Phage identification and host prediction methods based on nanopore and high-throughput sequencing data

Abstract

To provide phage identification and host prediction methods based on nanopore and high throughput sequence determination data, in a field of biological information.SOLUTION: The invention is configured to: combine, original nanopore sequence determination data and high throughput sequence determination data; design a quality management filtering line and perform, with respect to two kinds of pieces of sequence determination data, quality management and filtering; identify a potential phage sequence on the basis of a feature model of a phage gene in a sequence after hybrid assembly; extract a specific spacer sequence provided in an existing bacterial genome in a database; finally, collate the specific spacer sequence with a specific spacer sequence extracted from the database and mine a correspondence and predict a host relationship corresponding to the phage. The invention achieves efficient prediction of the phage host and efficient mining to a phage sequence in the nanopore and high throughput sequence determination data, and therefore the invention has a significant value in use and in popularization.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present application is in the field of bioinformatics, and specifically relates to phage identification and host prediction methods based on nanopore and high-throughput sequencing data. [Background technology]

[0002] Phages are a type of virus that specifically infect bacteria. On the one hand, phages have ecological significance, as phages can regulate bacterial populations by lysing bacteria. On the other hand, phages have important potential uses, as phages can precisely control the growth of pathogenic bacteria, thus avoiding widespread drug resistance caused by the mass use of antibiotics. In the wastewater treatment field, phages are expected to be used to control undesirable bacteria.

[0003] In the existing related technology, with regard to phage identification and host prediction, the patent application No. CN201910506027.0, filed on June 12, 2019, discloses a method for predicting temperate phages based on high-throughput sequencing data, which realizes effective prediction of temperate phages in bacterial genomes. However, the invention does not consider the method for identifying free phages and predicting hosts, and does not involve the method for analyzing phages with nanopore sequencing data. The patent application No. CN201911110250.X, filed on November 14, 2019, discloses a high-throughput screening method for phage host spectrum, which only requires one person to work for two days to complete phage host identification. However, the invention mainly uses traditional biological culture, not high-throughput sequencing data, and the phage host identification method is still time-consuming. Chinese Patent Application No. CN202010241653.4, filed on March 31, 2020, discloses a method for detecting functional prophages and their locations and sequences in bacteria, which can detect functional prophages and their locations and sequences in bacteria. However, the invention utilizes bacterial genomes, not phage genome sequencing data, and only considers mining phage information in high-throughput sequencing data.

[0004] As can be seen from the above, traditional phage identification methods use biological culture, which is time-consuming and very inefficient, making it difficult to quickly obtain phage information in environmental samples, and it is also very difficult to establish the infection relationship between phages and multiple hosts at the same time. In addition, with the rapid development of genome sequencing technology, nanopore sequencing technology, which is the third generation sequencing technology, is becoming more and more popular around the world, but currently there is a severe lack of phage identification and host prediction methods for nanopore sequencing data. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Chinese patent application no. CN201910506027.0 [Patent Document 2] Chinese patent application no. CN202010241653.4 Summary of the Invention [Problem to be solved by the invention]

[0006] The present application provides a phage identification and host prediction method based on nanopore and high-throughput sequencing data, which solves the problems and shortcomings associated with the prior art, and the method can simultaneously meet the analytical requirements of nanopore sequencing data and high-throughput sequencing data, accurately identify phage sequences in the sequencing data, and predict the corresponding relationship between phage and host. [Means for solving the problem]

[0007] The first aspect of the present application provides a method for phage identification and host prediction based on nanopore and high-throughput sequencing data, comprising obtaining original nanopore sequencing data and original high-throughput sequencing data from a test DNA sample, and then performing phage identification and host prediction; S1: setting a first quality value filtering parameter, and removing sequences from the original nanopore sequencing data whose average quality value is lower than the first quality value filtering parameter to obtain filtered nanopore sequences; S2: performing a length analysis on the filtered nanopore sequence, setting a first sequence length parameter, and removing short sequences whose sequence length is less than the first sequence length parameter to obtain a quality control filtered nanopore long sequence; S3: removing sequences containing linkers from the original high-throughput sequencing data to obtain filtered high-throughput sequencing data; S4: removing sequences with N content of more than 10% from the filtered high-throughput sequencing data, setting a second quality value filtering parameter, and further removing sequences with base quality values ​​of more than 50% of the removed high-throughput sequences that are lower than the second quality value filtering parameter to obtain high-throughput short sequences after quality control filtering; S5: hybrid assembly the quality control filtered nanopore long sequence and the quality control filtered high-throughput short sequence, splicing to obtain identified phage sequence information, set a second sequence length parameter, and remove the spliced ​​short sequence whose sequence length is shorter than the second sequence length parameter to obtain a hybrid assembled phage sequence; S6: performing gene prediction on the phage sequence after hybrid assembly to obtain gene characteristic information of the predicted sequence; S7: Matching the genetic characteristic information of the predicted sequence with a first type database containing phage genetic characteristic information and a second type database containing bacterial genetic characteristic information to obtain corresponding matching status data respectively; S8: A step of tabulating and judging the collation situation data, Identifying the predicted sequence as a phage sequence when the proportion of genes matching the first type database in the gene characteristic information of the predicted sequence is greater than or equal to 60% of the total amount of genes in the sequence, or the proportion of genes matching the first type database is greater than or equal to the number of genes matching the second type database, or the proportion of genes matching the first type database is greater than or equal to 10% and the proportion of genes matching the second type database is less than or equal to 40%; S9: Downloading a database containing all bacterial genome sequence information and extracting special spacer sequences from existing bacterial genome sequences; S10: Matching the phage sequence identified in step S8 with the special spacer sequence extracted in step S9 to obtain predicted phage host information.

[0008] Optionally, the quality value filtering parameter according to the method is a base quality value Q, the first quality value filtering parameter is Q7 and the second quality value filtering parameter is Q5.

[0009] Optionally, the method includes setting the first sequence length parameter to 1000 bp and the second sequence length parameter to 5000 bp.

[0010] Optionally, in step S7, the first type database is a VPF database, and the second type database is either one or both of a Pfam database and a KEGG database; and in step S8, tabulating and judging the matching situation data includes: identifying the sequence as a phage sequence when the number of genes matching the VPF database is greater than or equal to 60% of the total amount of genes in the sequence among the gene feature information of the predicted sequence; identifying the sequence as a phage sequence when the number of genes matching the VPF database is greater than or equal to the number of genes matching the Pfam database; identifying the sequence as a phage sequence when the number of genes matching the VPF database is greater than or equal to 10% of the total amount of genes in the sequence, and the number of genes matching the Pfam database is less than or equal to 40% of the total amount of genes in the sequence, and the number of genes matching the KEGG database is less than or equal to 20% of the total amount of genes in the sequence.

[0011] Optionally, in step S9, the database containing all the bacterial genome sequence information is an NCBI database.

[0012] Optionally, in step S10, the phage sequence identified in step S8 is matched with the special spacer sequence extracted in step S9 using Blast software, and the parameters used in the match include a coverage of 90%, a match rate of 97%, and an error count of 1.

[0013] A second aspect of the present application provides the use of said phage identification and host prediction method in the microbial analysis of environmental media samples. Effect of the Invention

[0014] As can be seen from the above, the method of the present application can simultaneously process the latest third generation nanopore sequencing data and the currently popular second generation high-throughput sequencing data, can rapidly and accurately predict phage host relationships on a large scale without biological culture, and can effectively mine phage sequences in nanopore and high-throughput sequencing data and effectively predict phage hosts, so it has great value in terms of popularization and use. Compared with the prior art, the beneficial effects of the method of the present application specifically include: (1) The present invention is a phage identification and host prediction method for both nanopore sequencing data and high-throughput sequencing data, in which the nanopore sequencing data is useful for identifying phages with longer and more complete genomes, and the high-throughput sequencing data is useful for identifying phages with higher genome accuracy, and the phages identified to utilize both data combine the advantages of these two.

[0015] (2) The phage identification and host prediction method of the present invention can be used not only for the sequencing data of prophage genomes, but also for the sequencing data of free phage genomes, which has a wider scope of application, a larger number of phages have been identified, and the phage database constructed is more comprehensive.

[0016] (3) Compared with the traditional method of identifying phage hosts through biological culture, the present invention can rapidly identify phages from environmental samples on a large scale and accurately predict phage-host relationships, greatly shortening the process of phage identification and host prediction; if there are sufficient computing resources, the process of phage identification and host prediction can be completed simultaneously for a large number of environmental samples through parallel processing using software tools, and the accuracy rate of host prediction is high. When tested with phages whose hosts are known in the NCBI database, the accuracy rate of host prediction reaches more than 90%. [Brief description of the drawings]

[0017] In the following, in order to more clearly explain the technical solutions of the present application, the drawings used in the embodiments are briefly introduced, and it goes without saying that those skilled in the art can obtain other drawings from these drawings without performing any novel work. [Figure 1] FIG. 1 is a flow chart of the phage identification and host prediction method of the present application. [Diagram 2] FIG. 2 is a distribution diagram of the number of phage sequences identified as satisfying the three conditions in the examples of this application. [Diagram 3] FIG. 3 is a genome arrangement diagram of one phage whose predicted host is nitrate bacteria in the examples of the present application. [Figure 4] FIG. 4 is a genome arrangement diagram of another phage whose predicted host is nitrite bacteria in the examples of the present application. [Diagram 5] FIG. 5 is a genome arrangement diagram of yet another phage whose host is predicted to be nitrite bacteria in the examples of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Hereinafter, the present application will be described in detail with reference to drawings and examples so that the above-mentioned objects, features and advantages of the present application will be more clearly and easily understood.

[0019] The present application provides a phage identification and host prediction method based on nanopore and high-throughput sequencing data, which extracts DNA from a test sample, obtains original nanopore sequencing data and original high-throughput sequencing data from the extracted test DNA sample, and then performs phage identification and host prediction.

[0020] The steps of the method of the present application mainly include data quality control, phage identification and host prediction, specifically, an efficient quality control method by nanopore and high-throughput sequencing data, a rapid phage identification method by nanopore and high-throughput sequencing data, and an accurate phage host prediction method by nanopore and high-throughput sequencing data.

[0021] As shown in FIG. 1, the method specifically includes the following steps: S1: Set a first quality value filtering parameter, and remove sequences whose average quality value is lower than the first quality value filtering parameter from the original nanopore sequencing data to obtain filtered nanopore sequences.

[0022] Specifically, in step S1, the sequencing data is filtered based on the average quality value of the nanopore sequencing data. In practical work, as one embodiment, the Q-value may be used as a quality value filtering parameter to filter the sequencing data with a low average quality value. The Q-value is a base quality score (Q-score), which is an integer form mapping of the base calling error probability in biophysics, where Q=-10×lgP, where P is the probability of base calling error. The Q-value is a quality value that is automatically calculated simultaneously in the nanopore sequencing process, and represents the probability of base sequencing error, and the higher the Q-value, the lower the error rate and the higher the accuracy rate of sequencing. For example, in this method, Q5 to Q7 can be selected as the first quality value filtering parameter, and the corresponding accuracy rate of sequencing is 70% to 80%. If the Q-value is low, it will have a negative impact on the effect of assembly, so the Q-value must be filtered to remove sequences with an average quality value lower than a predetermined Q-value.

[0023] S2: Perform a length analysis on the filtered nanopore sequence, set a first sequence length parameter, and remove short sequences whose sequence length is less than the first sequence length parameter to obtain a quality control filtered nanopore long sequence.

[0024] Specifically, the sequences are screened and filtered for sequence length, and the specific value of the first sequence length parameter can be set based on experience or according to practical requirements, for example, 500-1000 bp.

[0025] S3: Linker-containing sequences are removed from the original high-throughput sequencing data to obtain filtered high-throughput sequencing data.

[0026] Specifically, in this step, it is necessary to remove the linker fragment from the sequence because the linker is a short sequence artificially added in the sequencing process and its presence affects the efficiency of the subsequent assembly.

[0027] S4: Remove sequences with an N content of more than 10% from the filtered high-throughput sequencing data, set a second quality value filtering parameter, and further remove sequences with a base quality value of more than 50% lower than the second quality value filtering parameter from the removed high-throughput sequences to obtain high-throughput short sequences after quality control filtering.

[0028] Specifically, similar to step S1, the second quality value filtering parameter can be expressed as a Q value, which is a base quality value, and is set, for example, as Q5 to Q7, where Q5 to Q7 are converted into sequencing accuracy of approximately 70% to 80%.

[0029] S5: The nanopore long sequence after quality control filtering and the high-throughput short sequence after quality control filtering are hybrid assembled and spliced ​​to obtain identified phage sequence information, a second sequence length parameter is set, and the short sequence whose sequence length after splicing is shorter than the second sequence length parameter is removed to obtain a phage sequence after hybrid assembly.

[0030] Specifically, in this step, the sequence after hybrid assembly is screened and filtered for length, and the specific value of the second sequence length parameter can be set based on experience or according to actual requirements, for example, 5000 to 10000 bp.

[0031] S6: Gene prediction is performed on the phage sequences after hybrid assembly to obtain gene characteristic information of the predicted sequences.

[0032] S7: Match the gene characteristic information of the predicted sequence with a first type database including phage gene characteristic information and a second type database including bacterial gene characteristic information to obtain corresponding matching status data respectively.

[0033] Specifically, the first type of database is a database containing phage gene characteristic information, and the more the gene characteristic information of the predicted sequence matches the database of that type, the more likely the predicted sequence is a phage. Conversely, the second type of database contains bacterial gene characteristic information, and the more the gene characteristic information of the predicted sequence matches the database of that type, the more likely the predicted sequence is a bacterium rather than a phage. There may be multiple databases of each type, and they can be selected from existing databases that are widely used.

[0034] S8: The collation status data is tallied and judged.

[0035] Among the gene characteristic information of the predicted sequence, if the proportion of the number of genes matching the first type database is greater than or equal to 60% of the total number of genes of the sequence, or if the number of genes matching the first type database is greater than or equal to the number of genes matching the second type database, or if the proportion of the number of genes matching the first type database is greater than or equal to 10% and the proportion of the number of genes matching the second type database is less than or equal to 40%, the predicted sequence is identified as a phage sequence.

[0036] Specifically, three conditions are set in the present application, and a sequence that satisfies any one of the three conditions is identified as a phage sequence. The first condition indicates that if 60% or more of the genetic characteristic information of the test sequence matches the first type database (i.e., the database including phage genetic characteristic information), the sequence is considered to be a phage sequence. Similarly, the second condition indicates that if the number of genes in the test sequence that match the database including phage genetic characteristic information is greater than or equal to the number of genes that match the database including bacterial genetic characteristic information, the sequence is considered to be a phage sequence. Thus, the third condition indicates that if 10% or more of the genetic characteristic information of the test sequence matches the first type database (i.e., the database including phage genetic characteristic information) and only 40% or less of the genetic characteristic information matches the second type database (i.e., the database including bacterial genetic characteristic information), the sequence is unlikely to be a bacterium, and the sequence that meets the condition is determined to be a phage sequence.

[0037] When judging the matching status data, if the sequence satisfies any of the three conditions, it is identified as a phage sequence. In actual operation, the matching sequence is judged successively to see whether it meets the above conditions, and if a set of three sequences that meet the above three conditions is obtained, it is identified as a phage sequence, and then the sets of three sequences identified as phages are counted, and overlapping sequences are removed from the set of three to identify the phage sequences in the sample.

[0038] S9: Download a database containing all bacterial genome sequence information and extract special spacer sequences from existing bacterial genome sequences.

[0039] Specifically, in this step, the database downloaded is a database containing all bacterial genome sequence information, and can be selected from the most comprehensive database of bacterial genome sequence information that is widely used in this technical field, for example, the NCBI database. The special spacer sequence fragment in the database records the phage sequence fragment excised by bacteria when they defend against phage infection, and contains infection relationship information between phage and host.

[0040] S10: The phage sequences identified in step S8 are matched with the special spacer sequences extracted in step S9 to obtain predicted phage host information.

[0041] As can be seen from the above steps, the phage identification and host prediction method based on nanopore and high-throughput sequencing data provided by the present application combines the original nanopore sequencing data and high-throughput sequencing data, designs a quality control filtering line to perform quality control and filtering on two kinds of sequencing data respectively, identifies potential phage sequences based on the phage gene feature model in the sequence after hybrid assembly, then extracts the special spacer sequences of the existing bacterial genome in the database, and finally matches them with the special spacer sequences extracted from the database to mine the corresponding relationship and predict the host relationship corresponding to the phage. The present invention can realize the effective mining of phage sequences in nanopore and high-throughput sequencing data and the effective prediction of phage hosts, which is of great value in popularization and use.

[0042] Preferably, the quality value filtering parameter described in the embodiments of the present application is a base quality value Q, the first quality value filtering parameter is Q7, and the second quality value filtering parameter is Q5.

[0043] Specifically, Q5 corresponds to a sequencing accuracy of about 70%, and Q7 corresponds to a sequencing accuracy of about 80%.

[0044] Preferably, in the present embodiment, the first sequence length parameter is set to 1000 bp and the second sequence length parameter is set to 5000 bp.

[0045] Preferably, in step S7, the first type database is a VPF database, and the second type database is either one or both of a Pfam database and a KEGG database; and in step S8, tabulating and judging the matching situation data includes: determining the sequence as a phage sequence when the number of genes matching the VPF database is greater than or equal to 60% of the total amount of genes in the sequence among the gene feature information of the predicted sequence; determining the sequence as a phage sequence when the number of genes matching the VPF database is greater than or equal to the number of genes matching the Pfam database; or identifying the sequence as a phage sequence when the number of genes matching the VPF database is greater than or equal to 10% of the total amount of genes in the sequence, and the number of genes matching the Pfam database is less than or equal to 40% of the total amount of genes in the sequence, and the number of genes matching the KEGG database is less than or equal to 20% of the total amount of genes in the sequence.

[0046] Specifically, in this step, the Pfam database mainly contains bacterial gene characteristic information, and the more the gene characteristic information of the predicted sequence matches the database of the type, the more likely the predicted sequence is a bacterium rather than a phage, and by matching with the Pfam database, the possibility that the sequence is a potential bacterial sequence can be determined. The KEGG database is another database that mainly contains bacterial gene characteristic information, and by matching with the KEGG database, the possibility that the sequence is a potential bacterial sequence can be determined. The VPF database is a database that mainly contains phage gene characteristic information, where VPF refers to Viral Protein Families (VPFs), and the VPF database represents a database containing VPFs information. In a more specific embodiment, the VPF database is a database file that is derived from the IMG / VR database and is processed and can be used for hmmer matching. The more the gene characteristic information of the predicted sequence matches the database, the more likely the predicted sequence is a phage, and by matching with the VPF database, the possibility that the sequence is a potential phage sequence can be determined.

[0047] Preferably, in step S9, the database containing all the bacterial genome sequence information is an NCBI database.

[0048] Specifically, in this step, the NCBI database (National Center for Biotechnology Information) is currently the largest bacterial genome database in the world, and comprehensive and complete bacterial genome sequence information can be obtained from this database.

[0049] Preferably, in step S10, the phage sequence identified in step S8 is matched with the special spacer sequence extracted in step S9 using Blast software, and the parameters used for the match include a coverage of 90%, a degree of identity of 97%, and an error count of 1.

[0050] Specifically, the full name of Blast is Basic Local Alignment Search Tool, that is, it is a search tool using a basic local sequence matching algorithm, and is a program for matching the primary structure sequences of biopolymers developed and managed by the National Center for Biotechnology Information (NCBI). When the method of the present application is actually carried out, matching may be performed using Blast, and the parameters used are set to 90% coverage, 97% identity, and 1 error.

[0051] In addition, when the method of the present application is actually carried out, various software in the field of bioinformatics can be used in addition to the work, including biological software tools that are widely used in this field, such as NanoPlot and NanoFilt for length analysis and removal of short sequences, and OPERA-MS for hybrid assembly of sequences, which are not listed here one by one. In addition, the method is not limited to being carried out using a self-programmed computer program, and detailed description thereof is omitted here.

[0052] The present examples also provide the use of the phage identification and host prediction methods in the microbial analysis of environmental media samples.

[0053] Specifically, the environmental media samples include, but are not limited to, wastewater and sludge samples that are the subject of water treatment technology. By analyzing the microorganisms in these samples, the status of phages in the wastewater or sludge samples is determined, and the predicted phage host information is used to analyze the environmental ecological functions of phages and their hosts in the wastewater system.

[0054] The specific operation of the method will now be further illustrated by means of a specific application example.

[0055] Example 1 Through standard pretreatment and DNA extraction and sequencing steps, 50 Gb of nanopore sequencing data and 100 Gb of high-throughput sequencing data will be obtained from five wastewater samples.

[0056] This embodiment is carried out in the following steps. Step 101: Using NanoFilt software, remove sequences with average quality value lower than Q7 from the 50 Gb nanopore sequencing data.

[0057] Step 102: Perform length analysis on the nanopore sequences filtered in step 101 using NanoPlot, and use NanoFilt to remove short sequences less than 1000 bp in length to obtain quality-control filtered nanopore long sequences.

[0058] Step 103: Remove linker-containing sequences from the 100 Gb high-throughput sequencing data using cutadapt.

[0059] Step 104: From the high-throughput sequencing data filtered in step 103, sequences with an N content of more than 10% are removed using fastp, and from the removed high-throughput sequences, more than 50% of sequences with a base quality value lower than Q5 are removed using fastp to obtain high-throughput short sequences after quality control filtering.

[0060] Step 105: Perform hybrid assembly using OPERA-MS on the nanopore long sequences after quality control filtering in step 102 and the high-throughput short sequences after quality control filtering in step 104, splice to obtain identified phage sequence information, and use seqkit to remove sequences shorter than 5000 bp in length after splicing to obtain phage sequences after hybrid assembly.

[0061] Step 106: Prodigal is used to perform gene prediction on the phage sequence after hybrid assembly obtained in step 105 to obtain characteristic function information, i.e., gene characteristic information, of each gene in the sequence.

[0062] Step 107: Using hmmsearch, the gene characteristic information obtained in step 106 is compared with the Pfam database, which mainly contains bacterial gene characteristic information. By comparing with the Pfam database, it can be determined that the sequence is a potential bacterial sequence; Using diamond, the gene characteristic information obtained in step 106 is compared with the KEGG database, which is another database mainly containing bacterial gene characteristic information. By comparing with the KEGG database, the possibility that the sequence is a potential bacterial sequence can be further determined; Using hmmsearch, the gene characteristic information obtained in step 106 is compared with the VPF database, which is a database that mainly contains phage gene characteristic information. By comparing with the VPF database, the possibility that the sequence is a potential phage sequence can be further determined.

[0063] Step 108: The matching statuses obtained in step 107 are tallied.

[0064] If the number of genes matching the VPF is greater than or equal to 60% of the total number of genes in the sequence, the sequence is identified as a phage sequence. In this example, a total of 3699 phages meeting this condition were identified. If the number of genes matching VPF is greater than or equal to the number of genes matching Pfam, the sequence is identified as a phage sequence. In this example, a total of 30,781 phages meeting this condition were identified. If the number of genes matching KEGG is less than or equal to 20% of the total gene amount of the sequence, and the number of genes matching Pfam is less than or equal to 40% of the total gene amount of the sequence, and the number of genes matching VPF is greater than or equal to 10% of the total gene amount of the sequence, the sequence is identified as a phage sequence. In this example, a total of 17690 phages that meet the above conditions are identified.

[0065] The phages that satisfy the above three conditions are counted and duplicated sequences are removed, as shown in Figure 2. In this example, a total of 44270 phage sequences are identified, of which 691 are prophages and 43579 are free phages.

[0066] Step 109: Download all bacterial genome sequence information contained in the NCBI database, and use CRSIPRDetect to extract special spacer sequence fragments in the existing bacterial genome sequences in the NCBI Refseq V91 database.

[0067] Step 110: Using Blast, the 44,270 phage sequences identified in step 108 are matched with the special spacer sequences extracted in step 109, using parameters of 90% coverage, 97% identity, and 1 error, to predict the host information of the phage.

[0068] The results show that the host of 326 of the phages is predicted to be mycobacteria, and the host of three of the phages is predicted to be nitrate bacteria. Figures 3 to 5 show the genome layout of the three phages whose host is nitrate bacteria. The length of the phage shown in Figure 3 is about 140 kbp, and it is the only phage among the three that contains a tRNA gene. The length of the phage in Figure 4 is about 45 kbp, and the length of the phage in Figure 5 is about 35 kbp. The coding gene regions of the three phages are mainly hypothetical proteins.

[0069] The table below shows the infection-related test results according to the NCBI Refseq V91 version database. [Table 1]

[0070] The results show that when the parameters used in the matching method are 90% coverage, 97% concordance, and 1 error, the accuracy of host prediction reaches 93%.

[0071] Although the present application has been described in detail above using specific embodiments and illustrative examples, these descriptions are not intended to be limiting to the present application. Those skilled in the art will understand that various equivalent replacements, modifications or improvements can be made to the technical solutions and embodiments of the present application without departing from the spirit and scope of the present application, and these fall within the scope of the present application. The scope of the present application is governed by the appended claims.

Claims

1. A phage identification and host prediction method based on nanopore and high-throughput sequencing data, comprising obtaining original nanopore sequencing data and original high-throughput sequencing data from a test DNA sample, and then performing phage identification and host prediction, the test DNA sample being an environmental medium sample, the environmental medium sample including sewage and sludge samples; S1: Setting a first quality value filtering parameter, and removing sequences whose average quality value is lower than the first quality value filtering parameter from the original nanopore sequencing data to obtain a filtered nanopore sequence, the quality value filtering parameter is a base quality value Q, and the first quality value filtering parameter is Q7; S2: performing length analysis on the filtered nanopore sequence, setting a first sequence length parameter, and removing short sequences whose sequence length is less than the first sequence length parameter to obtain a quality control filtered nanopore long sequence, where the first sequence length parameter is 1000 bp; S3: removing sequences containing linkers from the original high-throughput sequencing data to obtain filtered high-throughput sequencing data; S4: removing sequences with N content of more than 10% from the filtered high-throughput sequencing data, setting a second quality value filtering parameter, and further removing sequences with base quality values ​​of more than 50% of the removed high-throughput sequences that are lower than the second quality value filtering parameter to obtain high-throughput short sequences after quality control filtering, wherein the second quality value filtering parameter is Q5; S5: hybrid assembly the quality control filtered nanopore long sequence and the quality control filtered high-throughput short sequence, splicing to obtain identified phage sequence information, set a second sequence length parameter, and remove the spliced ​​short sequence whose sequence length is shorter than the second sequence length parameter to obtain a hybrid assembled phage sequence, where the second sequence length parameter is 5000 bp; S6: performing gene prediction on the phage sequence after hybrid assembly to obtain gene characteristic information of the predicted sequence; S7: A step of collating the gene characteristic information of the predicted sequence with a first type database including phage gene characteristic information and a second type database including bacterial gene characteristic information to obtain corresponding collation status data, wherein the first type database is a VPF database, and the second type database is either one or both of a Pfam database and a KEGG database; S8: A step of tabulating and judging the collation situation data, specifically, If the number of genes matching the VPF database in the gene characteristic information of the predicted sequence is greater than or equal to 60% of the total number of genes in the sequence, the sequence is identified as a phage sequence; or identifying the sequence as a phage sequence if the number of gene matches in the VPF database is greater than or equal to the number of gene matches in the Pfam database; or identifying the sequence as a phage sequence if the number of genes matching the VPF database is greater than or equal to 10% of the total amount of genes in the sequence, and the number of genes matching the Pfam database is less than or equal to 40% of the total amount of genes in the sequence, and the number of genes matching the KEGG database is less than or equal to 20% of the total amount of genes in the sequence; S9: A step of downloading a database containing all bacterial genome sequence information, and extracting from the database a spacer sequence that records a phage sequence fragment excised when bacteria defend against phage infection in an existing bacterial genome sequence, the step of the database containing all bacterial genome sequence information being an NCBI database; S10: Using Blast software, the phage sequence identified in step S8 is compared with a spacer sequence that records the phage sequence fragment excised by the bacteria extracted in step S9 when defending against phage infection, and parameters used in the comparison include a coverage of 90%, a degree of identity of 97%, and an error count of 1, and a step of obtaining predicted phage host information.

2. Use of the phage identification and host prediction method according to claim 1 in the microbial analysis of environmental media samples.

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