Digital standard for verifying accuracy of metagenome biological information detection

Abstract

Provided in the present invention is a digital standard for verifying the accuracy of the metagenome biological information detection process. In the present invention, 24 viral genomes of particular concern to the Chinese Pharmacopoeia are selected, and a total of 100 samples are constructed. Each sample contains 24 different viral genome sequences. The abundance is randomly generated, and the viral abundance distribution of each sample is different. The digital standard of the present invention can be used for verifying the accuracy of the biological information process built on the basis of metagenome.

Description

Digital standards for verifying the accuracy of metagenomic bioinformatics detection Technical Field

[0001] The present invention relates to the field of bioinformatics, and more particularly to digital standards for verifying the accuracy of metagenomic bioinformatics detection. Background Art

[0002] Metagenomics involves extracting DNA from all microorganisms in a microbial sample, constructing a metagenomic library, and then applying genomic research strategies to analyze the genetic composition of the entire microbial population and the potential functions of the community. This omics technique, which does not rely on the isolation and pure culture of single bacteria, largely overcomes the difficulty in studying most microorganisms due to the inability to isolate and culture them. It also allows for the true composition of microorganisms in the ecological environment. In microbial ecosystem research related to human health, quantification of microorganisms based on metagenomic sequencing data is fundamental for understanding community composition, interspecies interactions, and the relationship between these communities and disease development. With the advancement of scientific research, an increasing number of studies have demonstrated the increasing importance of accurately annotating lower-level taxa within a given species, namely strains. Simply studying the relationship between bacteria and disease at a higher taxonomic level can lead to lumping together species that are positively, unrelatedly, or even negatively correlated with disease development. This is a significant biological and statistical error. Existing research findings urgently need to be corrected by improving the accuracy of microbial quantification or conducting more in-depth mechanistic studies.

[0003] In the biopharmaceutical testing industry, metagenomics is increasingly being used for exogenous virus detection. High-throughput metagenomics technology promises to detect all microorganisms (bacteria, viruses, fungi, and more) within a sample simultaneously. However, subsequent validation efforts must be rapidly implemented before this technology can be widely adopted.

[0004] The metagenomics technology developed in recent years bypasses traditional microbial isolation and culture methods and directly extracts total DNA from environmental samples. It obtains new functional genes and bioactive substances by constructing and screening metagenomic libraries. Metagenomic libraries include both culturable and unculturable microbial genetic information, thereby increasing the chance of obtaining new bioactive substances. Its quantitative method has a high resolution, that is, it can be annotated to the species or strain level, so it is currently widely used in pathogen detection.

[0005] In the field of biological product testing, the industry currently primarily relies on traditional methods such as PCR, electrophoresis, and culture. However, these traditional methods have low throughput and cannot simultaneously detect a large number of different virus types. Therefore, methods based on next-generation sequencing (NGS) technology are gradually being developed.

[0006] Although second-generation sequencing can make up for the disadvantage of low throughput, its high sensitivity, high background noise, and randomness of sequencing make it difficult to judge whether the test results are true or false positives. At the same time, the sample preparation, DNA and RNA extraction, and subsequent library construction processes of second-generation sequencing are prone to environmental or contamination of consumables. Therefore, while promoting the use of metagenomics for biological product safety testing, it is necessary to develop standards to test the entire metagenomic workflow, including dry experiments and wet experiments. Among them, the methods and tools of dry experiments (bioinformatics processes) are varied, and there is currently no recognized most reliable detection method and tool for everyone to use. Therefore, it is very necessary to build digital standards to verify the bioinformatics analysis process.

[0007] Therefore, there is a need in the art to develop a digital standard for verifying the accuracy of metagenomic bioinformatics detection.

[0008] Summary of the Invention

[0009] The purpose of the present invention is to provide a digital standard for testing the accuracy of metagenomic biological information detection.

[0010] In a first aspect of the present invention, a method for preparing a digital standard for genomic biological information detection is provided, comprising the following steps:

[0011] (i) providing n reference genomes of microorganisms, wherein n is an integer selected from 10-24, preferably 15-24, more preferably 20-24;

[0012] (ii) simulated community design: generating simulated communities based on the reference genome and taxonomic information;

[0013] (iii) read simulation: using a read simulator to sample from each genome of the simulated community to generate simulated samples, which are then mixed to obtain a simulated dataset;

[0014] (iv) Standard assembly: assembling the simulated data set to obtain the digital standard.

[0015] In another preferred embodiment, the microorganism is a virus.

[0016] In another preferred embodiment, the microorganisms are n microorganisms selected from the group consisting of Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type 1), Simian retrovirus 8 (simian retrovirus), Simian foamy virus (simian foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

[0017] In another preferred embodiment, the n microorganisms are composed of the following microorganisms: Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type 1), Simian retrovirus 8 (simian retrovirus), Simian foamy virus (simian foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

[0018] In another preferred embodiment, the reference genome is obtained from the NCBI genome database.

[0019] In another preferred embodiment, steps (ii) to (iv) are performed using CAMISIM software.

[0020] In another preferred embodiment, the simulated data set in step (ii) is generated by a method selected from the group consisting of de novo simulation, data set simulation, or a combination thereof.

[0021] In another preferred embodiment, the de novo simulation comprises the following steps:

[0022] A1) provide specified taxonomic information, including the name of the taxon and the abundance of the taxon;

[0023] A2) providing the genome taxonomic name of the reference genome in a known database, mapping the taxonomic unit name to the genome taxonomic name of the known database (e.g., NCBI database taxonomic ID), thereby matching the taxonomic unit with the reference genome;

[0024] A3) Based on the novelty level of the reference genome and the corresponding taxonomic unit name, m genomes are extracted from the reference genome to generate the simulated community.

[0025] In another preferred embodiment, the given taxonomic information is a CAMI file or a BIOM file.

[0026] In another preferred embodiment, the species classification unit name refers to the classification name of the classification operational unit (OTU).

[0027] In another preferred embodiment, in step A2), the OUT classification name is mapped to the NCBI classification ID.

[0028] In another preferred embodiment, in step A2), the taxonomic unit and the genome to which it is mapped satisfy the following conditions:

[0029] There is a minimal path between the taxon name and the genome taxon name calculated from the de Bruijn graph, and

[0030] The taxonomic unit name and the genome taxonomic name belong to at least the same family.

[0031] In another preferred embodiment, in step A2), if there is no genome that meets the conditions, the classification unit is assigned to a customized new "random" genome.

[0032] In another preferred embodiment, step A3) comprises the steps of:

[0033] 1) Based on the total number of genomes m, the same number of genomes were sampled from each novelty category (new strain, new species, new genus, new family, new order);

[0034] 2) If there are not enough genomes to sample from a certain novelty category, more genomes are sampled from other categories in equal amounts;

[0035] 3) Repeat steps 1) and 2) until m genomes are extracted.

[0036] In another preferred embodiment, the novelty category is determined based on the degree of similarity between the reference genome sequence and currently known viral sequences.

[0037] In another preferred embodiment, the m is 15-40, preferably 20-25.

[0038] In another preferred embodiment, the simulated community further comprises an additional genome simulated by the steps of:

[0039] a) selecting a specific genome from the reference genome and extracting a strain number from a geometric distribution, wherein the strain number represents the number of related strains created for the specific genome;

[0040] b) repeating this step until the specified total number of strains is reached;

[0041] c) Randomly selecting a designated strain number from the generated related strains, thereby obtaining the simulated additional genome.

[0042] In another preferred example, for each reference genome, 20-50 related strains, preferably 30-40 related strains are generated.

[0043] In another preferred embodiment, the genetic distance between each related strain and the starting strain increases in steps of 0.1%, and the total distance is 2%-5%, preferably 3%-4%.

[0044] In another preferred embodiment, the data set simulation comprises the following steps:

[0045] B1) Sampling from the lognormal distribution to create a set of abundance distribution samples;

[0046] B2) generating the simulated community based on the reference genome and the abundance distribution sample set.

[0047] In another preferred embodiment, in step B1), the default data of CAMISIM software is used, and the parameter mu of the lognormal distribution is set to 1, and the parameter sigma is set to 2.

[0048] In another preferred embodiment, in step B1), ≥2 abundance distribution samples, preferably ≥5, and more preferably ≥10, are independently extracted from the lognormal distribution using the same parameter settings.

[0049] In another preferred embodiment, in step B1), for the x+1th abundance distribution sample, a new value is sampled from the lognormal distribution, the new value is added to the value of the xth abundance distribution sample and divided by 2, thereby obtaining the x+1th abundance distribution sample.

[0050] In another preferred embodiment, in step (iii), simulated reads are generated according to specified parameters, wherein the parameters include: read length, insert size, error rate, or a combination thereof.

[0051] In another preferred embodiment, the read length is 50-500 bp, preferably 100-350 bp, and more preferably 150-250 bp.

[0052] In another preferred embodiment, the insert size is 120 bp-400 bp, preferably 220 bp-320 bp, more preferably 270 bp-280 bp.

[0053] In another preferred embodiment, the error rate is ≤10%, preferably ≤5%, and more preferably ≤1%.

[0054] In another preferred embodiment, in step (iii), each genome of the simulated community is sampled proportionally according to the designated genome abundance.

[0055] In another preferred embodiment, the simulated data set contains 10-500 simulated samples, preferably 50-200 simulated samples, and more preferably 100-150 simulated samples.

[0056] In another preferred embodiment, the designated abundance in each sample is different.

[0057] In another preferred embodiment, step (iii) further comprises the step of: generating a read simulation for a second time using the same genomic abundance and different average insert size, and mixing it with the previously obtained simulation sample.

[0058] In another preferred embodiment, in step (iii), the size of each sample in the simulated data set comprises 1-10G simulated reads, preferably 3-5G.

[0059] In another preferred embodiment, in step (iii), the generated simulated data set includes for each sample:

[0060] Sequences of simulated reads, such as FASTQ files; and

[0061] Alignment information of simulated reads to the reference genome, such as a BAM file.

[0062] In another preferred embodiment, step (iv) comprises the following steps:

[0063] iva) obtains the position of each read on the reference genome from the read simulation and calculates the coverage of each site;

[0064] ivb) Decomposing the reference genome sequence at zero coverage sites to obtain gold standard contig sequences.

[0065] In another preferred embodiment, step (iv) comprises the following steps:

[0066] ivc) The sequences of the simulated dataset are shuffled and anonymized to obtain the challenge dataset.

[0067] In another preferred embodiment, the method further comprises a step (v) after step (iv): filtering the data according to the bases and sequence quality of the sequencing data of the digital standard; and using sequence alignment software to compare and identify the digital standard with a database built based on a reference genome, and removing sequences that are aligned to non-target viral genomes.

[0068] In another preferred embodiment, the digital standard contains the reference genome species and corresponding abundance information contained in each simulated sample.

[0069] In a second aspect of the present invention, a digital standard for metagenomic bioinformatics detection is provided, wherein the digital standard comprises p samples, wherein each of the samples contains genome sequences derived from reference genomes of n different microorganisms,

[0070] The p samples have different genomic abundances.

[0071] In another preferred embodiment, the microorganism is a virus.

[0072] In another preferred embodiment, the microorganisms are n microorganisms selected from the group consisting of Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type 1), Simian retrovirus 8 (simian retrovirus), Simian foamy virus (simian foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

[0073] In another preferred embodiment, the n microorganisms are composed of the following microorganisms: Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type 1), Simian retrovirus 8 (simian retrovirus), Simian foamy virus (simian foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

[0074] In another preferred embodiment, p is an integer selected from 10-500, preferably 50-200, more preferably 100-150.

[0075] In another preferred embodiment, n is an integer selected from 10-24, preferably 15-24, more preferably 20-24.

[0076] In another preferred embodiment, the digital standard contains the reference genome species and corresponding abundance information contained in each sample.

[0077] In another preferred embodiment, the digital standard is filtered, and the filtering refers to using sequence alignment software to compare and identify the digital standard with a database built based on a reference genome, and removing sequences that are aligned to non-target viral genomes.

[0078] In another preferred embodiment, the digital standard is constructed by the method described in the first aspect of the present invention.

[0079] In a third aspect of the present invention, a device for preparing digital standards for metagenomic bioinformatics detection is provided, the device comprising:

[0080] 1) an input module, wherein the input module is used to input n reference genomes of microorganisms, wherein n is an integer selected from 10-24, preferably 15-24, and more preferably 20-24;

[0081] 2) Simulation module, which is used to:

[0082] generating a simulated community based on the reference genome and taxonomic information;

[0083] using a read simulator to sample from each genome of the simulated community to generate simulated samples, and mixing the simulated samples to obtain a simulated dataset;

[0084] assembling the simulated data sets to obtain the digital standards; and

[0085] 3) An output module, which is used to output the digital standard product.

[0086] In another preferred embodiment, the microorganism is a virus.

[0087] In another preferred embodiment, the microorganisms are n microorganisms selected from the group consisting of Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type 1), Simian retrovirus 8 (simian retrovirus), Simian foamy virus (simian foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

[0088] In another preferred embodiment, the n microorganisms are composed of the following microorganisms: Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type 1), Simian retrovirus 8 (simian retrovirus), Simian foamy virus (simian foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

[0089] In a fourth aspect of the present invention, a method for testing the accuracy of metagenomic biological information detection is provided, comprising the steps of:

[0090] 1) providing a metagenomic bioinformatics detection method to be tested;

[0091] II) using the detection method to detect each sample in the digital standard according to the second aspect of the present invention, thereby obtaining a detection result of the detection method;

[0092] III) comparing the detection results of the detection method with the reference genome species and corresponding abundance information of each sample in the digital standard to determine the accuracy of the detection method.

[0093] In another preferred embodiment, the method includes comparing the abundance information of the same virus in a standard substance with the detection result of the biological information detection method to be tested to see whether they are consistent.

[0094] In a fifth aspect of the present invention, a device for testing the accuracy of a metagenomic biological information detection method is provided, the device comprising:

[0095] 1) an input module, wherein the input module is used to input the metagenomic biological information detection method to be tested;

[0096] II) a calculation module, which uses the detection method to detect the digital standard according to the second aspect of the present invention, thereby obtaining a detection result of the method to be tested;

[0097] III) a comparison module, which compares the detection results of the detection method with the reference genome species and corresponding abundance information of each sample in the digital standard, thereby determining the accuracy of the bioinformatics detection method;

[0098] IV) an output module, which is used to output the accuracy judgment result of the biological information detection method.

[0099] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features described in detail below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be listed here one by one. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] The following drawings are used to illustrate specific embodiments of the present invention and are not used to limit the scope of the present invention defined by the claims.

[0101] Figure 1 shows a flowchart for constructing simulated data for a specific reference genome using CAMISIM.

[0102] Figure 2 shows an example of the results of using digital standards to verify the accuracy of bioinformatics detection alignment.

[0103] Figure 3 shows the overlap results of virus species in the standards and detection results using 59 viruses to construct digital standards.

[0104] Figure 4 shows an example of the results of validating the accuracy of bioinformatic detection alignment using digital standards constructed from 59 viruses. DETAILED DESCRIPTION

[0105] After extensive and in-depth research, the inventors have developed for the first time a digital standard for testing the accuracy of the metagenomic bioinformatics detection process. Specifically, the present invention selected 24 viral genomes that the Chinese Pharmacopoeia pays special attention to, and constructed digital standards that can be used to test the accuracy of the metagenomic bioinformatics detection process, including qualitative and quantitative. The present invention constructed a total of 100 samples, each of which contained 24 genome sequences of different viruses, the abundance of which was randomly generated, and the abundance distribution of viruses in each sample was different. Through this batch of digital standards, the accuracy of the bioinformatics process based on the metagenomic construction can be effectively tested. On this basis, the present invention was completed.

[0106] Standards

[0107] The digital standard of the present invention is constructed by the method described in the first aspect of the present invention. Specifically, it is constructed based on the known viral genomes selected from the following Table 1:

[0108] Table 1 Viral genome information

[0109] The size of the viral genome is at least between several K and tens of K. The base difference between the subtype of the virus and the original type may be only 100-200 bases, which accounts for a very small proportion of the entire genome. Since the reads length of the second-generation sequencing is usually 250-300bp, it is difficult to guarantee that sequences containing different bases can be extracted during the random library construction process. The current virus alignment method still relies on the accuracy of sequencing, and the rule usually allows a one-base mismatch. However, in the distinction between subtypes, a difference of one base may represent different subtypes of viruses, resulting in an incorrect alignment of the final sequencing results. In addition, most of the sequences between viruses in the current virus library are relatively conservative, and there is no sequence library that can completely distinguish between subtypes of viruses.

[0110] The present invention selected viruses of key concern from the Chinese Pharmacopoeia. During the screening of approximately 100 viruses of key concern in the Chinese Pharmacopoeia, it was found that since these viruses contain many viral subtypes and their genomes contain highly similar viral subtype sequences, the prepared digital standards often contain a large number of similar sequences. Using such digital standards to evaluate the accuracy of bioinformatics detection methods will cause interference, making it difficult to obtain true and reliable evaluation results.

[0111] After extensive trials and screening, the present invention selected 24 viruses shown in Table 1. These viruses have a wide range of origins, including bovine, mouse, human, and porcine. They contain or essentially contain no similar viral sequences, will not interfere with the test results of digital standards, and can be accurately used to evaluate bioinformatics detection methods. At the same time, these viruses are all viruses that are of key concern in the Chinese Pharmacopoeia, and the digital standards they constitute have extremely high clinical significance and application value.

[0112] CAMISIM

[0113] The digital standard preparation method of the present invention is performed using CAMISIM, a microbial community and metagenomic simulator. CAMISIM software can simulate different microbial abundance profiles, multi-sample time series and differential abundance studies, including real and simulated strain-level diversity, and generate second- and third-generation sequencing data.

[0114] CAMISIM allows customization of many properties of generated communities and datasets, such as the total number of genomes, species diversity, genome abundance distribution, sample size, number of replicates, and sequencing technology used. Sequencing data simulated by CAMISIM software are highly consistent with real-world data. Based on existing techniques, highly consistent results with real-world data were observed in two simulated multi-sample datasets of human and mouse gut microbiomes generated by CAMISIM software. Furthermore, the effects of varying genomic divergence, sequencing depth, and read errors on two popular metagenomic sequence assembly software, MEGAHIT and metaSPAdes, were investigated using thousands of small datasets generated by CAMISIM.

[0115] CAMISIM data simulation is divided into three stages:

[0116] 1) Community design, including selecting community members and their genomes and assigning their relative abundances; genome selection is based on a truncated geometric distribution, and abundance is based on a lognormal distribution.

[0117] 2) metagenomic sequencing data simulation;

[0118] 3) Post-processing, including binning and assembly steps.

[0119] Kraken2

[0120] In one embodiment, during the preparation of the digital standard of the present invention, alignment with known sequences can be performed to remove simulated sequences of non-target viral genomes and improve the accuracy of the final verification. This alignment can be performed using Kraken software.

[0121] Kraken and Kraken2 (an iterative version of Kraken) are the most commonly used software for microbiome analysis. Compared to other similar software, they are characterized by speed and high accuracy. Based on the k-mer algorithm, they are high-precision metagenomic sequence classification software that can quickly classify sequenced (or simulated) reads into species.

[0122] There are two steps to using Kraken software: preparation (library construction) and identification.

[0123] Step 1: Preparation (library construction)

[0124] · Establish a taxon database corresponding to k-mer (Box1)

[0125] Map the database and index files into memory

[0126] Step 2: Identification

[0127] Cut the sequence to be identified into k-mers

[0128] Align the k-mer to the database to obtain its LCA_taxon (Box 2) and the number of times it is aligned.

[0129] Build a classification tree from the above data, and then calculate the sum of all weights on each root-to-leaf. The largest sum is the classification tree for that sequence.

[0130] Digital Standard Construction Method

[0131] The digital standard construction method of the present invention requires providing: 1) CAMISIM tools, 2) a reference genome of a specific microorganism.

[0132] In one embodiment, the construction method of the present invention includes: 1) installing the CAMISIM tool; 2) downloading a reference genome of a specific microorganism; 3) referring to the CAMISIM tool teaching process, using the reference genome to simulate standard data (the process is shown in Figure 1); 4) using Kraken2 (reference) to compare the simulated data and remove sequences that are aligned to non-target viral genomes.

[0133] In the construction process of the digital standards of the present invention, a specific amount of simulated data, simulated read length, and insert size are used to obtain good simulated sequence accuracy. The read length used in the construction process of the present invention can be 50-500bp, preferably 100-350bp, more preferably 150-250bp; the insert size can be 120bp-400bp, preferably 220bp-320bp, more preferably 270bp-280bp; the size of each sample in the simulated data set can contain 1-10G simulated reads, preferably 3-5G. In a preferred embodiment, the construction process of the present invention uses a 150bp read length, an insert size of 270, and a data amount of 5G reads for each simulated sample.

[0134] When performing virus detection based on simulated data, there is a probability of matching to viruses other than the reference genome due to sequence similarity issues. Therefore, after simulating and obtaining digital standards, the method of the present invention can also include a step of filtering the digital standards. Specifically, the data is filtered based on the bases and sequence quality of the digital standard sequencing data; and sequence alignment software is used to compare and identify the digital standards with a database built based on the reference genome, removing sequences that match to non-target viral genomes. The digital standards obtained by the present invention have eliminated sequences that are easily mismatched to viruses that are not in the reference viral genome. Using the standards of the present invention, mismatches are essentially eliminated.

[0135] The main advantages of the present invention include:

[0136] 1) This invention can verify the accuracy of metagenomic bioinformatics processes in detecting specific viruses. It can help establish the accuracy and versatility of using second-generation sequencing technology for biological product testing, which is conducive to promoting the use of second-generation sequencing in the field of biological products;

[0137] 2) This paper has developed a set of metagenomic data covering 24 viruses from the Chinese Pharmacopoeia. With known virus species and abundance, this data can be used to assess the sensitivity and specificity of metagenomic bioinformatics detection processes. It can also verify the accuracy of the quantitative analysis process.

[0138] 3) The viruses selected in the present invention come from a wide range of sources, including bovine, mouse, human, and porcine sources; and contain no or essentially no similar viral sequences, significantly reducing the interference of viral subtypes on the test results of digital standards; at the same time, these viruses are all viruses of key concern in the Chinese Pharmacopoeia, and the digital standards they constitute have extremely high clinical significance and application value.

[0139] 4) The present invention screened for analog data volume, analog read length, and insert size that were more favorable for the construction of digital standards, and achieved better analog sequence accuracy.

[0140] The present invention will be further described below in conjunction with specific examples. It should be understood that these examples are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The experimental methods in the following examples, for which specific conditions are not specified, are generally performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or according to the conditions recommended by the manufacturer. Unless otherwise stated, percentages and parts are by weight.

[0141] Example 1 Construction of Metagenomic Bioinformatics Detection Standards

[0142] 1.1 Install CAMISIM tool

[0143] Use Git to download version 71a1d86 from https: / / github.com / CAMI-challenge / CAMISIM and install it on the server according to the instructions.

[0144] 1.2 Download the reference genome of a specific microorganism

[0145] The genomes of 24 Chinese Pharmacopoeia focus viruses were downloaded from the NCBI genome database (version dated November 19, 2022).

[0146] 1.3 Metagenomic simulation method 1: de novo metagenome simulation

[0147] 1.3.1 Input reference genome

[0148] For de novo metagenomic simulations, all sequences shorter than 1000 base pairs are removed from the provided genome, and the sequences are verified to contain only valid characters. The input genome can be a draft genome in FASTA format, which may contain ambiguous DNA characters in addition to the bases ACGT, such as "RYWSMKHBVDN". All non-base characters are removed, except for the "N" probability, which is retained.

[0149] 1.3.2 Taxonomic information

[0150] If the input taxonomic information is a CAMI or BIOM format taxonomic file, the taxonomic units are matched to closely related complete genomes (e.g., new genomes) in NCBI RefSeq or other reference files, so that the simulated metagenome (genomes and their abundance) reflects the input profile as closely as possible. This step is accomplished by matching the taxonomic names in the input configuration file. Given the taxonomic names of the Operational Taxonomic Units (OTUs) in the BIOM configuration file, the NCBI taxonomic ID can be inferred. For all reference genomes, the taxonomic ID is also required.

[0151] Next, the lineages between OTUs and genomes are compared, and OTUs are mapped to the genome with the minimum path between the OTU taxonomic ID and the genome taxonomic ID, as calculated using the de Bruijn graph. Because the minimum path can be very long, a threshold is set at the family level: OTUs are not mapped to genomes that do not belong to the same family, that is, for each mapped genome, they are guaranteed to belong to at least the same family. Typically, this results in some OTUs not being mapped, either because their scientific names do not correspond to NCBI taxonomic IDs or because no complete genome is available for the taxonomic ID. For unmapped OTUs, they can be assigned to "random" genomes in the reference collection to increase complexity through additional options.

[0152] 1.3.3 Community Design

[0153] For de novo community design, a specified number of sequences is selected from all input sequences (e.g., circular elements (i.e., a sequence that can represent a certain microorganism) or genomes) according to certain criteria to generate a specific dataset, where the specified number represents the number of viral sequence types to be simulated. The level of novelty is determined by the degree of similarity of the genome sequence combination to currently known viral sequences. The random selection of genomes for a specific dataset is based on their novelty level and membership in OTUs: to increase taxonomic spread, for a specified total number of genomes to be included in a specific dataset, the selection algorithm attempts to extract the same number of genomes from each novelty level (new strain, new species, new genus, new family, new order). If one category does not have enough genomes to come up with the specified number of genomes, more genomes are extracted in equal amounts from other categories, if possible. This operation is repeated until the specified number of genomes has been extracted.

[0154] 1.3.4 Additional genome additions

[0155] To increase strain-level diversity in the dataset, many additional genomes can be simulated using sgEvolver12, representing strains related to the reference genome selected above.

[0156] To do this, a genome was randomly selected and a strain number was drawn from a geometric distribution (p = 0.3), representing the number of related strains created for that particular genome. This process was repeated until the specified total number of strains to be generated was reached. For each genome, sgEvolver simulated 40 strains, each with increasing genetic distance from the previous strain (in steps of 0.1%), up to a total distance of 4%. From these 40 strains, the specified strain number was randomly selected and added to the genome set.

[0157] 1.4 Metagenomic Simulation Method 2: Dataset Simulation

[0158] For dataset simulations, both genome and circular element abundance distributions are required for each dataset.

[0159] For a single sample dataset, the abundance distribution is created by sampling from a lognormal distribution. By default, the logarithm mu of the lognormal distribution is set to 1 and sigma is set to 2. A higher sigma results in a relatively flatter and less peaked normal distribution. To reflect differential abundance experiments, two or more abundance samples can be independently drawn from the lognormal distribution using the same parameter settings.

[0160] For consecutive samples in a time series, the abundance of the next sample was calculated by sampling new values ​​from a lognormal distribution, adding these values ​​to the previous value, and dividing by two. The absolute abundance of circular elements can be set to 15 times the abundance of the microbial genome to replicate high natural plasmid abundance. For each sample, the taxonomic profile of the higher-ranking taxa was calculated based on the genome abundance values ​​and their taxonomic IDs.

[0161] 1.5 Reading Simulation

[0162] For each genome, coverage in the simulated sample is calculated based on the genomic abundance distribution, genome size, and the specified metagenomic sample output size. The selected read simulator then generates reads using the user-specified technology and properties (such as read length, insert size, or error rate (if applicable)), sampling each genome in proportion to the abundance specified in the community. The read length is set to 150bp and the insert size is set to 270bp. The read simulation of the dataset can be run a second time using a different average insert size and the same genomic abundance as before to replicate the use of hybrid technology on the same sample. The size of each sample for each dataset is set to 5G reads.

[0163] The output of this step is, for each sample in the dataset, a FASTQ and BAM file that specifies the position of each read in the input sequence.

[0164] 1.6 Standard product assembly

[0165] Gold standard assemblies were performed using SAMtools for each sample and for all samples per dataset (combined gold standards), respectively, including each sequence position with a coverage of at least 1. Specifically, the SAM file obtained from the read simulation specifies the position of each read on the reference genome and is used with SAMtools to calculate the coverage of each site. The sequences of the reference genome are then decomposed into gold standard contig sequences at zero coverage sites. The contig sequences are then labeled with a unique dataset and sample identification prefix and an increasing index as a suffix. In the final step, the sequences of the simulated dataset are shuffled and anonymized using the unix command "shuf" to create the challenge dataset.

[0166] Example 2: Establishing a Metagenomic Bioinformatics Analysis Process

[0167] Using ab initio simulation, digital standards were constructed according to the method described in Example 1.

[0168] 2.1 Build a self-developed metagenome bioinformatics data analysis process

[0169] a) Use common sequence quality control software such as FastQC or FastP to filter the data based on the bases and sequence quality of the digital standard sequencing data

[0170] b) Download virus sequence alignment software (such as Kraken2, centrifuge, etc.) and use these 24 virus genomes to build a virus database.

[0171] c) Using the above software and preset parameters, perform viral sequence alignment and identification on the sample, and remove sequences that align to non-target viral genomes.

[0172] 2.2 Accuracy test of bioinformatics detection methods

[0173] The virus detection results were verified based on the virus species and abundance of each digital standard provided by this patent. The results are shown in Figure 2. The use of the digital standards of this invention can determine the accuracy of the bioinformatics detection method.

[0174] Comparative Example

[0175] Due to the limitations of the current sequence alignment method and virus library, it is very difficult to use the alignment method to completely and correctly align subtype viruses. Before trying to use the 24 viruses this time, the inventors used 59 viruses that the Chinese Pharmacopoeia focused on for digital standard preparation and testing. It was found that the standards constructed using 59 viruses produced many false detection phenomena. As shown in Figure 3, the detected viruses should theoretically be around 59, but after detection using the bioinformatics process, a total of 136 virus species were detected. At the same time, the results in Figure 4 also show that some virus species in the standard have false detection and missed detection phenomena.

[0176] After analysis, it was found that this phenomenon may be due to the interference of subtypes in these viruses. The genome sequences in the digital standards are too similar to the sequences of other viruses that are not in the target virus, resulting in false detection.

[0177] All documents mentioned in this application are incorporated herein by reference, just as if each document were incorporated herein by reference individually. It should also be understood that after reading the above teachings of the present invention, those skilled in the art may make various changes or modifications to the present invention, and that such equivalents also fall within the scope of the claims appended hereto.

Claims

1. A method for preparing digital standards for metagenomic bioinformatics detection, characterized in that: The following steps are involved: (i) providing n reference genomes of microorganisms, wherein n is an integer selected from 10-24, preferably 15-24, more preferably 20-24; (ii) simulated community design: generating simulated communities based on the reference genome and taxonomic information; (iii) read simulation: using a read simulator to sample from each genome of the simulated community to generate simulated samples, and the simulated samples are mixed to obtain a simulated dataset; (iv) Standard assembly: assembling the simulated data set to obtain the digital standard.

2. The method for preparing a digital standard according to claim 1, characterized in that: The n microorganisms are composed of the following microorganisms: Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type I), Simian retrovirus 8 (monkey retrovirus), Simian foamy virus (monkey foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

3. The method for preparing a digital standard according to claim 1, characterized in that: The steps (ii) to (iv) are performed using CAMISIM software.

4. The method for preparing a digital standard according to claim 1, characterized in that: The simulated data set comprises 10-500 simulated samples, preferably 50-200 simulated samples, and more preferably 100-150 simulated samples, and the designated abundance in each sample is different.

5. The method for preparing a digital standard according to claim 1, characterized in that: The method further comprises a step (v) after step (iv): filtering the data according to the bases and sequence quality of the sequencing data of the digital standard; and using sequence alignment software to compare and identify the digital standard with a database built based on a reference genome, and removing sequences that are aligned to non-target viral genomes.

6. A digital standard for metagenomic bioinformatics detection, characterized in that: The digital standard comprises p samples, wherein each of the samples contains a genome sequence derived from a reference genome of n different microorganisms, wherein the p samples have different genomic abundances; and p is an integer selected from 10-500, preferably 50-200, more preferably 100-150; n is an integer selected from 10-24, preferably 15-24, more preferably 20-24.

7. The digital standard product according to claim 6, characterized in that: The n microorganisms are composed of the following microorganisms: Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type I), Simian retrovirus 8 (monkey retrovirus), Simian foamy virus (monkey foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

8. The digital standard product according to claim 6, characterized in that: The digital standard is filtered, and the filtering refers to using sequence alignment software to compare and identify the digital standard with a database built based on a reference genome, and removing sequences that are aligned to non-target viral genomes.

9. The digital standard product according to claim 6, characterized in that: The digital standard is constructed by the method as claimed in claim 1.

10. A device for preparing digital standards for metagenomic bioinformatics detection, characterized in that: The device comprises: 1) an input module, wherein the input module is used to input reference genomes of n microorganisms, wherein n is an integer selected from 10-24, preferably 15-24, and more preferably 20-24; 2) A simulation module, which is used for: generating a simulated community based on the reference genome and taxonomic information; Using a read simulator, sampling from each genome of the simulated community to generate simulated samples, and mixing the simulated samples to obtain a simulated data set; assembling the simulated data sets to obtain the digital standards; and 3) An output module, wherein the output module is used to output the digital standard product.

11. The preparation device according to claim 10, characterized in that: The n microorganisms are composed of the following microorganisms: Betapolyomavirus macacae (simian virus 40), Bluetongue virus (blue tongue virus), Bovine polyomavirus 3 (bovine polyomavirus), Human betaherpesvirus 6A (human herpesvirus-6), Human betaherpesvirus 6B (human herpesvirus-6), Fowl aviadenovirus A (exogenous avian adenovirus type I), Simian retrovirus 8 (monkey retrovirus), Simian foamy virus (monkey foamy virus), Human gammaherpesvirus 4 (human Epstein-Barr virus), Human polyomavirus 12 (human polyomavirus), Human circovirus VS6600022 (human circovirus), Human betaherpesvirus 5 (human cytomegalovirus), Human immunodeficiency virus 1 (human immunodeficiency virus), Murine respirovirus (Sendai virus), Murid betaherpesvirus 8 (mouse salivary gland virus_mouse cytomegalovirus), Nipah henipavirus (Nipah virus), Suid alphaherpesvirus 1 (pseudorabies virus), Swinepox virus (swine pox virus), Porcine reproductive and respiratory syndrome virus (porcine reproductive and respiratory syndrome virus), Suid betaherpesvirus 2 (porcine cytomegalovirus), Porcine circovirus 2 (porcine circovirus), Human gammaherpesvirus 8 (human herpesvirus-8), Bat rotavirus (mouse rotavirus), Murid betaherpesvirus 1 (mouse salivary gland virus_mouse cytomegalovirus).

12. A method for testing the accuracy of metagenomic biological information detection, characterized in that: Includes steps: 1) providing a method for detecting the bioinformatics of the metagenome to be tested; II) using the detection method to detect each sample in the digital standard according to any one of claims 6 to 10, thereby obtaining a detection result of the detection method; III) comparing the detection results of the detection method with the reference genome species and corresponding abundance information of each sample in the digital standard to determine the accuracy of the detection method.

13. A device for testing the accuracy of a metagenomic bioinformatics detection method, characterized in that: The device includes: 1) an input module, wherein the input module is used to input a metagenome bioinformatics detection method to be tested; II) a calculation module, wherein the calculation module uses the detection method to detect the digital standard product according to any one of claims 6 to 10, thereby obtaining a detection result of the method to be tested; III) a comparison module, which compares the detection results of the detection method with the reference genome species and corresponding abundance information of each sample in the digital standard, so as to determine the accuracy of the biological information detection method; IV) An output module, which is used to output the accuracy judgment result of the biological information detection method.

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