Environmental RNA virus omics analysis method

Abstract

The invention relates to a method for omics analysis of environmental RNA viruses, and particularly provides a method for analyzing RNA viruses in a sample. The method comprises the following steps: 1) performing macro transcriptome sequencing on a sample to obtain macro transcriptome data, then analyzing the macro transcriptome data through a homologous iteration method based on RdRp and a geNomad method based on a machine learning model, screening RNA virus sequences in the sample, and obtaining an RNA virus operation classification unit and a sequence thereof; 2) respectively performing abundance analysis, classification identification and host analysis on the RNA virus operation classification unit obtained in the step 1); wherein the classification identification comprises the following steps: respectively analyzing sequences of the RNA virus operation classification units through geNomad and CAT methods to obtain classification results of the RNA virus operation classification units in the sample. The method provided by the invention can provide a method technology for detection and analysis of RNA viruses in the environment, and has important practical significance.

Description

Technical Field

[0001] The invention belongs to the field of bioinformatics analysis, and in particular relates to an omics analysis method for environmental RNA viruses. Background Art

[0002] Viruses are the most numerous organisms on Earth, with an estimated 10 31 There are many virus particles, among which RNA viruses use RNA as genetic material, can infect a variety of organisms, and pose a major threat to humans and animal husbandry, such as the well-known COVID-19, swine and avian influenza viruses, etc. For more than a century, culture methods based on cell or animal models have been the gold standard for detecting viruses. However, due to its long cycle, high cost, high labor intensity, and the fact that many viruses are not easy to culture, it is difficult to efficiently detect the types of RNA viruses present in the environment. Since there is no need to isolate and culture viruses in a laboratory environment, metatranscriptome sequencing has become a new method for discovering new RNA viruses. RNA-dependent RNA polymerase (RdRp) is a key protease that is essential for the replication of RNA viruses and controls the replication and transcription process of RNA viruses. This provides a theoretical basis for the identification of RNA viruses based on the homology of the RdRp domain based on metatranscriptomics. However, due to the limited number of known RNA virus sequences, the sequences of unknown RdRp cannot be fully identified by homology analysis alone. Therefore, combining RdRp homology analysis with emerging machine learning, k-mer analysis and other different principles to identify RNA virus sequences in the macrotranscriptome can provide methods and technologies for the detection and analysis of RNA viruses in the environment, which has important practical significance. Summary of the invention

[0003] In view of the above problems, the purpose of the present invention is to provide an omics analysis method for environmental RNA viruses. First, the sample is subjected to macrotranscriptome sequencing, and the raw data of the macrotranscriptome sequencing is subjected to quality control analysis to obtain effective macrotranscriptome reads; then, contigs are assembled, and RNA virus contigs are further identified by multiple methods, and RNA virus operational taxonomic units (vOTUs) and their sequences are obtained by de-redundancy and clustering; then, the length, contamination, integrity and other characteristics of the RNA virus sequence in the sample are obtained through quality inspection; then, the relative abundance of different RNA viruses in the sample is determined through abundance analysis; then, through species classification analysis, two virus bioinformatics classification methods, the CAT method and the geNomad method, are creatively coupled to obtain classification results; then, the life history of RNA viruses is predicted by the VIBRANT software to obtain functional annotation results; then, the RNAvirHOST virus-host association method is used to clarify the host information of the RNA virus sequence, distinguish bacteriophages, animal viruses, plant viruses, etc., focus on human and animal health and economic crops, and clarify the potential risks of RNA viruses.

[0004] To achieve the above object, the present invention adopts the following technical scheme: a method for omics analysis of RNA viruses, comprising the following steps, wherein steps 3-7 can be performed simultaneously:

[0005] 1) Perform metatranscriptome sequencing on the sample, then decompress and perform quality control analysis on the raw data after the metatranscriptome sequencing to obtain valid reads;

[0006] 2) Assemble valid reads into contigs, identify RNA virus contigs using RdRp-based homology iteration and geNomad methods, remove redundancy and cluster the identified RNA virus contigs to obtain RNA virus operational taxonomic units (vOTUs) and their sequences;

[0007] 3) The RNA virus operational taxonomic unit (vOTU) sequence obtained in step 2) is subjected to checkV quality inspection, and based on the quality inspection results, the length, contamination, integrity and other characteristics of the RNA virus sequence in the sample are revealed;

[0008] 4) Use the coverM tool to perform abundance analysis on the sequences of RNA virus operational taxonomic units (vOTUs) obtained in step 2) to determine the relative abundance of different RNA viruses in the sample;

[0009] 5) Use geNomad software and CAT software to perform taxonomic analysis on the sequences of RNA virus operational taxonomic units (vOTUs) obtained in step 2), give priority to the classification results of geNomad, and then use the CAT results as a supplement, and obtain the taxonomic analysis results of RNA virus operational taxonomic units (vOTUs) at the phylum and family levels by combining the geNomad method or the CAT method;

[0010] 6) Analyze the sequences of RNA virus operational taxonomic units (vOTUs) obtained in step 2) using VIBRANT software, predict the life history of RNA viruses, and obtain the functional annotation results of RNA virus operational taxonomic units (vOTUs);

[0011] 7) Using the species classification information at the family level, the RNAvirHOST virus-host association method is used to analyze the sequences of the RNA virus operational taxonomic units (vOTUs) obtained in step 2) to determine the virus host information of the RNA virus operational taxonomic units (vOTUs).

[0012] In the above step 1), the sample is subjected to macrotranscriptome sequencing, and then the raw data after the macrotranscriptome sequencing is decompressed and quality controlled to obtain valid reads, including the following steps:

[0013] The samples were sequenced by macrotranscriptome sequencing, and the raw data were decompressed. The decompressed data were quality controlled using the read_qc module of MetaWrap software (v 1.3.2). Finally, high-quality reads without adapters, i.e. valid reads, were obtained after quality control filtering. The echo tool was used to count the valid reads (clean reads).

[0014] In the above step 2), the valid reads are assembled into contigs, and the RNA virus contig sequences are identified by the homology iteration method based on RdRp and the geNomad method based on the machine learning model, and the identified RNA virus contig sequences are de-redundant and clustered to obtain RNA virus operational taxonomic units (vOTUs) and their sequences, including the following steps, wherein steps 2.3-2.4 can be performed simultaneously:

[0015] 2.1) Use the assembly module of MetaWrap software (v 1.3.2) to assemble the valid reads of each sample into contigs.

[0016] 2.2) The contigs sequences with a length greater than 1.0 kb in the sample were screened out and merged into a preliminary RNA virus dataset, and the sequences of this dataset were predicted into a viral protein sequence dataset.

[0017] 2.3) Using the homology iteration method (i.e., using the hidden Markov model, iterated 5 times in total), the RdRp protein sequences in the viral protein sequence database were compared to select viral sequences with RdRp protein sequences.

[0018] 2.4) The preliminary RNA virus dataset was classified and RNA virus sequences were screened out using the machine learning-based geNomad (v 1.7.0) software.

[0019] 2.5) The RNA virus sequences obtained by the homology iteration method based on RdRp protein sequences and the machine learning-based geNomad (v 1.7.0) software were merged, and the sequences with 100% similarity were clustered based on CD-HIT-EST to obtain the RNA virus contig sequence data set.

[0020] 2.6) Cluster the RNA virus sequence dataset according to sequence similarity ≥ 90% and coverage ≥ 80% to obtain non-redundant RNA virus operational taxonomic units (vOTUs) and their sequences.

[0021] In the above step 3), the sequence of the RNA virus operational taxonomic unit (vOTU) obtained in step 2) is subjected to checkV quality inspection, and according to the quality inspection results, the length, contamination, integrity and other characteristics of the RNA virus sequence in the sample are revealed, including the following steps:

[0022] The checkV tool was used to perform quality inspection on RNA virus operational taxonomic unit (vOTU) sequences to obtain characteristics such as the length, contamination, and integrity of the RNA virus sequences in the samples.

[0023] In step 4), the coverM tool is used to perform abundance analysis on the sequences of RNA virus operational taxonomic units (vOTUs) obtained in step 2), thereby determining the relative abundance of different RNA viruses in the sample, including the following steps:

[0024] 4.1) Use the coverM tool to align the sequences of RNA virus operational taxonomic units (vOTUs) with valid reads to generate the number of covered bases (base) and RPKM abundance information of each RNA virus operational taxonomic unit (vOTU) in different samples.

[0025] 4.2) Divide the number of bases covered by each RNA virus operational taxonomic unit (vOTU) in the sample by its length (the result obtained in step 3 above). If it is ≥70% (i.e., coverage ≥70%), it means that the RNA virus operational taxonomic unit (vOTU) exists in the sample.

[0026] 4.3) The relative abundance of the RPKM of the RNA virus operational taxonomic units (vOTUs) that actually exist in the sample is calculated, that is, the abundance of all RNA viruses in a single sample is taken as 100%, thereby obtaining the relative abundance of RNA viruses in different samples.

[0027] In the above step 5), the sequences of RNA virus operational taxonomic units (vOTUs) obtained in step 2) are subjected to taxonomic analysis using geNomad software and CAT software, with the classification results of geNomad being given priority, and then supplemented by the CAT results. The taxonomic analysis results of RNA virus operational taxonomic units (vOTUs) at the phylum and family levels are obtained by combining the geNomad method or the CAT method, including the following steps, wherein steps 5.1-5.5 and 5.6-5.8 are performed simultaneously:

[0028] 5.1) Use geNomad (v 1.7.0) software to annotate the RNA virus operational taxonomic unit (vOTU) sequences obtained in step 2) to species.

[0029] 5.2) Use CAT (v5.2.3) software to annotate the RNA virus operational taxonomic unit (vOTU) sequences obtained in step 2) to species.

[0030] 5.3) The classification results of geNomad (v 1.7.0) were given priority, and then supplemented with the results of CAT (v5.2.3) to finally obtain the classification results of RNA virus operational taxonomic units (vOTUs).

[0031] 5.4) Use the coverM (v0.7.0) tool to perform abundance analysis on the sequences of non-redundant RNA virus operational taxonomic units (vOTUs) to determine different RNA virus populations and their relative abundance in the samples.

[0032] In the above step 6), the sequence of the RNA virus operational taxonomic unit (vOTU) obtained in step 2) is analyzed by VIBRANT software to predict the life history of the RNA virus. The functional annotation results of the RNA virus operational taxonomic unit (vOTU) are obtained by the following steps:

[0033] 6.1) Use VIBRANT software (v1.2.1) to predict the life history of RNA viruses;

[0034] 6.2) Use Prodigal (v2.6.3) to predict the gene sequences of non-redundant RNA virus operational taxonomic unit (vOTU) sequences, and compare them with the SARG and CARD databases based on the blastx method;

[0035] 6.3) Use the coverM (v0.7.0) tool to perform abundance analysis on non-redundant RNA virus operational taxonomic unit (vOTU) sequences to determine the relative abundance of different RNA virus populations in the sample;

[0036] 6.4) Based on the comparison results of the SARG and CARD databases, the prediction results of the VIBRANT software and the abundance results of coverM, the life history of RNA viruses, the occurrence characteristics of antibiotic resistance genes (ARGs) they carry and their relative abundance were determined.

[0037] In the above step 7), the sequence of the RNA virus operational taxonomic unit (vOTU) obtained in step 2) is analyzed by using the species classification information at the family level and the RNAvirHOST virus-host association method to determine the virus host information of the RNA virus operational taxonomic unit (vOTU), including the following steps:

[0038] 7.1) Use RNAvirHOST software (v1.0.5) to analyze the infective hosts of non-redundant RNA virus operational taxonomic units (vOTU) sequences;

[0039] 7.2) After classifying the non-redundant RNA virus operational taxonomic units (vOTUs), enter the family-level classification results on the ICTV (https: / / ictv.global / ) website, which will provide the infective host classification of the family, thereby obtaining the ICTV host characteristics;

[0040] 7.3) Merge the results of virus-host relationships obtained in steps 7.1)-7.2) to obtain host characteristics of non-redundant RNA virus operational taxonomic units (vOTUs).

[0041] the term:

[0042] Hidden Markov model is a statistical model known in the prior art, which is used to describe a Markov process with hidden unknown parameters. In the present invention, the hidden Markov model is used to perform homology iteration, wherein the sequence is compared with a known sequence (such as RdRp) according to the conventional operation method of the model, and then the compared sequence is included in the known sequence for the next round of homology comparison, and so on for 5 iterations, so as to mine as many viral sequences as possible.

[0043] geNomad is a tool for identifying viral and plasmid genomes from nucleotide sequences. It can quickly find mobile genetic elements from genomes and metagenomes. For a tutorial on how to use it, see https: / / github.com / apcamargo / genomad.

[0044] Specifically, the present invention relates to the following technical solutions:

[0045] 1. A method for analyzing RNA viruses in a sample, comprising the following steps:

[0046] 1) Perform metatranscriptome sequencing on the samples to obtain metatranscriptomics data, and then analyze the metatranscriptomics data using the homology iteration method based on RdRp and the geNomad method based on a machine learning model to screen RNA virus sequences in the samples and obtain RNA virus operational taxonomic units (vOTUs);

[0047] 2) Conduct abundance analysis, classification identification and host analysis on the RNA virus operational taxonomic units obtained in step 1);

[0048] The classification and identification includes analyzing the RNA virus operational classification unit sequences by geNomad and CAT methods respectively to obtain the classification results of the RNA virus operational classification units in the sample.

[0049] 2. The method according to item 1, wherein the host analysis includes analyzing the RNA virus operational taxonomic unit (vOTU) sequence obtained in step 1) using the RNAvirHOST method to obtain the host information of the RNA virus in the sample.

[0050] 3. The method according to item 1 or 2, wherein the abundance analysis includes performing abundance analysis on the RNA virus operational taxonomic unit sequences obtained in step 1) using the coverM tool to calculate the relative abundance of RNA viruses in the sample.

[0051] 4. The method according to item 1 or 2, wherein step 2) also includes subjecting the RNA virus operational classification unit sequence obtained in step 1) to checkV quality inspection to reveal the length, contamination, integrity and other characteristics of the RNA virus sequence in the sample.

[0052] 5. The method according to item 1 or 2, wherein step 2) further comprises analyzing the RNA virus operational classification unit sequence obtained in step 1) using VIBRANT software to predict the life history of the RNA virus in the sample.

[0053] 6. The method according to item 1, wherein in step 1), the sample is subjected to metatranscriptome sequencing to obtain raw metatranscriptomics data; the raw data is subjected to quality control analysis to obtain valid reads; the valid reads are assembled into overlapping groups; the overlapping groups are then screened by homology iteration method and geNomad method to obtain RNA virus overlapping groups in the sample respectively; the RNA virus overlapping groups obtained by homology iteration method and geNomad method respectively are merged and clustered to obtain RNA virus operational taxonomic units (vOTUs) and their sequences.

[0054] 7. The method of claim 1, wherein the sample comprises soil, water, atmospheric particulate matter, a composting environment, sludge, river or seabed sediment, or an anaerobic digestion environment.

[0055] 8. The method according to item 7, wherein the anaerobic digestion environment includes livestock and poultry manure and food waste.

[0056] The present invention adopts the above technical solution, which has the following advantages:

[0057] 1. The present invention innovatively combines the analysis methods of the RdRp-based homology iteration method and the machine learning-based geNomad method to identify RNA virus sequences present in samples, which can greatly enhance the ability to detect RNA viruses in omics data.

[0058] 2. When classifying RNA viruses, the present invention creatively couples two virus bioinformatics classification methods, the CAT method and the geNomad method, to make the obtained taxonomic analysis results richer and more representative.

[0059] 3. The present invention innovatively combines the family-level species classification to determine the host, virus and host information, and uses humans, economic animals and plants as key hosts to determine potential high-risk RNA virus vOTU.

[0060] Therefore, the present invention can be widely applied to the field of omics analysis of environmental RNA viruses. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the process of the omics analysis method of environmental RNA viruses of the present invention;

[0062] Figure 2 This is a distribution diagram of the environmental RNA virus quality inspection results of Example 3 of the present invention;

[0063] Figure 3 This is a frequency distribution diagram of environmental RNA viruses in samples according to Example 4 of the present invention;

[0064] Figure 4The taxonomic results of environmental RNA viruses at the phylum and family levels in Example 5 of the present invention; wherein, Figure 4 A shows the species composition at the RNA virus phylum level. Figure 4 B shows the species composition at the RNA virus family level.

[0065] Figure 5 This is the RNA life history distribution diagram of Example 6 of the present invention;

[0066] Figure 6 This is a diagram of RNA virus host composition and abundance distribution in Example 7 of the present invention; wherein, Figure 6 A shows the number of vOTUs that RNA viruses can infect hosts. Figure 6 B shows the relative abundance of RNA viruses that can infect the host. DETAILED DESCRIPTION

[0067] The present invention is further illustrated by the following examples, but any example or combination thereof should not be interpreted as limiting the scope of the present invention or the embodiments. The scope of the present invention is limited by the appended claims. In combination with this specification and the general knowledge in the art, a person of ordinary skill in the art can clearly understand the scope of the claims. Without departing from the spirit and scope of the present invention, a person of ordinary skill in the art can make any modifications and changes to the technical solution of the present invention, and such modifications and changes are also included in the scope of the present invention.

[0068] Example 1 Data acquisition and quality control

[0069] The raw data of metatranscriptomics of anaerobic digestion of manure from different livestock and poultry (chicken, pig and cattle) around the world were collected and downloaded from the public databases of NCBI (https: / / www.ncbi.nlm.nih.gov) and GSA (https: / / ngdc.cncb.ac.cn), with a total of 36 samples. Alternatively, the raw data samples can also be obtained by performing metatranscriptomics sequencing on environmental samples.

[0070] According to the software operating instructions, the read_qc module of MetaWrap software (v 1.3.2) was used to perform quality control on the raw data of the samples. That is, the module used FastQC (v0.11.8) software to check the overall quality of the raw reads, and used Trimmomactic (v0.39) software to remove the double-end adapter sequences and low-quality sequences of the sequences, and finally obtained high-quality reads without adapters after quality control filtering, namely effective reads (clean reads), totaling 640 GB.

[0071] The specific operations of MetaWrap software in this example are as follows:

[0072] for F in 01RAW_READS / *_1.fastq; do

[0073] R=${F%_*}_2.fastq

[0074] BASE=${F##* / }

[0075] SAMPLE=${BASE%_*}

[0076] metawrap read_qc -1 $F -2 $R -t 20 --skip-bmtagger -o 01READ_QC / $SAMPLE

[0077] pigz $F $R

[0078] done

[0079] Example 2 Identification of RNA virus sequences and analysis of viral operational taxonomic units (vOTUs)

[0080] According to the software operating instructions, the assembly module of MetaWrap software (v 1.3.2) was used to splice the valid reads to obtain the contigs sequence. That is, the module called MEGAHIT (v1.1.3) to splice the contigs sequence, and the contigs sequences with a length greater than 1.0 kb in the sample were screened out and merged into a preliminary RNA virus data set RNA_contigs1000bp.fasta.

[0081] The specific operations of MetaWrap software are as follows:

[0082] for F in 01Reads_fq / *_1.fastq; do

[0083] R=${F%_*}_2.fastq

[0084] BASE=${F##* / }

[0085] SAMPLE=${BASE%_*}

[0086] metawrap assembly -1 $F -2 $R -m 500 -t 50 -o 05ASSEMBLY / $SAMPLE

[0087] rm -rf 05ASSEMBLY / $SAMPLE / megahit

[0088] done

[0089] for F in 01Reads_fq / *_1.fastq; do

[0090] R=${F%_*}_2.fastq

[0091] BASE=${F##* / }

[0092] SAMPLE=${BASE%_*}

[0093] sed "s / k141 / $SAMPLE / g" 05ASSEMBLY / $SAMPLE / final_assembly.fasta |seqtk seq -L 1000 >06VCs / 00Contigs / $SAMPLE.fa

[0094] done

[0095] cat 06VCs / 00Contigs / *.fa >06VCs / 00Contigs / RNA_contigs1000bp.fasta

[0096] The above-obtained preliminary RNA virus dataset RNA_contigs1000bp.fasta was used to predict the corresponding protein sequence using Prodigal (v2.6.3) (refer to https: / / github.com / hyattpd / Prodigal for usage). Then, the RdRp protein sequence in the contig protein sequence was compared by homology iteration (i.e., using the hidden Markov model, a total of 5 iterations), and the virus sequence with the RdRp protein sequence was selected, obtaining a total of 1696 RNA virus contig sequences.

[0097] The specific method is:

[0098] . / rdrp-search -f "test / *.faa" -t 50 -g -o test / out

[0099] samtools faidx test / out / final-hits.fa;

[0100] In addition, the preliminary RNA virus dataset RNA_contigs1000bp.fasta was screened by geNomad (v1.7.0) to obtain a total of 1811 RNA virus contig sequences (refer to https: / / github.com / apcamargo / genomad for usage).

[0101] Then, 1696 RNA virus contigs obtained by homology iteration were combined with 1811 RNA virus contigs obtained by geNomad, and 1994 RNA virus contigs were obtained by clustering the sequences with 100% similarity based on CD-HIT-EST (v 4.7), which were 17.57% and 10.1% higher than the homology iteration (1696) and geNomad (1811) methods, respectively. Finally, these sequences were clustered according to sequence similarity ≥ 90% and coverage ≥ 80% to obtain 729 RNA virus operational taxonomic units (vOTUs) and their sequences.

[0102] The specific operations of this embodiment are as follows:

[0103] 1) Decompress the raw data after the macrotranscriptome sequencing, and use the read_qc module of MetaWrap software (version 1.3.2) to perform quality control on the decompressed data, and use the echo tool to count the effective reads (cleanreads).

[0104] 2) Use the metawrap assembly module to assemble the valid reads of each sample into contigs.

[0105] 3) The overlapping sequences in the samples with a length greater than 1.0 kb were screened out and merged into a preliminary RNA virus dataset, and the sequences of this dataset were predicted to become a viral protein sequence dataset.

[0106] 4) Through the homology iteration method (5 iterations in total), the RdRp protein sequences in the viral protein sequence database were compared to select viral sequences with RdRp protein sequences.

[0107] 5) The preliminary RNA virus dataset was classified and RNA virus sequences were screened out using the machine learning-based geNomad (v 1.7.0) software.

[0108] 6) The RNA virus sequences obtained by the RdRp protein sequence iteration method and the machine learning-based geNomad software were merged, and the sequences with 100% similarity were clustered based on CD-HIT-EST to obtain the RNA virus contig sequence data set.

[0109] 7) Cluster the RNA virus sequence dataset according to sequence similarity ≥ 90% and coverage ≥ 80% to obtain non-redundant RNA virus operational taxonomic units (vOTUs) and their sequences.

[0110] Example 3 RNA virus quality inspection and analysis

[0111] After the obtained 729 RNA virus operational taxonomic unit (vOTU) sequences were quality checked by checkV (v1.0.1) (reference for usage: https: / / bitbucket.org / berkeleylab / checkv / src / master / ), it was found that the length of the virus sequences was between 1000–10786bp. According to the quality inspection evaluation criteria of the software (such as length, contamination, and integrity), 81, 136, and 283 virus sequences were identified as high-quality, medium-quality, and low-quality, respectively, among the 729 sequences, and the quality of the other 229 virus sequences could not be identified ( Figure 2 ).

[0112] Example 4 Abundance Analysis of RNA Viruses

[0113] The coverM (v0.7.0) tool was used to analyze the abundance of the RNA virus operational taxonomic unit (vOTU) sequences obtained in Example 2 (reference for usage: https: / / github.com / wwood / CoverM), and the coverage (≥70%) screening (common standard reported in literature) revealed that RNA viruses were found in only 29 of the 36 samples, and that most viruses (475) actually appeared at very low frequencies (≤3) in the 29 samples, and the virus with the highest frequency appeared in only 15 samples ( Figure 3 ).

[0114] The specific operations of this embodiment are as follows:

[0115] 1) Use the coverM (v0.7.0) tool to align the sequences of the RNA virus operational taxonomic units (vOTUs) obtained in Example 2 with the valid reads to generate the number of bases covered (base) and RPKM abundance information of each RNA virus operational taxonomic unit (vOTU) in different samples.

[0116] 2) Divide the number of bases covered by each RNA virus operational taxonomic unit (vOTU) sequence in the sample by its length (the result obtained in Example 3). If it is ≥70% (i.e., coverage ≥70%), it means that the RNA virus operational taxonomic unit (vOTU) exists in the sample.

[0117] 3) The relative abundance of the RPKM of the RNA virus operational taxonomic units (vOTUs) that actually exist in the sample is calculated, that is, the abundance of all RNA viruses in a single sample is taken as 100%, thereby obtaining the relative abundance of RNA viruses in different samples.

[0118] Example 5 RNA virus taxonomic analysis

[0119] The sequences of 729 RNA virus operational classification units (vOTUs) obtained in Example 2 were subjected to taxonomic analysis by geNomad (v1.7.0, use method refer to https: / / github.com / apcamargo / genomad) and CAT (v5.2.3, use method refer to https: / / github.com / MGXlab / CAT_pack), and it was found that geNomad and CAT obtained classification results of 647 and 670 RNA virus operational classification units (vOTUs), respectively. By combining the methods, i.e. giving priority to the classification results of geNomad, and then supplementing them with the CAT results, the classification results of 727 RNA virus operational classification units (vOTUs) were finally obtained.

[0120] Figure 4 The final taxonomic analysis results of RNA viruses in this example at the phylum and family levels are shown. During the anaerobic digestion of livestock and poultry manure, RNA viruses were mainly composed of Duplopiviricetes (28.71%), Pisoniviricetes (17.75%), Leviviricetes (11.52%), and Howeltoviricetes (9.12%). Figure 4 A); At the family level, a total of 36 RNA virus families were identified, among which five RNA virus families, Picobirnaviridae, Partitiviridae, Mitoviridae, Leviviridae and Picornaviridae, were found to be the main core virus families in the anaerobic digestion of livestock and poultry manure (present in more than 80% of the samples) ( Figure 4 B).

[0121] The specific operations of this embodiment are as follows:

[0122] 1) Use geNomad (v 1.7.0) software to perform species annotation on the non-redundant RNA virus operational taxonomic unit (vOTU) sequences obtained in Example 2;

[0123] 2) Using CAT (v5.2.3) software to perform species annotation on the non-redundant RNA virus operational taxonomic unit (vOTU) sequences obtained in Example 2;

[0124] 3) The classification results of geNomad (v 1.7.0) were given priority, and then supplemented with the results of CAT (v5.2.3) to finally obtain the classification results of RNA virus operational taxonomic units (vOTUs);

[0125] 4) The coverM (v0.7.0) tool was used to perform abundance analysis on non-redundant RNA virus operational taxonomic units (vOTUs) to determine the relative abundance of different RNA virus populations in the samples.

[0126] Example 6 RNA virus life cycle and function analysis

[0127] The life history of RNA viruses was predicted by VIBRANT software (v1.2.1). It was found that only 28 RNA virus operational taxonomic units (vOTUs) could be predicted as lytic phages, accounting for an average relative abundance of 1.72% in 29 samples. Figure 5 ).

[0128] In addition, after comparison with antibiotic resistance gene (ARGs) databases such as CARD and SARG, a vOTU471 identified as Partitiviridae was found to carry the aadS resistance gene, with an average relative abundance of 0.04% in 29 samples.

[0129] The specific operations of this embodiment are as follows:

[0130] 1) Use VIBRANT software (v1.2.1) to predict the life history of RNA viruses;

[0131] 2) Use Prodigal (v2.6.3) to predict the gene sequence of the non-redundant RNA virus operational taxonomic unit (vOTU) sequence obtained in Example 2, and compare it with the SARG and CARD databases based on the blastx method;

[0132] 3) Use the coverM (v0.7.0) tool to perform abundance analysis on non-redundant RNA virus operational taxonomic unit (vOTU) sequences to determine the relative abundance of different RNA virus populations in the samples;

[0133] 4) Based on the comparison results of the SARG and CARD databases, the prediction results of the VIBRANT software and the abundance results of coverM, the life history of RNA viruses, the occurrence characteristics of antibiotic resistance genes (ARGs) they carry and their relative abundance were determined.

[0134] Example 7 Analysis of RNA infection host and transmission risk

[0135] The virus-host association method of RNAvirHOST software (v1.0.5) was used through the species classification information at the family level. Among them, the classification name of the virus family was searched on the ICTV (https: / / ictv.global / ) website to find the host it infected, thereby determining the virus host of 474 vOTUs, and the virus host of 370 vOTUs was obtained through RNAvirHOST software (v1.0.5), and finally the virus host information of 585 vOTUs. These RNA viruses are mainly vertebrate viruses (227), bacteriophage viruses (149) and fungal viruses (97), while the number of invertebrate viruses (57) and plant viruses is relatively small (55) ( Figure 6 A). Through abundance analysis, the RNA viruses in livestock and poultry feces are mainly vertebrate viruses with an average relative abundance of 27.62%±24.95%, followed by invertebrate viruses (25.36%±23.55%), while the average relative abundance of high-risk viruses that can infect primates, mammals and plants is 21.03%±25.25% ( Figure 6 B).

[0136] The specific operations of this embodiment are as follows:

[0137] 1) RNAvirHOST software (v1.0.5) was used to analyze the infectible hosts of non-redundant RNA virus operational taxonomic units (vOTUs);

[0138] 2) After classifying the non-redundant RNA virus operational taxonomic units (vOTUs), the family-level classification results were input into the ICTV (https: / / ictv.global / ) website, which provided the infectible host classification of the family, thereby obtaining the ICTV host characteristics;

[0139] 3) Pool the obtained host results to finally obtain the host characteristics of non-redundant RNA virus operational taxonomic units (vOTUs).

Claims

1. A method for analyzing RNA viruses in a sample, comprising the following steps: 1) Perform metatranscriptome sequencing on the samples to obtain metatranscriptomics data, and then analyze the metatranscriptomics data using the homology iteration method based on RdRp and the geNomad method based on a machine learning model to screen RNA virus sequences in the samples and obtain RNA virus operational taxonomic units (vOTUs) and their sequences; 2) Conduct abundance analysis, classification identification and host analysis on the RNA virus operational taxonomic units obtained in step 1); The classification and identification includes analyzing the RNA virus operational classification unit sequences by geNomad and CAT methods respectively to obtain the classification results of the RNA virus operational classification units in the sample.

2. The method according to claim 1, wherein: The host analysis includes analyzing the RNA virus operational taxonomic unit (vOTU) sequence obtained in step 1) by using the RNAvirHOST method to obtain the host information of the RNA virus in the sample.

3. The method according to claim 1 or 2, wherein: The abundance analysis includes performing abundance analysis on the RNA virus operational taxonomic unit sequences obtained in step 1) using the coverM tool to calculate the relative abundance of RNA viruses in the sample.

4. The method according to claim 1 or 2, wherein: Step 2) also includes subjecting the RNA virus operational classification unit sequence obtained in step 1) to checkV quality inspection to reveal characteristics such as the length, contamination, and integrity of the RNA virus sequence in the sample.

5. The method according to claim 1 or 2, wherein: Step 2) also includes analyzing the RNA virus operational classification unit sequence obtained in step 1) through VIBRANT software to predict the life history of the RNA virus in the sample.

6. The method according to claim 1, wherein: In step 1), the sample is subjected to metatranscriptome sequencing to obtain raw metatranscriptomics data; the raw data is subjected to quality control analysis to obtain valid reads; the valid reads are assembled into contigs; the contigs are then screened by homology iteration method and geNomad method to obtain RNA virus contigs in the sample; the RNA virus contigs obtained by homology iteration method and geNomad method are merged and clustered to obtain RNA virus operational taxonomic units (vOTUs) and their sequences.

7. The method according to claim 1, wherein: The samples include soil, water, atmospheric particulate matter, compost, sludge, river or seabed sediment, or anaerobic digestion environments.

8. The method according to claim 7, wherein: The anaerobic digestion environment includes livestock and poultry excrement and food waste.

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