than 1 or greater than 1 The best assembly result is selected from multiple sampling assembly results.
[0008] In some embodiments of this application, after each sampling assembly, a step of removing assembly redundancy is also included: The assembled sequences were self-aligned; Based on the alignment results, determine whether the tail sequence matches the head sequence. If they match, remove the tail sequence.
[0009] In some embodiments of this application, the results of multiple sampling assemblies are evaluated to screen for the best assemblies. The evaluation steps include: Add a 20-200bp sequence from the beginning and end of the assembled sequence to obtain the circle sequence; The original reads are compared with the circle sequence, and the depth of each position in the circle sequence is calculated. Modify the depth position so that it starts from the beginning of the assembly sequence; Calculate coverage and ringing rate: (1) Calculate the coverage of the original reads after the beginning, i.e., the reads coverage; (2) Calculate the depth before the head. <D C The proportion is a cyclic index, where D C =1~50, The best assembly result was selected based on the number of assembled sequences, read coverage, and circularization index.
[0010] In some embodiments of this application, the preset plasmid size LP is 5000.
[0011] In some embodiments of this application, a 20-200 bp sequence is added to the beginning of the assembled sequence to obtain a circle sequence.
[0012] In some embodiments of this application, D C The specific value is chosen based on the sample depth. If the sample depth is high, for example, >1000X, then D C A larger value can be chosen, such as 50X; if the sample depth is low, such as ~20X, then D C You can choose a smaller value, such as 3.
[0013] In some embodiments of this application, firstly, a setting is established j The target depths are sorted in ascending order. The target depth D1 for the first sampling assembly is selected as the second or later value in the sort. If the first sampling assembly is successful, the target depth closest to D1' is removed, and the remaining target depth is used as the target depth for subsequent sampling assemblies.
[0014] In some embodiments of this application, if two of the set target depths are closest to D1', then one of them can be arbitrarily selected.
[0015] In some specific embodiments of this application, the number of samplings is set to 5, and the five target depths, from smallest to largest, are 30X, 60X, 80X, 150X, and 200X. The target depth D1 for the first sampling assembly is selected as 60X. If the first sampling assembly is successful and the assembly depth D1' is closest to 60X, then the target depths D2, D3, D4, and D5 for the second to fifth sampling assembly are 30X, 80X, 150X, and 200X, respectively. Similarly, if the first sampling assembly is successful and the assembly depth D1' is closest to 30X, then the target depths D2, D3, D4, and D5 for the second to fifth sampling assembly are 60X, 80X, 150X, and 200X, respectively.
[0016] In some embodiments of this application, R i At least one part is less than 1, and another part is greater than 1. In some specific embodiments of this application, R2, R3, R4 and R5 are 0.5, 2, 0.3 and 3, respectively.
[0017] In this application, if the calculated number of sampled reads exceeds the total number of reads obtained from sequencing, the total number of reads obtained from sequencing is used as the number of sampled reads.
[0018] In some specific embodiments of this application, those skilled in the art can arbitrarily set the number of samplings, target depth, and sampling ratio, and sort them according to the calculated number of samples from smallest to largest. If the number of samples in a certain sampling exceeds the number of reads obtained from sequencing, then subsequent sampling will not be performed.
[0019] In some embodiments of this application, the third-generation sequencing assembly software is Flye.
[0020] A second aspect of this application provides a system for assembling third-generation sequencing data based on adaptive sampling, comprising the following modules: The data input module is used to obtain the third-generation sequencing data and to obtain the average read length (LR) of the third-generation sequencing data. A sampling module, connected to the data input module, is used to determine the number of sampled items and record the number of samplings, and to extract data from the data input module. If it is the first sampling, the number of sampling strips N1 assembled for the first sampling is determined according to the preset plasmid size LP, target depth D1 and the average read length LR and sampling is performed. If it is the first i The second sampling is then determined based on the results of the first sampling assembly. i Number of samples in the second sampling: If the first sampling assembly is successful, then based on the number of samples N1 in the first sampling assembly, the assembly depth D1' of the first sampling assembly, and the... i The target depth D of the sub-sampling assembly i Determine the number N of samples to be assembled in the i-th sampling. i N i Calculate using the following formula and round up: N i =N1 / D1'*D i ; If the first sampling assembly fails, then based on the number of samples assembled in the first sampling assembly and the number of samples assembled in the second sampling assembly... i The target proportion R of the second sampling assembly i Determine the first i The number of sample items N in the sub-sampling assembly i N i Calculate using the following formula and round up: N i =N1*R i , Among them, 2≦ i ≦ j And it is an integer. j ≧2, D1~D j R is an integer value selected from 20 to 500.i Less than 1 or greater than 1; An assembly module, connected to the sampling module, is used by the third-generation sequencing assembly software to assemble the data extracted by the sampling module. If it is the first sampling assembly, it is also used to return the result of whether the assembly was successful to the sampling module. If the first sampling assembly is successful, it is also used to return the assembly depth to the sampling module. An evaluation module, connected to the assembly module, is used to select the best assembly result from multiple sampled assembly results.
[0021] In some embodiments of this application, a redundancy removal module is further included between the assembly module and the evaluation module, for removing assembly redundancy based on the following steps: The assembled sequences were self-aligned; Based on the alignment results, determine whether the tail sequence matches the head sequence. If they match, remove the tail sequence.
[0022] In some embodiments of this application, the evaluation module evaluates the assembly results based on the following steps: Add a 20-200bp sequence from the beginning and end of the assembled sequence to obtain the circle sequence; The original reads are compared with the circle sequence, and the depth of each position in the circle sequence is calculated. Modify the depth position so that it starts from the beginning of the assembly sequence; Calculate coverage and ringing rate: (1) Calculate the coverage of the original reads after the beginning, i.e., the reads coverage; (2) Calculate the depth before the head. <D C The proportion is a cyclic index, where D C =1~50.
[0023] In some embodiments of this application, the optimal assembly result is selected based on the number of assembled sequences, read coverage, and circularization index. In some specific embodiments of this application, if the number of assembled sequences is 1, the read coverage is 1, and the circularization index is 0, then the assembly is optimal.
[0024] In this application, if the number of sampled lines calculated in the sampling module exceeds the total number of reads obtained from sequencing, then the total number of reads obtained from sequencing is used as the number of sampled lines.
[0025] A third aspect of this application provides an electronic device including at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods described in the first aspect of this application.
[0026] A fourth aspect of this application provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform any of the methods described in the first aspect of this application.
[0027] Compared with the prior art, this application has the following advantages: The methods, systems, devices, and media of this application are based on an adaptive strategy that can assemble both small and large plasmids, greatly expanding the application range of plasmids.
[0028] The methods, systems, devices, and media of this application comprehensively evaluate the results of multiple sampling assemblies and select the best assemblies as the final results, thus ensuring the accuracy of the assembly.
[0029] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description
[0030] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of this application are illustrated in the drawings by way of example and not limitation, in which: Figure 1 A schematic diagram of the assembly process of third-generation sequencing data based on adaptive sampling in Embodiment 1 of this application is shown; Figure 2 A schematic diagram of the process for evaluating sequencing results in Embodiment 1 of this application is shown. Detailed Implementation
[0031] Unless otherwise stated, implied from the context, or as is customary in the art, all parts and percentages in this application are based on weight, and all testing and characterization methods used are concurrent with the filing date of this application. Where applicable, any patent, patent application, or disclosure relating to this application is incorporated herein by reference in its entirety, and its equivalent patent families are also incorporated herein by reference, particularly the definitions of relevant terms in the art disclosed in such documents. If any definition of a specific term disclosed in the prior art is inconsistent with any definition provided in this application, the definition provided in this application shall prevail.
[0032] To make the technical problems, technical solutions and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments.
[0033] The following examples are used to illustrate preferred embodiments of this application. Those skilled in the art will understand that the techniques disclosed in the examples represent technologies discovered by the inventors that can be used to implement this application, and therefore can be considered preferred embodiments of this application. However, those skilled in the art should understand from this specification that many modifications can be made to the specific embodiments disclosed herein, still yielding the same or similar results, without departing from the spirit or scope of this application.
[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains, and all materials cited herein and referenced by them are incorporated herein by reference.
[0035] Those skilled in the art will recognize, or can learn through routine experimentation, many equivalents of the specific embodiments of the invention described herein. These equivalents will be included in the claims.
[0036] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the instruments and equipment used in the following examples are all conventional laboratory instruments and equipment; unless otherwise specified, the experimental materials used in the following examples were all purchased from conventional biochemical reagent stores.
[0037] Example 1: A method for assembling long-read sequencing data based on adaptive sampling The general procedure for assembling third-generation sequencing data using Flye is to extract a quantitative number of reads multiple times (e.g., 5 times), perform multiple sampling assemblies, and finally select a successfully assembled result.
[0038] However, if the plasmid is large and the sequencing depth is low, extracting a fixed number of reads each time can lead to assembly failure.
[0039] To address this, the inventors proposed an adaptive sampling method for third-generation sequencing data, compatible with all plasmid sizes. (Reference) Figure 1 The specific steps are as follows: Before assembly, first determine the number of samples. j ( j =2~50) and the corresponding number of target depths (integer values in 20X~500X), sort the target depths from smallest to largest.
[0040] 1. First sampling assembly The number of samples N1 for the first sampling assembly is calculated based on the plasmid size (LP), the target depth of the first sampling assembly (D1), and the average read length LR (calculated based on the average read length in the sequencing data). N1 = LP * D1 / LR.
[0041] To accommodate large plasmids, the target depth for the first sampling assembly can be selected as the second or later target depth in the sequence, for example, by setting the number of samplings. j The target depths for the five samplings are 30X, 60X, 80X, 150X, and 200X, respectively. The target depth of the second-ranked sample is selected during the first sampling assembly, which is 60X.
[0042] Next, the sequencing data were sampled according to the determined number of sample strips N1 for the first sampling assembly, and then assembled using Flye software.
[0043] 2. Determine the number of subsequent samples. Based on the results of the first sampling assembly, the set number of sampling times, and the target depth or target proportion, the number of sampling items for subsequent sampling is automatically determined: (1) The first sampling assembly was successful. After successful assembly, Flye will output the assembly depth (D1') of the first sample assembly. First, remove the target depths closest to D1' from the set target depths; the remaining target depths are used as the target depths for subsequent sampling. Similarly, setting 5 samples with target depths of 30X, 60X, 80X, 150X, and 200X, and selecting a target depth of 60X for the first sample assembly, assuming the first sample assembly depth D1' is 34X, which is closest to the target depth of 30X, then the target depths D2, D3, D4, and D5 for the subsequent 4 samples are 60X, 80X, 150X, and 200X, respectively.
[0044] In subsequent sampling, the number of samples (N1) from the first sampling, the assembly depth (D1') from the first sampling assembly, and the... i The target depth (D) of the second sampling i Determine the subsequent... i Number of samples (N) in the sampling i ), N i =N1 / D1'*D i ,in, i =2~ j .
[0045] (2) The first sampling assembly failed. In this situation, the number of subsequent sampled items is calculated based on the number of already sampled items (N1), the set number of sampling attempts, and the target proportion. i =N1*Ri , where N i For the first i The number of samples in the sub-sample, R i For the first i The target proportion of the second sampling. i =2~ j .
[0046] When using Flye for assembly, both excessive and insufficient data can lead to assembly failure. Since the first sampling assembly fails, it's impossible to determine whether the failure is due to too much or too little data. Therefore, the target proportions for the subsequent four sampling assemblies need to vary; that is, the number of samples in each subsequent assembly needs to be partially greater than the number in the first assembly, and partially less, i.e., R0. i Less than 1 or greater than 1.
[0047] 3. Multiple sampling assembly The sequencing data is sampled multiple times according to the determined "sampling number," and assembled using Flye software after each sampling. Regardless of the sampling method, if the calculated sampling number exceeds the total number of reads obtained from sequencing, the total number of reads obtained from sequencing is used as the sampling number.
[0048] 4. Eliminate assembly redundancy Since plasmids are usually circular sequences, the Flye software may assemble redundant sequences, even two or three copies. In this case, it is necessary to remove the redundant parts of the assembled sequence. The steps are as follows: (1) Perform self-alignment on the assembled sequence; (2) Based on the alignment results, determine whether the tail sequence is aligned with the head sequence (at least 150 bp), that is, whether the circular sequence overlaps head to tail. If they overlap, it means that the overlapping part is a redundant sequence, and the overlapping tail sequence is removed.
[0049] 5. Evaluation of assembly results.
[0050] Because the assembly was performed multiple times, multiple assembly results were obtained. In order to select the best assembly result, the inventor evaluated each assembly result.
[0051] The evaluation of the assembly results mainly involves analyzing the coverage and ringing of the assembly results. The detailed steps are as follows: (1) Add a tail sequence (20bp-200bp, 100bp in subsequent evaluations) to the beginning of the assembled sequence to obtain the circle sequence; (2) Align the original reads with the circle sequence; (3) Calculate the depth at each position in the circle sequence; (4) Modify the depth position so that it starts from the beginning of the assembly sequence (0bp); (5) Calculate coverage and ringing rate: i. Calculate the coverage of reads after the beginning (0bp), i.e., read coverage; ii. The depth is almost zero before calculating 0bp ( <D C The proportion of ) is the cyclic index, where D C =1~50, D C The specific value is chosen based on the sample depth. If the sample depth is high, for example, >1000X, then D C A larger value can be chosen, such as 50X; if the sample depth is low, such as ~20X, then D C You can choose a smaller value, such as 3X.
[0052] 6. Select the best assembly result The best assembly result is selected according to the following priority criteria: (1) The number of assembly strips should be minimized (normally it should be 1 strip, 1 is optimal); (2) The cyclization index is the lowest (between 0 and 1, with 0 being the best); (3) The highest read coverage (between 0 and 1, with 1 being the best) Specifically, from multiple assembly results, the best (1) is selected first, then the best (2) is selected from the selected results, and then the best (3) is selected from the selected results. If all three indicators of multiple assembly results are optimal, then the sequence with the middle sequence length is selected.
[0053] Example 2: Application of the method established in Example 1 in 8 large plasmids Sequencing data for eight plasmids were obtained using a carbon-3 sequencing platform. After low-quality filtering, the data are shown in Table 1. Table 1: Sequencing data of 8 plasmids .
[0054] First, determine the number of samplings and the target depth. In this embodiment, the number of samplings is set to 5, and the target depths are set from smallest to largest as 30X, 60X, 80X, 150X, and 200X.
[0055] 1. First sampling assembly Since the plasmid size is unknown, the number of samples cannot be determined based on the plasmid size. Plasmid sizes typically range from 1kb to 1000kb, with commonly used laboratory plasmids ranging from 2kb to 10kb. Therefore, we assume the plasmid size LP is 5kb. Furthermore, to ensure compatibility with larger plasmids, the first sampling assembly will use the second target depth, 60X.
[0056] Taking AC17 as an example, the number of samples N1 in the first sampling assembly is calculated as follows: N1=LP*D1 / LR=5000*60 / 5494.5=54.6, which is rounded up to 55.
[0057] The number of sampling items for the first sampling assembly of each sample is shown in Table 2.
[0058] Table 2: Number of sampling rows assembled in the first sampling for each sample .
[0059] Sequencing data for each plasmid is sampled according to a predetermined number of sampling lines, and then assembled using Flye.
[0060] After assembly, redundancy removal and result evaluation were performed according to the method in Example 1 (when evaluating the cyclone index, D...). C =3X), the results are shown in Table 3.
[0061] Table 3: Assembly results after the first sampling of each sample .
[0062] This shows that all samples were successfully assembled.
[0063] 2. Subsequent sampling and assembly After the first sampling and successful assembly, first determine which of the five target depths D1' is closest to, thus determining the target depths for the second to fifth sampling assemblies as the other four target depths; then, based on the number of samplings (N1) in the first sampling assembly, the assembly depth (D1') in the first sampling assembly, and the target depth (D... i , i =2~5) Calculate the number of sampled items (N) for the 2nd to 5th sampling assemblies. i Specifically, the calculation is performed according to the following formula and then rounded up: N i =N1 / D1'*D i .
[0064] Taking sample AC17 as an example, D1' is 12.84X, which is closest to 30X. Therefore, the target depths D2, D3, D4, and D5 for the 2nd to 5th sampling assemblies are 60X, 80X, 150X, and 200X, respectively. The number of sampling lines is calculated as follows: N2=N1 / D1'*D2=55 / 12.84*60=257; N3=N1 / D1'*D3=55 / 12.84*80=343; N4=N1 / D1'*D4=55 / 12.84*150=643; N5=N1 / D1'*D5=55 / 12.84*200=857.
[0065] The calculations were performed on the other samples in turn, and the results are shown in Table 4.
[0066] Table 4: Number of sampling rows assembled for each sample in the 2nd to 5th sampling iterations .
[0067] After each sampling assembly, redundancy removal and result evaluation were performed according to the method in Example 1 (when evaluating the cyclization index, D...). C =3X), the results are shown in Tables 5 to 8.
[0068] Table 5: Assembly results after the second sampling of each sample .
[0069] Table 6: Assembly results after the third sampling of each sample .
[0070] Table 7: Assembly results after the 4th sampling for each sample .
[0071] Table 8: Assembly results after the 5th sampling for each sample .
[0072] Based on the evaluation data of multiple sampling assembly results, the best assembly result was selected and polished, as shown in Table 9.
[0073] Table 9: Final assembly results for each sample .
[0074] Example 3: Application of the method established in Example 1 in 21 small plasmids Sequencing data for 21 plasmids were obtained using a carbon-3 sequencing platform. After low-quality filtering, the data are shown in Table 10. Table 10: Sequencing data of 21 plasmids .
[0075] Similarly, the number of samplings and the target depth are determined first. As in Example 1, the number of samplings is set to 5, and the target depths are set from largest to smallest as 30X, 60X, 80X, 150X and 200X.
[0076] 1. First sampling assembly Similar to Example 1, it is assumed that the plasmid size LP is 5kb and that D1 is set to 60X.
[0077] Taking BC01 as an example, the number of samples N1 for the first sampling assembly is calculated as follows: N1 = LP * D1 / LR = 5000 * 60 / 2784 = 108.
[0078] The number of sampling items for the first sampling assembly of each sample is shown in Table 11.
[0079] Table 11: Number of sampling rows assembled in the first sampling of each sample .
[0080] Sequencing data for each plasmid is sampled according to a predetermined number of sampling lines, and then assembled using Flye.
[0081] After assembly, redundancy removal and result evaluation were performed according to the method in Example 1 (when evaluating the cyclone index, D...). C =3X), the results are shown in Table 12.
[0082] Table 12: Assembly results after the first sampling of each sample .
[0083] This shows that, except for sample BC17, all samples were successfully assembled.
[0084] 2. Subsequent sampling and assembly For the samples successfully assembled in the first sampling, first determine which of the five target depths D1' is closest to, thus determining the target depths for the second to fifth sampling assemblies as the other four target depths; then, based on the number of samples (N1) from the first sampling assembly, the assembly depth (D1') from the first sampling assembly, and the target depth (D... i , i =2~5) Calculate the number of sampled items (N) for the 2nd to 5th sampling assemblies. i Specifically, calculate according to the following formula and round up: N i =N1 / D1'*D i .
[0085] Taking sample BC01 as an example, D1' is 65.87X, which is closest to 60X. Therefore, the target depths D2, D3, D4, and D5 for the 2nd to 5th sampling assemblies are 30X, 80X, 150X, and 200X, respectively. The number of sampling lines is calculated as follows: N2=N1 / D1'*D2=108 / 65.87*30=49; N3=N1 / D1'*D3=108 / 65.87*80=131; N4=N1 / D1'*D4=108 / 65.87*150=246; N5=N1 / D1'*D5=108 / 65.87*200=328.
[0086] The calculations were performed sequentially on the other samples that were successfully assembled in the first sampling, and the results are shown in Table 13.
[0087] Table 13: Number of sampling rows assembled for each sample in the 2nd to 5th sampling iterations .
[0088] For sample BC17, which failed in the first sampling assembly, based on the number of sampling items in the first sampling assembly and the... i The target proportion of the sub-sample assembly (R) i , i =2~5) Determine the first i Number of sample items (N) in a sub-sample assembly i Furthermore, calculate and round up according to the following formula: N i =N1*R i .
[0089] In this embodiment, the sampling proportions R2, R3, R4, and R5 for the 2nd to 5th samplings are 0.5, 2, 0.3, and 3, respectively. Therefore, for sample BC17: N2 = N1 * R2 = 105 * 0.5 = 53; N3 = N1 * R3 = 105 * 2 = 210; N4 = N1 * R4 = 105 * 0.3 = 31.5, rounded up to 32; N5=N1 / *D5=105*3=315.
[0090] Sampling was performed according to the determined number of samples, and the samples were assembled separately. After each sampling, redundancy removal and result evaluation were performed according to the method in Example 1 (when evaluating the cycloidization index, D...). C =3X), the results are shown in Tables 14 to 17.
[0091] Table 14: Assembly results after the second sampling of each sample .
[0092] Table 15: Assembly results of each sample after the third sampling .
[0093] Table 16: Assembly results after the 4th sampling for each sample .
[0094] Table 17: Assembly results of each sample after the 5th sampling .
[0095] Based on the evaluation data of multiple sampling assembly results, the best assembly result was selected and polished, as shown in Table 18.
[0096] Table 18: Final assembly results for each sample .
[0097] Therefore, it can be seen that the method of Example 1 can also be used to assemble small plasmids successfully.
[0098] Furthermore, it should be understood that after reading the foregoing content of this application, those skilled in the art can make various alterations or modifications to this application, and these equivalent forms also fall within the scope defined by the appended claims.
Claims
1. A method for assembling third-generation sequencing data based on adaptive sampling, characterized in that, Includes the following steps: Based on the third-generation sequencing data, the average read length (LR) was obtained. First sampling assembly: The number of samples N1 for the first sampling assembly is determined based on the preset plasmid size LP, the target depth D1 of the first sampling assembly, and the average read length LR, and sampling is performed. The sampled data is then assembled using third-generation sequencing assembly software. No. i Second sampling assembly: Determine the second sampling assembly based on the results of the first sampling assembly. i Number of samples in the second sampling: If the first sampling assembly is successful, then based on the number of samples N1 in the first sampling assembly, the assembly depth D1' of the first sampling assembly, and the... i The target depth D of the sub-sampling assembly i Determine the first i The number of sample items N in the sub-sampling assembly i N i Calculate using the following formula and round up: N i =N1 / D1'*D i ; If the first sampling assembly fails, then based on the number of samples assembled in the first sampling assembly and the number of samples assembled in the second sampling assembly... i The target proportion R of the second sampling assembly i Determine the first i The number of sample items N in the sub-sampling assembly i N i Calculate using the following formula and round up: N i =N1*R i , Among them, 2≦ i ≦ j And it is an integer. j ≧2, D1~D j R is an integer value selected from 20 to 500. i Less than 1 or greater than 1 The results of multiple sampling assemblies are evaluated to select the best assemblies.
2. The method according to claim 1, characterized in that, After each sampling assembly, the process also includes a step to remove assembly redundancy: The assembled sequences were self-aligned; Based on the alignment results, determine whether the tail sequence matches the head sequence. If they match, remove the tail sequence.
3. The method according to claim 2, characterized in that, The evaluation steps include: Add a 20-200bp sequence from the beginning and end of the assembled sequence to obtain the circle sequence; The original reads are compared with the circle sequence, and the depth of each position in the circle sequence is calculated. Modify the depth position so that it starts from the beginning of the assembly sequence; Calculate coverage and ringing rate: (1) Calculate the coverage of the original reads after the beginning, i.e., the reads coverage; (2) Calculate the depth before the head. <D C The proportion is a cyclic index, where D C =1~50, The best assembly result was selected based on the number of assembled sequences, read coverage, and circularization index.
4. The method according to any one of claims 1 to 3, characterized in that, The preset plasmid size LP is 5000.
5. The method according to any one of claims 1 to 3, characterized in that, First, set j The target depths are sorted in ascending order. The target depth D1 of the first sampling assembly is selected as the second or later value in the sort. If the first sampling assembly is successful, the target depth closest to D1' is removed, and the remaining target depth is used as the target depth for subsequent sampling.
6. The method according to any one of claims 1 to 3, characterized in that, The third-generation sequencing assembly software is Flye.
7. A system for assembling third-generation sequencing data based on adaptive sampling, characterized in that, Includes the following modules: The data input module is used to obtain the third-generation sequencing data and to obtain the average read length (LR) of the third-generation sequencing data. A sampling module, connected to the data input module, is used to determine the number of sampled items and record the number of samplings, and to extract data from the data input module. If it is the first sampling, the number of sampling strips N1 for the first sampling assembly is determined according to the preset plasmid size LP, the target depth D1 of the first sampling assembly, and the average read length LR, and sampling is performed. If it is the first i The second sampling is then determined based on the results of the first sampling assembly. i Number of samples in the second sampling: If the first sampling assembly is successful, then based on the number of samples N1 in the first sampling assembly, the assembly depth D1' of the first sampling assembly, and the... i The target depth D of the sub-sampling assembly i Determine the number N of samples to be assembled in the i-th sampling. i N i =N1 / D1'*D i ; If the first sampling assembly fails, then based on the number of samples assembled in the first sampling assembly and the number of samples assembled in the second sampling assembly... i The target proportion R of the second sampling assembly i Determine the first i The number of sample items N in the sub-sampling assembly i N i =N1*R i , Among them, 2≦ i ≦ j And it is an integer. j ≧2, D1~D j R is an integer value selected from 20 to 500. i Less than 1 or greater than 1; An assembly module, connected to the sampling module, is used to assemble the data extracted by the sampling module using third-generation sequencing assembly software. If it is the first sampling assembly, it is also used to return the result of whether the assembly is successful to the sampling module. If the first sampling assembly is successful, it is also used to return the assembly depth to the sampling module. An evaluation module, connected to the assembly module, is used to evaluate the results of multiple sampling assembly processes in order to select the best assembly result.
8. The system according to claim 7, characterized in that, Between the assembly module and the evaluation module, a redundancy removal module is also included, used to remove assembly redundancy based on the following steps: The assembled sequences were self-aligned; Based on the alignment results, determine whether the tail sequence matches the head sequence. If they match, remove the tail sequence.
9. 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 to enable the at least one processor to perform the method of any one of claims 1-6.
10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.