WO2025137825A1 - Nucleic acid molecule sequencing method and related device - Google Patents

Abstract

A nucleic acid molecule sequencing method of the present disclosure, comprising: first acquiring a sequencing image of a target nucleic acid sample; performing image processing on the sequencing image by means of an image processing model, so as to obtain a target image, the image processing model being a model obtained by training constructed nucleic acid molecule true value images and corresponding simulation nucleic acid images; and performing nucleic acid molecule sequencing on the basis of the target image, so as to obtain a sequencing result corresponding to the target nucleic acid sample.

Description

Nucleic acid molecule sequencing method and related device Technical Field

The present disclosure relates to the field of image processing and the field of biological molecule sequencing technology, and in particular to a nucleic acid molecule sequencing method and related devices.

Background Art

High-throughput sequencing is a technology for sequencing nucleic acid molecules, which can measure the parallel sequences of a large number of nucleic acid molecules at a time. It can be pointed out that high-throughput sequencing can fix nucleic acid molecules on arrayed sequencing chips. Through each round of reaction between nucleic acid molecules, specific enzymes and fluorescent probes, different bases will emit fluorescent signals of different wavelengths. This process is collected by the imaging system, and the collected images are reconstructed and identified on this basis, and the base sequence can be determined from them.

According to relevant technologies, in the above-mentioned process of determining the base sequence, reducing the distance between adjacent nucleic acid molecules and increasing the arrangement density of nucleic acid molecules on the chip can effectively increase the number of bases in a unit field of view, thereby increasing the sequencing throughput and reducing the sequencing cost per unit throughput. However, due to the optical diffraction limit, when the distance between nucleic acid molecules is less than the resolution of the imaging system, the fluorescent signals of adjacent nucleic acid molecules will crosstalk, thereby greatly affecting the accuracy of base calling. Therefore, how to effectively improve the accuracy of base calling in the process of sequencing nucleic acid molecules has become a major problem that needs to be solved urgently in the industry.

Summary of the invention

The present disclosure aims to solve at least one of the technical problems existing in the prior art. To this end, the present disclosure proposes a nucleic acid molecule sequencing method and a related device, which can effectively improve the accuracy of base calling in the process of sequencing nucleic acid molecules.

The nucleic acid molecule sequencing method according to the first aspect of the present disclosure includes:

Acquire a sequencing image of the target nucleic acid sample, wherein the sequencing image is an image acquired by collecting an image of the target nucleic acid sample using a preset optical sequencing system;

Performing image processing on the sequencing image based on an image processing model to obtain a target image, wherein the image processing model is a model trained using a constructed true value image of nucleic acid molecules and a corresponding simulated nucleic acid image, wherein the simulated nucleic acid image is an image obtained by performing simulated sequencing on a simulated nucleic acid sample corresponding to the true value image of nucleic acid molecules based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system;

Nucleic acid molecule sequencing is performed according to the target image to obtain a sequencing result corresponding to the target nucleic acid sample.

According to some embodiments of the present disclosure, the performing image processing on the sequencing image based on the image processing model to obtain the target image further includes training the image processing model, specifically including:

Constructing the true value image of the nucleic acid molecule based on the simulated nucleic acid sample;

Performing simulated sequencing based on the simulated nucleic acid sample by the simulated optical system to obtain the simulated nucleic acid image;

Inputting the true value image of the nucleic acid molecule and the simulated nucleic acid image corresponding to the true value image of the nucleic acid molecule into the original image processing model, and iteratively training the image processing model;

When the image processing model meets the first predetermined condition during iterative training, the trained image processing model is obtained.

According to some embodiments of the present disclosure, performing simulated sequencing based on the simulated nucleic acid sample by the simulated optical system to obtain the simulated nucleic acid image includes:

Simulating optical calibration information of the preset optical sequencing system;

The nucleic acid distribution information is simulated based on the simulated optical calibration information to obtain the simulated nucleic acid image.

According to some embodiments of the present disclosure, the nucleic acid molecules in the simulated nucleic acid sample include a plurality of bases, each of which is labeled with a fluorescent signal;

The simulating the optical calibration information of the preset optical sequencing system includes:

Determine the pixel size of the preset optical sequencing system; wherein the pixel size is the size of a single pixel in the preset optical sequencing system corresponding to the imaging plane;

Performing optical imaging analysis based on the fluorescence signal corresponding to the base in the simulated nucleic acid sample to obtain an optical transfer function;

The pixel size is integrated with the optical transfer function to obtain the simulated optical calibration information.

According to some embodiments of the present disclosure, performing optical imaging analysis on the fluorescence signal corresponding to the base in the simulated nucleic acid sample to obtain an optical transfer function includes:

Scanning and imaging the simulated nucleic acid sample to obtain a scanned nucleic acid image;

Performing a local maximum search based on the scanned nucleic acid image to obtain a fluorescent image reflecting each of the fluorescent signals;

Performing Gaussian fitting and averaging processing on the fluorescence images corresponding to the multiple fluorescence signals to obtain a point spread function;

Performing Fourier transform on the point spread function to obtain the optical transfer function.

According to some embodiments of the present disclosure, integrating the pixel size with the optical transfer function to obtain the simulated optical calibration information includes:

Extracting noise from blank areas between the nucleic acid molecules in the simulated nucleic acid sample to obtain simulated noise information;

The pixel size, the optical transfer function and the simulated noise information are integrated to obtain the simulated optical calibration information.

According to some embodiments of the present disclosure, simulating the nucleic acid distribution information based on the simulated optical calibration information to obtain the simulated nucleic acid image includes:

Mapping the nucleic acid distribution information to an imaging space based on the pixel size to obtain a first simulated image of the simulated nucleic acid sample in the imaging space;

Performing imaging performance simulation on the first simulation image based on the optical transfer function to obtain a second simulation image;

Environmental noise simulation is performed on the second simulated image based on the simulated noise information to obtain the simulated nucleic acid image.

According to some embodiments of the present disclosure, the step of inputting the true value image of the nucleic acid molecule and the simulated nucleic acid image corresponding to the true value image of the nucleic acid molecule into the original image processing model and iteratively training the image processing model includes:

In each round of iterative training, the simulated nucleic acid image is input into the image processing model for image processing training to obtain the processing result of this round;

Comparing the processing result of this round with the true value image of the nucleic acid molecule to obtain training deviation data;

Based on the training bias data, weight parameters of the image processing model are updated.

According to some embodiments of the present disclosure, when the image processing model meets the first predetermined condition in iterative training, obtaining the trained image processing model includes:

When the training deviation data reflects that the image processing model converges during iterative training, it is determined that the image processing model meets the first predetermined condition during the iterative training, and the trained image processing model is obtained.

According to the second aspect of the present disclosure, a nucleic acid molecule sequencing device includes:

An image acquisition module, used to acquire a sequencing image of a target nucleic acid sample, wherein the sequencing image is an image acquired by acquiring an image of the target nucleic acid sample using a preset optical sequencing system;

An image processing module, used for performing image processing on the sequencing image based on an image processing model to obtain a target image, wherein the image processing model is a model trained by using a constructed true value image of nucleic acid molecules and a corresponding simulated nucleic acid image, wherein the simulated nucleic acid image is an image obtained by performing simulated sequencing on a simulated nucleic acid sample corresponding to the true value image of nucleic acid molecules based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system;

The sequence determination module is used to perform nucleic acid molecule sequencing according to the target image to obtain a sequencing result corresponding to the target nucleic acid sample.

In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising: a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the nucleic acid molecule sequencing method as described in any one of the embodiments of the first aspect of the present disclosure.

In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement a nucleic acid molecule sequencing method as described in any one of the embodiments of the first aspect of the present disclosure.

The nucleic acid molecule sequencing method and related device according to the embodiments of the present disclosure have at least the following beneficial effects:

The disclosed nucleic acid molecule sequencing method can first obtain a sequencing image of a target nucleic acid sample, wherein the sequencing image is an image obtained by using a preset optical sequencing system to collect an image of the target nucleic acid sample; the sequencing image is processed based on an image processing model to obtain a target image, wherein the image processing model is a model trained using a constructed nucleic acid molecule true value image and a corresponding simulated nucleic acid image, wherein the simulated nucleic acid image is an image obtained by performing simulated sequencing on a simulated nucleic acid sample corresponding to the nucleic acid molecule true value image based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system; and then nucleic acid molecule sequencing is performed according to the target image to obtain a sequencing result corresponding to the target nucleic acid sample. Since the image processing model is trained with the nucleic acid molecule true value image and the corresponding simulated nucleic acid image, and the simulated nucleic acid image is an image obtained by performing simulated sequencing on a simulated nucleic acid sample corresponding to the nucleic acid molecule true value image based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system. Therefore, the image processing model is used to process the sequencing image, and the resolution can be improved, so that the accuracy of base calling can be effectively improved in the process of sequencing nucleic acid molecules.

Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or will be learned through practice of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG1 is a flow chart of a nucleic acid molecule sequencing method provided by an embodiment of the present disclosure;

FIG2 is a schematic diagram of an image processing model of a deep residual channel attention neural network structure provided by an embodiment of the present disclosure;

FIG3 is a schematic diagram of the principle of the nucleic acid molecule sequencing method provided by an embodiment of the present disclosure;

Flow chart of training the image processing model before step S102 in FIG4 ;

FIG5 is a flow chart of step S402 in FIG4 ;

FIG6 is a flow chart of step S501 in FIG5 ;

FIG7 is a flow chart of step S602 in FIG6 ;

FIG8(a) to FIG8(g) are schematic diagrams of obtaining optical transfer functions provided by embodiments of the present disclosure;

FIG9 is a flow chart of step S603 in FIG6 ;

FIG10 is a schematic diagram of determining the average value, standard deviation of the signal in the blank area and the average maximum value of the nucleic acid molecule area provided by an embodiment of the present disclosure;

FIG11 is a flow chart of step S502 in FIG5 ;

FIG. 12( a) to FIG. 12( c) are schematic diagrams of obtaining simulated nucleic acid images provided by embodiments of the present disclosure;

FIG13 is a flow chart of step S403 in FIG4 ;

FIG14 is a sequence result of a multi-spaced nucleic acid molecule chip by a sequencer provided in a specific embodiment of the present disclosure;

FIG. 15 is a sequencing result of a multi-spacing nucleic acid molecule chip using a high sampling optical machine provided in the second specific embodiment of the present disclosure;

FIG16 is a schematic diagram of the structure of a nucleic acid molecule sequencing device provided in an embodiment of the present disclosure;

FIG. 17 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure.

DETAILED DESCRIPTION

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

In the description of the present disclosure, the meaning of "a plurality" is more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

In the description of the present disclosure, it can be understood that the descriptions involving orientations, such as up, down, left, right, front, back, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, rather than indicating or implying that the device or element referred to has a specific orientation, is constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present disclosure.

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

In the description of the present disclosure, it can be explained that, unless otherwise clearly defined, the terms such as setting, installing, connecting, etc. should be understood in a broad sense, and the technical personnel in the relevant technical field can reasonably determine the specific meanings of the above terms in the present disclosure in combination with the specific content of the technical solution. In addition, the identification of specific steps below does not represent a limitation on the order of steps and execution logic, and the execution order and execution logic between each step should be understood and inferred with reference to the contents described in the embodiment.

High-throughput sequencing is a technology for sequencing nucleic acid molecules, which can perform parallel sequence determination on a large number of nucleic acid molecules at a time. It can be pointed out that in the high-throughput sequencing process, nucleic acid molecules can be connected based on the solid surface, complementary probes with fluorescent groups can be connected to nucleic acid molecules, and then the base sequences can be confirmed in sequence through fluorescent imaging.

According to relevant technologies, nucleic acid molecules are fixed on arrayed sequencing chips. Through each round of reaction between nucleic acid molecules, specific enzymes and fluorescent probes, different bases will emit fluorescent signals of different wavelengths. This process is collected by the imaging system. On this basis, the collected images are reconstructed and identified, and the base sequence can be determined from them. Among them, reducing the distance between adjacent nucleic acid molecules and increasing the arrangement density of nucleic acid molecules on the chip can effectively increase the number of bases per unit field of view, thereby increasing the sequencing throughput and reducing the sequencing cost per unit throughput. However, due to the optical diffraction limit, when the distance between nucleic acid molecules is less than the resolution of the imaging system, the fluorescent signals of adjacent nucleic acid molecules will crosstalk, thereby greatly affecting the accuracy of base calling. Therefore, how to effectively improve the accuracy of base calling in the process of sequencing nucleic acid molecules has become a major problem that needs to be solved in the industry.

The present disclosure aims to solve at least one of the technical problems existing in the prior art. To this end, the present disclosure proposes a nucleic acid molecule sequencing method and a related device, which can effectively improve the accuracy of base calling in the process of sequencing nucleic acid molecules.

The following description is based on the accompanying drawings.

1 , the nucleic acid molecule sequencing method provided according to an embodiment of the present disclosure may include, but is not limited to, the following steps S101 to S103 .

Step S101, obtaining a sequencing image of a target nucleic acid sample, where the sequencing image is an image obtained by collecting an image of the target nucleic acid sample using a preset optical sequencing system;

Step S102, performing image processing on the sequencing image based on an image processing model to obtain a target image, wherein the image processing model is a model trained using a constructed true value image of a nucleic acid molecule and a corresponding simulated nucleic acid image, wherein the simulated nucleic acid image is an image obtained by performing simulated sequencing on a simulated nucleic acid sample corresponding to the true value image of the nucleic acid molecule based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating a preset optical sequencing system;

Step S103, performing nucleic acid molecule sequencing according to the target image to obtain a sequencing result corresponding to the target nucleic acid sample.

According to the nucleic acid molecule sequencing method shown in steps S101 to S103 of the present disclosure, a sequencing image of a target nucleic acid sample can be first obtained, and the sequencing image is an image obtained by collecting an image of the target nucleic acid sample using a preset optical sequencing system; the sequencing image is image processed based on an image processing model to obtain a target image, and the image processing model is a model trained using a constructed nucleic acid molecule true value image and a corresponding simulated nucleic acid image, and the simulated nucleic acid image is an image obtained by simulated sequencing of a simulated nucleic acid sample corresponding to the nucleic acid molecule true value image based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system; and then nucleic acid molecule sequencing is performed according to the target image to obtain a sequencing result corresponding to the target nucleic acid sample. Since the image processing model is trained with the nucleic acid molecule true value image and the corresponding simulated nucleic acid image, and the simulated nucleic acid image is an image obtained by simulated sequencing of a simulated nucleic acid sample corresponding to the nucleic acid molecule true value image based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system, the image processing model is used to process the sequencing image, and the resolution can be improved, so that the accuracy of base calling can be effectively improved in the process of sequencing nucleic acid molecules.

In step S101 of some embodiments, a sequencing image of a target nucleic acid sample is obtained, and the sequencing image is an image obtained by capturing an image of the target nucleic acid sample using a preset optical sequencing system. It can be explained that in order to sequence nucleic acid molecules, a sequencing image of the target nucleic acid sample can be first obtained. Among them, the target nucleic acid sample refers to a nucleic acid sample that serves as a sequencing target, and by capturing an image of the target nucleic acid sample, a sequencing image of the target nucleic acid sample can be obtained. It should be understood that image capture for the target nucleic acid sample can rely on a preset optical sequencing system, and the preset optical sequencing system refers to a nucleic acid image capture and sequencing system that is pre-set with certain optical conditions. It can be pointed out that capturing images of the target nucleic acid sample based on a preset optical sequencing system under different optical conditions will have different effects.

In step S102 of some embodiments, the sequencing image is processed based on the image processing model to obtain the target image, the image processing model is a model obtained by training with the constructed true value image of nucleic acid molecules and the corresponding simulated nucleic acid image, the simulated nucleic acid image is an image obtained by simulating sequencing of the simulated nucleic acid sample corresponding to the true value image of nucleic acid molecules based on the simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system. It can be emphasized that the nucleic acid molecules are fixed on the arrayed sequencing chip, and different bases will emit fluorescent signals of different wavelengths through each round of reaction between nucleic acid molecules, specific enzymes and fluorescent probes. This process is collected by the imaging system, and the collected image is reconstructed and identified on this basis, and the base sequence can be determined therefrom. It can be pointed out that both the target nucleic acid sample and the simulated nucleic acid sample can be prepared in the above manner, the difference being that the target nucleic acid sample is a nucleic acid sample used as a sequencing target in actual applications, and the simulated nucleic acid sample is used to construct the training data of the image processing model.

Some embodiments can effectively increase the number of bases per unit field of view by reducing the distance between adjacent nucleic acid molecules and increasing the arrangement density of nucleic acid molecules on the chip, thereby increasing sequencing throughput and reducing unit throughput sequencing costs. However, due to the optical diffraction limit, when the distance between nucleic acid molecules is less than the resolution of the imaging system, the fluorescent signals of adjacent nucleic acid molecules will crosstalk, thereby greatly affecting the accuracy of base calling.

In order to solve this problem, in step S102 of the embodiment of the present disclosure, the sequencing image can be processed based on the image processing model to obtain the target image. It can be pointed out that the image processing of the sequencing image based on the image processing model is intended to improve the resolution of the sequencing image through the image processing model, that is, super-resolution processing. Among them, super-resolution processing is to improve the resolution of the original image through hardware or software methods, and the process of obtaining a high-resolution image through a series of low-resolution images is super-resolution reconstruction.

It can be clearly understood that the image processing model is a model obtained by training with the constructed true value image of nucleic acid molecules and the corresponding simulated nucleic acid image, wherein the simulated nucleic acid image is an image obtained by simulating sequencing of a simulated nucleic acid sample corresponding to the true value image of nucleic acid molecules based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating a preset optical sequencing system. It should be understood that the image obtained by simulating sequencing of a simulated nucleic acid sample corresponding to the true value image of nucleic acid molecules by a simulated optical system can more realistically simulate the actual acquisition of the sequencing image. Therefore, using the true value image of nucleic acid molecules and the corresponding simulated nucleic acid image to train the image processing model can improve the image processing model's super-resolution processing capability for images. After the sequencing image is processed based on the image processing model, a target image with a higher resolution can be obtained, so as to facilitate nucleic acid molecule sequencing according to the target image in subsequent steps. In this way, when the spacing between nucleic acid molecules is less than the resolution of the imaging system, the crosstalk effect caused by the fluorescent signals of adjacent nucleic acid molecules can be reduced, and the bases can be effectively improved in the process of sequencing nucleic acid molecules. The accuracy of the judgment.

Referring to Figure 2, in some more specific embodiments, the image processing model can be a deep residual channel attention neural network structure. The target image can be obtained by performing image processing on the sequencing image based on the image processing model. Specifically, the sequencing image can be first subjected to shallow feature extraction to obtain the sequencing image features, and then sequentially passed through multiple layers of RG and Conv for output, and input together with the initial sequencing image features into the reconstruction module to generate the target image. Among them, RG: residual shallow feature extraction module, FCAB: channel attention module, Conv: convolution layer, RELU: linear rectification activation layer, FFT: Fourier transform layer; Sigmoid: S-type function activation layer.

In step S103 of some embodiments, nucleic acid molecules are sequenced according to the target image to obtain sequencing results corresponding to the target nucleic acid sample. It can be explained that, since the sequencing image is processed by the image processing model, the resolution of the nucleic acid molecules in the target image is improved compared to the resolution of the nucleic acid molecules in the sequencing image. On this basis, sequencing nucleic acid molecules according to the target image helps to reduce the crosstalk caused by the fluorescent signals of adjacent nucleic acid molecules when the spacing between nucleic acid molecules is less than the resolution of the imaging system, so that the accuracy of base calling can be effectively improved in the process of sequencing nucleic acid molecules.

In some embodiments, a gene sequencer can be used for nucleic acid molecule sequencing. It can be explained that a gene sequencer, also known as a DNA sequencer, is an instrument for determining the base sequence, type and quantity of DNA fragments. It is mainly used in human genome sequencing, genetic diagnosis of human genetic diseases, infectious diseases and cancer, forensic paternity testing and individual identification, screening of bioengineering drugs, animal and plant hybrid breeding, etc. At present, the working principle of a DNA sequencer is mainly based on the dideoxy chain end termination method or the chemical degradation method. Although these two methods are different in principle, they are both based on starting the extension of the nucleotide chain at a fixed site, randomly terminating at a certain specific base, and generating a series of nucleotide chains of four groups of different lengths with A, T, C, and G as the ends. The fragments are separated and detected by electrophoresis on a denaturing polyacrylamide gel, thereby obtaining a DNA sequence. Since the dideoxy chain end termination method is simpler and more suitable for optical automatic detection, it is widely used in fully automatic DNA sequencers that are simply intended to determine the DNA sequence. The chemical degradation method has important application value in studying the secondary structure of DNA and protein-DNA interactions. It should be understood that there are various ways to perform nucleic acid molecule sequencing based on the target image, which may include, but not be limited to, the specific embodiments listed above.

Referring to the schematic diagram of the principle of the nucleic acid molecule sequencing method shown in FIG3 . The simulated nucleic acid sample corresponding to the true value image of the nucleic acid molecule obtains a simulated nucleic acid image after simulated sequencing, and then the image processing model is trained based on the true value image of the nucleic acid molecule and the corresponding simulated nucleic acid image. In the process of nucleic acid molecule sequencing, the sequencing image of the target nucleic acid sample is first obtained, and then the sequencing image is processed based on the image processing model to obtain the target image, and the nucleic acid molecule is sequenced according to the target image to obtain the sequencing result corresponding to the target nucleic acid sample. The image processing model is used for image processing of the sequencing image to achieve an improvement in resolution, so that the accuracy of base calling can be effectively improved in the process of sequencing nucleic acid molecules.

Since steps S101 to S103 of the embodiment of the present disclosure have been described in detail above, the steps that may be included before step S102 will be described in detail below.

The following is a description of an embodiment of the present disclosure of training an image processing model before step S102.

4 , according to some embodiments of the present disclosure, before performing image processing on the sequencing image based on the image processing model to obtain the target image in step S102 , it may also include training the image processing model, which may specifically include, but is not limited to, the following steps S401 to S404 .

Step S401, constructing a true value image of nucleic acid molecules based on a simulated nucleic acid sample;

Step S402, performing simulated sequencing based on the simulated nucleic acid sample by a simulated optical system to obtain a simulated nucleic acid image;

Step S403, inputting the true value image of the nucleic acid molecule and the simulated nucleic acid image corresponding to the true value image of the nucleic acid molecule into the original image processing model, and iteratively training the image processing model;

Step S404: When the image processing model meets the first predetermined condition during iterative training, a trained image processing model is obtained.

In some embodiments, step S401 constructs a true value image of nucleic acid molecules based on a simulated nucleic acid sample. It can be emphasized that nucleic acid molecules are fixed on an arrayed sequencing chip, and through each round of reaction between nucleic acid molecules, specific enzymes and fluorescent probes, different bases will emit fluorescent signals of different wavelengths. This process is collected by the imaging system, and the collected images are reconstructed and identified on this basis, and the bases can be determined from them. It can be pointed out that the simulated nucleic acid sample can be prepared in the above manner, and the simulated nucleic acid sample is used to construct the training data of the image processing model. It can be pointed out that the base sequence of the simulated nucleic acid sample is known, and on this basis, the true value image of the nucleic acid molecule can be constructed based on the known base sequence of the simulated nucleic acid sample.

According to some more specific embodiments, each nucleic acid molecule of the simulated nucleic acid sample is regularly loaded into the sequencing chip for arrangement, and the arrangement unit of the nucleic acid arrangement is called a block. Each imaging field of view contains multiple arrangement unit blocks, and the spacing between adjacent arrangement unit blocks is called a tracking line. It can be pointed out that the information such as the number of arrangement unit blocks, the arrangement order, and the spacing between nucleic acid molecules divided in each imaging field of view of the simulated nucleic acid sample is recorded in the mask file. Since the mask file contains the known nucleic acid arrangement in the simulated nucleic acid sample, the true value image of the nucleic acid molecule can be constructed based on the mask file to obtain the true value image of the nucleic acid molecule. Among them, a group of simulations can include multiple mask files describing different imaging fields.

According to some more specific embodiments, each nucleic acid molecule is composed of a base sequence, which can be randomly generated or from a standard genome library such as Escherichia coli and humans. In the process of constructing a true value image of a nucleic acid molecule based on a mask file, one base can be imaged in turn when the nucleic acid molecule is imaged for multiple rounds. After several rounds of imaging of the nucleic acid molecule in each imaging field of view, the base of the nucleic acid molecule in each imaging field of view can be clearly presented in the true value image of the nucleic acid molecule obtained by simulation sequencing. Among them, since different bases can emit fluorescent signals of different wavelengths, the brightness of the fluorescent signal corresponding to each base can be represented by a set of normalized four-dimensional vectors [i_a, i_c, i_g, i_t]. When the base type is one of A, C, G, and T, the brightness of the corresponding position is set to a certain range of values. If there is no base at a certain position, the four elements of the vector are all 0. In this way, the true value of the simulated nucleic acid sample can be clearly presented in the true value image of the nucleic acid molecule.

In step S402 of some embodiments, a simulated nucleic acid sample is simulated and sequenced by a simulated optical system to obtain a simulated nucleic acid image. It can be explained that the simulated optical system is an optical system obtained by simulating a preset optical sequencing system, and thus the simulated optical system simulates the optical conditions of the preset optical sequencing system, and can perform simulated sequencing based on the simulated nucleic acid sample to obtain a simulated nucleic acid image.

In the process of executing the nucleic acid molecule sequencing method disclosed in the present invention, the sequencing image is an image obtained by collecting the target nucleic acid sample using a preset optical sequencing system. Therefore, in order to enable the trained image processing model to be adapted to super-resolution processing of the sequencing image, in the process of training the image processing model, a simulated nucleic acid sample can be simulated sequenced by a simulated optical system to obtain a simulated nucleic acid image.

5 , according to some embodiments of the present disclosure, step S402 performs simulated sequencing based on a simulated nucleic acid sample through a simulated optical system to obtain a simulated nucleic acid image, which may include, but is not limited to, the following steps S501 to S502 .

Step S501, simulating optical calibration information of a preset optical sequencing system;

Step S502 , simulating the nucleic acid distribution information based on the simulated optical calibration information to obtain a simulated nucleic acid image.

In step S501 of some embodiments, the optical calibration information of the preset optical sequencing system is simulated. It can be explained that since the simulated optical system is an optical system obtained by simulating the preset optical sequencing system, the simulated optical system simulates the optical conditions of the preset optical sequencing system, and can perform simulated sequencing based on the simulated nucleic acid sample to obtain a simulated nucleic acid image. Specifically, the simulated optical system simulates the optical conditions of the preset optical sequencing system, which can be a simulation of the optical calibration information of the preset optical sequencing system. The so-called optical calibration information means the optical physical quantity information that can be obtained by the simulated optical system in the image acquisition process of simulating the preset optical sequencing system.

In some embodiments, since the preset optical sequencing system is a nucleic acid image acquisition and sequencing system that is pre-set with certain optical conditions, the optical calibration information of the preset optical sequencing system can be simulated by querying its pre-set parameters.

6 , according to some more specific embodiments of the present disclosure, the nucleic acid molecules in the simulated nucleic acid sample include a plurality of bases, each base being labeled with a fluorescent signal. Step S501 simulates the optical calibration information of the preset optical sequencing system, which may include, but is not limited to, the following steps S601 to S603.

Step S601, determining the pixel size of a preset optical sequencing system; wherein the pixel size is the pixel size corresponding to a single pixel in the preset optical sequencing system. The size of the imaging plane;

Step S602, performing optical imaging analysis based on the fluorescence signals corresponding to the bases in the simulated nucleic acid sample to obtain an optical transfer function;

Step S603: Integrate the pixel size and the optical transfer function to obtain simulated optical calibration information.

In step S601 of some embodiments, the pixel size of the preset optical sequencing system is determined; wherein the pixel size is the size of a single pixel in the preset optical sequencing system corresponding to the imaging plane. It can be explained that the preset optical sequencing system uses a camera to collect images, then the pixel size represents the size of a single pixel in the camera corresponding to the imaging plane, for example, the physical size of the camera pixel is 2μm, and the magnification of the preset optical sequencing system is 20 times, then the corresponding pixel size of the preset optical sequencing system is 0.1μm. It can be pointed out that the determination of the pixel size of the preset optical sequencing system is intended to establish a mapping of the simulated nucleic acid sample to the camera imaging space.

In some more specific embodiments, the pixel size of the preset optical sequencing system can be determined by using a high-precision stage to move the sequencing chip loaded with the simulated nucleic acid sample a large distance (e.g., 100 μm), and in this process, the number of displacement pixels at the corresponding position is measured from the image, and then the distance moved is divided by the number of displacement pixels to calculate the system pixel size. It should be understood that there are various implementation methods for determining the pixel size of the preset optical sequencing system, which may include, but are not limited to, the specific embodiments listed above.

In step S602 of some embodiments, optical imaging analysis is performed based on the fluorescent signals corresponding to the bases in the simulated nucleic acid sample to obtain an optical transfer function. It can be explained that the optical transfer function is used to model the imaging performance of the optical system, and the modeled imaging performance can be used to generate a simulated nucleic acid image. By performing optical imaging analysis based on the fluorescent signals corresponding to the bases in the simulated nucleic acid sample, that is, modeling the imaging performance of the optical system, the corresponding optical transfer function can be obtained.

7 , according to some more specific embodiments of the present disclosure, step S602 performs optical imaging analysis based on the fluorescence signals corresponding to the bases in the simulated nucleic acid sample to obtain the optical transfer function, which may include, but is not limited to, the following steps S701 to S704 .

Step S701, scanning and imaging the simulated nucleic acid sample to obtain a scanned nucleic acid image;

Step S702, performing a local maximum search based on the scanned nucleic acid image to obtain a fluorescent image reflecting each fluorescent signal;

Step S703, performing Gaussian fitting and averaging processing on the fluorescence images corresponding to the multiple fluorescence signals to obtain a point spread function;

Step S704: Perform Fourier transform on the point spread function to obtain an optical transfer function.

In step S701 of some embodiments, the simulated nucleic acid sample is scanned and imaged to obtain a scanned nucleic acid image. It can be explained that scanning imaging refers to the process of capturing a physical object to accurately represent its geometric shape in a digital environment. Since the optical transfer function is used to model the imaging performance of an optical system, some embodiments can scan and image a sparsely dotted area in a simulated nucleic acid sample, so that a scanned nucleic acid image can be obtained more efficiently in a sparsely dotted simulated nucleic acid sample. It should be understood that the sparsely dotted area in a simulated nucleic acid sample refers to an area where the spacing between nucleic acid molecules is much greater than the optical resolution.

In steps S702 to S704 of some embodiments, a local maximum search is first performed based on the scanned nucleic acid image to obtain a fluorescent image reflecting each fluorescent signal; then the fluorescent images corresponding to multiple fluorescent signals are subjected to Gaussian fitting and averaging to obtain a point spread function; the point spread function is subjected to Fourier transformation to obtain an optical transfer function. It can be explained that the scanned nucleic acid image can also be filtered by a Gaussian difference function, and then a single fluorescent sphere corresponding to each base is extracted through a local maximum search to form a fluorescent image containing multiple fluorescent spheres, and then a point spread function is obtained through Gaussian fitting and averaging, and then the system optical transfer function can be obtained after Fourier transformation.

In the embodiment of the present disclosure shown by steps S701 to S704, the simulated nucleic acid sample is first scanned and imaged to obtain a scanned nucleic acid image; then a local maximum search is performed based on the scanned nucleic acid image to obtain a fluorescent image reflecting each fluorescent signal; the fluorescent images corresponding to multiple fluorescent signals are Gaussian-fitted and averaged to obtain a point spread function; and the point spread function is Fourier transformed to obtain an optical transfer function. The optical transfer function obtained in this way can more accurately model the imaging performance of the optical system.

Some embodiments of the present disclosure for obtaining optical transfer functions are provided with reference to Figures 8(a) to 8(g). It can be emphasized that the nucleic acid molecules in the simulated nucleic acid sample include a plurality of bases, each of which is labeled with a fluorescent signal.

In FIG8(a), the simulated nucleic acid sample is scanned and imaged to obtain a scanned nucleic acid image. In the scanned nucleic acid image shown in FIG8(a), A plurality of fluorescent microspheres discretely distributed on a sequencing chip loaded with a simulated nucleic acid sample;

In FIG8( b ), the region where the fluorescent microspheres are located is extracted based on the scanned nucleic acid image;

In FIG8(c), a filtering algorithm (such as local maximum search) is used to extract a sparse and discrete single fluorescent sphere region that meets the requirements, and a fluorescent image reflecting each fluorescent signal is obtained;

FIG8( d ) shows one of the fluorescence images of a single fluorescent microsphere, and FIG8( e ) shows another fluorescence image of a single fluorescent microsphere;

A Gaussian function is used to fit the intercepted single fluorescent ball image to obtain the image center, and multiple fluorescent ball images are aligned and averaged, that is, the fluorescent images corresponding to multiple fluorescent signals are Gaussian-fitted and averaged to obtain the point spread function;

FIG8(f) and FIG8(g) are schematic diagrams of performing Fourier transform on the point spread function to obtain the optical transfer function.

In step S603 of some embodiments, the pixel size is integrated with the optical transfer function to obtain simulated optical calibration information. It can be explained that the pixel size is used to establish a mapping from the simulated nucleic acid sample to the camera imaging space, and the optical transfer function is used to model the imaging performance of the optical system. By integrating the pixel size with the optical transfer function, the simulated optical system can be obtained as an image acquisition process of a simulated preset optical sequencing system, thereby determining the simulated optical calibration information.

9 , according to some embodiments of the present disclosure, step S603 integrates the pixel size with the optical transfer function to obtain simulated optical calibration information, which may include, but is not limited to, steps S901 to S902 .

Step S901, extracting noise from blank areas between nucleic acid molecules in a simulated nucleic acid sample to obtain simulated noise information;

Step S902 , integrating the pixel size, the optical transfer function and the simulated noise information to obtain simulated optical calibration information.

Through the embodiment shown in step S901 to step S902, noise is extracted from the blank areas between the nucleic acid molecules in the simulated nucleic acid sample to obtain simulated noise information, and then the pixel size, optical transfer function and simulated noise information are integrated to obtain simulated optical calibration information. It can be explained that the blank areas between the nucleic acid molecules in the simulated nucleic acid sample do not contain nucleic acids that can be sequenced, so the blank areas between the nucleic acid molecules are suitable for extracting noise therefrom to obtain simulated noise information. The disclosed embodiment aims to incorporate the optical noise of the preset optical sequencing system during image acquisition into the consideration of simulation modeling, improve the accuracy of the optical calibration information in simulating the nucleic acid distribution information, and obtain a higher quality simulated nucleic acid image.

In some more specific embodiments, the optical noise can be extracted manually or by algorithm. When manually extracted, the image software can read the average signal bg_ave, standard deviation bg_std of the blank area on the tracking line, and the maximum average sig of the nucleic acid molecule area; when extracted by algorithm, the algorithm can automatically locate the tracking line and the blank area and extract the corresponding signal. When simulating noise, a normalized Gaussian noise can be generated first, and each pixel is independent. The average value of the Gaussian noise is bg_ave/sig, and the standard deviation is bg_std/sig. Then, the Gaussian noise can be added to the simulated signal and the pixel value of the superimposed shot noise can be calculated by the Poisson distribution function.

Referring to the embodiment shown in FIG10 , a more specific embodiment of the present disclosure for determining the average value bg_ave, standard deviation bg_std of the blank area signal and the maximum average sig of the nucleic acid molecule area is provided. In FIG10 , the white box is the tracking line background area, and the average value and standard deviation of the signal in this area are measured as bg_ave and bg_std. The white cross indicates that the bright spot is a nucleic acid molecule signal, and the brightness of the center position of 10 bright spots is measured and the average value is sig.

In the embodiment of the present disclosure shown by steps S601 to S603, the pixel size of the preset optical sequencing system is first determined; wherein the pixel size is the size of a single pixel in the preset optical sequencing system corresponding to the imaging plane; optical imaging analysis processing is performed based on the fluorescence signal corresponding to the base in the simulated nucleic acid sample to obtain the optical transfer function; and the pixel size and the optical transfer function are then integrated to obtain optical calibration information. And the pixel size in the optical calibration information can be used to establish a mapping from the simulated nucleic acid sample to the camera imaging space, and the optical transfer function in the optical calibration information can be used to model the imaging performance of the optical system. It should be understood that the optical calibration information obtained in this way helps to simulate the nucleic acid distribution information more accurately and obtain a higher quality simulated nucleic acid image.

In step S502 of some embodiments, the nucleic acid distribution information is simulated based on the simulated optical calibration information to obtain a simulated nucleic acid image. It can be explained that after simulating the optical calibration information of the preset optical sequencing system, the nucleic acid distribution information can be simulated based on the simulated optical calibration information. The simulated optical calibration information is simulated to obtain a simulated nucleic acid image. Since the simulated optical calibration information is obtained by simulating a preset optical sequencing system, the simulated nucleic acid image can more realistically simulate the actual collection of sequencing images.

11 , according to some embodiments of the present disclosure, step S502 simulates the nucleic acid distribution information based on the simulated optical calibration information to obtain a simulated nucleic acid image, which may include, but is not limited to, the following steps S1101 to S1103 .

Step S1101, mapping nucleic acid distribution information to an imaging space based on pixel size to obtain a first simulated image of a simulated nucleic acid sample in the imaging space;

Step S1102, performing imaging performance simulation on the first simulation image based on the optical transfer function to obtain a second simulation image;

Step S1103, performing environmental noise simulation on the second simulated image based on the simulated noise information to obtain a simulated nucleic acid image.

In steps S1101 to S1103 of some embodiments, the nucleic acid distribution information is first mapped to the imaging space based on the pixel size to obtain a first simulated image of the simulated nucleic acid sample in the imaging space; then the imaging performance of the first simulated image is simulated based on the optical transfer function to obtain a second simulated image; the environmental noise of the second simulated image is simulated based on the simulated noise information to obtain a simulated nucleic acid image. It can be explained that the pixel size is used to establish a mapping of the simulated nucleic acid sample to the camera imaging space. Therefore, the nucleic acid distribution information can be mapped to the imaging space based on the pixel size to obtain the first simulated image of the simulated nucleic acid sample in the imaging space. Since the optical transfer function is used to model the imaging performance of the optical system, the imaging performance of the first simulated image can be simulated based on the optical transfer function to obtain the second simulated image. Since the simulated noise information is the optical noise when the preset optical sequencing system performs image acquisition, the environmental noise of the second simulated image can be simulated based on the simulated noise information, and finally the simulated nucleic acid image is obtained. It should be understood that, based on the optical calibration information including pixel size, optical transfer function and simulated noise information, the optical calibration information helps to simulate the preset optical sequencing system more accurately, so that the simulated nucleic acid image obtained by simulation can more realistically simulate the actual acquisition of the sequencing image.

Referring to some more specific embodiments shown in Figures 12(a) to 12(c), during simulation, the distribution of nucleic acid molecules in each imaging field of view can be first determined, and a simulated nucleic acid molecule list can be generated, as shown in Figure 12(a); the nucleic acid molecule list can be mapped to a two-color or four-color imaging space, as shown in Figure 12(b), which is mapping the nucleic acid molecule list to a four-color imaging space; the mapped image can then be low-pass filtered using the measured optical transfer function, and finally simulated noise can be added to obtain a sequencing image that is close to the low-resolution during nucleic acid molecule sequencing, that is, a simulated nucleic acid image, as shown in Figure 12(c).

In some more specific embodiments, when performing multiple rounds of simulation, the actual signal and noise levels are extracted based on the images actually captured by the system, and the decrease in signal-to-noise ratio in long-cycle sequencing can also be simulated.

Through the embodiment of the present disclosure shown in step S501 to step S502, the optical calibration information of the preset optical sequencing system is first simulated, and then the nucleic acid distribution information is simulated based on the simulated optical calibration information to obtain a simulated nucleic acid image. The optical calibration information obtained by simulating the preset optical sequencing system is used to simulate the simulated nucleic acid image, which helps to improve the simulation effect, so that the simulated nucleic acid image can more realistically simulate the actual collection of the sequencing image.

In step S403 of some embodiments, the true value image of nucleic acid molecules and the simulated nucleic acid image corresponding to the true value image of nucleic acid molecules are input into the original image processing model, and the image processing model is iteratively trained. It can be explained that the simulated nucleic acid image is an image obtained by simulating the simulated nucleic acid sample corresponding to the true value image of nucleic acid molecules, which can simulate the acquisition of sequencing images more realistically. Inputting the simulated nucleic acid image into the original image processing model for training helps to make the trained image processing model adaptable to super-resolution processing of sequencing images; the true value image of nucleic acid molecules can reflect the real and accurate arrangement of base sequences in the simulated nucleic acid sample, and inputting the simulated nucleic acid image into the original image processing model for training can improve the super-resolution processing capability of the image processing model. Therefore, inputting the true value image of nucleic acid molecules and the simulated nucleic acid image corresponding to the true value image of nucleic acid molecules into the original image processing model, and iteratively training the image processing model can continuously improve the super-resolution processing capability of the image processing model for images during iterative training, and make the image processing model adaptable to super-resolution processing of sequencing images.

13, according to some embodiments of the present disclosure, step S403 inputs the true value image of the nucleic acid molecule and the simulated nucleic acid image corresponding to the true value image of the nucleic acid molecule into the original image processing model, and iteratively trains the image processing model, which may include, but is not limited to, the following steps S1301 to S1302. Step S1303:

Step S1301, in each round of iterative training, the simulated nucleic acid image is input into the image processing model for image processing training to obtain the processing result of this round;

Step S1302, comparing the processing result of this round with the true value image of the nucleic acid molecule to obtain training deviation data;

Step S1303, updating the weight parameters of the image processing model based on the training deviation data.

In step S1301 of some embodiments, in each round of iterative training, the simulated nucleic acid image is input into the image processing model for image processing training to obtain the processing result of this round. It can be explained that since the simulated nucleic acid image is an image obtained by simulating the simulated nucleic acid sample corresponding to the true value image of the nucleic acid molecule, it can more realistically simulate the collection of sequencing images, and several rounds of iterative training are intended to optimize the super-resolution processing capability of the image processing model, therefore, in each round of iterative training, the simulated nucleic acid image can be input into the image processing model for image processing training to obtain the processing result of this round.

In step S1302 of some embodiments, the processing results of this round are compared with the true image of the nucleic acid molecule to obtain training deviation data. It can be explained that the true image of the nucleic acid molecule can reflect the real and accurate arrangement of the base sequence in the simulated nucleic acid sample, so the processing results of this round obtained in each round of iterative training can be compared with the true image of the nucleic acid molecule. Thereby, the training deviation data between the processing results of this round and the true image of the nucleic acid molecule is obtained. It can be pointed out that when the training deviation data between the processing results of this round and the true image of the nucleic acid molecule reflects that the processing results of this round are closer to the true image of the nucleic acid molecule, it means that the image processing model has a better effect in improving the resolution, and the image processing model has a stronger super-resolution processing capability for the image.

In step S1303 of some embodiments, the weight parameters of the image processing model are updated based on the training deviation data. It can be explained that after obtaining the training deviation data, the weight parameters of the image processing model can be updated. It can be explained that there are generally two types of parameters in the neural network model: one type of parameter is the tuning parameters in the machine learning algorithm, which can be flexibly set according to existing or existing experience, also known as hyperparameters. For example, the regularization coefficient λ, the depth of the tree in the decision tree model. A hyperparameter is also a parameter, which has the characteristics of a parameter, such as unknown, that is, it is not a known constant, but a configurable value, which can be assigned a "correct" value based on existing or existing experience, that is, a flexibly set value, which is not obtained through system learning; another type of parameter can be learned and estimated from the data, called a model parameter, that is, a learnable parameter of the model itself. For example, the weight coefficient (slope) and the deviation term (intercept) of the linear regression line are both model parameters. Learnable parameters specifically refer to parameter values learned during the training process of the neural network model. For learnable parameters, they usually start from a set of random values, and then update these values in an iterative manner as the neural network model learns. In fact, when the "neural network model is learning", it is more accurate to mean that the parameters of the neural network model are in the process of iterative updating, and the appropriate values of these parameters are gradually determined. It can be pointed out that the so-called appropriate value can be a value that minimizes or converges the loss function. Therefore, in some embodiments of the present disclosure, in order to gradually determine the appropriate values of these model parameters while the data classification model is in the process of iterative updating, the weight parameters of the image processing model can be updated based on the training bias data.

In the embodiment of the present disclosure shown by steps S1301 to S1303, in each round of iterative training, the simulated nucleic acid image is input into the image processing model for image processing training to obtain the processing result of this round, and then the processing result of this round is compared with the true value image of the nucleic acid molecule to obtain the training deviation data, and then the weight parameters of the image processing model are updated based on the training deviation data. In this way, the super-resolution processing capability of the image processing model for images can be continuously optimized in several rounds of iterative training.

In step S404 of some embodiments, when the image processing model meets the first predetermined condition in iterative training, a trained image processing model is obtained. It can be explained that when the image processing model meets the first predetermined condition in iterative training, it means that the image processing model's super-resolution processing capability for images has reached the expected level for practical application, and the image processing model can be adapted to super-resolution processing of sequencing images. In this case, the iterative training of the image processing model can be terminated to obtain the trained image processing model.

In some embodiments, the image processing model meets the first predetermined condition in the iterative training, which may be that after the image processing model performs image processing training on the simulated nucleic acid image in the iterative training, the processing result reaches the resolution level of the true value image of the nucleic acid molecule; The first predetermined condition is met during training, or the image processing model's super-resolution processing capability for images converges to a certain level during iterative training.

According to some more specific embodiments of the present disclosure, step S404, when the image processing model meets the first predetermined condition in iterative training, the trained image processing model is obtained, which may include, but is not limited to: when the training deviation data reflects that the image processing model converges in the iterative training, it is determined that the image processing model meets the first predetermined condition in the iterative training, and the trained image processing model is obtained. It can be explained that when the training deviation data reflects that the image processing model converges in the iterative training, it means that the super-resolution processing capability of the image processing model for the image has been optimized to a current limit level and is difficult to continue to improve. At this time, it can be determined that the image processing model meets the first predetermined condition in the iterative training, and the trained image processing model is obtained.

It should be understood that there are various embodiments for determining whether an image processing model meets the first predetermined condition during iterative training, which may include, but are not limited to, the specific embodiments listed above.

According to the disclosed embodiment shown in step S401 to step S404, a true image of nucleic acid molecules is first constructed based on a simulated nucleic acid sample; then a simulated sequencing is performed based on the simulated nucleic acid sample through a simulated optical system to obtain a simulated nucleic acid image; the true image of nucleic acid molecules and the simulated nucleic acid image corresponding to the true image of nucleic acid molecules are input into the original image processing model, and the image processing model is iteratively trained; when the image processing model meets the first predetermined condition in the iterative training, a trained image processing model is obtained. In this way, the image processing model can be improved in the process of training the image processing model for super-resolution processing of images, until the image processing model for super-resolution processing of images has reached the expected level that can be actually applied, and the image processing model can be adapted to super-resolution processing of sequencing images. On this basis, the trained image processing model is used for image processing of sequencing images, which helps to improve the image resolution, and helps to reduce the crosstalk caused by the fluorescent signals of adjacent nucleic acid molecules when the spacing between nucleic acid molecules is less than the resolution of the imaging system, so that the accuracy of base calling can be effectively improved in the process of sequencing nucleic acid molecules.

Next, two more specific embodiments are provided to illustrate the technical effects of the nucleic acid molecule sequencing method disclosed in the present invention.

Specific embodiment 1: sequencing a multi-spaced nucleic acid molecule chip on a sequencer optical machine.

The sequencer uses a 0.8NA air mirror, with an optical resolution of about 500-600nm, a sampling resolution of 566nm, and a nucleic acid molecule spacing of 715nm in normal use. When sequencing the E. coli genome library, the single-stranded 100-round (SE100) alignment rate can reach more than 90%. On this basis, this specific embodiment will sequence a multi-spacing sequencing chip (576nm, 480nm, 450nm).

First, nucleic acid molecule patterns with spacing of 576nm, 480nm, and 450nm are generated according to the chip template, and the true value images of nucleic acid molecules and the corresponding simulated nucleic acid images of multiple rounds of sequencing are generated to train the image processing model. Taking 480nm as an example, each imaging field of view FoV of the chip template contains 10*10 blocks with a total of 100 blocks. The number of rows/columns of nucleic acid molecules contained in each block is 145, 120, 180, 210, 240, 240, 210, 180, 120, 145, and the tracking line width between adjacent blocks is 1440nm. First, a list of nucleic acid molecules required for simulation is generated according to the template, and the nucleic acid molecules are mapped to the corresponding four-color imaging space according to the measured pixel size of 566nm.

The nucleic acid molecules on the sequencing chip are sequenced on the sequencer, and multiple rounds of sequencing images are taken. The image signal-to-noise ratio is extracted for each round of images, and simulation data pairs are generated based on the system optical transfer function and signal-to-noise ratio for training the image processing model.

After the resequencing image is processed by the image processing model, the bases of the target image are called and compared.

14 , the sequencing results show that after sequencing a multi-spacing nucleic acid molecule chip using the nucleic acid molecule sequencing method disclosed herein, the matching rate of nucleic acid molecules with a spacing of 576 nm can reach 90%, and the matching rate of nucleic acid molecules with a spacing of 480 nm can reach 75%, which is 18% higher than the traditional algorithm.

Specific embodiment 2: sequencing a multi-spacing nucleic acid molecule chip on a self-built high-sampling optical machine.

The self-built high-sampling optical machine uses a 0.8NA air mirror, with an optical resolution of approximately 500-600nm and a sampling resolution of 260nm. The nucleic acid molecular spacing used in normal use is 715nm.

When the E. coli genome library is sequenced using the nucleic acid molecular sequencing method disclosed in the present invention, the single-strand 100 round (SE100) alignment rate can reach more than 90%. The nucleic acid molecular sequencing method disclosed in the present invention is used to sequence a multi-spacing sequencing chip (576nm, 480nm, 450nm, 400nm, 360nm). The implementation steps refer to the above-mentioned specific embodiment 1.

Referring to Figure 15, the final sequencing results show that compared with the traditional algorithm, the matching rates of nucleic acid molecules with 576nm spacing, 480nm spacing, 450nm spacing, 400nm spacing and 360nm spacing using the algorithm described in this technical solution are increased by 10%, 14%, 18%, 17% and 24% respectively.

16 , according to some embodiments, the present disclosure provides a nucleic acid molecule sequencing device 1600, comprising:

An image acquisition module 1601 is used to acquire a sequencing image of a target nucleic acid sample, where the sequencing image is an image acquired by acquiring an image of the target nucleic acid sample using a preset optical sequencing system;

An image processing module 1602 is used to perform image processing on the sequencing image based on an image processing model to obtain a target image, wherein the image processing model is a model trained using a constructed true value image of a nucleic acid molecule and a corresponding simulated nucleic acid image, wherein the simulated nucleic acid image is an image obtained by performing simulated sequencing on a simulated nucleic acid sample corresponding to the true value image of the nucleic acid molecule based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating a preset optical sequencing system;

The sequence determination module 1603 is used to perform nucleic acid molecule sequencing according to the target image to obtain a sequencing result corresponding to the target nucleic acid sample.

It can be seen that the contents in the above-mentioned nucleic acid molecule sequencing method embodiment are all applicable to the embodiment of the present nucleic acid molecule sequencing device. The functions specifically implemented by the present nucleic acid molecule sequencing device embodiment are the same as those in the above-mentioned nucleic acid molecule sequencing method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned nucleic acid molecule sequencing method embodiment.

Referring to FIG. 17 , FIG. 17 illustrates a hardware structure of an electronic device according to another embodiment. The electronic device includes:

Processor 1701 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits to execute related programs to implement the technical solutions provided by the embodiments of the present disclosure;

The memory 1702 may be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1702 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1702, and are called by the processor 1701 to execute the nucleic acid molecule sequencing method of the embodiments of the present disclosure;

Input/output interface 1703, used to implement information input and output;

Communication interface 1704, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

A bus 1705 that transmits information between each component of the device (e.g., the processor 1701, the memory 1702, the input/output interface 1703, and the communication interface 1704);

The processor 1701 , the memory 1702 , the input/output interface 1703 and the communication interface 1704 are connected to each other in communication within the device via the bus 1705 .

The present disclosure also provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, so that the computer device executes and implements the above-mentioned nucleic acid molecule sequencing method.

The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It is understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

It should be understood that in this disclosure, "at least one (item)" means one or more, and "more" means two or more. "And/or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and/or B" can mean: only A exists, only B exists. And there are three situations where A and B exist at the same time, where A and B can be singular or plural. The character "/" generally indicates that the objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

It should be understood that in the description of the embodiments of the present disclosure, the meaning of multiple (or multiple items) is more than two, greater than, less than, exceed, etc. are understood to not include the number, and above, below, within, etc. are understood to include the number.

In the several embodiments provided in the present disclosure, it can be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including multiple instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of each embodiment method of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

It should also be understood that the various implementations provided in the embodiments of the present disclosure can be combined arbitrarily to achieve different technical effects.

The above is a specific description of the implementation methods of the present disclosure, but the present disclosure is not limited to the above implementation methods. Technical personnel familiar with the art can also make various equivalent modifications or substitutions without violating the spirit of the present disclosure. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present disclosure.

Claims (13)

[Corrected 15.07.2024 in accordance with Article 91]
A method for nucleic acid molecule sequencing, comprising: Acquire a sequencing image of the target nucleic acid sample, wherein the sequencing image is an image acquired by collecting an image of the target nucleic acid sample using a preset optical sequencing system; Performing image processing on the sequencing image based on an image processing model to obtain a target image, wherein the image processing model is a model trained using a constructed true value image of nucleic acid molecules and a corresponding simulated nucleic acid image, wherein the simulated nucleic acid image is an image obtained by performing simulated sequencing on a simulated nucleic acid sample corresponding to the true value image of nucleic acid molecules based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system; Nucleic acid molecule sequencing is performed according to the target image to obtain a sequencing result corresponding to the target nucleic acid sample. The method according to claim 1, characterized in that, before performing image processing on the sequencing image based on the image processing model to obtain the target image, it also includes training the image processing model, including: Constructing the true value image of the nucleic acid molecule based on the simulated nucleic acid sample; Performing simulated sequencing based on the simulated nucleic acid sample by the simulated optical system to obtain the simulated nucleic acid image; Inputting the true value image of the nucleic acid molecule and the simulated nucleic acid image corresponding to the true value image of the nucleic acid molecule into the original image processing model, and iteratively training the image processing model; When the image processing model meets the first predetermined condition during iterative training, the trained image processing model is obtained. The method according to claim 2, characterized in that the step of performing simulated sequencing based on the simulated nucleic acid sample by the simulated optical system to obtain the simulated nucleic acid image comprises: Simulating optical calibration information of the preset optical sequencing system; The nucleic acid distribution information is simulated based on the simulated optical calibration information to obtain the simulated nucleic acid image. The method according to claim 3, characterized in that the nucleic acid molecule in the simulated nucleic acid sample comprises a plurality of bases, each of which is marked with a fluorescent signal; The simulating the optical calibration information of the preset optical sequencing system includes: Determine the pixel size of the preset optical sequencing system; wherein the pixel size is the size of a single pixel in the preset optical sequencing system corresponding to the imaging plane; Performing optical imaging analysis based on the fluorescence signal corresponding to the base in the simulated nucleic acid sample to obtain an optical transfer function; The pixel size is integrated with the optical transfer function to obtain the simulated optical calibration information. The method according to claim 4, characterized in that the optical imaging analysis processing based on the fluorescence signal corresponding to the base in the simulated nucleic acid sample to obtain the optical transfer function comprises: Scanning and imaging the simulated nucleic acid sample to obtain a scanned nucleic acid image; Performing a local maximum search based on the scanned nucleic acid image to obtain a fluorescent image reflecting each of the fluorescent signals; Performing Gaussian fitting and averaging processing on the fluorescence images corresponding to the multiple fluorescence signals to obtain a point spread function; Performing Fourier transform on the point spread function to obtain the optical transfer function. The method according to claim 4, characterized in that the step of integrating the pixel size with the optical transfer function to obtain the simulated optical calibration information comprises: Extracting noise from blank areas between the nucleic acid molecules in the simulated nucleic acid sample to obtain simulated noise information; The pixel size, the optical transfer function and the simulated noise information are integrated to obtain the simulated optical calibration information. The method according to claim 6, characterized in that the simulating the nucleic acid distribution information based on the simulated optical calibration information to obtain the simulated nucleic acid image comprises: Mapping the nucleic acid distribution information to an imaging space based on the pixel size to obtain a first simulated image of the simulated nucleic acid sample in the imaging space; Performing imaging performance simulation on the first simulation image based on the optical transfer function to obtain a second simulation image; Environmental noise simulation is performed on the second simulated image based on the simulated noise information to obtain the simulated nucleic acid image. The method according to claim 2, characterized in that the step of inputting the true value image of the nucleic acid molecule and the simulated nucleic acid image corresponding to the true value image of the nucleic acid molecule into the original image processing model and iteratively training the image processing model comprises: In each round of iterative training, the simulated nucleic acid image is input into the image processing model for image processing training to obtain the processing result of this round; Comparing the processing result of this round with the true value image of the nucleic acid molecule to obtain training deviation data; Based on the training bias data, weight parameters of the image processing model are updated. The method according to claim 8, characterized in that when the image processing model meets the first predetermined condition in iterative training, obtaining the trained image processing model comprises: When the training deviation data reflects that the image processing model converges during iterative training, it is determined that the image processing model meets the first predetermined condition during the iterative training, and the trained image processing model is obtained. A nucleic acid molecule sequencing device, comprising: An image acquisition module, used to acquire a sequencing image of a target nucleic acid sample, wherein the sequencing image is an image acquired by acquiring an image of the target nucleic acid sample using a preset optical sequencing system; An image processing module, used for performing image processing on the sequencing image based on an image processing model to obtain a target image, wherein the image processing model is a model trained by using a constructed true value image of nucleic acid molecules and a corresponding simulated nucleic acid image, wherein the simulated nucleic acid image is an image obtained by performing simulated sequencing on a simulated nucleic acid sample corresponding to the true value image of nucleic acid molecules based on a simulated optical system, and the simulated optical system is an optical system obtained by simulating the preset optical sequencing system; The sequence determination module is used to perform nucleic acid molecule sequencing according to the target image to obtain a sequencing result corresponding to the target nucleic acid sample. An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the nucleic acid molecule sequencing method according to any one of claims 1 to 9 when executing the computer program. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the nucleic acid molecule sequencing method according to any one of claims 1 to 9. A computer program product, comprising a computer program, wherein the computer program is read and executed by a processor of a computer device, so that the computer device executes the nucleic acid molecule sequencing method according to any one of claims 1 to 9.