Pathogenic microorganism detection system based on nanopore sequencing

Abstract

The invention relates to the technical field of pathogenic microorganism detection, in particular to a pathogenic microorganism detection system based on nanopore sequencing, which comprises: a microflow sample introduction module, which uses nanopore sequencing, regulates and controls a microorganism liquid channel and adjusts the opening of a sample introduction valve at the same time, setting the flow rate of the trace liquid and comparing the ion concentration of the conductive pore channel with the pH value of the solution by using a sensor. According to the invention, liquid channel regulation and control are carried out through nanopore sequencing, and accurate control of a fluid environment is realized by combining opening adjustment of a sample introduction valve, flow velocity setting and comparison of ion concentration and pH value of a conductive pore channel, so that a microbial sample is ensured to enter a sequencing system under stable physical and chemical conditions, and the stability of sequencing data is improved. In the cell lysis process, the nucleic acid release rate is adjusted through temperature monitoring, and the particle removal operation is combined, so that the nucleic acid component in the sample is more uniform, and the reliability of subsequent detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pathogenic microorganism detection, and in particular to a pathogenic microorganism detection system based on nanopore sequencing. Background Art

[0002] The technical field of pathogenic microorganism detection involves the identification, quantitative analysis, and typing detection of microorganisms such as viruses, bacteria, fungi, and parasites. This field encompasses multiple disciplines such as molecular biology, microbiology, medical laboratory science, and bioinformatics, and is widely applied in scenarios such as clinical diagnosis, food safety, environmental monitoring, public health, and biosafety.

[0003] In the prior art, there are problems of uneven sample processing during microorganism detection. The liquid injection method is limited by the equipment accuracy, and the instability of the flow rate and chemical environment leads to large fluctuations in sequencing data, affecting the accuracy of microorganism identification. During cell lysis, traditional methods cannot precisely control the nucleic acid release rate, easily resulting in nucleic acid degradation or excessive impurity residues, affecting the amplification and sequencing quality. When screening pathogens, due to the limited ability of the detection method to eliminate non-target nucleic acids, the non-specific background interference increases, reducing the accuracy of pathogen recognition. Summary of the Invention

[0004] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a pathogenic microorganism detection system based on nanopore sequencing.

[0005] To achieve the above purpose, the present invention adopts the following technical scheme: A pathogenic microorganism detection system based on nanopore sequencing includes: A microfluidic injection module that uses nanopore sequencing, simultaneously regulates the microbial liquid channel and adjusts the opening of the injection valve, sets the flow rate of trace liquid, and uses a sensor to compare the ion concentration and solution pH value of the conductance pore, monitors the current value read by the protein pore in the microfluidic chip, and generates ion recognition data; A pathogen lysis module that, based on the ion recognition data, performs microbial cell disruption and separates cell membrane fragments, controls the temperature of the lysis solution and monitors the fluctuation of nucleic acid content in the sample with a thermometer, removes particles and detects the distribution concentration of pathogen fragments, and forms a cell lysis mixture; A multimodal information module that, based on the cell lysis mixture, screens for pathogenic microorganism DNA and eliminates irrelevant nucleotide fragments, measures the read length of microbial RNA and uses a probe to record the current signal when passing through the protein pore, injects enzyme preparations and monitors the change in the amplification amount, and forms a characteristic correlation sequence; The sequence allocation module divides the sequencing data channels based on the feature association sequence, groups and marks them according to the sequence length, calls the microbial classification tags and matches the ion signal peaks, adjusts the data reading timing and compares the multi-channel voltage differences, records the number of mismatch errors during the sequencing cycle, and forms a reading allocation map.

[0006] Preferably, it further includes: The multi-gradient segmentation module counts the lengths of the sequencing fragments based on the reading allocation map, records the number of each fragment by cumulative counting, selects the segmentation sites and compares the base arrangement rules, locates and summarizes the base change values after segmentation, screens the microbial RNA regions and detects the distribution of repeated fragments, and forms a gradient segmented sequence; The multi-level cache module temporarily stores the segmented data based on the gradient segmented sequence and records the amount of each cache write, counts the reading frequencies and compares the occurrence times of common sequence fragments, counts the cache hit numbers and records the occupancy rate, and forms cache scheduling information.

[0007] Preferably, the microfluidic injection module includes: The channel regulation sub-module uses nanopore sequencing, regulates the microbial liquid channel and adjusts the opening of the injection valve at the same time, records the valve position and opening values through a sensor, counts the flow rate changes after each adjustment and monitors the flow rate fluctuations, and generates valve adjustment information; The flow rate setting sub-module sets the flow rate of the trace liquid and collects the ion concentration records based on the valve adjustment information, counts the ion concentration differences during each flow rate setting, compares the ion concentration in the conductance pore and the solution pH value using a sensor, and generates flow rate status data; The signal monitoring sub-module monitors the reading current value of the protein pore in the microfluidic chip and records the real-time current fluctuations based on the flow rate status data, counts the number of current peaks and compares the peak intensities, analyzes the correspondence between the flow rate and the current, and generates ion recognition data.

[0008] Preferably, the pathogen lysis module includes: The cell disruption sub-module performs microbial cell disruption and separates the cell membrane fragments based on the ion recognition data, monitors the morphology of the membrane fragments and records the mass of the membrane fragments, evaluates the disruption efficiency and sorts the cell membrane and cytoplasmic components, marks the number of separated particles, and generates cell separation information; The temperature control sub-module controls the temperature of the lysis solution and monitors the fluctuation of the nucleic acid content in the sample with a thermometer based on the cell separation information, records the correlation between the temperature change and the nucleic acid concentration, marks the nucleic acid change range corresponding to the temperature interval, and generates temperature monitoring information; The fragment detection sub-module, based on the temperature monitoring information, removes particles and detects the distribution concentration of pathogen fragments, summarizes the number of residual particles and registers the content of microbial fragments, and calculates the removal efficiency to form a cell lysis mixture.

[0009] Preferably, the multi-modal information module includes: The DNA screening sub-module, based on the cell lysis mixture, screens the DNA of pathogenic microorganisms and removes irrelevant nucleotide fragments, records the number of DNA fragments and the sequencing adaptability after screening, compares the DNA retention rate and marks the positions of the retained fragments, calculates the total amount of removed fragments, and generates a DNA screening result; The RNA read length sub-module, based on the DNA screening result, measures the read length of microbial RNA and uses a probe to record the current signal when passing through the protein pore, calculates the read length range and current response information, and generates read length monitoring data; The enzyme preparation injection sub-module, based on the read length monitoring data, injects the enzyme preparation and records the change in the amplification amount, monitors the current fluctuation during the enzyme action and measures the amplification peak, and forms a characteristic correlation sequence.

[0010] Preferably, the sequence allocation module includes: The channel division sub-module, based on the characteristic correlation sequence, divides the sequencing data channels and groups and marks them according to the sequence length, calculates the number of each channel group and registers the sequence interval, and generates grouped marking information; The label calling sub-module, based on the grouped marking information, calls the microbial classification labels and matches the ion signal peaks, records the positions of the signal peaks and associates them with the sequence categories, and generates classification matching data; The timing adjustment sub-module, based on the classification matching data, adjusts the data reading timing and compares the multi-channel voltage differences, calculates the number of mismatch errors and marks the abnormal reading positions, and forms a reading allocation mapping.

[0011] Preferably, the multi-gradient segmentation module includes: The fragment statistics sub-module, based on the reading allocation mapping, calculates the length of the sequencing fragments and records the number of each fragment by cumulative counting, calculates the fragment proportion and identifies the length distribution characteristics, and generates fragment statistics data; The segmentation selection sub-module, based on the fragment statistics data, selects the segmentation sites and compares the base arrangement rules, annotates the sequence intervals and marks the repeated regions, and generates segmentation positioning information; The base summary sub-module, based on the segmentation positioning information, performs post-segmentation positioning and summarizes the base change values, checks the microbial RNA region and detects the distribution of repeated fragments, calculates the change types and frequencies, and forms a gradient segmentation sequence.

[0012] Preferably, the multi-level cache module includes: A data temporary storage sub-module, based on the gradient segmentation sequence, performs segmented data temporary storage, records the amount of each cache write, traces the write time sequence, registers the storage location, and generates temporary storage management data; A cache statistics sub-module, based on the temporary storage management data, performs read frequency statistics and compares the occurrence times of common sequence segments, and generates frequency comparison information; A resource allocation sub-module, based on the frequency comparison information, counts the cache hits, records the occupancy rate, analyzes the storage partition and the available buffer space, and forms cache scheduling information.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through nanopore sequencing for liquid channel regulation, combined with the adjustment of the injection valve opening, flow rate setting, and the comparison of the ion concentration and pH value in the conductance pore, precise control of the fluid environment is achieved, ensuring that microbial samples enter the sequencing system under stable physical and chemical conditions, and improving the stability of sequencing data. During the cell lysis process, the nucleic acid release rate is regulated by temperature monitoring, and combined with particle removal operations, the nucleic acid components in the sample are made more uniform, improving the reliability of subsequent detections. During the pathogenic microorganism screening process, the separation of DNA and RNA adopts a probe determination method to reduce the interference of irrelevant nucleic acid fragments, and combined with the protein pore signal recognition mechanism, the accuracy of data reading is optimized. The sequencing data channel division is grouped and marked according to the sequence length, matching the ion signal peak value, ensuring the accuracy of data classification, and combined with timing adjustment to reduce mismatch errors, improving the data analysis quality. The entire process from fluid control, sample lysis, nucleic acid screening to sequencing data matching is centered around obtaining the genetic information of pathogens, improving data integrity, sequencing accuracy, and signal analysis accuracy, reducing background noise interference, and optimizing data analysis efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] Please refer to Figure 1 , the present invention provides a technical solution: A pathogenic microorganism detection system based on nanopore sequencing includes: Microfluidic injection module, using nanopore sequencing, simultaneously regulates the microbial liquid channel and adjusts the opening degree of the injection valve, sets the flow rate of trace liquid, and uses a sensor to compare the ion concentration in the conductance pore and the pH value of the solution, monitors the current value read by the protein pore in the microfluidic chip, and generates ion recognition data; Pathogen lysis module, based on the ion recognition data, performs microbial cell lysis and separates cell membrane fragments, controls the temperature of the lysis solution and monitors the fluctuation of nucleic acid content in the sample with a thermometer, removes particles and detects the distribution concentration of pathogen fragments, and forms a cell lysis mixture; Multimodal information module, based on the cell lysis mixture, screens for pathogenic microbial DNA and eliminates irrelevant nucleotide fragments, measures the read length of microbial RNA and records the current signal when passing through the protein pore with a probe, injects enzyme preparations and monitors the change in the amplification amount, and forms a characteristic correlation sequence; Sequence allocation module, based on the characteristic correlation sequence, divides the sequencing data channels and groups and marks them according to the sequence length, calls the microbial classification tags and matches the ion signal peaks, adjusts the data reading timing and compares the multi-channel voltage differences, records the number of mismatch errors during the sequencing cycle, and forms a reading allocation map; Multi-gradient segmentation module, based on the reading allocation map, counts the length of the sequencing fragments and records the number of each fragment by cumulative counting, selects the segmentation sites and compares the base arrangement rules, locates after segmentation and summarizes the base change values, screens the microbial RNA region and detects the distribution of repeated fragments, and forms a gradient segmentation sequence; Multi-level cache module, based on the gradient segmentation sequence, temporarily stores the segmented data and records the amount written to the cache each time, counts the reading frequency and compares the number of occurrences of common sequence fragments, counts the cache hit number and records the occupancy rate, and forms cache scheduling information.

[0017] The microfluidic injection module includes: Channel regulation sub-module, using nanopore sequencing, simultaneously regulates the microbial liquid channel and adjusts the opening degree of the injection valve, records the valve position and opening degree values through a sensor, counts the flow rate change after each adjustment and monitors the flow rate fluctuation, and generates valve adjustment information; Flow rate setting sub-module, based on the valve adjustment information, sets the flow rate of trace liquid and collects ion concentration records, counts the ion concentration difference during each flow rate setting, uses a sensor to compare the ion concentration in the conductance pore and the pH value of the solution, and generates flow rate status data; Signal monitoring sub-module, based on the flow rate status data, monitors the current value read by the protein pore in the microfluidic chip and records the real-time current fluctuation, counts the number of current peak occurrences and compares the peak intensities, analyzes the corresponding relationship between the flow rate and the current, and generates ion recognition data.

[0018] Specifically, the channel regulation process is based on the fluid delivery requirements for nanopore sequencing. First, a mapping table of valve opening to flow rate is prepared. The opening range in this table can be set from 0% to 100%, and the reference flow rate is recorded every 5%. The recording method is to inject the microbial solution in the range of 0 to 1 milliliter per minute and observe the actual flow velocity. If the difference in the flow rate measured in a certain measurement relative to the previous set value is greater than 0.02 milliliters per minute, and this threshold is obtained through multiple preliminary experiments, it is considered that a large fluctuation has occurred and the re - correction process is initiated. During the correction process, the valve position is adjusted by 1% to 3% of the opening in the direction of the target value according to the measured value, and the corrected flow rate change is recorded again. This process is repeated until the flow rate difference is less than 0.02 milliliters per minute. At the same time, an in - built sensor is used to detect the valve rotation angle and convert it into numerical information, recording the amplitude of each adjustment and the corresponding flow rate result. The instantaneous flow rate measured each time can be compared with the set range of 0 to 1 milliliter per minute. If the valve opening and the corresponding flow rate fluctuation directions do not match, the opening is corrected in the reverse direction by 2% to 5% during the next adjustment, and the actual flow rate is measured again. Through this method, a flow rate closer to the expected value can be obtained after multiple iterations. All time - series data are arranged with timestamps, and the valve positions and flow rate data at each measurement stage are associated to generate valve adjustment information.

[0019] The flow rate setting is based on the valve adjustment information obtained previously. After associating the valve opening and the target flow rate value, a flow rate parameter table is established. In this table, the flow rate is segmented and recorded in steps of 0.1 milliliters per minute, and a reference range of ion concentration is set within each step interval. This reference range can be set as the average value obtained from previous experimental statistics ±5%. For example, the solution pH value is compared in the range of pH5 to pH9, and the ion concentration is compared in the range of 0.01 to 0.5 moles per liter. When comparing successively, if the solution pH value is lower than pH5 or higher than pH9, it is considered that the pH value deviates from the range. This determination criterion is derived from the characteristics of the culture medium and is confirmed through multiple measurements. If the ion concentration is less than 0.01 moles per liter or greater than 0.5 moles per liter, it is considered that the ions are too low or too high. During the automatic acquisition process, an ion concentration measurement is performed for each flow rate setting, and at the same time, the corresponding relationship between the valve position and the actual flow rate is recorded, and the measured pH value is compared with the set interval. When the ion concentration or pH value deviates, a second measurement is performed to exclude accidental errors. If the result of the second measurement still deviates, the valve opening is adjusted down or up by a fixed step size according to the valve adjustment information obtained previously. The fixed step size can be empirically set to 2%. After repeated measurements, it is checked whether it returns to the reference range. The pH value and ion concentration data measured each time are summarized and the actual setting situation of the current flow rate is recorded to generate flow rate status data.

[0020] Based on the flow velocity state data obtained previously, the signal monitoring section first determines the current flow rate setting and the range of solution pH value. Then, it uses the current sensing electrodes on the microfluidic chip to perform real-time sampling of the ion flow through the protein pore. It collects 100 current data points per second and obtains the corresponding time stamps. If the current value in a certain period exceeds 2 microamperes or is lower than 0.2 microamperes, it is regarded as abnormal ion flow. These upper and lower threshold values are obtained by comparing the normal range and the device indicators during the laboratory test stage. The values outside this range are reconfirmed. If there are still deviations after reconfirmation, this interval is marked as an abnormal period. At the same time, by comparing the set flow rate in the current flow velocity state data, it is judged whether the pore produces non-linear changes due to too large or too small flow rate. During the judgment process, the time point when the current peak appears is corresponding to the flow rate of the solution injection. If there is a regular deviation between the peak frequency and the flow rate in a specific section, the current is collected again by fine-tuning the flow rate. The collected data are summarized in chronological order and the peak intensity is statistically analyzed. The records with peak intensity greater than 2 microamperes and the number of occurrences exceeding the set standard (such as exceeding 2 microamperes continuously three times) are marked separately, and further comparison is made in combination with the pH information obtained previously. After all the records are completed, the peak frequency during this period is arranged in the summary table to generate ion recognition data.

[0021] The pathogen lysis module includes: The cell disruption sub-module, based on the ion recognition data, performs microbial cell disruption treatment, separates the cell membrane fragments, monitors the morphology of the membrane fragments and records the mass of the membrane fragments, evaluates the disruption efficiency, sorts the cell membrane and cytoplasmic components, marks the number of separated particles, and generates cell separation information; The temperature control sub-module, based on the cell separation information, controls the temperature of the lysis solution and monitors the fluctuation of the nucleic acid content in the sample with a thermometer, records the correlation between the temperature change and the nucleic acid concentration, marks the nucleic acid change range corresponding to the temperature interval, and generates temperature monitoring information; The fragment detection sub-module, based on the temperature monitoring information, removes the particles and detects the distribution concentration of the pathogen fragments, summarizes the number of remaining particles and registers the content of the microbial fragments, statistically analyzes the removal efficiency, and forms a cell lysis mixture.

[0022] Specifically, based on the ion recognition data obtained previously, the cell disruption process first places the microbial sample in a mechanical oscillation device for preliminary grinding. It is continuously ground for 2 minutes at a grinding rate of 500 revolutions per minute. This rate is selected after comparing the degree of microbial damage through multiple experiments. Then, the sample is transferred to an ultrasonic disruptor and the ultrasonic frequency is set to 20 kHz. The duration of each ultrasonic treatment can be set to 30 seconds, with a 10-second break in between. If the average size of the membrane fragments continuously exceeds 5 microns (this 5-micron threshold is derived from the best disruption degree judgment standard in the literature), the ultrasonic duration is extended to 45 seconds and the average size is measured again. At the same time, the peak periods marked as abnormal in the ion recognition data are recorded and compared with the timing tags of the grinding and ultrasonic processes. If it is found that there is cell mass aggregation during some high-peak periods, the rotation speed of the oscillation device is reduced to 300 revolutions per minute and the appearance of the membrane fragments is detected again. Images are taken using an optical microscope, and after collecting multiple images, the morphology of the membrane fragments is estimated through an image analysis algorithm. The mass of the membrane fragments is recorded and classified into three categories based on the disruption degree: 0.1 mg to 0.5 mg, 0.5 mg to 1 mg, and above 1 mg. Subsequently, the cell membrane and cytoplasmic components are sorted, and the number of separated particles is counted in a summary to generate cell separation information.

[0023] The temperature control process is based on the cell separation information obtained previously. First, the separated cell mixture is placed in a specific container and a lysis buffer with a conventional formulation is injected. The initial temperature is set to 40 °C on the heating device. This temperature refers to the condition under which nucleic acids generally remain relatively stable. The current temperature is read every 5 minutes using a thermometer and the lysis buffer is stirred. If the temperature exceeds 60 °C after continuous heating (this value is the safety upper limit determined by comparing the nucleic acid degradation degree during the experiment), the operation of the heating device is paused and the lysis buffer is allowed to cool naturally to around 50 °C. During this process, the change in nucleic acid concentration is recorded. If the measured nucleic acid content significantly decreases and is lower than the previously set 0.01 mg / ml (this threshold comes from the statistical results of parallel experiments on multiple groups of microorganisms), the temperature is maintained at 50 °C and observed for another 5 minutes. During this period, if the nucleic acid content rises above 0.01 mg / ml, indicating that there are still relatively active nucleic acid components in the sample, this temperature is continued to be maintained and the nucleic acid content is measured repeatedly at 2-minute intervals. If the temperature is lower than 40 °C, the heating device is restarted and a slow heating mode is set. The heating rate can be set to increase by 1 °C per minute until the temperature reaches 50 °C again, and then the nucleic acid concentration is measured. The specific values of the relationship between temperature change and nucleic acid concentration are recorded and the temperature range is annotated to generate temperature monitoring information.

[0024] The debris detection process is based on the previously obtained temperature monitoring information. When the temperature stabilizes in the range of 50 degrees Celsius, centrifugation is performed on the samples. The centrifugation speed can be set at 8000 revolutions per minute and maintained for 5 minutes. This speed is the optimal speed selected from multiple rounds of experimental comparisons. After removing the supernatant, optical detection is performed on the precipitate. If the number of detected particles exceeds 2000 particles per milliliter (this 2000 particles per milliliter threshold is obtained based on the statistical analysis of the common debris concentration in the microbial lysis mixture), then secondary centrifugation is continued and the detection is repeated. At the same time, a particle counting device is used to record the number of residual particles detected each time. If the debris content is still higher than 2000 particles per milliliter after secondary centrifugation, the centrifugation duration is extended to 10 minutes and detection is performed again after centrifugation. The collected concentration data is compared with the previously set effective range (0 to 2000 particles per milliliter). Samples that meet this range are no longer subjected to subsequent centrifugation. The particle content of all samples is registered in a table to distinguish the level of residual debris. After confirming that the debris concentration of most samples is below 2000 particles per milliliter, a unified mixing operation is carried out. The microbial lysis solutions of the same type are combined and gently stirred evenly. After summarizing all the detection data, the statistical result of the number of residual particles is obtained and the removal efficiency is calculated to form a cell lysis mixture.

[0025] The multi-modal information module includes: The DNA screening sub-module, based on the cell lysis mixture, screens for pathogenic microbial DNA and eliminates irrelevant nucleotide fragments, records the number of DNA fragments and sequencing adaptability after screening, compares the DNA retention rate and marks the positions of the retained fragments, and statistically calculates the total amount of eliminated fragments to generate a DNA screening result; The RNA read length sub-module, based on the DNA screening result, measures the microbial RNA read length and uses a probe to record the current signal when passing through the protein pore, statistically calculates the read length range and current response information, and generates read length monitoring data; The enzyme preparation injection sub-module, based on the read length monitoring data, injects the enzyme preparation and records the change in the amplification amount, monitors the current fluctuation during the enzyme action and measures the amplification peak value to form a characteristic correlation sequence.

[0026] Specifically, DNA screening is based on the cell lysis mixture obtained previously. First, the nucleotide information in the pathogenic microorganism sample is classified and marked. The microbial gene sequence characteristics listed in the relevant literature can be pre-disassembled at the initial stage, and the key fragments in the literature and the control group sequence information are matched item by item. Then, the possible difference range of microbial DNA and other unrelated nucleotide fragments is recorded. If the base ratio of the DNA fragment is detected to deviate from the reference comparison value by more than 10% during the comparison process, and the 10% value comes from the average deviation statistics of multiple batches of microbial samples, then the fragment will be marked as a suspicious unrelated fragment and the base type and length information will be further verified. During the verification, the continuous sequence of bases is read and compared with that in the reference literature. The probability of appearing in the literature is calculated. If the probability is less than 5%, it is considered to be eligible for elimination. The number of eliminated fragments and their actual lengths are recorded at the same time, and a detailed elimination list is accumulated. The sequencing fitness of the retained DNA fragments is measured. Sequencing fitness can be measured by two indicators: base distribution uniformity and sequence integrity. If the base distribution uniformity does not reach the set threshold of 60%, which is derived from the public data of similar sequencing platforms, the compatibility of this fragment on the sequencer is rechecked. If it still does not reach 60%, it is listed as a non-priority sequencing fragment. The amount of fragments retained after all screening and the number of eliminated fragments are summarized separately. Finally, the DNA retention rate is confirmed and the specific position of the retained fragments in the sequencing sequence is marked to generate the DNA screening results.

[0027] RNA read lengths are determined based on the DNA screening results obtained previously. The retained DNA fragments are matched against a pre-recorded database of pathogenic microbial RNAs. By querying the database for RNA segment identifiers, the RNA fragments whose read lengths need to be measured are identified. During this process, a probe is connected to the protein pore to record the current changes as the pore passes through it. Each measured current value is compared with the theoretical length of the RNA fragment. If multiple measurements show an RNA fragment length deviation exceeding 50 bases (the upper limit of 50 bases is based on statistical data for similar microbial species), the RNA fragment is re-measured to confirm whether there is a length mutation. Simultaneously, the current response information recorded by the probe is counted, and a table is created that maps peak intensities to RNA sizes. If the maximum peak deviation between the two measurements is within 5%, which is determined by the instrument's measurement accuracy settings, the read length data is considered stable. Outliers are further examined for abnormal fluctuations in the current curve. If continuous high current peaks are present, the pore status is re-checked. After multiple measurements stabilize, the read length range of all RNA fragments is calculated, and the corresponding current responses are collated to generate read length monitoring data.

[0028] Based on the read length monitoring data obtained previously, the enzyme preparation is injected. First, referring to the existing experimental records of microbial RNA amplification, the typical RNA amplification starting points and peaks are obtained. Before the formal injection, a low-dose group of enzyme preparations can be injected first and the RNA amplification amount changing with time is recorded. If it is found that the amplification amount exceeds the set threshold of 0.05 micrograms per milliliter, and this threshold comes from the statistical results of the amplification effect of microbial RNA through repeated laboratory tests, it indicates that the reaction has been significantly initiated. Then, the enzyme preparation can be continuously supplemented into the system in stages. The time interval and dosage of each supplement should be recorded and compared with the previous amplification amount. If the amplification amount in a certain stage is significantly lower than 90% of the previous measurement value, the enzyme preparation concentration can be adjusted. The adjustment range can refer to the recommended range (0.01 to 0.1 milliliters) listed in the literature, and then observe the change of the current fluctuation again. When the current curve shows a peak exceeding 2 microamperes and lasting for 3 acquisition cycles, this 2-microampere value combines the range index of the device and the experience of previous enzyme injections, and it can be judged that the reaction between the enzyme and RNA is at a high activity level. At this time, the monitored amplification peak and the time stamp are recorded together. After the whole process is completed, check the relationship between all injection amounts and the amplification peaks to form a characteristic correlation sequence.

[0029] The sequence allocation module includes: The channel division sub-module, based on the characteristic correlation sequence, divides the sequencing data channels and groups and marks them according to the sequence length, counts the number of each channel group and registers the sequence interval to generate the grouped marking information; The label calling sub-module, based on the grouped marking information, calls the microbial classification labels and matches the ion signal peaks, records the signal peak positions and associates the sequence categories to generate the classification matching data; The timing adjustment sub-module, based on the classification matching data, adjusts the data reading timing and compares the multi-channel voltage differences, calculates the mismatch error number and marks the abnormal reading positions to form the reading allocation mapping.

[0030] Specifically, the channel division is based on the feature correlation sequence obtained previously. First, sequences of different lengths are compared to summarize several groups. To determine the grouping criteria, one can first refer to the sequence length distribution data of the same type of pathogenic microorganism. For example, ranges such as less than 500 bases, 500 to 1000 bases, 1000 to 2000 bases, and more than 2000 bases are defined as pre-established grouping intervals. Then, the actually measured sequence lengths are recorded one by one and assigned to the corresponding intervals. After confirming the interval assignment, they are assigned to different channel numbers. For example, the first channel is responsible for recording sequences less than 500 bases, the second channel corresponds to the range of 500 to 1000 bases, and subsequent channels are sequentially assigned sequences of greater lengths. At the same time, the number of sequences already assigned in each channel is recorded. If the number of sequences in a certain channel exceeds the pre-estimated capacity, which can be set to 200 sequences per channel based on experimental experience, then a new channel number with the same number is created and a secondary number is marked after its name. Repeat this process and compare the actual grouping information with the corresponding sequence intervals in a list form to finally form grouping marker information.

[0031] The label call is based on the grouping marker information obtained previously. By combining the pre-decomposed microbial classification label comparison table, label matching is performed on the sequences of each group. This comparison table can be established by collecting the characteristic peak ranges and label types of known pathogenic microorganisms. When performing the matching, the peak positions recorded in the ion signal acquisition are compared one by one with the corresponding sequence categories in the comparison table. If the similarity is greater than 80%, this 80% value is obtained by averaging the multiple comparisons between the label in the comparison table and the measured peak, then it is determined that this sequence belongs to this label type. For sequences that do not meet the 80% similarity, it is necessary to additionally observe their peak shapes and position distributions. If it is found that the matching degree with another label is higher instead, then update the corresponding label identifier, record the specific peak positions and label correspondence relationships, and at the same time confirm whether this classification is within the pre-established classification boundary values. For example, compare the peak current in the interval of 0.1 to 3 microamperes, and compare the peak occurrence frequency in the interval of 10 times / second to 200 times / second, etc. After comparing each item one by one, attach classification information to each sequence to generate classification matching data.

[0032] Based on the classification matching data obtained previously, the timing adjustment compares the starting moment of data reading with the multiplex voltages of each channel, and records the differences in this comparison for each time period. If it is found that the voltage deviation of a certain channel exceeds 0.5 volts, and this 0.5 volts is the threshold summarized during previous multiple measurements, then the reading moment of this channel is postponed by 0.1 seconds and then re-acquired, and it is compared whether the newly acquired data still exceeds this threshold. If it still deviates, it continues to be postponed backward. When the multiplex voltages are within the normal range (0 volts to 24 volts), the data of all channels are compared again. If the peak time period of any sequence shows a difference of more than 2 seconds among different channels, it is statistically analyzed according to the calculation method of the mismatch error number, and the time points where errors occur are marked item by item. After confirming all abnormal time periods, the relationship between the deviation value and the voltage difference of each time period is output, and is summarized through a unified timing table to obtain the final reading allocation correspondence relationship, forming a reading allocation mapping.

[0033] The multi-gradient segmentation module includes: The fragment statistics sub-module, based on the reading allocation mapping, conducts sequencing fragment length statistics, records the quantity of each fragment by cumulative counting, calculates the fragment proportion, and identifies the length distribution characteristics, generating fragment statistical data; The segmentation selection sub-module, based on the fragment statistical data, conducts segmentation site selection, compares the base arrangement rules, annotates the sequence interval, and marks the repeated regions, generating segmentation positioning information; The base summary sub-module, based on the segmentation positioning information, conducts positioning after segmentation and summarizes the base change values, checks the microbial RNA region, detects the distribution of repeated fragments, statistically analyzes the change types and frequencies, and forms a gradient segmented sequence.

[0034] Specifically, the fragment statistics are based on the previously obtained read allocation mapping. First, they are summarized according to the length intervals corresponding to each sequence in the mapping, registering the occurrence times one by one from the minimum length to the maximum length. If it is found during the preliminary statistics that the lengths of some fragments are less than 100 bases or more than 30,000 bases (these two boundary values are the reference ranges set based on previous experience and literature data), these abnormal fragments are marked separately and their length recording deviations are checked again. Only after confirming no deviation can they be included in the statistical summary table. Then, the cumulative count method is used to record the cumulative quantity of each length segment. For example, a step of 200 bases can be used, and the intervals from 0 to 200 bases, 200 to 400 bases, and so on are calculated in turn until the count stops when exceeding the 30,000-base interval. After each interval is counted, it is compared with the previously set valid range. For example, the lengths of each interval are compared with the macroscopic interval of 0 to 50,000 bases. If the number of fragments in an individual interval exceeds 300 (this upper limit of 300 comes from the average load capacity in multiple tests), these intervals need to be split into finer sub-intervals, which can make the subsequent cumulative statistics more accurate. Subsequently, the percentage occupied by each sub-interval is calculated according to the number of fragments in the sub-interval, and the intervals with a fragment proportion greater than 10% are rechecked. This 10% value is the conventional warning line obtained from the statistics of a large amount of sequencing historical data. If no obvious errors are confirmed during the inspection process, these intervals are marked as high-frequency distribution segments in the statistical results. Finally, by integrating all length intervals and their cumulative count results, they are unified and summarized to generate a length distribution characteristic table, generating fragment statistical data.

[0035] Segment selection is based on the fragment statistical data obtained previously. First, retrieve the distribution of each length interval listed in the statistical data. Consider the intervals with relatively higher proportions as key segment candidates and those with relatively lower proportions as general segment candidates. In this step, it is necessary to record the starting base position and ending base position of each candidate interval. If the base proportion of a certain segment exceeds 15% (this 15% is a boundary obtained from previous sequencing experience and statistical distribution results), then this segment can be preferentially processed when comparing the base arrangement rules. When comparing the base rules, the sequence is sliced into equal-length segments in the order of bases A, T, C, G, and the number of repeated base combinations within the segment is compared. If the number of repetitions of a certain combination is higher than 20 times (this 20 times is the repetition alarm threshold summarized in the original sequencing data analysis stage), then mark this combination in the annotation list and further check the sequence interval. Then, when summarizing this annotation list, it is necessary to perform parallel comparison with the interval information of the candidate segments. If there are multiple repeated combinations in the same segment, then further subdivide this segment. For example, split an interval from 500 bases to 700 bases into two small segments of 250 bases each, re-statistics and note the positions of the repeated bases in a list. All regions marked as repeated need to be tracked during the subsequent segment positioning process, so that the actual repeated regions can be more accurately located after the segment sites are selected. Finally, summarize all annotation situations and output the corresponding sequence interval marking information to generate segment positioning information.

[0036] Base summarization is based on the segment positioning information obtained previously. Scan each base one by one within the segmented range after splitting and record its change value. If there are a large number of deletions or insertions in the base composition within a certain segment, for example, it is detected that the number of inserted bases accounts for more than 5% of the total number of bases in this segment (this 5% is obtained from the deletion and insertion ratio statistics during the test process), then conduct additional inspection on this segment and confirm the insertion position again. At the same time, special attention needs to be paid to the sequences in the microbial RNA region. It can be checked whether the detected base deletions or substitutions have exceeded 10 consecutive bases by comparing with the RNA reference sequence (this upper limit of 10 consecutive bases is derived from the typical RNA change length in the reference literature). If there are indeed changes within such a range, then make a note of it. Subsequently, judge whether there are more repeated segments within this segment. If the repeated segments are widely distributed and exceed 10% of the total length within the segment, then mark this segment as a segment with a relatively high repeatability, record the types of repeated bases and their occurrence frequencies. When merging all the segmented data that have been positioned, ensure that the statistics of base changes and the registration of repeated segments within each segment are included in the final difference table. Finally, combine the summary situations of all segments, summarize the change types and change frequencies according to the sequence positions to form a gradient segmented sequence.

[0037] The multi-level cache module includes: The data temporary storage submodule temporarily stores segmented data based on the gradient segmentation sequence and records the amount of cache writes each time, tracks the write time sequence and registers the storage location, and generates temporary storage management data; The cache statistics submodule performs read frequency statistics based on temporary storage management data and compares the number of occurrences of common sequence fragments to generate frequency comparison information; The resource allocation submodule counts the number of cache hits and records the occupancy rate based on the frequency comparison information, analyzes the storage partitions and available buffer space, and forms cache scheduling information.

[0038] Specifically, data temporary storage is based on the gradient segmentation sequence obtained previously. First, each segment in the sequence is grouped according to different identification numbers. Each identification number corresponds to a segment range, for example, identification number 1 corresponds to the range of 1 to 500 bases, identification number 2 corresponds to the range of 501 to 1000 bases, etc. Before these segmented data are temporarily stored, a write timestamp needs to be assigned to each segment and the actual write amount needs to be recorded. If the segment size exceeds 2 megabytes, the 2 megabyte threshold is determined by the sequencing system cache capacity assessment, and then this segment needs to be split into several sub-segments Then write in batches, and record the corresponding storage location after each write is completed. The storage location can be represented by a sequence number and an address offset value. If it is found that the existing temporary storage space is insufficient during writing, the free position will be searched in sequence according to the size of the previous identification number, and a part of the sub-segment will be written to the available address area, and the start and end time of each write operation will be tracked. If a data conflict or discontinuous timestamp is encountered during the writing process, the writing of the segment must be suspended and traced back to the previous confirmation record. After finding the cause of the conflict, it can be written again. Finally, the temporary storage of all segments is completed and temporary storage management data is generated.

[0039] Cache statistics are based on the temporary storage management data obtained previously. First, a timestamp and storage location comparison table for each segment's write is listed, and then the read frequency statistics for all segments are calculated. If some segments are read more than 10 times within a period of time (the 10-time standard comes from the analysis of common search needs), they are classified as common high-frequency segments and included in a high-priority table. Segments that appear less than 5 times are registered in a low-frequency list. If a segment is found to be read more than 10 times twice in a short period of time, more information needs to be temporarily extracted to determine whether there are abnormal repeated reads. During the judgment process, the data can be compared with a pre-set common read range (for example, 0 to 5 times per day) or the peak load of the storage partition (for example, a single partition can support up to 100 reads). Segments with frequencies significantly higher or lower than this range are further classified. Finally, the number of times different segments appear under high and low priority is recorded. These compared statistical data are integrated into frequency comparison information to generate frequency comparison information.

[0040] Resource allocation is based on the frequency comparison information obtained previously. Before counting the cache hit count, it is necessary to first determine which segments have been listed as high-priority and query their actual occupancy in the cache. If the actual occupancy rate exceeds 70% (this 70% value comes from the stable limit test results of the cache hardware), then before the next write, a part of the buffer space needs to be reserved for these high-priority segments. At the same time, count the number of cache blocks occupied by low-priority segments. If the number of blocks occupied by low-priority segments exceeds the pre-set upper limit (for example, not exceeding 30% of the total cache blocks), then some blocks need to be vacated for high-priority segments to overwrite. When analyzing the storage partition, available buffer units can be allocated from the current free area first. If it is still insufficient, look for segments that can be released in the low-priority area. After releasing the occupancy, high-priority segments migrate into this area. Record all such movement operations in a time series table to track the resource allocation process. Stop the partition adjustment when the overall occupancy rate drops below 70% and generate cache scheduling information.

Claims

1. A pathogenic microorganism detection system based on nanopore sequencing, characterized in that, The system includes: A microfluidic injection module that uses nanopore sequencing to simultaneously regulate the microbial liquid channel, adjust the opening of the injection valve, set the flow rate of trace liquid, compare the ion concentration and solution pH value of the conductance pore with a sensor, monitor the current value read by the protein pore in the microfluidic chip, and generate ion recognition data; A pathogen lysis module that, based on the ion recognition data, performs microbial cell lysis and separates cell membrane fragments, controls the temperature of the lysis solution, monitors the fluctuation of nucleic acid content in the sample with a thermometer, removes particles, and detects the distribution concentration of pathogen fragments to form a cell lysis mixture; A multi-modal information module that, based on the cell lysis mixture, screens for pathogenic microbial DNA and removes irrelevant nucleotide fragments, measures the read length of microbial RNA, records the current signal when passing through the protein pore with a probe, injects enzyme preparations, and monitors the change in amplification amount to form a characteristic correlation sequence; A sequence allocation module that, based on the characteristic correlation sequence, divides the sequencing data channels, groups and marks them according to the sequence length, calls the microbial classification tags and matches the ion signal peaks, adjusts the data reading timing, compares the multi-channel voltage differences, records the number of mismatch errors during the sequencing cycle, and forms a reading allocation map; 2. The pathogenic microorganism detection system based on nanopore sequencing according to claim 1, wherein It also includes: A multi-gradient segmentation module that, based on the reading allocation map, statistically analyzes the length of the sequencing fragments, records the number of each fragment by cumulative counting, selects the segmentation sites and compares the base arrangement rules, locates after segmentation and summarizes the base change values, screens the microbial RNA region and detects the distribution of repeated fragments to form a gradient segmentation sequence; A multi-level cache module that, based on the gradient segmentation sequence, temporarily stores the segmented data and records the amount written to the cache each time, statistically analyzes the reading frequency and compares the occurrence times of common sequence fragments, counts the cache hit number and records the occupancy rate to form cache scheduling information; 3. The pathogenic microorganism detection system based on nanopore sequencing according to claim 1, wherein The microfluidic injection module includes: A channel regulation sub-module that uses nanopore sequencing to simultaneously regulate the microbial liquid channel, adjust the opening of the injection valve, record the valve position and opening value with a sensor, statistically analyze the flow rate change after each adjustment, monitor the flow rate fluctuation, and generate valve adjustment information; A flow rate setting sub-module that, based on the valve adjustment information, sets the flow rate of trace liquid, collects ion concentration records, statistically analyzes the ion concentration difference during each flow rate setting, compares the ion concentration and solution pH value of the conductance pore with a sensor, and generates flow rate state data; A signal monitoring sub-module that, based on the flow rate state data, monitors the current value read by the protein pore in the microfluidic chip, records the real-time current fluctuation, statistically analyzes the number of current peak occurrences and compares the peak intensity, analyzes the corresponding relationship between the flow rate and the current, and generates ion recognition data; 4. The pathogenic microorganism detection system based on nanopore sequencing according to claim 1, wherein The pathogen lysis module includes: A cell lysis sub-module that, based on the ion recognition data, performs microbial cell lysis and separates cell membrane fragments, monitors the morphology of the membrane fragments and records the mass of the membrane fragments, evaluates the lysis efficiency, sorts the cell membrane and cytoplasmic components, marks the number of separated particles, and generates cell separation information; The temperature control sub-module, based on the cell separation information, controls the temperature of the lysis solution and monitors the fluctuation of nucleic acid content in the sample with a thermometer, records the correlation between temperature changes and nucleic acid concentration, marks the nucleic acid change range corresponding to the temperature range, and generates temperature monitoring information; The debris detection sub-module, based on the temperature monitoring information, removes particles and detects the distribution concentration of pathogen debris, summarizes the number of residual particles and registers the content of microbial debris, and calculates the removal efficiency to form a cell lysis mixture.

5. The pathogenic microorganism detection system based on nanopore sequencing according to claim 1, characterized in that, The multi-modal information module includes: The DNA screening sub-module, based on the cell lysis mixture, screens for pathogenic microorganism DNA and removes irrelevant nucleotide fragments, records the number of DNA fragments and sequencing adaptability after screening, compares the DNA retention rate and marks the positions of the retained fragments, and calculates the total amount of removed fragments to generate a DNA screening result; The RNA read length sub-module, based on the DNA screening result, measures the read length of microbial RNA and records the current signal when passing through the protein pore with a probe, calculates the read length range and current response information, and generates read length monitoring data; The enzyme preparation injection sub-module, based on the read length monitoring data, injects the enzyme preparation and records the change in amplification amount, monitors the current fluctuation during enzyme action and measures the amplification peak to form a characteristic correlation sequence.

6. The pathogenic microorganism detection system based on nanopore sequencing according to claim 1, wherein, The sequence allocation module includes: The channel division sub-module, based on the characteristic correlation sequence, divides the sequencing data channels and groups and marks them according to the sequence length, calculates the number of each channel group and registers the sequence interval to generate grouped marking information; The tag call sub-module, based on the grouped marking information, calls the microbial classification tags and matches the ion signal peaks, records the positions of the signal peaks and correlates them with the sequence categories to generate classification matching data; The timing adjustment sub-module, based on the classification matching data, adjusts the data reading timing and compares the multi-channel voltage differences, calculates the number of mismatch errors and marks the abnormal reading positions to form a reading allocation mapping.

7. The pathogenic microorganism detection system based on nanopore sequencing according to claim 2, wherein The multi-gradient segmentation module includes: The fragment statistics sub-module, based on the reading allocation mapping, calculates the length of the sequencing fragments and records the number of each fragment by cumulative counting, calculates the fragment ratio and identifies the length distribution characteristics to generate fragment statistics data; The segment selection sub-module, based on the fragment statistics data, selects the segment sites and compares the base arrangement rules, annotates the sequence intervals and marks the repeated regions to generate segment positioning information; The base summary sub-module, based on the segment positioning information, locates after segmentation and summarizes the base change values, checks the microbial RNA region and detects the distribution of repeated fragments, calculates the change types and frequencies to form a gradient segmented sequence.

8. The pathogen microorganism detection system based on nanopore sequencing according to claim 2, wherein The multi-level cache module includes: The data temporary storage sub-module, based on the gradient segmented sequence, temporarily stores the segmented data and records the amount of each cache write, tracks the write time sequence and registers the storage location to generate temporary storage management data; The cache statistics sub-module, based on the temporary storage management data, calculates the read frequency and compares the occurrence times of common sequence fragments to generate frequency comparison information; A resource allocation sub-module, based on the frequency comparison information, counts the cache hits and records the occupancy rate, analyzes the storage partitions and the available buffer space, and forms cache scheduling information.

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