Method, device and equipment for recognizing gene cluster of bacterial genome and storage medium

Abstract

The application discloses a bacterial genome gene cluster identification method, device, equipment and storage medium, the method comprises the steps: constructing a gene cluster identification model by using a preprocessing unit, an ESM protein language unit, a cosine approximation degree calculation unit, a normalized point-by-point mutual information unit, a term frequency-inverse document frequency unit, a graph convolution unit, a BERT unit and a softmax function unit; inputting a BGC sequence training sample of a bacterial genome into the gene cluster identification model, training the gene cluster identification model by using a cross-entropy loss function, and iteratively optimizing the training process by using an Adam optimizer; inputting a BGC sequence data set of a bacterial genome to be identified into a target gene cluster identification model for identification; correcting fragments in the preliminary identification result to obtain a final identification result; by constructing an adjacency matrix and inputting the adjacency matrix into the graph convolution unit, global features contained in Pfam domain nodes in the BGC sequence are extracted, and the identification and prediction performance of the model is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gene cluster identification of bacterial genomes, and in particular to a gene cluster identification method, device, equipment and storage medium for bacterial genomes. BACKGROUND

[0002] Antibiotics are an important drug in modern medicine. However, due to the overuse of antibiotics, microorganisms have developed resistance to antibiotics, reducing or even inhibiting the bacteriostatic effect of antibiotics, so that they lose their therapeutic effect. In order to cope with the increasing impact of antibiotic resistance, biologists and medical scientists around the world are actively developing new antibiotics. Among them, natural compounds with rich chemical structures and biological activities are a valuable resource, some of which have antibacterial, anticancer or other immunomodulatory effects, so they have attracted the attention of drug developers. These natural compounds are encoded by biosynthesis gene clusters (BGC), which are usually composed of a series of interrelated genes that encode a series of different enzymes for the common synthesis of biologically active molecules. Many BGCs of natural microorganisms are the source of many drugs (mainly antibiotics), which are discovered by discovering their corresponding BGC. Accurate positioning of BGC in the genome and prediction of its products and activities can be used for the discovery of new drugs. Current BGC prediction tools can be divided into two main methods: sequence similarity-based methods and machine learning methods that can learn more complex patterns.

[0003] Sequence similarity-based methods, such as antiSMASH and NP.searcher, use only existing databases and tools for alignment, and are difficult to capture structural features in BGC, and have weak recognition ability for novel or dissimilar BGC.

[0004] Existing machine learning-based methods, such as DeepBGC and BiGCARP, use deep learning models to learn the structural features contained in BGC, improving the ability to identify BGC. However, due to the small number of BGCs in the database, the features of BGC cannot be well learned, and existing machine learning methods only use local features in BGC sequences, but do not incorporate global features between BGC sequences. SUMMARY

[0005] The purpose of the embodiments of the present application is to propose a gene cluster identification method, device, equipment and storage medium for bacterial genomes, to solve the problem of weak recognition ability of existing methods for gene clusters in microorganisms.

[0006] In order to solve the above technical problems, the present invention provides a method for identifying gene clusters in bacterial genomes, which adopts the following technical solutions, including:

[0007] Step 100: Obtain a preprocessing unit, an ESM protein language unit, a cosine approximation calculation unit, a normalized point-by-point mutual information unit, a term frequency-inverse document frequency unit, a graph convolution unit, and a BERT unit, and use the preprocessing unit, the ESM protein language unit, the cosine approximation calculation unit, the normalized point-by-point mutual information unit, the term frequency-inverse document frequency unit, the graph convolution unit, the BERT unit, and the softmax function unit to construct a gene cluster recognition model;

[0008] Step 200: Input the BGC sequence training sample of the bacterial genome into the gene cluster recognition model, train the gene cluster recognition model using the cross entropy loss function, and iteratively optimize the training process using the Adam optimizer to obtain the target gene cluster recognition model;

[0009] Step 300: Input the BGC sequence dataset of the bacterial genome to be identified into the target gene cluster recognition model for identification to obtain a preliminary identification result;

[0010] Step 400: Correct the segments in the preliminary recognition result to obtain a final recognition result;

[0011] Among them, the cosine approximation calculation unit is used to calculate the similarity of the embedded vector of the Pfam domain node, the normalized point-by-point mutual information unit and the term frequency-inverse document frequency unit are used to construct the Pfam domain node adjacency matrix, and the graph convolution unit is used to extract the global features of the Pfam domain node.

[0012] Furthermore, the step 200 includes:

[0013] Step 210: using the pre-processing unit to convert the BGC sequence into a Pfam sequence;

[0014] Step 220: extracting the embedding vector of the Pfam sequence using the ESM protein language unit;

[0015] Step 230: Calculate the similarity of the embedding vectors based on the Pfam database and using the cosine approximation calculation unit.

[0016] Furthermore, the step 200 further includes:

[0017] Step 240: performing data enhancement on the BGC sequence according to the similarity to obtain an enhanced BGC sequence;

[0018] Step 250: constructing a Pfam adjacency matrix based on the input BGC sequence and using the normalized point-by-point mutual information unit and the term frequency-inverse document frequency unit;

[0019] Step 260: Input the Pfam adjacency matrix into the graph convolution unit to extract Pfam global features.

[0020] Furthermore, the step 240 includes:

[0021] Step 241: using the Pfam database, obtaining all Pfam domains in the BGC sequence whose similarity is greater than a first predetermined threshold;

[0022] Step 242: Obtain the number of Pfam domains in the BGC, and screen out target BGC sequences whose number is greater than a second predetermined threshold;

[0023] Step 243: randomly replace the Pfam domains in a specified number of BGCs in the target BGC sequence whose similarity is greater than the first specified threshold to obtain the enhanced BGC sequence;

[0024] The enhanced BGC sequence includes a BGC sequence that has not replaced the Pfam domain and a BGC sequence that has replaced the Pfam domain.

[0025] Furthermore, the step 200 further includes:

[0026] Step 270: Input the Pfam global feature into the BERT unit, and use the BERT unit to fuse the Pfam global feature with the local features in the enhanced BGC sequence to obtain a first fused feature;

[0027] Step 280: Input the first fusion feature into the softmax function unit to obtain a preliminary recognition result.

[0028] Furthermore, the step 200 further includes:

[0029] Step 290: Calculate the function value of the cross entropy loss function using the preliminary recognition result and the true label;

[0030] Step 291: Update the gene cluster recognition model parameters using the function value of the cross entropy loss function and the back propagation algorithm.

[0031] Furthermore, the step 400 includes:

[0032] Step 410: Obtain the distance between any two consecutive segments predicted to constitute the Pfam domain in the BGC sequence;

[0033] Step 420, if the interval distance is less than a third specified threshold, the continuous fragments are merged into one fragment.

[0034] Step 430, if the length of the continuous fragment is less than a fourth specified threshold and there is no adjacent fragment, the continuous fragment is discarded.

[0035] To solve the above problems, a bacterial genome gene cluster identification device is also provided, which adopts the bacterial genome gene cluster identification method, comprising:

[0036] The construction module is configured to obtain a preprocessing unit, an ESM protein language unit, a cosine similarity calculation unit, a normalized point-wise mutual information unit, a term frequency-inverse document frequency unit, a graph convolution unit, and a BERT unit, and construct a gene cluster identification model by using the preprocessing unit, the ESM protein language unit, the cosine similarity calculation unit, the normalized point-wise mutual information unit, the term frequency-inverse document frequency unit, the graph convolution unit, the BERT unit, and a softmax function unit.

[0037] The training module is configured to input a BGC sequence training sample of a bacterial genome into the gene cluster identification model, train the gene cluster identification model by using a cross-entropy loss function, and iteratively optimize the training process by using an Adam optimizer to obtain a target gene cluster identification model.

[0038] The identification module is configured to input a BGC sequence data set of a bacterial genome to be identified into the target gene cluster identification model for identification to obtain a preliminary identification result.

[0039] The correction module is configured to correct the fragments in the preliminary identification result to obtain a final identification result.

[0040] To solve the above problems, the embodiments of the present application further provide a computer device, which comprises a memory and a processor, the memory stores computer readable instructions, and the processor executes the computer readable instructions to realize the steps of the bacterial genome gene cluster identification method.

[0041] To solve the above problems, the embodiments of the present application further provide a computer readable storage medium, which stores computer readable instructions, and the computer readable instructions are executed by a processor to realize the steps of the bacterial genome gene cluster identification method.

[0042] Compared with the prior art, by utilizing the ESM protein language unit, the cosine similarity calculation unit calculates the Pfam domain similarity in the BGC sequence, and randomly replaces the BGC sequence with a Pfam domain similarity greater than a specified threshold, the BGC sequence data is enhanced, so that the model can better learn the high-dimensional features contained in different types of BGCs, by adopting the normalized point-wise mutual information unit and the term frequency-inverse document frequency unit to construct an adjacency matrix, and inputting it into the graph convolution unit, the global features contained in the Pfam domain nodes in the BGC sequence are extracted, and the recognition and prediction performance of the model is improved. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the scheme in the present application, the drawings needed in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0044] Figure 1 is a flowchart of an embodiment of a bacterial genome gene cluster recognition method of the present application;

[0045] Figure 2 is Figure 1 is a flowchart of a specific embodiment of S200 in the present application;

[0046] Figure 3 is Figure 2 is a flowchart of a specific embodiment after S230 in the present application;

[0047] Figure 4 is Figure 3 is a flowchart of a specific embodiment of S240 in the present application;

[0048] Figure 5 is Figure 3 is a flowchart of a specific embodiment after S260 in the present application;

[0049] Figure 6 is Figure 5 is a flowchart of a specific embodiment after S280 in the present application;

[0050] Figure 7 is Figure 1 is a flowchart of a specific embodiment of S400 in the present application;

[0051] Figure 8 is a flowchart of a bacterial genome gene cluster recognition method of the present application;

[0052] Figure 9is a module structure schematic diagram of one embodiment of a bacterial genome gene cluster identification device of the present application;

[0053] Figure 10 is a module structure schematic diagram of a computer device of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0055] In this document, reference to“an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase“in an embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. Those skilled in the art will appreciate that embodiments described herein can be combined with other embodiments in various ways.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application; the use herein of terms such as“comprise”,“comprises” or“comprising” are used in the inclusive, open sense and are not intended to be limiting; the use herein of terms such as“include”,“includes” or“including” are used in the inclusive, open sense and are not intended to be limiting; the use herein of terms such as“first”,“second”,“third” and the like are used only to describe a particular embodiment and are not intended to be limiting; the use herein of terms such as“a”,“an” and“the” are used in the singular sense and are not intended to be limiting; the use herein of terms such as“plurality” means two or more; the use herein of the term“about” means that a value can vary from the stated value by up to 10% of the value.

[0057] In addition, the terms“first”,“second”, and the like are used only to describe purposes and can not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with“first”,“second” can explicitly or implicitly include one or more features. In the description of the present application, the meaning of“plurality” is two or more, unless otherwise specifically limited.

[0058] Embodiments of the present application aim to provide a bacterial genome gene cluster identification method, device, equipment and storage medium to solve the problem of weak gene cluster identification capability in microorganisms in the prior art.

[0059] To solve the above technical problems, the embodiments of the present application provide a bacterial genome gene cluster identification method, which adopts the following technical scheme, such as Figure 1 , Figure 1 is a flowchart of one embodiment of a bacterial genome gene cluster identification method of the present application; comprising:

[0060] S100, a preprocessing unit, an ESM protein language unit, a cosine similarity calculation unit, a normalized pointwise mutual information unit, a term frequency-inverse document frequency unit, a graph convolution unit, and a BERT unit are obtained, and a gene cluster identification model is constructed using the preprocessing unit, the ESM protein language unit, the cosine similarity calculation unit, the normalized pointwise mutual information unit, the term frequency-inverse document frequency unit, the graph convolution unit, the BERT unit, and a softmax function unit.

[0061] In this embodiment, the cosine similarity calculation unit is used to calculate the similarity of the embedding vectors of the Pfam domain nodes, the normalized pointwise mutual information unit and the term frequency-inverse document frequency unit are used to construct the Pfam domain node adjacency matrix, and the graph convolution unit is used to extract the global features of the Pfam domain nodes.

[0062] In this embodiment, by performing similarity calculation, data augmentation on BGC sequences, and constructing an adjacency matrix and extracting global features using a graph convolution unit, the recognition performance of the gene cluster identification model is improved.

[0063] In this embodiment, the preprocessing unit is used to convert the original BGC sequence into a Pfam sequence, and the ESM protein language unit is used to obtain the embedding vectors of each Pfam domain node in the BGC sequence.

[0064] In this embodiment, the ESM protein language unit model is a deep learning model for processing protein tasks, which is based on the Transformer architecture, uses stacked Transformer Encoder Layers, and learns the dependency between residues by predicting the type of Masked residues through random Masking of residues.

[0065] In this embodiment, the cosine similarity calculation unit is used to calculate the similarity of each Pfam domain node and the domains in the Pfam database, in preparation for enhancing BGC data.

[0066] In this embodiment, the normalized pointwise mutual information (NPMI) unit model is used to determine the correlation strength between Pfam domain nodes, and the term frequency-inverse document frequency unit model is used to calculate the frequency of occurrence of a certain Pfam domain node. In this embodiment, an adjacency matrix is constructed using the normalized pointwise mutual information unit and the term frequency-inverse document frequency unit.

[0067] In this embodiment, the BERT unit is used to fuse the local features and global features of the Pfam domains, and the softmax function unit is used for classification recognition.

[0068] S200, inputting the BGC sequence training sample of the bacterial genome into the gene cluster recognition model, training the gene cluster recognition model using the cross entropy loss function, and iteratively optimizing the training process using the Adam optimizer to obtain the target gene cluster recognition model.

[0069] In a preferred embodiment, Figure 2 , Figure 2 yes Figure 1 Flowchart of a specific implementation of S200; S200 includes: S210, using a preprocessing unit to convert a BGC sequence into a Pfam sequence; S220, using an ESM protein language unit to extract an embedding vector of the Pfam sequence; S230, calculating the similarity of the embedding vector based on the Pfam database and using a cosine approximation calculation unit.

[0070] In this embodiment, if Figure 8 , Figure 8 It is a flow chart of a method for identifying gene clusters in a bacterial genome of the present application; there are multiple BGCs in a BGC sequence, and each BGC is represented by multiple Pfam domain nodes, that is, a Pfam sequence.

[0071] In this embodiment, when using the ESM protein language unit to extract the embedding vector of the Pfam domain node, the ESM protein language unit model first obtains the Pfam sequence and generates a corresponding embedding representation for each amino acid in the Pfam sequence. Then, based on the definition of the Pfam family and the correspondence between the known protein sequence and the Pfam family, the Pfam-related feature vector is extracted from the above embedding representation.

[0072] In this embodiment, the cosine value of the Pfam node embedding vector in the BGC sequence and the related vector in the database is used to determine the similarity. A cosine value of 1 indicates that the two are exactly the same, and a cosine value of 0 indicates that the two vectors are orthogonal and have no similarity.

[0073] In a preferred embodiment, Figure 3 , Figure 3 yes Figure 2 A flowchart of a specific implementation method after S230 in the embodiment; S200 also includes: S240, performing data enhancement on the BGC sequence according to the similarity to obtain an enhanced BGC sequence; S250, constructing a Pfam adjacency matrix based on the input BGC sequence and using the normalized point-by-point mutual information unit and the term frequency-inverse document frequency unit; S260, inputting the Pfam adjacency matrix into the graph convolution unit to extract the Pfam global features.

[0074] In the embodiment, by performing similarity calculation, Pfam domains with high similarity in the BGC sequence are obtained, and then data augmentation is performed on the BGC sequence by replacement, so as to alleviate the problem of insufficient sample quantity in the current BGC database.

[0075] In a preferred embodiment, as Figure 4 , Figure 4 is Figure 3 a flowchart of one specific implementation of S240; S240 includes: S241, using a Pfam database, obtaining all Pfam domains in the BGC sequence with similarity greater than a first specified threshold; S242, obtaining the number of Pfam domains in the BGC, and screening target BGC sequences greater than a second specified threshold; S243, randomly replacing a specified number of Pfam domains in the target BGC sequence with similarity greater than the first specified threshold, to obtain an augmented BGC sequence; wherein the augmented BGC sequence includes BGC sequences without replaced Pfam domains and BGC sequences with replaced Pfam domains.

[0076] In the embodiment, the first specified threshold is preferably 95%, which is intended to screen Pfam domains with similarity greater than 95%. In the embodiment, the second specified threshold is preferably 10, which is intended to exclude BGCs with less than 10 Pfam domains. In BGC sequences with more than 10 Pfam domains, 5-10 BGCs are randomly selected for Pfam domain replacement to enhance the functional characteristics of the BGC sequence data.

[0077] In the embodiment, when constructing the Pfam adjacency matrix, first calculate the TF-IDF value, term frequency (TF), for each Pfam domain in each protein sequence, count the number of times the Pfam domain appears in the sequence. For example, if Pfam domain A appears 3 times in sequence 1, and the total Pfam domain annotation number in sequence 1 is 10, then the TF value of Pfam domain A in protein sequence 1 is 3 / 10 = 0.3. The inverse document frequency (IDF) value, in the embodiment, each Pfam sequence is regarded as a document. Calculate the IDF value of each Pfam domain. For example, there are a total of 100 Pfam sequences, and Pfam domain A appears in 20 of them, then IDF(A) = log(100 / 20) = log(5). The TF-IDF value is the TF value of the family in the sequence multiplied by its IDF value, and the normalized pointwise mutual information (NPMI) is calculated

[0078] For any two Pfam domains A and B, calculate the NPMI between them. Count the number of Pfam sequences in which both A and B occur (denoted as N(A,B)), the number of Pfam sequences in which A occurs (denoted as N(A)), the number of Pfam sequences in which B occurs (denoted as N(B)), and the total number of Pfam sequences (denoted as N). The pointwise mutual information (PMI) is calculated using the formula: PMI(A,B) = log(N(A,B)*N / (N(A)*N(B))). The PMI is then normalized to obtain the NPMI. A commonly used normalization formula is: NPMI(A,B) = PMI(A,B) / -log(N(A,B) / N).

[0079] Calculate the TF-IDF(A) value, TF-IDF(B) value, and NPMI(A,B) value, and multiply them together to obtain the elements of the adjacency matrix. Based on this principle, calculate the values ​​of all elements to obtain the adjacency matrix.

[0080] In a preferred embodiment, the graph convolution unit model adopts a three-layer graph convolutional neural network (GCN), which is a deep learning architecture for processing graph structured data. The core of the graph convolutional network is to update the feature representation of the node by aggregating the information of neighboring nodes. In each layer of graph convolution, the node calculates its own new features based on the features of its neighboring nodes and the weights of the edges connected to the neighboring nodes. The first layer: The input is the original node feature matrix and the Pfam node adjacency matrix of the graph. The second layer: The Pfam node features output by the first layer are used as input to further aggregate the information of more distant neighbors. The third layer: Based on the first two layers, the final information fusion and feature extraction are performed. The Pfam node features output by this layer can contain higher-level structural and semantic information of the graph, which is used for downstream Pfam node classification.

[0081] In a preferred embodiment, Figure 5 , Figure 5 yes Figure 3 A flowchart of a specific implementation method after S260 in the embodiment; S200 also includes: S270, inputting the Pfam global feature into the BERT unit, using the BERT unit to fuse the Pfam global feature with the local feature in the enhanced BGC sequence to obtain a first fused feature; S280, inputting the first fused feature into the softmax function unit to obtain a preliminary recognition result.

[0082] In this embodiment, if Figure 8, the BERT unit model obtains the global feature (Global Feature) from the graph convolution unit model (GCN), and fuses it with the local features of the BGC enhanced sequence to obtain the fused feature, which is output to the softmax function unit for classification and recognition.

[0083] In a preferred embodiment, Figure 6 , Figure 6 yes Figure 5 A flowchart of a specific implementation method after S280; S200 also includes: S290, using the preliminary recognition results and the true label to calculate the function value of the cross entropy loss function; S291, using the function value of the cross entropy loss function and the back propagation algorithm to update the gene cluster recognition model parameters.

[0084] S300: Input the BGC sequence data set of the bacterial genome to be identified into the target gene cluster recognition model for recognition, and obtain a preliminary recognition result.

[0085] In this embodiment, if Figure 8 , and the prediction results are as follows:

[0086]

[0087] The product with the highest predicted probability is regarded as the identified product. Then, on the next S axis, the fragment of the product is corrected to avoid missed reports and false positives.

[0088] In this embodiment, the above table is only an output example of the classification model and does not represent the actual prediction result.

[0089] S400: Correct the segments in the preliminary recognition result to obtain a final recognition result.

[0090] In a preferred embodiment, Figure 7 , Figure 7 yes Figure 1 A flowchart of a specific implementation of S400 in the method; S400 includes: S410, obtaining the predicted interval distance between any two consecutive fragments constituting the Pfam domain in the BGC sequence; S420, if the interval distance is less than a third specified threshold, merging the consecutive fragments into one segment; S430, if the length of the consecutive fragment is less than a fourth specified threshold and there is no adjacent fragment, discarding the consecutive fragment.

[0091] In this embodiment, if Figure 8 The third threshold is preferably 10 units. When the distance between any two consecutive segments is less than 10 units, they are merged into one segment. The fourth threshold is preferably 5 units. When the length of a segment is less than 5 units, it is discarded.

[0092] By correcting the fragments of the predicted Pfam domain, filtering out false positives and false negatives, the accuracy of the target gene cluster recognition model is further improved, and more accurate and reliable prediction results are obtained.

[0093] In one embodiment, as Figure 8 After obtaining the recognition result (which has been determined as a BGC sequence), the BGC sequence is then classified according to the category.

[0094] By using the ESM protein language unit, the cosine similarity calculation unit calculates the Pfam domain similarity in the BGC sequence, and randomly replaces the BGC sequence with a Pfam domain similarity greater than a specified threshold, to enhance the BGC sequence data, so that the model can better learn the high-dimensional features contained in different types of BGCs. By using the normalized point-wise mutual information unit and the term frequency-inverse document frequency unit to construct an adjacency matrix, and inputting it into the graph convolution unit, the global features contained in the Pfam domain nodes in the BGC sequence are extracted, and the recognition and prediction performance of the model is improved.

[0095] To solve the above problems, a bacterial genome gene cluster recognition device is also provided, which adopts a bacterial genome gene cluster recognition method, such as Figure 9 , Figure 9 is a module structure diagram of an embodiment of a bacterial genome gene cluster recognition device 500 of the present application; comprising:

[0096] The construction module 501 is used to obtain the preprocessing unit, the ESM protein language unit, the cosine similarity calculation unit, the normalized point-wise mutual information unit, the term frequency-inverse document frequency unit, the graph convolution unit and the BERT unit, and to construct a gene cluster recognition model using the preprocessing unit, the ESM protein language unit, the cosine similarity calculation unit, the normalized point-wise mutual information unit, the term frequency-inverse document frequency unit, the graph convolution unit, the BERT unit and the softmax function unit; the training module 502 is used to input the BGC sequence training sample of the bacterial genome into the gene cluster recognition model, train the gene cluster recognition model using the cross-entropy loss function, and iteratively optimize the training process using the Adam optimizer to obtain a target gene cluster recognition model; the recognition module 503 is used to input the BGC sequence data set of the bacterial genome to be recognized into the target gene cluster recognition model for recognition, and obtain a preliminary recognition result; the correction module 504 is used to correct the fragments in the preliminary recognition result to obtain a final recognition result.

[0097] In order to solve the above problems, an embodiment of the present application also proposes a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of a gene cluster identification method for a bacterial genome.

[0098] In order to solve the above problems, an embodiment of the present application further proposes a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the steps of a method for identifying gene clusters in a bacterial genome are implemented.

[0099] In order to solve the above problems, Figure 10 , Figure 10 Schematic diagram of the module structure of a computer device according to the present application. The present application also proposes a computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of a method for identifying gene clusters in a bacterial genome.

[0100] The computer device can be a computer, server, workstation or other device, or a mobile phone, tablet, vehicle-mounted mobile terminal or other device with program execution capability. The internal structure diagram of the computer device can be as follows: Figure 10 As shown, Figure 10 This is a structural diagram of an embodiment of a computer device according to the present application. The computer device includes a processor, a memory and a communication module. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, instructions or code. The internal memory provides an environment for the operation of the operating system and instructions or code in the non-volatile storage medium. When the instructions or code are executed by the processor, the functions or steps of a method for fine-tuning the above-mentioned pre-trained model are realized. The communication module of the computer device may include a network interface and / or a wireless communication module, and the computer device may communicate with other devices or service platforms through the communication module. In addition, the computer device may also include a display screen and an input device, etc.

[0101] The memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to invoke the program instructions. When the processor executes the instructions or codes, the following steps are implemented: S100, obtaining a preprocessing unit, an ESM protein language unit, a cosine approximation degree calculation unit, a normalized point-by-point mutual information unit, a term frequency-inverse document frequency unit, a graph convolution unit, and a BERT unit, constructing a gene cluster identification model by using the preprocessing unit, the ESM protein language unit, the cosine approximation degree calculation unit, the normalized point-by-point mutual information unit, the term frequency-inverse document frequency unit, the graph convolution unit, the BERT unit, and a softmax function unit; S200, inputting a BGC sequence training sample of a bacterial genome into the gene cluster identification model, training the gene cluster identification model by using a cross-entropy loss function, and iteratively optimizing the training process by using an Adam optimizer to obtain a target gene cluster identification model; S300, inputting a BGC sequence data set of a bacterial genome to be identified into the target gene cluster identification model for identification to obtain a preliminary identification result; and S400, correcting a fragment in the preliminary identification result to obtain a final identification result.

[0102] To solve the above problems, the embodiment of the application further provides a computer readable storage medium, which adopts the following technical scheme:

[0103] The computer readable storage medium stores a computer program, the computer program comprises program instructions, and the program instructions make the processor execute a gene cluster identification method of a bacterial genome when the processor executes the program instructions.

[0104] The embodiment of the application further provides a computer readable storage medium, which stores a computer program, and the computer program comprises program instructions. Figures 1 to 7 The computer readable storage medium stores a computer program, the computer program comprises program instructions, and the program instructions make the processor execute a gene cluster identification method of a bacterial genome when the processor executes the program instructions.

[0105] The computer readable storage medium can be a standard mouth shape speaker face video generation device provided by any of the foregoing embodiments, or an internal storage unit of the terminal device, such as a hard disk or a memory of a computer device. The computer readable storage medium can also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0106] Furthermore, the computer-readable storage medium may include both an internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0107] However, it should be understood that implementation of all illustrated components is not required, and more or fewer components may be implemented instead. Those skilled in the art will appreciate that a computer device herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, and the like.

[0108] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0109] Compared with the existing technology, the BGC sequence data is enhanced by using ESM protein language units and cosine approximation calculation units to calculate the Pfam domain approximation in BGC sequences, and randomly replacing BGC sequences with Pfam domain approximation greater than a specified threshold, so that the model can better learn the high-dimensional features contained in different types of BGCs. By using normalized point-by-point mutual information units and term frequency-inverse document frequency units to construct an adjacency matrix and input it into the graph convolution unit, the global features contained in the Pfam domain nodes in the BGC sequence are extracted, thereby improving the recognition and prediction performance of the model.

[0110] The non-Company software tools or components appearing in the embodiments of this application are merely examples and do not represent actual use.

[0111] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying gene clusters in a bacterial genome, comprising: Step 100: Obtain a preprocessing unit, an ESM protein language unit, a cosine approximation calculation unit, a normalized point-by-point mutual information unit, a term frequency-inverse document frequency unit, a graph convolution unit, and a BERT unit, and use the preprocessing unit, the ESM protein language unit, the cosine approximation calculation unit, the normalized point-by-point mutual information unit, the term frequency-inverse document frequency unit, the graph convolution unit, the BERT unit, and the softmax function unit to construct a gene cluster recognition model; Step 200: Input the BGC sequence training sample of the bacterial genome into the gene cluster recognition model, train the gene cluster recognition model using the cross entropy loss function, and iteratively optimize the training process using the Adam optimizer to obtain the target gene cluster recognition model; Step 300: Input the BGC sequence dataset of the bacterial genome to be identified into the target gene cluster recognition model for identification to obtain a preliminary identification result; Step 400: Correct the segments in the preliminary recognition result to obtain a final recognition result; The cosine approximation calculation unit is used to calculate the similarity of the embedded vectors of the Pfam domain nodes, the normalized point-by-point mutual information unit and the term frequency-inverse document frequency unit are used to construct the Pfam domain node adjacency matrix, and the graph convolution unit is used to extract the global features of the Pfam domain nodes. The step 200 includes: Step 210: using the pre-processing unit to convert the BGC sequence into a Pfam sequence; Step 220: extracting the embedding vector of the Pfam sequence using the ESM protein language unit; Step 230: Calculate the similarity of the embedding vectors based on the Pfam database and using the cosine approximation calculation unit; Step 240: performing data enhancement on the BGC sequence according to the similarity to obtain an enhanced BGC sequence; Step 250: constructing a Pfam adjacency matrix based on the input BGC sequence and using the normalized point-by-point mutual information unit and the term frequency-inverse document frequency unit; Step 260: Input the Pfam adjacency matrix into the graph convolution unit to extract Pfam global features; Step 270: Input the Pfam global feature into the BERT unit, and use the BERT unit to fuse the Pfam global feature with the local features in the enhanced BGC sequence to obtain a first fused feature; Step 280: Input the first fusion feature into the softmax function unit to obtain a preliminary recognition result.

2. The method for identifying gene clusters in bacterial genomes according to claim 1, wherein: The step 240 includes: Step 241: using the Pfam database, obtaining all Pfam domains in the BGC sequence whose similarity is greater than a first predetermined threshold; Step 242: Obtain the number of Pfam domains in the BGC, and screen out target BGC sequences whose number is greater than a second predetermined threshold; Step 243: randomly replace the Pfam domains in a specified number of BGCs in the target BGC sequence whose similarity is greater than the first specified threshold to obtain the enhanced BGC sequence; The enhanced BGC sequence includes a BGC sequence that has not replaced the Pfam domain and a BGC sequence that has replaced the Pfam domain.

3. The method for identifying gene clusters in bacterial genomes according to claim 1, wherein: The step 200 further includes: Step 290: Calculate the function value of the cross entropy loss function using the preliminary recognition result and the true label; Step 291: Update the gene cluster recognition model parameters using the function value of the cross entropy loss function and the back propagation algorithm.

4. The method for identifying gene clusters in bacterial genomes according to claim 1, wherein The step 400 includes: Step 410: Obtain the distance between any two consecutive segments predicted to constitute the Pfam domain in the BGC sequence; Step 420: If the interval distance is less than a third predetermined threshold, merge the consecutive segments into one segment; Step 430: If the length of the continuous segment is less than a fourth specified threshold and there is no adjacent segment, the continuous segment is discarded.

5. A bacterial genome gene cluster identification device, using the bacterial genome gene cluster identification method according to any one of claims 1 to 4, characterized in that: include: A construction module is used to obtain a preprocessing unit, an ESM protein language unit, a cosine approximation calculation unit, a normalized point-by-point mutual information unit, a term frequency-inverse document frequency unit, a graph convolution unit, and a BERT unit, and to construct a gene cluster recognition model using the preprocessing unit, the ESM protein language unit, the cosine approximation calculation unit, the normalized point-by-point mutual information unit, the term frequency-inverse document frequency unit, the graph convolution unit, the BERT unit, and the softmax function unit; A training module is used to input the BGC sequence training sample of the bacterial genome into the gene cluster recognition model, train the gene cluster recognition model using the cross entropy loss function, and iteratively optimize the training process using the Adam optimizer to obtain the target gene cluster recognition model; The recognition module is used to input the BGC sequence data set of the bacterial genome to be identified into the target gene cluster recognition model for identification to obtain a preliminary identification result; The correction module is used to correct the segments in the preliminary recognition result to obtain the final recognition result.

6. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the steps of the method for identifying gene clusters of a bacterial genome according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the method for identifying gene clusters in a bacterial genome according to any one of claims 1 to 4.

Images