A quality evaluation method and system for DNA information storage biochemical experiments
By employing single and combined quality assessment methods, the lack of a unified quality expression in biochemical experiments for DNA information storage was addressed. This approach provides assessments of coverage depth, positional error rate, single base and k-mer motif error preferences, calculates coverage and error quality fractions, and achieves a unified quality assessment without the need for experience-based judgment, thereby improving the applicability of experimental data.
Patent Information
- Application Number
- CN202611156895.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-31
- Publication Date
- 2026-11-06
AI Technical Summary
Current technologies lack a unified expression of experimental quality, and cover quality and error quality are observed separately. Researchers need to rely on experience to judge the applicability of DNA information storage biochemical experiments, making it difficult to provide unified quality assessment results without human experience.
We employed single-item quality assessment and joint quality assessment methods to evaluate coverage depth, positional error rate, single-base error preference, and k-mer motif error preference by acquiring input data. We then combined the normalized amplitude limiting function and weighting coefficients to calculate coverage quality fractions and error quality fractions, which were mapped to standardized quality levels to determine the applicability of the experimental data.
It enables the provision of standardized experimental quality assessment results without relying on human experience, avoiding misjudgments, directly reflecting the true usability of DNA information storage experiments, and facilitating researchers' decision-making.
Smart Images
Figure CN123053252A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of quality detection technology for biochemical experiments in DNA information storage, specifically relating to a quality assessment method and system for biochemical experiments in DNA information storage. Background Technology
[0002] DNA information storage typically involves several stages, including information encoding, DNA sequence synthesis, amplification and preservation, sequencing and reading, and decoding and recovery. Problems such as insufficient coverage, uneven coverage, insertions, deletions, and substitutions may occur during synthesis, amplification, preservation, and sequencing. Therefore, after the experiment, it is necessary to determine whether the batch of data has the quality foundation for downstream decoding.
[0003] Existing related technologies can be mainly divided into two categories: one category focuses on DNA storage encoding, error correction code design, and decoding recovery; the other category focuses on quality control of sequencing data or oligonucleotide libraries. For example, quality control tools such as SOLQC can perform statistical analysis and visualization of oligonucleotide libraries or sequencing results, helping researchers to observe local quality problems.
[0004] However, the aforementioned technologies typically lack a unified expression of experimental quality for biochemical experiments involving DNA information storage; moreover, coverage quality and error quality are often observed separately, and researchers still need to rely on experience to judge whether experimental data is suitable for decoding, whether the experiment needs to be repeated, or whether experimental conditions need to be adjusted. Summary of the Invention
[0005] The purpose of this invention is to provide a quality assessment method and system for biochemical experiments on DNA information storage, in order to solve the technical problem of how to provide both individual quality assessment results and joint quality assessment results for the results of biochemical experiments on DNA information storage without relying on human experience, so as to determine whether the batch of experimental data is suitable for downstream decoding.
[0006] To achieve the objectives of this invention, the technical solution provided by this invention is as follows: First aspect This invention provides a quality assessment method for biochemical experiments on DNA information storage, comprising the following steps: Step S1: Obtain input data: reference oligonucleotide sequence set, sequencing read sequence set; compare the sequencing read sequence with the reference oligonucleotide sequence, and count the sequencing coverage data of each reference oligonucleotide, as well as the distribution data of base insertion, deletion, and substitution errors in sequence position, base type, and k-mer motif dimension; Step S2: Perform a single quality assessment, which completes four assessment logics in parallel and outputs four independent quality characterization results: coverage depth distribution, position error rate distribution, single base error preference, and k-mer motif error preference; The evaluation criteria include: Coverage depth assessment: statistically analyzing the distribution of read counts across all reference oligonucleotide sequencing to determine total sequencing coverage and uniformity; Position error rate assessment: statistically analyzing the probability of insertion, deletion, and substitution errors by base position to identify systematically high-error regions at fixed sequence positions; Single-base error preference assessment: statistically analyzing the frequency distribution of various errors occurring in A, T, C, and G bases to characterize systematic error bias at the base level; and k-mer motif error preference assessment: identifying systematic sequencing errors caused by local sequence context that are not statistically identifiable by single bases, used to characterize sequences of length... The relationship between the context of consecutive DNA base segments and the probability of base errors; Step S3: Perform a joint quality assessment based on the output results of the individual quality assessments; Step S4: Output the evaluation results.
[0007] Further, step S3 includes the following sub-steps: Step S301: Apply a normalized limiting function For the index constraints, the normalized limiting function satisfies: Output 0 at time. Output x at that time. Output 1 when the time is right; Step S302: Calculate coverage sufficiency (Average coverage depth / Target coverage depth), Coverage completeness (Percentage of 1-zero covered oligonucleotides); According to the formula Calculate the coverage mass sub-unit ; in, , , When calculating the coverage quality sub-component, an additional coverage depth dispersion index can be introduced into the calculation, and the weighting coefficients can be adjusted accordingly. , ; Step S303: According to the formula Calculation error mass fraction ; In the formula, The overall error level E is a globally uniform total error rate. Alternatively, weights can be assigned to each of the base insertion, deletion, and substitution errors, and then a weighted calculation can be performed. Preset error tolerance threshold; Step S304: According to Calculate joint score The joint score is mapped to a standardized quality level, which is divided into at least four levels: excellent, good, medium, and poor. Based on the quality level, it is determined whether the DNA information storage biochemical experimental data is suitable for downstream decoding.
[0008] Furthermore, step S2 includes a length configuration sub-step when performing k-mer motif error preference assessment: the length of the k-mer motif is adaptively set to 3, 5, or 7 based on the experimental sequence length and the total number of sequencing samples.
[0009] Furthermore, in step S303, the overall error level is... The calculation steps are as follows: Based on the experimental requirements of different DNA synthesis and sequencing platforms, weighting coefficients are customized for the three types of base errors: insertion, deletion, and substitution, and the overall error level is obtained after weighting; or a globally unified overall error level can be directly adopted.
[0010] Furthermore, it also includes a parameter preset step: before executing step S3, a preset error tolerance threshold is manually configured in advance based on the DNA information storage and decoding error tolerance requirements. .
[0011] Furthermore, the decoding and determination step in step S304 is as follows: when the quality level corresponding to the joint score is excellent or good, the batch data can be directly entered into the downstream decoding; when the quality level is medium or poor, it indicates that there is a coverage defect or base error defect, and the sequencing experiment needs to be re-conducted after adjusting the biochemical experimental conditions.
[0012] Second aspect Corresponding to the above method, the present invention provides a quality assessment system for biochemical experiments on DNA information storage, comprising the following units: The data acquisition unit is used to acquire input data: a set of reference oligonucleotide sequences and a set of sequencing read sequences; it compares the sequencing read sequences with the reference oligonucleotide sequences, calculates the sequencing coverage data for each reference oligonucleotide, and the distribution data of base insertion, deletion, and substitution errors in sequence position, base type, and k-mer motif dimension; A single quality assessment unit is used to perform a single quality assessment, which completes four assessment logics in parallel and outputs four independent quality characterization results: coverage depth distribution, position error rate distribution, single base error preference, and k-mer motif error preference. The evaluation criteria include: Coverage depth assessment: statistically analyzing the distribution of read counts across all reference oligonucleotide sequencing to determine total sequencing coverage and uniformity; Position error rate assessment: statistically analyzing the probability of insertion, deletion, and substitution errors by base position to identify systematically high-error regions at fixed sequence positions; Single-base error preference assessment: statistically analyzing the frequency distribution of various errors occurring in A, T, C, and G bases to characterize systematic error bias at the base level; and k-mer motif error preference assessment: identifying systematic sequencing errors caused by local sequence context that are not statistically identifiable by single bases, used to characterize sequences of length... The relationship between the context of consecutive DNA base segments and the probability of base errors; The joint quality assessment unit is used to perform joint quality assessments based on the output results of individual quality assessments. The result output unit is used to output the evaluation results.
[0013] Furthermore, the joint quality assessment unit is used to perform the following sub-steps: Step S301: Apply a normalized limiting function For the index constraints, the normalized limiting function satisfies: Output 0 at time. Output x at that time. Output 1 when the time is right; Step S302: Calculate coverage sufficiency (Average coverage depth / Target coverage depth), Coverage completeness (Percentage of 1-zero covered oligonucleotides); According to the formula Calculate the coverage mass sub-unit ; in, , , When calculating the coverage quality sub-component, an additional coverage depth dispersion index can be introduced into the calculation, and the weighting coefficients can be adjusted accordingly. , ; Step S303: According to the formula Calculation error mass fraction ; In the formula, The overall error level E is a globally uniform total error rate. Alternatively, weights can be assigned to each of the base insertion, deletion, and substitution errors, and then a weighted calculation can be performed. Preset error tolerance threshold; Step S304: According to Calculate joint score The joint score is mapped to a standardized quality level, which is divided into at least four levels: excellent, good, medium, and poor. Based on the quality level, it is determined whether the DNA information storage biochemical experimental data is suitable for downstream decoding.
[0014] Furthermore, the k-mer motif error preference assessment includes a length configuration sub-step: adaptively setting the length of the k-mer motif to 3, 5, or 7 based on the experimental sequence length and the total number of sequencing samples.
[0015] Furthermore, the overall error level The calculation steps are as follows: Based on the experimental requirements of different DNA synthesis and sequencing platforms, weighting coefficients are customized for the three types of base errors: insertion, deletion, and substitution, and the overall error level is obtained after weighting; or a globally unified overall error level can be directly adopted.
[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention first performs individual quality assessments, followed by a joint quality assessment. This approach can pinpoint whether the problem stems from insufficient coverage, incorrect positions, base bias, or k-mer motif bias, and provides a unified conclusion regarding experimental quality. Compared to simply presenting a few statistical results, this invention facilitates batch comparisons and downstream decoding decisions for researchers.
[0017] This invention uses both coverage quality fraction and error quality fraction to determine the joint score, which can avoid misjudgment caused by a single indicator. For example, a high coverage but a high error rate, or a low error rate but a large number of zero-coverage oligonucleotides, should not be rated as high quality. The joint score of this invention can more directly reflect the true usability of DNA information storage experiments. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of a quality assessment method for biochemical experiments on DNA information storage, provided as an embodiment of the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0020] It should be noted that the acquisition of data and collection of information in this application are legal, compliant, or obtained with the consent of the subject of the data collection.
[0021] like Figure 1As shown in this embodiment, the quality assessment method for biochemical experiments on DNA information storage includes the following steps: Step S1: Obtain input data, including a set of reference oligonucleotide sequences and a set of read sequences obtained from sequencing. By mapping the read sequences to the reference oligonucleotide sequences, the read coverage of each reference oligonucleotide is statistically analyzed, and the distribution of insertion, deletion, and substitution errors at different positions, with different bases, and under different k-mer motif conditions is statistically analyzed.
[0022] Step S2: This invention mainly includes two main stages. The first stage is a single-item quality assessment, which outputs the coverage depth distribution, position error rate distribution, single-base error preference, and k-mer motif error preference.
[0023] Coverage depth assessment is used to determine whether the number of sequencing reads obtained from different reference oligonucleotides is sufficient and uniform. Oligonucleotides with insufficient or zero coverage directly increase the risk of downstream decoding failure.
[0024] Positional error rate assessment is used to determine whether errors are concentrated at specific positions in a sequence. If certain positions consistently exhibit high error rates, it can indicate position-related risks in synthesis, sequencing, or sequence design.
[0025] Single-base error preference assessment is used to determine whether there is a significant bias in the occurrence of errors among the four bases A, T, C, and G, thereby reflecting a systematic error tendency at the base level.
[0026] The k-mer motif error preference assessment is used to statistically analyze the relationship between local sequence fragments of length k and error occurrence. This assessment can uncover contextual error patterns that are difficult to detect with single-base statistics, representing a significant improvement in the single-item quality assessment stage of this invention.
[0027] Step S3: The second stage is the joint quality assessment, which calculates the coverage quality sub-score, error quality sub-score, joint score, and quality grade based on the results of the first stage.
[0028] After obtaining the individual quality assessment results, this invention further calculates the coverage quality fraction and the error quality fraction. The coverage quality fraction comprehensively considers the average coverage depth and the proportion of zero-coverage oligonucleotides, while the error quality fraction is determined based on the overall error level or the error level weighted by error type.
[0029] To avoid intermediate results such as coverage ratios and error ratios from exceeding limits and affecting the score, this invention employs a normalized amplitude limiting function. Limitations are imposed on relevant indicators: That is, when When 0 is taken, When x is taken, Take 1 at the time.
[0030] Adequate coverage (Average coverage depth / Target coverage depth).
[0031] Completeness of coverage (Percentage of 1-zero covered oligonucleotides).
[0032] Coverage quality sub-segment ,in , , .
[0033] Error quality sub-segment ,in This represents the overall error rate or the error level weighted by error type. This indicates the error tolerance threshold.
[0034] The joint scoring formula is: ,in Indicates joint scoring. Indicates the coverage quality sub-segment, This represents the erroneous mass sub-sample.
[0035] The above joint scoring formula is used to avoid a situation where a high score for one subset masks a low score for another. A higher joint score indicates that the biochemical results of this batch of DNA storage are more suitable for downstream decoding.
[0036] The joint score is mapped to a standardized quality level, which is divided into at least four levels: excellent, good, medium, and poor. Based on the quality level, it is determined whether the DNA information storage biochemical experimental data is suitable for downstream decoding.
[0037] It should be noted that the inputs, outputs, and functions of the two evaluation stages of this invention are shown in Table 1.
[0038] Table 1
[0039] Output the evaluation results.
[0040] In addition, the present invention provides a quality assessment system for biochemical experiments on DNA information storage, comprising the following units: The data acquisition unit is used to acquire input data: a set of reference oligonucleotide sequences and a set of sequencing read sequences; it compares the sequencing read sequences with the reference oligonucleotide sequences, calculates the sequencing coverage data for each reference oligonucleotide, and the distribution data of base insertion, deletion, and substitution errors in sequence position, base type, and k-mer motif dimension; A single quality assessment unit is used to perform a single quality assessment, which completes four assessment logics in parallel and outputs four independent quality characterization results: coverage depth distribution, position error rate distribution, single base error preference, and k-mer motif error preference. The evaluation criteria include: Coverage depth assessment: statistically analyzing the distribution of read counts across all reference oligonucleotide sequencing to determine total sequencing coverage and uniformity; Position error rate assessment: statistically analyzing the probability of insertion, deletion, and substitution errors by base position to identify systematically high-error regions at fixed sequence positions; Single-base error preference assessment: statistically analyzing the frequency distribution of various errors occurring in A, T, C, and G bases to characterize systematic error bias at the base level; and k-mer motif error preference assessment: identifying systematic sequencing errors caused by local sequence context that are not statistically identifiable by single bases, used to characterize sequences of length... The relationship between the context of consecutive DNA base segments and the probability of base errors; The joint quality assessment unit is used to perform joint quality assessments based on the output results of individual quality assessments. The result output unit is used to output the evaluation results.
[0041] Finally, it should be noted that the above embodiments are merely illustrative and explanatory of the present invention, and are not intended to limit the present invention to the scope of the described embodiments. Furthermore, those skilled in the art will understand that the present invention is not limited to the above embodiments, and many more variations and modifications can be made based on the teachings of the present invention, all of which fall within the scope of protection claimed by the present invention.
Claims
1. A quality assessment method for biochemical experiments involving DNA information storage, characterized in that, Includes the following steps: Step S1: Obtain input data: reference oligonucleotide sequence set, sequencing read sequence set; The sequencing reads were compared with reference oligonucleotide sequences, and the sequencing coverage data of each reference oligonucleotide, as well as the distribution data of base insertion, deletion, and substitution errors in sequence position, base type, and k-mer motif dimension were calculated. Step S2: Perform a single quality assessment, which completes four assessment logics in parallel and outputs four independent quality characterization results: coverage depth distribution, position error rate distribution, single base error preference, and k-mer motif error preference; Among them, coverage depth assessment: statistically analyze the distribution of the number of reads of all reference oligonucleotide sequencing to determine the total sequencing coverage and uniformity; position error rate assessment: statistically analyze the probability of insertion, deletion and substitution errors by base position to identify systematic high error regions at fixed positions in the sequence. Single-base error preference assessment: Statistical analysis of the frequency distribution of various errors in four base classes (A, T, C, G) to characterize systematic error bias at the base level; k-mer motif error preference assessment: Identifying systematic sequencing errors caused by local sequence context that are statistically unrecognizable by single bases, used to characterize sequences of length... The relationship between the context of consecutive DNA base segments and the probability of base errors; Step S3: Perform a joint quality assessment based on the output results of the individual quality assessments; Step S4: Output the evaluation results.
2. The quality assessment method for biochemical experiments oriented towards DNA information storage according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S301: Apply a normalized limiting function For the index constraints, the normalized limiting function satisfies: Output 0 at time. Output x at that time. Output 1 when the time is right; Step S302: Calculate coverage sufficiency (Average coverage depth / Target coverage depth), Coverage completeness (Percentage of 1-zero covered oligonucleotides); According to the formula Calculate the coverage mass sub-unit ; in, , , When calculating the coverage quality subset, an additional coverage depth dispersion index can be introduced into the calculation, and the weighting coefficients can be adjusted accordingly. , ; Step S303: According to the formula Calculation error mass fraction ; In the formula, The overall error level E is a globally uniform total error rate. Alternatively, weights can be assigned to each of the base insertion, deletion, and substitution errors, and then a weighted calculation can be performed. Preset error tolerance threshold; Step S304: According to Calculate joint score The joint score is mapped to a standardized quality level, which is divided into at least four levels: excellent, good, medium, and poor. Based on the quality level, it is determined whether the DNA information storage biochemical experimental data is suitable for downstream decoding.
3. The quality assessment method for biochemical experiments oriented towards DNA information storage according to claim 2, characterized in that, The step S2 includes a length configuration sub-step when performing k-mer motif error preference assessment: the length of the k-mer motif is adaptively set to 3, 5 or 7 based on the experimental sequence length and the total number of sequencing samples.
4. The quality assessment method for biochemical experiments oriented towards DNA information storage according to claim 3, characterized in that, The comprehensive error level in step S303 The calculation steps are as follows: Based on the experimental requirements of different DNA synthesis and sequencing platforms, weighting coefficients are customized for the three types of base errors: insertion, deletion, and substitution, and the overall error level is obtained after weighting; or a globally unified overall error level can be directly adopted.
5. The quality assessment method for biochemical experiments oriented towards DNA information storage according to claim 4, characterized in that, It also includes a parameter preset step: before executing step S3, a preset error tolerance threshold is manually configured in advance based on the DNA information storage and decoding error tolerance requirements. .
6. The quality assessment method for biochemical experiments oriented towards DNA information storage according to claim 5, characterized in that, The decoding and determination steps in step S304 are as follows: when the quality level corresponding to the joint score is excellent or good, the batch data can be directly entered into the downstream decoding; when the quality level is medium or poor, it indicates that there is a coverage defect or a base error defect, and the sequencing experiment needs to be restarted after adjusting the biochemical experimental conditions.
7. A quality assessment system for biochemical experiments involving DNA information storage, characterized in that, Includes the following units: The data acquisition unit is used to acquire input data: a set of reference oligonucleotide sequences and a set of sequencing read sequences; it compares the sequencing read sequences with the reference oligonucleotide sequences, calculates the sequencing coverage data for each reference oligonucleotide, and the distribution data of base insertion, deletion, and substitution errors in sequence position, base type, and k-mer motif dimension; A single quality assessment unit is used to perform a single quality assessment, which completes four assessment logics in parallel and outputs four independent quality characterization results: coverage depth distribution, position error rate distribution, single base error preference, and k-mer motif error preference. The evaluation criteria include: Coverage depth assessment: statistically analyzing the distribution of read counts across all reference oligonucleotide sequencing to determine total sequencing coverage and uniformity; Position error rate assessment: statistically analyzing the probability of insertion, deletion, and substitution errors by base position to identify systematically high-error regions at fixed sequence positions; Single-base error preference assessment: statistically analyzing the frequency distribution of various errors occurring in A, T, C, and G bases to characterize systematic error bias at the base level; and k-mer motif error preference assessment: identifying systematic sequencing errors caused by local sequence context that are not statistically identifiable by single bases, used to characterize sequences of length... The relationship between the context of consecutive DNA base segments and the probability of base errors; The joint quality assessment unit is used to perform joint quality assessments based on the output results of individual quality assessments. The result output unit is used to output the evaluation results.
8. The quality assessment system for biochemical experiments on DNA information storage according to claim 7, characterized in that, The joint quality assessment unit is used to perform the following sub-steps: Step S301: Apply a normalized limiting function For the index constraints, the normalized limiting function satisfies: Output 0 at time. Output x at that time. Output 1 when the time is right; Step S302: Calculate coverage sufficiency (Average coverage depth / Target coverage depth), Coverage completeness (Percentage of 1-zero covered oligonucleotides); According to the formula Calculate the coverage mass sub-unit ; in, , , When calculating the coverage quality subset, an additional coverage depth dispersion index can be introduced into the calculation, and the weighting coefficients can be adjusted accordingly. , ; Step S303: According to the formula Calculation error mass fraction ; In the formula, The overall error level E is a globally uniform total error rate. Alternatively, weights can be assigned to each of the base insertion, deletion, and substitution errors, and then a weighted calculation can be performed. Preset error tolerance threshold; Step S304: According to Calculate joint score The joint score is mapped to a standardized quality level, which is divided into at least four levels: excellent, good, medium, and poor. Based on the quality level, it is determined whether the DNA information storage biochemical experimental data is suitable for downstream decoding.
9. The quality assessment system for biochemical experiments oriented towards DNA information storage according to claim 8, characterized in that, The process of performing k-mer motif error preference assessment includes a length configuration sub-step: the length of the k-mer motif is adaptively set to 3, 5, or 7 based on the experimental sequence length and the total number of sequencing samples.
10. The quality assessment system for biochemical experiments oriented towards DNA information storage according to claim 9, characterized in that, The overall error level The calculation steps are as follows: Based on the experimental requirements of different DNA synthesis and sequencing platforms, weighting coefficients are customized for the three types of base errors: insertion, deletion, and substitution, and the overall error level is obtained after weighting; or a globally unified overall error level can be directly adopted.