Method and device for constructing human source sequence filtering index and computer equipment

Abstract

The invention relates to a human source sequence filtering index construction method and device and computer equipment. The method comprises the following steps: acquiring an HG38 human genome and a human genome of at least one human group different from the HG38 human genome, and respectively determining non-redundant target genome sequences, which are not matched with the HG38 human genome, in the human genome of each human group; and constructing a human source sequence filtering index according to the non-redundant target genome sequence. By adopting the method, the filtering index can be constructed on the basis of multiple human genome sequences which are from different sources and are subjected to redundancy elimination processing, so that the sequencing data can be fully filtered out of the human sequences on the basis of the filtering index, the redundant sequences are prevented from being repeatedly analyzed, and the filtering efficiency of the human sequences is improved.

Description

Technical Field

[0001] The present application relates to the field of gene sequencing technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for constructing a human sequence filtering index. Background Art

[0002] As a molecular biology technology, metagenomic sequencing can obtain all microbial genome information directly from environmental samples to fully understand the composition, function and dynamic changes of microbial communities. It can reveal the diversity of human microbial communities, promote disease diagnosis and treatment, promote the development of new therapies, and help understand the interaction between microorganisms and the immune system. In the metagenomic sequencing analysis process, the host and background sequences that occupy the vast majority are generally removed first, and then the filtered sequences are annotated into multi-species genomes to analyze species composition and biological significance.

[0003] Traditional technologies mainly use the HG38 human genome sequence as a reference for sequence filtering and removal. However, traditional technologies cannot meet the requirements of sequence polymorphism and often cause deviations in sequencing data analysis. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for constructing a human sequence filtering index that can improve the filtering efficiency of human sequences in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for constructing a human sequence filtering index, comprising:

[0006] Obtaining the HG38 human genome and the human genome of at least one human ethnic group different from the HG38 human genome, and respectively determining non-redundant target genome sequences in the human genomes of the human ethnic groups that do not match the HG38 human genome;

[0007] A human sequence filtering index is constructed based on the non-redundant target genome sequence.

[0008] In one embodiment, the method further comprises:

[0009] Acquire a non-redundant additional sequence, wherein the non-redundant additional sequence is a non-redundant target mitochondrial sequence and / or a non-redundant target RNA sequence;

[0010] Wherein, the non-redundant target mitochondrial sequence is a sequence in the human mitochondrial sequence database that does not match the non-redundant target genome sequence and does not match each other, and the non-redundant target RNA sequence is a sequence that does not match the HG38 human genome and does not match each other;

[0011] The step of constructing a human sequence filtering index according to the non-redundant target genome sequence comprises:

[0012] A human sequence filtering index is constructed based on the non-redundant target genome sequence and the non-redundant additional sequence.

[0013] In one embodiment, the human genome of the at least one human population includes but is not limited to at least one of the T2T-CHM13v2.0 human genome, the T2T-Yao human genome, the NH1.0 human genome, the YH2.0 human genome, or the HX1 human genome.

[0014] In one embodiment, the determining of the non-redundant target genome sequence in the human genome of each of the human populations that does not match the HG38 human genome comprises:

[0015] For each chromosome in the human genome of each of the human populations, starting from the first base of each chromosome, each base and a preset number of bases after each base are obtained as the genome sequence to be analyzed;

[0016] The genome sequence to be analyzed is compared with the HG38 human genome, and bases / base sequences that do not match the HG38 human genome are screened out from the genome sequence to be analyzed as the non-redundant target genome sequence.

[0017] In one embodiment, obtaining the non-redundant additional sequence includes:

[0018] For each mitochondrial sequence in the human mitochondrial sequence database, starting from the first base of each mitochondrial sequence, each base and a preset number of bases after each base are obtained as the mitochondrial sequence to be analyzed;

[0019] The mitochondrial sequence to be analyzed is compared with the non-redundant target genome sequence, and bases / base sequences that do not match the non-redundant target genome sequence are screened out from the mitochondrial sequence to be analyzed as the non-redundant target mitochondrial sequence.

[0020] In one embodiment, after the step of comparing the mitochondrial sequence to be analyzed with the non-redundant target genome sequence, the method further comprises:

[0021] For any one of the mitochondrial sequences to be analyzed, constructing an initial mitochondrial index according to bases / base sequences in any one of the mitochondrial sequences to be analyzed that do not match the non-redundant target genome sequence;

[0022] The initial mitochondrial index is updated according to the bases / base sequences that do not match the non-redundant target genome sequence in the mitochondrial sequences to be analyzed except any one of the mitochondrial sequences to be analyzed, so as to obtain a mitochondrial index.

[0023] In one embodiment, obtaining the non-redundant additional sequence includes:

[0024] According to the positional overlap or similarity between the RNAs in the RNA sequence, the RNAs are divided into at least one set;

[0025] For the set, starting from the first base of each RNA in the set, each base and a preset number of bases after each base are obtained as the RNA sequence to be analyzed corresponding to the set;

[0026] The RNA sequences to be analyzed corresponding to the set are compared with the HG38 human genome, and bases / base sequences that do not match the HG38 human genome are screened out from the RNA sequences to be analyzed corresponding to the set as the non-redundant target RNA sequences.

[0027] In one embodiment, after the step of comparing the RNA sequence to be analyzed corresponding to the set with the HG38 human genome, the method further comprises:

[0028] For each of the sets, constructing an initial RNA index corresponding to the set according to the bases / base sequences in the RNA sequence to be analyzed corresponding to the set that do not match the HG38 human genome;

[0029] According to the initial RNA indexes other than any initial RNA index in the initial RNA indexes corresponding to the sets, the any initial RNA index is updated to obtain the RNA indexes corresponding to the sets;

[0030] The index sequences of the RNA indexes corresponding to the sets are integrated to obtain the non-redundant target RNA sequence.

[0031] In a second aspect, the present application also provides a method for filtering human sequences, the method comprising:

[0032] Get the sequence to be filtered;

[0033] The sequence to be filtered is filtered using the HG38 human genome and human sequence filtering index to obtain a filtered sequence; wherein the human sequence filtering index is constructed by a non-redundant target genome sequence, a non-redundant target mitochondrial sequence and a non-redundant target RNA sequence; the non-redundant target genome sequence is obtained from a genome sequence that does not match the HG38 human genome in a human genome of at least one human ethnic group different from the HG38 human genome; the non-redundant target mitochondrial sequence is obtained from mitochondrial sequences in a human mitochondrial sequence database that do not match the non-redundant target genome sequence and do not match each other; the non-redundant target RNA sequence is obtained from RNA sequences that do not match the HG38 human genome and do not match each other in an RNA sequence corresponding to the HG38 human genome.

[0034] In one embodiment, the filtering of the sequence to be filtered using the HG38 human genome and the human sequence filtering index includes:

[0035] When the sequence to be filtered is filtered using the human sequence filtering index, mitochondrial sequences in the sequence to be filtered that do not have base insertions or base deletions compared with the human sequence filtering index are deleted from the sequence to be filtered.

[0036] In a third aspect, the present application also provides a device for constructing a human sequence filtering index, comprising:

[0037] A genome analysis module, for obtaining the HG38 human genome and the human genome of at least one human ethnic group different from the HG38 human genome, respectively determining target genome sequences in the human genomes of the human ethnic groups that do not match the HG38 human genome, and generating a genome index based on the target genome sequences and the HG38 human genome;

[0038] An index building module is used to build a human sequence filtering index based on the genome index.

[0039] In a fourth aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0040] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the above method when executed by a processor.

[0041] In a sixth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0042] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for constructing a human sequence filtering index obtain the HG38 human genome and the human genome of at least one human ethnic group different from the HG38 human genome, and respectively determine the non-redundant target genome sequences in the human genomes of each human ethnic group that do not match the HG38 human genome; thereby accurately removing the redundant genome sequences in the human genomes of each human ethnic group and obtaining accurate non-redundant target genome sequences; constructing a human sequence filtering index based on the non-redundant target genome sequence, thereby constructing a human sequence filtering index for human sequence filtering based on the non-redundant target genome sequence, and being able to construct a filtering index based on human genome sequences from multiple different sources that have been de-redundanted, so as to fully filter out human sequences from sequencing data based on the filtering index, avoid repeated analysis of redundant sequences, and improve the filtering efficiency of human sequences. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 This is an application environment diagram of a method for constructing a human sequence filtering index in one embodiment;

[0045] Figure 2 A schematic diagram of a process for constructing a human sequence filtering index in one embodiment;

[0046] Figure 3 A schematic diagram of a process for constructing a human sequence filtering index in one embodiment;

[0047] Figure 4 It is a structural block diagram of a device for constructing a human sequence filtering index in one embodiment;

[0048] Figure 5 The figure is a diagram of the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] The method for constructing a human sequence filtering index provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the HG38 human genome and the human genome of at least one human ethnic group different from the HG38 human genome, and respectively determines the non-redundant target genome sequence that does not match the HG38 human genome in the human genome of each human ethnic group; the server 104 constructs a human sequence filtering index according to the non-redundant target genome sequence. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0051] In an exemplary embodiment, Figure 2 As shown, a method for constructing a human sequence filtering index is provided, and the method is applied to a server as an example for explanation, including the following steps S202 to S204. Among them:

[0052] Step S202, obtaining the HG38 human genome and the human genome of at least one human ethnic group different from the HG38 human genome, and determining the non-redundant target genome sequences in the human genomes of each human ethnic group that do not match the HG38 human genome.

[0053] Among them, the HG38 human genome may refer to the information obtained from a public database for characterizing the human reference genome.

[0054] The human genome of a human population may refer to information representing the human genomes of different human populations. In practical applications, the human population may include but is not limited to human populations in different regions.

[0055] Among them, the non-redundant target genome sequence may refer to a genome sequence obtained by removing the genome sequences (such as repetitive sequences) in the human genomes of various human ethnic groups that match the HG38 human genome.

[0056] As an example, the server can first obtain the HG38 human genome and the human genome of at least one human ethnic group, and then the server can compare the human genome of each human ethnic group with the HG38 human genome one by one, and determine the genome sequences in the human genome of each human ethnic group that do not match the HG38 human genome as non-redundant target genome sequences.

[0057] Step S204: construct a human sequence filtering index based on the non-redundant target genome sequence.

[0058] The human sequence filtering index may refer to an index / information used to filter human sequences.

[0059] As an example, the server can integrate information of non-redundant target genome sequences and construct a human sequence filtering index so as to remove human sequences during genome sequencing analysis to avoid the impact of human sequences on genome sequencing.

[0060] In the above-mentioned method for constructing a human sequence filtering index, by obtaining the HG38 human genome and the human genome of at least one human ethnic group, the non-redundant target genome sequences in the human genome of each human ethnic group that do not match the HG38 human genome are determined respectively; thereby accurately removing the redundant genome sequences in the human genome of each human ethnic group and obtaining accurate non-redundant target genome sequences; constructing a human sequence filtering index based on the non-redundant target genome sequence, thereby constructing a human sequence filtering index for human sequence filtering based on the non-redundant target genome sequence, and being able to construct a filtering index based on human genome sequences from multiple different sources that have been de-redundanted, so as to fully filter out human sequences from sequencing data based on the filtering index, avoid repeated analysis of redundant sequences, and improve the filtering efficiency of human sequences.

[0061] In an exemplary embodiment, the above method also includes: obtaining non-redundant additional sequences, the non-redundant additional sequences are non-redundant target mitochondrial sequences, and / or non-redundant target RNA sequences; wherein the non-redundant target mitochondrial sequences are sequences in the human mitochondrial sequence database that do not match the non-redundant target genome sequences and do not match each other, and the non-redundant target RNA sequences are sequences corresponding to the HG38 human genome that do not match the HG38 human genome and do not match each other; constructing a human sequence filtering index based on the non-redundant target genome sequence, including: constructing a human sequence filtering index based on the non-redundant target genome sequence and the non-redundant additional sequences.

[0062] The non-redundant additional sequences may include non-redundant target mitochondrial sequences and / or non-redundant target RNA sequences.

[0063] Among them, the human mitochondrial sequence database may refer to a pre-set database consisting of information representing the genetic material present in the mitochondria of human cells.

[0064] Among them, the non-redundant target mitochondrial sequence may refer to the mitochondrial sequence obtained by removing the mitochondrial sequences (such as repetitive sequences) that match the non-redundant target genome sequence in the human mitochondrial sequence database and the mitochondrial sequences (such as repetitive sequences) that match each other in the human mitochondrial sequence database.

[0065] Among them, the RNA sequence corresponding to the HG38 human genome may refer to the RNA sequence in the RNA database corresponding to the HG38 human genome.

[0066] Among them, the non-redundant target RNA sequence may refer to an RNA sequence obtained by removing the RNA sequence (such as a repetitive sequence) that matches the HG38 human genome from the RNA sequence corresponding to the HG38 human genome.

[0067] As an example, a human sequence filtering index can be constructed from a non-redundant target genome sequence and a non-redundant additional sequence, and the non-redundant additional sequence can be a non-redundant target mitochondrial sequence and / or a non-redundant target RNA sequence. Therefore, the human sequence filtering index can be obtained from a non-redundant target genome sequence and a non-redundant target mitochondrial sequence, or from a non-redundant target genome sequence and the non-redundant target mitochondrial sequence, or from a non-redundant target genome sequence, a non-redundant target mitochondrial sequence, and a non-redundant target mitochondrial sequence. For example: the server can first obtain a human mitochondrial sequence database, and then the server can compare the human mitochondrial sequences in the human mitochondrial sequence database with the non-redundant target genome sequences one by one. The server can also compare the human mitochondrial sequences in the human mitochondrial sequence database with each other in pairs, and determine the mitochondrial sequences in the human mitochondrial sequence database that do not match the non-redundant target genome sequence and do not match each other as non-redundant target mitochondrial sequences. After that, the server can integrate the information of the non-redundant target genome sequence and the non-redundant target mitochondrial sequence, and construct a human sequence filtering index. The server can also first obtain each RNA sequence in the RNA database corresponding to the HG38 human genome, and then the server can compare each RNA sequence in the RNA database corresponding to the HG38 human genome with the HG38 human genome one by one, and determine the non-redundant target RNA sequences in the RNA sequence that do not match the HG38 human genome and do not match each other. After that, the server can integrate the information of the non-redundant target genome sequence and the non-redundant target RNA sequence, and construct a human sequence filtering index. It is understandable that the server can also integrate the information of the non-redundant target genome sequence, the non-redundant target mitochondrial sequence, and the non-redundant target RNA sequence, and construct a human sequence filtering index. Therefore, the server can construct a human sequence filtering index based on at least one of the non-redundant target genome sequence, the non-redundant target mitochondrial sequence, and the non-redundant target RNA sequence, so that when performing genome sequencing analysis, the human sequence can be flexibly and accurately removed to avoid the influence of the human sequence on the genome sequencing.

[0068] In this embodiment, by obtaining a human mitochondrial sequence database, non-redundant target mitochondrial sequences in the human mitochondrial sequence database that do not match the non-redundant target genome sequence and do not match each other are determined; thereby accurately removing redundant mitochondrial sequences in the human mitochondrial sequence database, and obtaining accurate non-redundant target mitochondrial sequences; obtaining an RNA sequence corresponding to the HG38 human genome, and determining non-redundant target RNA sequences in the RNA sequence that do not match the HG38 human genome and do not match each other; thereby accurately removing redundant RNA sequences in the RNA sequence, and obtaining accurate non-redundant target RNA sequences; determining non-redundant additional sequences based on the non-redundant target mitochondrial sequence and / or the non-redundant target RNA sequence, and constructing a human sequence filtering index based on the non-redundant target genome sequence and the non-redundant additional sequence, thereby constructing a human sequence filtering index for human sequence filtering in combination with the non-redundant target genome sequence, the non-redundant target mitochondrial sequence and / or the non-redundant target RNA sequence, and being able to construct a filtering index based on human sequences (genomes / mitochondria / RNA) from multiple different sources that have been de-redundantly processed, so as to fully filter out human sequences from sequencing data based on the filtering index, avoid repeated analysis of redundant sequences, and improve the filtering efficiency of human sequences.

[0069] In an exemplary embodiment, the human genome of at least one human population includes but is not limited to at least one of the T2T-CHM13v2.0 human genome, the T2T-Yao human genome, the NH1.0 human genome, the YH2.0 human genome, or the HX1 human genome.

[0070] Among them, the T2T-CHM13v2.0 human genome can represent the end-to-end genome of the population / human ethnic group within a certain region.

[0071] Among them, the T2T-Yao human genome can represent the end-to-end genome of a population / human ethnic group in another regional range that is different from a certain regional range.

[0072] Among them, the NH1.0 human genome can represent the genome of the northern population / human ethnic group in another regional range that is different from a certain regional range.

[0073] Among them, the HX1 human genome can represent the genome of the southern population / human ethnic group in another regional range that is different from a certain regional range.

[0074] In practical applications, the human genome of at least one human ethnic group may also include the genome of the southern population / human ethnic group in another regional range different from a certain regional range (such as the YH2.0 human genome) and various human genomes of the population / human ethnic group in different regional ranges.

[0075] In some embodiments, non-redundant target genome sequences that do not match the HG38 human genome are determined in the human genome of each human population, including: for each chromosome in the human genome of each human population, starting from the first base of each chromosome, each base and a preset number of bases after each base are obtained as the genome sequence to be analyzed; the genome sequence to be analyzed is compared with the HG38 human genome, and the bases / base sequences that do not match the HG38 human genome are screened out from the genome sequence to be analyzed as the non-redundant target genome sequence.

[0076] The preset number may include but is not limited to 199. In actual applications, the preset number may be flexibly adjusted and set based on actual needs.

[0077] As an example, for each chromosome in the human genome of each human ethnic group, the server can start from the first base of each chromosome, obtain each base and a preset number of bases after each base as the genome sequence to be analyzed, and then the server can compare the genome sequence to be analyzed with the HG38 human genome, screen out and delete the repeated sequences compared with the HG38 human genome from the genome sequence to be analyzed, thereby screening out the bases / base sequences in the genome sequence to be analyzed that do not match the HG38 human genome as non-redundant target genome sequences.

[0078] In this embodiment, for each chromosome in the human genome of each human ethnic group, starting from the first base of each chromosome, each base and a preset number of bases after each base are obtained as the genome sequence to be analyzed; the genome sequence to be analyzed is compared with the HG38 human genome, and the bases / base sequences that do not match the HG38 human genome are screened out from the genome sequence to be analyzed as non-redundant target genome sequences. This can accurately screen out non-redundant target genome sequences from the human genome of each human ethnic group, thereby improving the accuracy of the non-redundant target genome sequences.

[0079] In some embodiments, obtaining non-redundant additional sequences includes: for each mitochondrial sequence in a human mitochondrial sequence database, starting from the first base of each mitochondrial sequence, obtaining each base and a preset number of bases after each base as the mitochondrial sequence to be analyzed; comparing the mitochondrial sequence to be analyzed with the non-redundant target genome sequence, and screening out bases / base sequences that do not match the non-redundant target genome sequence from the mitochondrial sequence to be analyzed as the non-redundant target mitochondrial sequence.

[0080] The preset number may include but is not limited to 199. In actual applications, the preset number may be flexibly adjusted and set based on actual needs.

[0081] As an example, for each mitochondrial sequence in the human mitochondrial sequence database, the server can start from the first base of each mitochondrial sequence, obtain each base and a preset number of bases after each base as the mitochondrial sequence to be analyzed, and then the server can compare the mitochondrial sequence to be analyzed with the non-redundant target genome sequence, filter out and delete the sequences that are repeated compared with the non-redundant target genome sequence from the mitochondrial sequence to be analyzed, and the server can also compare each mitochondrial sequence to be analyzed in pairs, filter out and delete the sequences that are repeated compared with another mitochondrial sequence to be analyzed from the mitochondrial sequence to be analyzed, thereby filtering out the bases / base sequences that do not match the non-redundant target genome sequence in the mitochondrial sequence to be analyzed as non-redundant target mitochondrial sequences.

[0082] In this embodiment, for each mitochondrial sequence in the human mitochondrial sequence database, starting from the first base of each mitochondrial sequence, each base and a preset number of bases after each base are obtained as the mitochondrial sequence to be analyzed; the mitochondrial sequence to be analyzed is compared with the non-redundant target genome sequence, and the base / base sequence that does not match the non-redundant target genome sequence is screened out from the mitochondrial sequence to be analyzed as the non-redundant target mitochondrial sequence, so that the non-redundant target mitochondrial sequence can be accurately screened out from each mitochondrial sequence in the human mitochondrial sequence database, thereby improving the accuracy of the non-redundant target mitochondrial sequence.

[0083] In some embodiments, after the step of comparing the mitochondrial sequence to be analyzed with the non-redundant target genome sequence, the method further includes: for any mitochondrial sequence to be analyzed among the mitochondrial sequences to be analyzed, constructing an initial mitochondrial index according to the bases / base sequences in any mitochondrial sequence to be analyzed that do not match the non-redundant target genome sequence; updating the initial mitochondrial index according to the bases / base sequences in the mitochondrial sequences to be analyzed other than any mitochondrial sequence to be analyzed that do not match the non-redundant target genome sequence, to obtain a mitochondrial index.

[0084] The initial mitochondrial index may refer to a data set consisting of bases / base sequences in the mitochondrial sequence to be analyzed that do not match the non-redundant target genome sequence.

[0085] As an example, for any mitochondrial sequence to be analyzed in each mitochondrial sequence to be analyzed, the server can first determine the base / base sequence in any mitochondrial sequence to be analyzed that does not match the non-redundant target genome sequence, and then integrate the base / base sequence in the mitochondrial sequence to be analyzed that does not match the non-redundant target genome sequence to construct an initial mitochondrial index. After that, the server can analyze the base / base sequence in each mitochondrial sequence to be analyzed except any mitochondrial sequence to be analyzed that does not match the non-redundant target genome sequence one by one, and use the base / base sequence in each mitochondrial sequence to be analyzed except any mitochondrial sequence to be analyzed that does not match the non-redundant target genome sequence to update the initial mitochondrial index one by one to obtain the mitochondrial index. In a specific implementation, the server can also compare each mitochondrial sequence to be analyzed in pairs, determine the base / base sequence that does not match between each mitochondrial sequence to be analyzed, and use the base / base sequence that does not match between each mitochondrial sequence to be analyzed to update the initial mitochondrial index one by one to obtain the mitochondrial index. In practical applications, the mitochondrial sequence in the mitochondrial index can be used as a non-redundant target mitochondrial sequence.

[0086] In this embodiment, an initial mitochondrial index is constructed for any mitochondrial sequence to be analyzed among the mitochondrial sequences to be analyzed according to the bases / base sequences in any mitochondrial sequence to be analyzed that do not match the non-redundant target genome sequence; the initial mitochondrial index is updated according to the bases / base sequences in the mitochondrial sequences to be analyzed other than any mitochondrial sequence to be analyzed that do not match the non-redundant target genome sequence, to obtain a mitochondrial index. The initial mitochondrial index can be first constructed based on any mitochondrial sequence to be analyzed, and the non-redundant target mitochondrial sequence can be accurately screened out from the mitochondrial sequences in the human mitochondrial sequence database in combination with the initial mitochondrial index, thereby improving the accuracy of the non-redundant target mitochondrial sequence.

[0087] In some embodiments, obtaining non-redundant additional sequences includes: dividing each RNA into at least one set according to the positional overlap or similarity between the RNAs in the RNA sequence; for the set, starting from the first base of each RNA in the set, obtaining each base and a preset number of bases after each base as the RNA sequence to be analyzed corresponding to the set; comparing the RNA sequence to be analyzed corresponding to the set with the HG38 human genome, and screening out bases / base sequences that do not match the HG38 human genome from the RNA sequence to be analyzed corresponding to the set as non-redundant target RNA sequences.

[0088] As an example, since the variable shearing of the same gene is highly similar, index reconstruction between variable shearing can compress the number of non-redundant target RNA sequences. In actual applications, the server can first analyze the positional overlap or similarity between each RNA in the RNA sequence, and then the server can divide the RNA sequences / transcripts with continuous overlap into the same set based on the positional overlap or similarity between each RNA in the RNA sequence, thereby dividing each RNA into at least one set. For the set, the server can start from the first base of each RNA in the set, and obtain each base and a preset number of bases after each base as the RNA sequence to be analyzed corresponding to the set. The server can then compare the RNA sequence to be analyzed corresponding to the set with the HG38 human genome, and screen out the repeated sequences in the RNA sequence to be analyzed corresponding to each set compared with the HG38 human genome, thereby screening out the bases / base sequences that do not match the HG38 human genome in the RNA sequence to be analyzed corresponding to the set as non-redundant target RNA sequences.

[0089] In this embodiment, each RNA is divided into at least one set according to the positional overlap relationship or similarity between each RNA in the RNA sequence; for the set, starting from the first base of each RNA in the set, each base and a preset number of bases after each base are obtained as the RNA sequence to be analyzed corresponding to the set; the RNA sequence to be analyzed corresponding to the set is compared with the HG38 human genome, and the bases / base sequences that do not match the HG38 human genome and do not match each other are screened out from the RNA sequence to be analyzed corresponding to the set as non-redundant target RNA sequences. The RNA sequence can be divided into several sets first, and the non-redundant target RNA sequences can be screened for each set, thereby improving the screening efficiency and accuracy of the non-redundant target RNA sequences.

[0090] In some embodiments, after the step of comparing the RNA sequence to be analyzed corresponding to the set with the HG38 human genome, the above method also includes: for each set, constructing an initial RNA index corresponding to the set according to the base / base sequence that does not match the HG38 human genome in the RNA sequence to be analyzed corresponding to the set; updating any initial RNA index except any initial RNA index in the initial RNA index corresponding to each set to obtain the RNA index corresponding to each set; integrating the index sequences of the RNA indexes corresponding to each set to obtain a non-redundant target RNA sequence.

[0091] As an example, for each set, the server can first analyze whether there are sequences (such as repeated sequences) that need to be de-redundanted between the RNA sequence to be analyzed corresponding to the set and the HG38 human genome. After removing the repeated sequences, the server can use the remaining sequences as bases / base sequences that do not match the HG38 human genome in the RNA sequence to be analyzed corresponding to the set, and then the server can integrate the bases / base sequences that do not match the HG38 human genome in the RNA sequence to be analyzed corresponding to the set to construct the initial RNA index corresponding to the set. Each set can construct the corresponding initial RNA index in the above manner. The server can update any initial RNA index according to the initial RNA index other than any initial RNA index in the initial RNA index corresponding to each set to obtain the RNA index corresponding to each set. Finally, the server can integrate the index sequence (such as RNA sequence) of the RNA index corresponding to each set to obtain a non-redundant target RNA sequence.

[0092] In this embodiment, for each set, an initial RNA index corresponding to the set is constructed according to the bases / base sequences in the RNA sequence to be analyzed corresponding to the set that do not match the HG38 human genome; any initial RNA index is updated according to the initial RNA index other than any initial RNA index in the initial RNA index corresponding to each set to obtain the RNA index corresponding to each set; the index sequences of the RNA indexes corresponding to each set are integrated to obtain a non-redundant target RNA sequence, so that the initial RNA index can be first constructed based on the RNA sequence to be analyzed corresponding to any set, and the non-redundant target RNA sequence can be accurately screened out from the RNA sequence corresponding to the HG38 human genome in combination with the initial RNA index, thereby improving the accuracy of the non-redundant target RNA sequence.

[0093] In an exemplary embodiment, a human sequence filtering method is provided, which is described by taking the method applied to a server as an example. The human sequence filtering method includes: obtaining a sequence to be filtered; filtering the sequence to be filtered using the HG38 human genome and a human sequence filtering index to obtain a filtered sequence; wherein the human sequence filtering index is constructed by a non-redundant target genome sequence, a non-redundant target mitochondrial sequence, and a non-redundant target RNA sequence; the non-redundant target genome sequence is obtained from a genome sequence that does not match the HG38 human genome in a human genome of at least one human ethnic group different from the HG38 human genome; the non-redundant target mitochondrial sequence is obtained from a mitochondrial sequence in a human mitochondrial sequence database that does not match the non-redundant target genome sequence and does not match each other; the non-redundant target RNA sequence is obtained from an RNA sequence corresponding to the HG38 human genome that does not match the HG38 human genome and does not match each other.

[0094] The sequence to be filtered may refer to a sequence that needs to be sequenced. In practical applications, the sequence to be filtered may refer to information used to characterize the sequence that needs to be sequenced.

[0095] Among them, the filtered sequence may refer to the sequence obtained after filtering the sequence to be filtered by human sequence using the HG38 human genome and / or a pre-constructed human sequence filtering index. In practical applications, the filtered sequence may refer to the information used to characterize the sequence after human sequence filtering.

[0096] As an example, in the metagenomic sequencing analysis process, the host and background sequences that occupy the vast majority are generally removed first, and then the filtered sequences are annotated into the multi-species genome to analyze the species composition and biological significance. In clinical applications, the host is usually human, so gene sequencing requires the removal of human fragments in the sequence to be filtered. In actual applications, after obtaining information characterizing the sequence to be filtered, the server can use the HG38 human genome and / or a pre-constructed human sequence filtering index to filter the sequence to be filtered and obtain the filtered sequence, which can be used for subsequent gene sequencing. In practical applications, the pre-constructed human sequence filtering index can be constructed by information integration of at least one of the non-redundant target genome sequence, the non-redundant target mitochondrial sequence and the non-redundant target RNA sequence. In specific implementations, the server can filter out the genome sequences that do not match the HG38 human genome in the human genome of at least one human ethnic group different from the HG38 human genome to obtain the non-redundant target genome sequence. The server can filter out the mitochondrial sequences that do not match the non-redundant target genome sequence and do not match each other in the human mitochondrial sequence database to obtain the non-redundant target mitochondrial sequence. The server can filter out the RNA sequences that do not match the HG38 human genome and do not match each other in the RNA sequence corresponding to the HG38 human genome to obtain the non-redundant target RNA sequence.

[0097] In some embodiments, the HG38 human genome and human sequence filtering index are used to filter the sequence to be filtered, including: when the human sequence filtering index is used to filter the sequence to be filtered, the mitochondrial sequences in the sequence to be filtered that do not have base insertions and base deletions compared to the human sequence filtering index are deleted from the sequence to be filtered.

[0098] As an example, when using the human sequence filter index to filter the sequence to be filtered, the server can first screen out the mitochondrial sequences in the sequence to be filtered that do not have base insertions and base deletions compared to the human sequence filter index. Since mitochondria have high homology in eukaryotes, false positive filtering is easily caused. Therefore, in the filtering process, the mitochondrial sequence is required to be filtered only when it does not have base insertions and base deletions compared to the human sequence filter index. In actual applications, the server can delete the mitochondrial sequences in the sequence to be filtered that do not have base insertions and base deletions compared to the human sequence filter index, thereby reducing interference with subsequent parasite-related analysis. In a specific implementation, any similar part in the non-mitochondrial sequence in the sequence to be filtered compared to the human sequence filter index is removed.

[0099] In this embodiment, by using the human sequence filtering index to filter the sequence to be filtered, the mitochondrial sequences in the sequence to be filtered that do not have base insertions and base deletions compared to the human sequence filtering index are deleted from the sequence to be filtered. This can avoid inaccurate filtering when using the human sequence filtering index to filter the sequence to be filtered, thereby avoiding interference with parasite-related analysis and improving the accuracy of filtering the sequence to be filtered.

[0100] In one exemplary embodiment, Figure 3 As shown, a schematic diagram of the process of constructing a human sequence filtering index is provided. The construction of the human sequence filtering index can include the steps of genome de-redundancy, mitochondrial de-redundancy, RNA de-redundancy, sequence integration, etc. Genome de-redundancy, mitochondrial de-redundancy, and RNA de-redundancy can respectively adopt different de-redundancy strategies according to the sequence type. Sequence integration can be used to integrate the results of de-redundancy and establish additional indexes for comparison (such as human sequence filtering index).

[0101] Taking the human genome and HG38 genome corresponding to different human ethnic groups as examples, the steps of genome de-redundancy are explained: the server first confirms the human genome combination. In addition to the universal HG38 genome, the server can also select the end-to-end genome T2T-CHM13v2.0, the end-to-end genome T2T-Yao, the population genome NH1.0, and the population genome HX1. In order to remove redundant sequences, the server can align the four genomes except HG38 to the genome index updated in each round in turn, obtain the base positions that cannot be aligned or have insertions and deletions, and generate a genome region record after aggregation. The corresponding genome sequence is obtained according to the region record, and the sequences outside the region are removed. In practical applications, the server can download the T2T-CHM13v2.0 genome sequence from the NCBI database, download the T2T-Yao, NH1.0, and HX1 genome sequences from the Genome Science Data Center, and use a python script to read the T2T-CHM13v2.0 genome sequence. Then the server can start from the first base of each chromosome, obtain each base and the next 199 bases, a total of 200bp long sequence, name the sequence with the chromosome name + the position on the chromosome, convert it into fastq format output, use alignment tools (such as bwa-mem2) to align with the HG38 genome index, and use a python script to synchronously read the alignment results. The server can record the base positions that cannot be aligned or have soft cuts / insertions / deletions based on the alignment results. The base positions that cannot be aligned or have soft cuts / insertions / deletions can be used as non-redundant target genome sequences. The server can summarize the position of each base as a genome region record. After that, the server can obtain the corresponding regional sequence on the genome according to the regional record, summarize it into a de-redundant T2T-CHM13v2.0 sequence, and add the de-redundant T2T-CHM13v2.0 sequence to the HG38 genome sequence, and use bwa-mem2 to create an index. Similarly, for the T2T-Yao, NH1.0, and HX1 genome sequences, the server can also use the above method to determine the corresponding alignment results, and based on the alignment results, record the base positions that cannot be aligned or have soft cuts / insertions / deletions, summarize the position of each base as a genome region record, and the server can update the HG38 genome index based on the base positions that cannot be aligned or have soft cuts / insertions / deletions in the T2T-Yao, NH1.0, and HX1 genome sequences to obtain the de-redundant genome sequence. In the specific implementation, the T2T-CHM13v2.0, T2T-Yao, NH1.0, and HX1 genome sequences contain a total of 11.2GB of sequences. After de-redundancy processing, the server can compress the genome sequences actually used for human sequence filtering contained in the T2T-CHM13v2.0, T2T-Yao, NH1.0, and HX1 genome sequences to 1.48GB.

[0102] Taking human mitochondrial sequences as an example, the steps of mitochondrial de-redundancy are explained: human mitochondrial sequences are smaller than genome sequences, but there are more than 60,000 different versions of human mitochondrial regions in the database. They have high homology and differences from each other. The total size of all versions of sequences is 1.1GB, which needs to be further compressed. Due to the high homology of mitochondria, each genome alignment summarizes non-redundant sequences to reconstruct the index, and further requires no mismatches on the basis of no insertions and deletions. In practical applications, the server can download 1.1GB of mitochondrial sequences from NCBI based on the complete human mitochondrial database on MITOMAP, a total of 66,000 human mitochondrial sequences, and use bwa to index the four de-redundant genome sequences in the genome de-redundancy step, and use a python script to read the downloaded mitochondrial sequences. After that, the server can start from the first base of each mitochondrial sequence, obtain each base and the following 199 bases, a total of 200bp long sequences, name the sequences with mitochondrial id + position on the mitochondria, convert them into fastq format output, use bwa to compare with the indexes corresponding to the four de-redundant genome sequences and the HG38 genome index, and use python scripts to synchronously read the comparison results. The server can adjust the filtering scheme according to the mitochondrial characteristics, record the base positions that cannot be matched or have any soft-cut insertion and deletion mismatches, summarize the position of each base as a mitochondrial regional record, obtain the corresponding regional sequence on the mitochondria according to the regional record, summarize it into a de-redundant mitochondrial sequence, and use bwa to index the de-redundant mitochondrial sequence. For each mitochondrial sequence, the server can record the base positions that cannot be matched or have any soft-cut insertion and deletion mismatches, re-convert them into fastq format output, use bwa to compare with the de-redundant mitochondrial sequence index, use python scripts to synchronously read the comparison results, record the base positions that cannot be matched or have any soft-cut insertion and deletion mismatches, and summarize the position of each base as a mitochondrial regional record. The server can obtain the corresponding regional sequence on the mitochondria according to the regional record, integrate the redundant mitochondrial sequence, and use bwa to index the de-redundant mitochondrial sequence until the last mitochondrial sequence is de-redundanted to obtain the final de-redundant mitochondrial sequence. In practical applications, the mitochondrial sequence in the de-redundant mitochondrial sequence can be used as a non-redundant target mitochondrial sequence. In specific implementations, the size of human mitochondrial sequences can be reduced from 1.1GB to 30MB.

[0103] Take the RNA sequence in the RNA database corresponding to the HG38 genome as an example to illustrate the steps of RNA de-redundancy: the RNA database of HG38 contains 700MB, a total of 180,000 transcripts, and the variable shearing of the same gene is highly similar. Index reconstruction between variable shearing can enhance its compression rate, but index reconstruction for all transcripts will greatly increase the construction time. Therefore, the gene set is divided into regions where there are no genes, and index reconstruction is performed within each sub-gene set, and finally the indexes of all sub-gene sets are summarized. In practical applications, the server can download the RNA sequence and GTF annotation file corresponding to the HG38 genome from NCBI, read the GTF annotation file, obtain the starting and ending positions of each transcript, divide the transcripts with continuous overlap into the same set, and separate different sets by the intergenic region where no genes exist. After that, the server can use a python script to read the RNA sequence, starting from the first base of each transcript, obtain each base and the next 199 bases, a total of 200bp long sequence, name the sequence with transcript id + position on the transcript, and convert it into fastq format for output. The server can use bwa-mem2 to align with the HG38 genome index, use a python script to synchronously read the alignment results, record the base positions that cannot be matched or have any soft cut insertion and deletion mismatches, generate sequences based on the base positions that cannot be matched or have any soft cut insertion and deletion mismatches for the first transcript in the set, and use bwa to index the generated sequences. After analyzing and de-redundant processing for each transcript in each set, the server can re-convert the final base positions that cannot be matched or have any soft cut insertion and deletion mismatches into fastq format output, and use bwa to align the fastq format output results with the sequences generated based on the base positions that cannot be matched or have any soft cut insertion and deletion mismatches, use a python script to synchronously read the alignment results, record the base positions that cannot be matched or have any soft cut / insertion / deletion / mismatches, and summarize the positions of each base as transcript region records. The server can obtain the corresponding region sequence on the transcript based on the region record, integrate it into the sequence generated by the first transcript based on the base positions that cannot be matched or have any soft cut insertion and deletion mismatches, and re-use bwa to index the generated sequence. After the transcript cycle in the collection is completed, the server can integrate the index sequence generated by this collection into the final de-redundant RNA sequence. In the specific implementation, the RNA sequence in the de-redundant RNA sequence can be used as a non-redundant target RNA sequence. After all collection cycles are completed, the final summary is the de-redundant RNA sequence. The size of the RNA sequence in the RNA database corresponding to the HG38 genome can be compressed from 700MB to 90MB.

[0104] During sequence integration, the server can integrate the de-redundant genome sequence, de-redundant mitochondrial sequence, and de-redundant RNA sequence to obtain the human sequence filter index, thereby compressing the size of the original sequence from 13GB to 1.6GB after de-redundancy. In actual applications, the server can also use preset DNA / RNA samples to test the human sequence filter index, increasing the loading speed by 700% and the comparison speed by 100%, greatly reducing the filtering time.

[0105] Since an overly large index will reduce the filtering speed, and HG38 can filter most human sequences, the server can first use bwa / bwa-mem2 to filter human sequences based on the HG38 index. When filtering with bwa / bwa-mem2, the alignment results in sam format will be output in real time. Thus, the sam output is converted to a fastq file synchronously during filtering, and the conversion is completed when the filtering is completed, which saves the conversion time and makes the operation easier. Since mitochondria have a high homology in eukaryotes and are prone to false positive filtering, during the conversion process, additional adjustments are made to the mitochondrial sequences on the alignment, requiring no insertions or deletions to be filtered, reducing interference with subsequent parasite-related analysis.

[0106] In this embodiment, the sequencing data is fully filtered out by selecting human sequences (genome / mitochondria / RNA) from multiple different sources. In order to alleviate the speed reduction caused by the large index, the redundant index sequences are compared with each other to remove the same or similar sequences, thereby ensuring the filtering effect while reducing the size of the index sequence as much as possible. After filtering using the HG38 human genome, the human sequence filtering index can further filter out the remaining fragments, thereby increasing the index loading speed and comparison speed. When using the index for filtering, by splitting the HG38 and human sequence filtering indexes into separate filtering steps, the time consumption caused by the human sequence filtering index can also be reduced, thereby further filtering out human fragments efficiently and quickly through the human sequence filtering index.

[0107] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0108] Based on the same inventive concept, the embodiment of the present application also provides a device for constructing a human sequence filtering index for implementing the method for constructing a human sequence filtering index involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the device for constructing one or more human sequence filtering indexes provided below can refer to the limitations of the method for constructing a human sequence filtering index above, and will not be repeated here.

[0109] In an exemplary embodiment, Figure 4 As shown, a device for constructing a human sequence filtering index is provided, comprising: a genome analysis module 402 and an index construction module 404, wherein:

[0110] The genome analysis module 402 is used to obtain the HG38 human genome and the human genome of at least one human ethnic group different from the HG38 human genome, respectively determine the target genome sequences in the human genomes of each of the human ethnic groups that do not match the HG38 human genome, and generate a genome index based on the target genome sequences and the HG38 human genome.

[0111] The index building module 404 is used to build a human sequence filtering index according to the genome index.

[0112] In one of the exemplary embodiments, the index construction module 404 is specifically used to obtain non-redundant additional sequences, wherein the non-redundant additional sequences are non-redundant target mitochondrial sequences and / or non-redundant target RNA sequences; wherein the non-redundant target mitochondrial sequences are sequences in the human mitochondrial sequence database that do not match the non-redundant target genome sequences and do not match each other, and the non-redundant target RNA sequences are sequences corresponding to the HG38 human genome that do not match the HG38 human genome and do not match each other; a human sequence filtering index is constructed based on the non-redundant target genome sequence and the non-redundant additional sequences.

[0113] In one exemplary embodiment, the human genome of the at least one human population includes but is not limited to at least one of the T2T-CHM13v2.0 human genome, the T2T-Yao human genome, the NH1.0 human genome, the YH2.0 human genome, or the HX1 human genome.

[0114] In one of the exemplary embodiments, the genome analysis module 402 is specifically used to obtain each base and a preset number of bases after each base as the genome sequence to be analyzed for each chromosome in the human genome of each of the human populations, starting from the first base of each chromosome; the genome sequence to be analyzed is compared with the HG38 human genome, and the bases / base sequences that do not match the HG38 human genome are screened out from the genome sequence to be analyzed as the non-redundant target genome sequence.

[0115] In one of the exemplary embodiments, the index construction module 404 is specifically used to obtain, for each mitochondrial sequence in the human mitochondrial sequence database, starting from the first base of each mitochondrial sequence, each base and a preset number of bases after each base as the mitochondrial sequence to be analyzed; compare the mitochondrial sequence to be analyzed with the non-redundant target genome sequence, and screen out the bases / base sequences that do not match the non-redundant target genome sequence from the mitochondrial sequence to be analyzed as the non-redundant target mitochondrial sequence.

[0116] In one of the exemplary embodiments, the index construction module 404 is specifically used to construct an initial mitochondrial index for any mitochondrial sequence to be analyzed among the mitochondrial sequences to be analyzed, according to the bases / base sequences in any mitochondrial sequence to be analyzed that do not match the non-redundant target genome sequence; update the initial mitochondrial index according to the bases / base sequences in the mitochondrial sequences to be analyzed other than any mitochondrial sequence to be analyzed among the mitochondrial sequences to be analyzed that do not match the non-redundant target genome sequence, so as to obtain a mitochondrial index.

[0117] In one of the exemplary embodiments, the index construction module 404 is specifically used to divide the RNAs into at least one set according to the positional overlap or similarity between the RNAs in the RNA sequence; for the set, starting from the first base of each RNA in the set, each base and a preset number of bases after each base are obtained as the RNA sequence to be analyzed corresponding to the set; the RNA sequence to be analyzed corresponding to the set is compared with the HG38 human genome, and the bases / base sequences that do not match the HG38 human genome are screened out from the RNA sequence to be analyzed corresponding to the set as the non-redundant target RNA sequence.

[0118] In one of the exemplary embodiments, the index construction module 404 is specifically used to construct, for each of the sets, an initial RNA index corresponding to the set based on the bases / base sequences in the RNA sequence to be analyzed corresponding to the set that do not match the HG38 human genome; update any initial RNA index except any initial RNA index in the initial RNA index corresponding to each set to obtain the RNA index corresponding to each set; integrate the index sequences of the RNA indexes corresponding to each set to obtain the non-redundant target RNA sequence.

[0119] Each module in the above-mentioned human sequence filtering index construction device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0120] In an exemplary embodiment, a human sequence filtering device is provided, comprising: a sequence acquisition module and a sequence filtering module, wherein:

[0121] The sequence acquisition module is used to obtain the sequence to be filtered.

[0122] A sequence filtering module, for filtering the sequence to be filtered using the HG38 human genome and a human sequence filtering index to obtain a filtered sequence; wherein the human sequence filtering index is constructed from a non-redundant target genome sequence, a non-redundant target mitochondrial sequence and a non-redundant target RNA sequence; the non-redundant target genome sequence is obtained from a genome sequence that does not match the HG38 human genome in a human genome of at least one human ethnic group different from the HG38 human genome; the non-redundant target mitochondrial sequence is obtained from mitochondrial sequences in a human mitochondrial sequence database that do not match the non-redundant target genome sequence and do not match each other; the non-redundant target RNA sequence is obtained from RNA sequences that do not match the HG38 human genome and do not match each other in an RNA sequence corresponding to the HG38 human genome.

[0123] In one of the exemplary embodiments, the sequence filtering module is specifically used to delete, from the sequence to be filtered, mitochondrial sequences in the sequence to be filtered that do not have base insertions and base deletions compared to the human sequence filtering index when the sequence to be filtered is filtered using the human sequence filtering index.

[0124] Each module in the above-mentioned human sequence filtering device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0125] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as human genomes, human mitochondrial sequences and RNA sequences. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for constructing a human sequence filtering index is implemented.

[0126] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0129] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0130] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0131] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0132] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0133] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for constructing a human sequence filtering index, characterized in that: The method comprises: Obtaining the HG38 human genome and the human genome of at least one human ethnic group different from the HG38 human genome, and respectively determining non-redundant target genome sequences in the human genomes of the human ethnic groups that do not match the HG38 human genome; A human sequence filtering index is constructed based on the non-redundant target genome sequence.

2. The method according to claim 1, characterized in that The method further comprises: Acquire a non-redundant additional sequence, wherein the non-redundant additional sequence is a non-redundant target mitochondrial sequence and / or a non-redundant target RNA sequence; Wherein, the non-redundant target mitochondrial sequence is a sequence in the human mitochondrial sequence database that does not match the non-redundant target genome sequence and does not match each other, and the non-redundant target RNA sequence is a sequence that does not match the HG38 human genome and does not match each other; The step of constructing a human sequence filtering index according to the non-redundant target genome sequence comprises: A human sequence filtering index is constructed based on the non-redundant target genome sequence and the non-redundant additional sequence.

3. The method according to claim 1, characterized in that: The human genome of the at least one human population includes but is not limited to at least one of the T2T-CHM13v2.0 human genome, the T2T-Yao human genome, the NH1.0 human genome, the YH2.0 human genome or the HX1 human genome.

4. The method according to claim 1, characterized in that: The step of determining the non-redundant target genome sequence in the human genome of each of the human populations that does not match the HG38 human genome comprises: For each chromosome in the human genome of each of the human populations, starting from the first base of each chromosome, each base and a preset number of bases after each base are obtained as the genome sequence to be analyzed; The genome sequence to be analyzed is compared with the HG38 human genome, and bases / base sequences that do not match the HG38 human genome are screened out from the genome sequence to be analyzed as the non-redundant target genome sequence.

5. The method according to claim 2, characterized in that: The obtaining of the non-redundant additional sequence comprises: For each mitochondrial sequence in the human mitochondrial sequence database, starting from the first base of each mitochondrial sequence, each base and a preset number of bases after each base are obtained as the mitochondrial sequence to be analyzed; The mitochondrial sequence to be analyzed is compared with the non-redundant target genome sequence, and bases / base sequences that do not match the non-redundant target genome sequence are screened out from the mitochondrial sequence to be analyzed as the non-redundant target mitochondrial sequence.

6. The method according to claim 5, characterized in that After the step of comparing the mitochondrial sequence to be analyzed with the non-redundant target genome sequence, the method further comprises: For any one of the mitochondrial sequences to be analyzed, constructing an initial mitochondrial index according to bases / base sequences in any one of the mitochondrial sequences to be analyzed that do not match the non-redundant target genome sequence; The initial mitochondrial index is updated according to the bases / base sequences that do not match the non-redundant target genome sequence in the mitochondrial sequences to be analyzed except any one of the mitochondrial sequences to be analyzed, so as to obtain a mitochondrial index.

7. The method according to claim 2, characterized in that The obtaining of the non-redundant additional sequence comprises: According to the positional overlap or similarity between the RNAs in the RNA sequence, the RNAs are divided into at least one set; For the set, starting from the first base of each RNA in the set, each base and a preset number of bases after each base are obtained as the RNA sequence to be analyzed corresponding to the set; The RNA sequences to be analyzed corresponding to the set are compared with the HG38 human genome, and bases / base sequences that do not match the HG38 human genome are screened out from the RNA sequences to be analyzed corresponding to the set as the non-redundant target RNA sequences.

8. The method according to claim 7, characterized in that After the step of comparing the RNA sequence to be analyzed corresponding to the set with the HG38 human genome, the method further comprises: For each of the sets, constructing an initial RNA index corresponding to the set according to the bases / base sequences in the RNA sequence to be analyzed corresponding to the set that do not match the HG38 human genome; According to the initial RNA indexes other than any initial RNA index in the initial RNA indexes corresponding to the sets, the any initial RNA index is updated to obtain the RNA indexes corresponding to the sets; The index sequences of the RNA indexes corresponding to the sets are integrated to obtain the non-redundant target RNA sequence.

9. A human sequence filtering method, characterized in that: The method comprises: Get the sequence to be filtered; The sequence to be filtered is filtered using the HG38 human genome and human sequence filtering index to obtain a filtered sequence; Among them, the human sequence filtering index is constructed by a non-redundant target genome sequence, a non-redundant target mitochondrial sequence and a non-redundant target RNA sequence; the non-redundant target genome sequence is obtained from a genome sequence in a human genome of at least one human ethnic group different from the HG38 human genome that does not match the HG38 human genome; the non-redundant target mitochondrial sequence is obtained from mitochondrial sequences in a human mitochondrial sequence database that do not match the non-redundant target genome sequence and do not match each other; the non-redundant target RNA sequence is obtained from RNA sequences corresponding to the HG38 human genome that do not match the HG38 human genome and do not match each other.

10. The method according to claim 9, characterized in that The filtering of the sequence to be filtered by using the HG38 human genome and the human sequence filtering index includes: When the sequence to be filtered is filtered using the human sequence filtering index, mitochondrial sequences in the sequence to be filtered that do not have base insertions or base deletions compared with the human sequence filtering index are deleted from the sequence to be filtered.

11. A device for constructing a human sequence filtering index, characterized in that: The device comprises: A genome analysis module, for obtaining the HG38 human genome and the human genome of at least one human ethnic group different from the HG38 human genome, respectively determining target genome sequences in the human genomes of the human ethnic groups that do not match the HG38 human genome, and generating a genome index based on the target genome sequences and the HG38 human genome; An index building module is used to build a human sequence filtering index based on the genome index.

12. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

14. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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