CN109215736B - High-throughput detection method and application of enterovirus group - Google Patents
High-throughput detection method and application of enterovirus group Download PDFInfo
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Abstract
The invention discloses a high-throughput detection method of an enterovirus group, wherein the method is characterized in that an enterovirus component annotation platform and an enterovirus component analysis platform of an existing sample database are established on the basis of a virus database (derived from Refseq) of Kraken, the enterovirus component analysis platform analyzes data obtained by the enterovirus component annotation platform, a prediction model and an enterovirus data set are established, then the enterovirus group of a high-throughput sequencing sample to be detected is compared with the prediction model, the comparison result is used for judging the blood pressure of a host and comparing with a set threshold value, and one group of data with the highest probability value after comparison is the comparison result of the sample to be detected. The invention provides a method for predicting hypertension by using enterovirus group samples by taking a virus group in a human intestinal microflora as a research object based on a microbiome and bioinformatics thinking, and the method has the characteristics of short period, noninvasive sampling, high universality, high added value and the like.
Description
Technical Field
The invention relates to a high-flux detection method of enterovirus, belonging to the field of microbiology and bioinformatics.
Background
Viruses are ubiquitous in the microbial community of our body and natural environment and have a high diversity (Rodriguez-Brito et al, 2010). It can alter the structural and functional composition of microbial communities by altering the host's adaptability and facilitating the exchange of genetic material (Andersson and Banfield, 2008). Furthermore, it is estimated that 15-20% of human cancers are associated with viruses, such as prostate, breast and brain cancers (McLaughlin-drufin and Munger, 2008). It is becoming increasingly clear that viruses play a key role in different ecosystems, and it is therefore highly desirable to rapidly identify viruses in the human gut microflora.
The human microbial community, which consists of over 100 trillion microbial cells, is mainly present in the gut and forms a large community (McKenna et al, 2008; Qin et al, 2010; Glasner,2017), which is an integral part of the host. The potential role of the gut microbiome has been demonstrated over the last decades, suggesting that the composition of gut microbiome has profound effects on the immune system (Hooper et al, 2012), the central nervous system (Sharon et al, 2016), and the link between gut microbiome and various diseases has also been demonstrated. On the basis of these studies, several microbiologic and functional biomarkers for these diseases were found, and fecal microflora transplantation is being used as a therapeutic strategy for treating specific diseases such as Inflammatory Bowel Disease (IBD) (Anderson et al, 2012). However, most of the existing microbiome studies have focused on bacteria and archaea and filtered the classification information of viruses, but viruses play an important role in modeling the human microbiome and the asset effects on the host (Carding et al, 2017; Nikolich-Zugich et al, 2017). Therefore, the analysis of "dark matter" viruses in microbial communities becomes crucial.
With the development of sequencing technology, the high-throughput sequencing technology has the advantages of large throughput and high cost performance, so that people can perform microorganism sequencing on different types of samples, can research 99% of non-culturable microorganisms, and can perform high-precision analysis on species composition and functional composition of a microbial community structure. With the increase of sequencing data, how to expand the relevance between the sequencing data and clinical diseases and increase the high added value of the sequencing data becomes more important. The evaluation of hypertension is performed by instruments, which is convenient, but has instability. The method has the biggest defect that the method cannot predict in advance, can well predict whether the host is at risk of suffering from hypertension by using the relevance between the virus and the hypertension, and has good sensitivity and accuracy. Therefore, it is very necessary to provide a method for predicting hypertension by using a sample of enterovirus group by using the concept of microbiome and bioinformatics.
Disclosure of Invention
In view of the above problems of the prior art, the present invention aims to obtain a method for high-throughput detection of enterovirus.
In order to achieve the above object, the present invention adopts the following technical scheme:
the method is characterized in that a virus library (derived from Refseq) carried by Kraken is used as a basis, an enterovirus component annotation platform and an enterovirus component analysis platform of the existing sample database are established, the enterovirus component analysis platform analyzes data obtained by the enterovirus component annotation platform, a prediction model and an enterovirus data set are established, then enterovirus groups of a high-throughput sequencing sample to be detected are compared with the prediction model, the comparison result is used for judging the blood pressure of a host and comparing with a set threshold, and one group of data with the highest probability value after comparison is used as the comparison result of the sample to be detected.
The existing sample is derived from human intestinal microorganism metagenome data in published articles related to hypertension, and can be updated in real time with the continuous and deep research.
Preferably, the existing three groups of samples include: a healthy control sample group, a pre-hypertensive sample group, and a hypertensive sample group.
The method is characterized in that three groups of data (20 samples in each group) are mixed and assembled based on known metagenome data to obtain contigs, ORF, protein and virus types and classifications are predicted, a data set and a training model are constructed based on different virus compositions of 3 groups of samples, reading sections (reads) of new samples are directly drawn (mapped) into the data set, and the reading sections are imported into the model to obtain a group with the maximum probability value, so that the group is the group to which the samples belong.
The enterovirus component annotation platform is a platform for processing collected database samples, and the processing comprises the following steps: data quality control processing, mixed assembly, gene and protein prediction, identification of virus protein and content calculation of virus components.
The enterovirus component analysis platform is a platform for analyzing known sample information in a database, a hypertensive enterovirus data set is constructed, and the analysis comprises the following steps: virus biomarker analysis between different groups, virus type analysis of samples, network analysis between bacteria and viruses, etc. The analysis content may vary according to the variation of the prediction index, and as long as the analysis object is based on the data in the platform, the analysis content is considered to fall within the protection scope of the analysis platform of the present invention.
The establishment of the enterovirus component annotation platform and the analysis platform is to better establish a hypertensive enterovirus data set, the sequence of the virus identified by the assembly of the used intestinal metagenome data establishes the hypertensive enterovirus data set, and the establishment process of the hypertensive enterovirus data set comprises the following steps:
i. carrying out mixed assembly on the virus data samples to obtain fragments with the length not less than 1000 bp;
ii, predicting an open reading frame of the long sequence fragment obtained in the step i to obtain a corresponding gene sequence and a corresponding protein sequence;
performing redundancy removal treatment on the gene sequence and the protein sequence obtained in the step ii to obtain a non-redundant gene and protein data set;
and iv, identifying the non-redundant gene and protein data set obtained in the step iii by taking a virus library owned by Kraken (a virus library derived from Refseq) as a basis, obtaining a hypertensive enterovirus data set and setting the data set as a threshold value.
The database related to the data set is a virus library owned by Kraken (virus library derived from Refseq), and the related bioinformatics software comprises MEGAHIT, prodigal, CD-HIT, Kraken, R language, Bowtie2, metahlan 2 and the like. The used bioinformatics database, such as the virus library of Kraken, has the characteristics of pertinence and moderate data volume, is easy to download and use, and has low threshold. Bioinformatics tools such as MEGAHIT, prodigal, CD-HIT, Kraken, Bowtie2 and metaphlan2 are used, which are easy to download, install and use, and have high efficiency and convenience.
Preferably, the mixed assembly is performed for 20 samples in each of two groups, a healthy control sample group, a pre-hypertension sample group and a hypertension sample group.
Specifically, the construction process of the hypertensive enterovirus dataset comprises the following steps:
mixing and assembling 60 samples (20 samples in each group of a healthy control sample group, a hypertensive early stage sample group and a hypertensive sample group) through MEGAHIT to obtain long-sequence fragments (contigs);
predicting the Open Reading Frames (ORFs) of the long sequence fragments by using prodigal software to obtain corresponding gene sequences and protein sequences;
redundancy removal processing is respectively carried out on the gene and protein sequences through CD-HIT to obtain a non-redundant gene and protein data set;
the gene and protein data sets are identified by Kraken to obtain the hypertensive enterovirus data set.
Preferably, the construction of the prediction model is performed by a random forest method in machine learning. More preferably, the predictive model is constructed using the R language. The random forest model selects a specific virus combination as a characteristic vector, and the prediction result has high accuracy.
After the hypertensive enterovirus data set is constructed, sampling detection is carried out on enteroviruses of a host, namely unknown enteroviruses, and the unknown samples are classified and subjected to de novo prediction by utilizing high-throughput sequencing data and an established prediction model, and whether the hypertensive enteroviruses are in a hypertensive stage or not is judged. The specific detection steps comprise the steps of extracting host metagenome DNA, high-throughput sequencing, data comparison, predicting conclusion and the like.
Wherein, the metagenome DNA is all DNA in a host sample and comprises genetic materials of bacteria, archaea, viruses and the like.
And (3) breaking and splicing the metagenome DNA of the unknown sample by adopting Bowtie2 to perform high-throughput sequencing. In order to obtain high quality metagenomic data, host DNA sequences are preferably removed from the sequencing results. Since the virus library derived from Kraken (virus library derived from Refseq) is used as a reference, the obtained virus species are only those described in the virus library derived from Kraken (virus library derived from Refseq).
And during data comparison, calculating the virus composition and content in the host sample according to the data set, inputting a calculation result into the prediction model, and obtaining a prediction conclusion according to the model.
Preferably, the enterovirus group is derived from a host fecal sample, and the fecal sample is processed according to the prior art, which is not described herein.
The invention also discloses an application of the high-throughput detection method of the enterovirus group, and the detection method adopts the high-throughput detection method of the enterovirus group to detect whether the blood pressure of a sample to be detected reaches a threshold value. That is, the method for predicting hypertension by enterovirus of the present invention can be used in the detection of host blood pressure at various stages and provide accurate detection results for reference.
Compared with the prior art, the invention takes the virome in the human intestinal microflora as a research object, and provides a method for predicting hypertension by using an enterovirus group sample based on the microbial group and bioinformatics thinking. Compared with the traditional method, the method has the following advantages:
(1) the period is short and the accuracy is high; by using the data set obtained in the invention, the virus composition in the fecal sample can be rapidly identified, and the blood pressure state of the host can be accurately predicted by using the established model.
(2) Sampling without wound; the sample that this patent used belongs to external excrement for the excrement sample, gathers easily, does not have the wound to the host. Meanwhile, the influence of the instant state of the host is small, and the influence of emotion during manual pressure measurement can be reduced.
(3) The universality is improved; the used biological information tool and database are easy to download, install and use, and have high universality.
(4) The added value is high; based on the high-throughput sequencing data, the analysis can expand the added value of the data, and meanwhile, based on the analysis, the detection about the hypertension state can be provided.
Drawings
FIG. 1 is a flow chart of the detection method of the present invention;
FIG. 2 is a schematic view of the detection method of the present invention;
FIGS. 3 to 8 are tables showing the types and compositions of viruses of samples in example 1 of the present invention;
FIGS. 9 to 15 are tables showing the types and compositions of viruses of samples in example 2 of the present invention;
FIGS. 16 to 24 are tables showing the types and compositions of viruses as samples in example 3 of the present invention.
Detailed Description
The method for high-throughput detection of enterovirus provided by the present invention is further described in detail and fully below with reference to examples. The following examples are illustrative only and are not to be construed as limiting the invention.
The experimental procedures in the following examples are conventional unless otherwise specified. The experimental materials used in the following examples were all commercially available unless otherwise specified.
Example 1
The database related to the embodiment includes a virus library owned by Kraken (derived from Refseq), the related bioinformatics software includes MEGAHIT, prodigal, CD-HIT, Kraken, R language, Bowtie2, metahlan 2, etc., the host 1 is taken as a prediction object, and the prediction process is as shown in fig. 1-2, specifically as follows:
a) building a library: collecting human intestinal microorganism metagenome data in published articles related to hypertension, classifying the sample data into 3 groups according to physiological indexes, wherein the group comprises the following steps: establishing a special hypertension enterovirus data set as a search database for a health control sample group, a hypertension early stage sample group and a hypertension sample group;
b) establishing an annotation platform: analyzing and processing collected database samples, including data quality control processing, mixed assembly, gene and protein prediction, identification of virus proteins and content calculation of virus components;
c) known sample analysis: constructing a hypertensive enterovirus data set by analyzing different collected known samples, including virus biomarker analysis among different groups, virus type analysis of the samples and network analysis among bacteria and viruses; specifically, the construction process of the hypertensive enterovirus dataset comprises the following steps:
i. mixing and assembling 60 samples (20 samples in each group of a healthy control sample group, a pre-hypertension sample group and a hypertension sample group) through MEGAHIT to obtain fragments (contigs) with the length of not less than 1000 bp;
predicting the Open Reading Frames (ORFs) of the long sequence fragments by using prodigal software to obtain corresponding gene sequences and protein sequences;
respectively carrying out redundancy removal treatment on the gene and protein sequences through CD-HIT to obtain a non-redundant gene and protein data set;
identifying the gene and protein data sets by Kraken to obtain the number of the hypertensive enteroviruses
And (6) collecting data.
d) Constructing a prediction model: constructing a prediction model by using a random forest method in machine learning, and optimizing and evaluating a virus model to obtain an optimal R language prediction model;
the model divides all samples into a training set test set according to the proportion of 4:1, and performs 10-fold optimization for 50 times by using a randomForest packet and a caret packet of an R language to obtain a model with the highest accuracy rate, wherein the model is used as a final model and obtains the most important virus classification in the model.
e) Prediction of unknown samples: collecting and processing unknown samples of a host, predicting sample classification by using high-throughput sequencing data and an established prediction model, and judging whether the samples are in a hypertension stage or not. The specific prediction steps are as follows:
i. sampling: collecting a host excrement sample by adopting the existing excrement sample collection method, placing the collected sample in an ice box, keeping the temperature below 0 ℃, storing the sample in a refrigerator at minus 80 ℃ within 2h, and keeping the sample for subsequent use;
extracting DNA: performing DNA extraction on the excrement sample, namely extracting metagenome DNA, wherein the metagenome DNA is all DNA in the sample and comprises genetic materials such as bacteria, archaea, viruses and the like;
high throughput sequencing: breaking the extracted metagenome by using Bowtie2 according to the flow of library building sequencing, adding a joint, and performing high-throughput sequencing on the metagenome to obtain sequencing data; removing host DNA sequences in the data to obtain high-quality macro genome data; the virus library derived from Kraken (virus library derived from Refseq) is used as a reference, so that the obtained virus species are only the species described in the virus library derived from Kraken (virus library derived from Refseq);
alignment data: taking a hypertensive enterovirus data set as a search data set, comparing macro genome data with high quality with the data set, and calculating virus composition and content in a sample;
predicting hypertension: inputting virus composition in a sample by adopting an R language platform, evaluating whether a host has hypertension risk according to a prediction result,
the prediction result can also predict that the sample is from a normal person, a patient before hypertension or a patient with hypertension.
The data comparison results of the embodiment 1 are shown in fig. 3 to 8, and based on the virus composition of the sample, the model in the method is used for prediction, and the probability value of the sample with normal blood pressure in the prediction results is highest, so that the sample is normal in blood pressure (has no disease), the prediction results are consistent with the recorded information of the collected sample (normal in blood pressure), the prediction is accurate, the detection method is feasible, and the prediction data set and the prediction model are accurate and comprehensive.
Example 2
Example 2 differs from example 1 only in the origin of the sample. In the embodiment 2, the prediction model in the method is used for prediction, the data comparison result is shown in fig. 9-15, the probability value of the sample at the early stage of hypertension in the prediction result is the highest, so that the sample is at the early stage of hypertension and has a risk of developing hypertension, and the prediction result is consistent with the recorded information of the collected sample (the early stage of hypertension).
Example 3
In embodiment 3, the prediction model in the method is used for prediction, the data comparison result is shown in fig. 16 to 24, and the probability value of the sample with high blood pressure in the prediction result is the highest, so that the sample is in a high blood pressure state, and the prediction result is consistent with the recorded information of the collected sample (high blood pressure).
Finally, it must be said here that: the above embodiments are only used for further detailed description of the technical solutions of the present invention, and should not be understood as limiting the scope of the present invention, and the insubstantial modifications and adaptations made by those skilled in the art according to the above descriptions of the present invention are within the scope of the present invention.
Claims (10)
1. A method for high throughput detection of enterovirus comprising: the method is characterized in that an enterovirus component annotation platform and an enterovirus component analysis platform of the existing sample database are established on the basis of a virus database carried by Kraken, the enterovirus component analysis platform analyzes data obtained by the enterovirus component annotation platform and constructs an enterovirus data set, a prediction model is constructed on the basis of the enterovirus data set, and an unknown sample is classified and predicted by utilizing high-throughput sequencing data and the established prediction model, and whether the unknown sample is in a hypertension stage or not is judged.
2. The method for high-throughput detection of enterovirus according to claim 1, wherein the enterovirus component annotation platform is a platform for processing collected database samples, and the processing comprises: data quality control processing, mixed assembly, gene and protein prediction, identification of virus protein and content calculation of virus components.
3. The method for high-throughput detection of enterovirus group according to claim 1, wherein the enterovirus component analysis platform is a platform for analyzing known sample information in a database, and a hypertensive enterovirus dataset is constructed, and the analysis comprises: virus biomarker analysis between different groups, virus type analysis of samples and network analysis between bacteria and viruses.
4. The method for high-throughput detection of enterovirus group according to claim 3, wherein the construction process of the hypertensive enterovirus dataset comprises:
i. carrying out mixed assembly on the virus data samples to obtain fragments with the length not less than 1000 bp;
ii, predicting an open reading frame of the long sequence fragment obtained in the step i to obtain a corresponding gene sequence and a corresponding protein sequence;
performing redundancy removal treatment on the gene sequence and the protein sequence obtained in the step ii to obtain a non-redundant gene and protein data set;
and iv, identifying the non-redundant gene and protein data set obtained in the step iii by taking a virus library carried by Kraken as a basis to obtain a hypertensive enterovirus data set.
5. The method for high throughput detection of enterovirus group according to claim 1, wherein the existing sample comprises three groups: a healthy control sample group, a pre-hypertensive sample group, and a hypertensive sample group.
6. The method for high-throughput detection of enterovirus according to claim 5, wherein: and carrying out mixed assembly on macro-base factor data of three groups of existing samples, wherein each group comprises 20 samples.
7. The method for high-throughput detection of enterovirus according to claim 1, wherein: and constructing a prediction model by adopting a random forest method in machine learning.
8. The method for high-throughput detection of enterovirus according to claim 7, wherein: the prediction model is constructed by adopting an R language.
9. The method for high-throughput detection of enterovirus according to claim 1, wherein the high-throughput sequencing is performed by disrupting and splicing metagenomic DNA of an unknown sample with bowtie.
10. The application of the high-throughput detection method of the enterovirus group is characterized in that: the detection method adopts the high-throughput detection method of the enterovirus group according to any one of claims 1 to 9 to detect whether the blood pressure of a sample to be detected reaches a threshold value.
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