A method for constructing a dehalogenating microorganism functional genome database and a method for analyzing dehalogenating microorganisms

Abstract

The present application relates to the technical field of bioinformatics, and particularly relates to a construction method of a dehalogenating microorganism functional genome database and an analysis method of dehalogenating microorganisms. The construction method comprises the following steps: obtaining dehalogenation-related genes; obtaining seed sequences and homologous sequences of a family to which the dehalogenation-related genes belong based on a Uniprot protein database to obtain a core database; comparing the core database with a homologous protein database, merging sequences of dehalogenation functional gene families obtained through comparison with the core database to obtain a preliminary database; and comparing the preliminary database to a Refseq database of microorganisms in NCBI, and obtaining a complete database through classification, merging of homologous sequences and de-redundancy. The present application constructs an accurate dehalogenation functional gene database based on analysis and search results of multiple databases, which can be used to conveniently and accurately analyze dehalogenating microorganism functional communities in the environment, and has important application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bioinformatics, and particularly relates to a construction method of a dehalogenating microorganism functional genome database and an analysis method of dehalogenating microorganisms. BACKGROUND

[0002] Organohalides are organic compounds containing fluorine, chlorine, bromine, iodine and other halogens, which are mainly produced by industrial and agricultural processes, such as the production of pesticides, drugs and personal care products, and widely exist in nature, and are toxic, persistent and bioaccumulative. These organic halides are often discharged into the environment through various channels, causing pollution, and long-term harm to human health and ecosystems. Microorganisms in the environment usually exist in the form of communities, and through the interaction between the environment and each other, they drive the global geochemical cycle and significantly change the living environment. These complex microorganisms can participate in the dehalogenation process of organic halides and realize the effective degradation of such substances. Microbial dehalogenation reactions are catalyzed by dehalogenase that cleaves carbon-halogen bonds, and are the key initial step of organic halide mineralization and detoxification.

[0003] The dehalogenase-promoted dehalogenation reaction has a similar catalytic mechanism, which replaces halogen through nucleophilic substitution. There are currently seven main dehalogenation reactions, which are reductive dehalogenation, oxidative dehalogenation, hydrolytic dehalogenation, thiolysis dehalogenation, dehydrohalogenation, methyl transfer dehalogenation and intramolecular substitution reaction, which are under the action of reductase, oxidase (monooxygenase or dioxygenase) and halide dehalogenase, and involve the participation of superoxide radicals, hydrogen electrons and hydroxyl groups in the bond breaking process. Microbial degradation of such complex organic halides involves the action of multiple functional genes and enzymes, such as the dehalogenation of halogenated alkanes catalyzed by dehalogenase encoded by dhmA, dhaAF and linB, and the reductive dehalogenation of chloroform involving enzymes encoded by cfrA gene. With the continuous development of high-throughput sequencing technology, existing technical personnel can clearly understand the classification, function and phylogenetic diversity of various organic halide biodegradation genes through 16S rRNA and metagenomic sequencing methods, and clearly understand the microbial degradation process of various organic halides in complex environments.

[0004] There are also existing methods of constructing dehalogenating microbial functional genome database, such as purification culture, molecular biology and sequencing. The method of purification culture is to screen part of microorganisms in the environmental sample, obtain microorganisms with dehalogenation function, and then further study the degradation capacity, degradation rate and adaptability. Usually, dilution plate method or separation culture method is used for separation, and single microorganism with dehalogenation function is gradually obtained. The molecular biology method directly extracts DNA from the environmental sample, and uses PCR technology to amplify the genes related to dehalogenation reaction, such as rdhA gene (reductive dehalogenase gene), pceA gene (tetrachloroethylene reductase gene), tceA gene (trichloroethylene reductase gene) and the like. Through the detection of these genes, microorganisms with dehalogenation capacity can be quickly screened. The sequencing method can analyze the microbial community through high-throughput sequencing of 16S rRNA gene, and understand the types and community structure of microorganisms with dehalogenation function in the environment. However, most of the dehalogenating microorganisms in the environment are difficult to culture under laboratory conditions, and it is also difficult to isolate these microorganisms, so the bacterial culture method and the PCR amplification method have great limitations, and are time-consuming and laborious. Moreover, these methods are highly dependent on the quality of available sequence databases. Although common public sequence databases such as eggNOG, KEGG, Uniprot and the like contain rich sequence information, there are problems such as inconsistent functional annotation, low coverage of target gene family, difficulty in distinguishing homologous genes and long database search time in the aspect of metagenome analysis. SUMMARY

[0005] In order to solve the problems existing in the prior art, the present application provides a method for constructing a dehalogenating microbial functional genome database and an analysis method of dehalogenating microorganisms.

[0006] In a first aspect, the present application provides a method for constructing a dehalogenating microbial functional genome database, comprising:

[0007] (1) obtaining dehalogenation reaction related genes;

[0008] (2) based on Uniprot protein database, obtaining seed sequences and homologous sequences of the family to which the dehalogenation reaction related genes belong to obtain a core database;

[0009] (3) comparing the core database with the homologous protein database, merging the sequences of the dehalogenation functional gene family obtained by comparison with the core database to obtain a preliminary database;

[0010] (4) comparing the preliminary database to the Refseq database of microorganisms in NCBI, and obtaining a complete database through classification, merging homologous sequences and removing redundancy.

[0011] In recent years, more and more data are generated by sequencing technology, but the existing database cannot meet the needs of dehalogenating microorganism and related functional gene species and abundance analysis, the present application provides a construction method, Uniprot protein database is the most comprehensive protein database at present, all protein entries in Swiss-Prot sub-library are manually annotated by experts and are supported by literatures, the data has high reliability, is the source of protein sequences for constructing seed sequences in the present application, the construction of core database is to compare the seed sequences with TrEMBL database in UniProt protein database, TrEMBL sub-library contains a large number of protein sequences which are not manually annotated and can be used to supplement the seed sequences, collect the protein sequences which are not manually annotated but have dehalogenation function in UniProt database into the core database, then compare the core database with homologous protein databases arCOG, KOG, COG, eggCOG and KEGG database and extract sequences, so as to improve the comprehensiveness of the database, identify homologous gene families and include them into the complete database to reduce false positives that may occur in database retrieval. The five databases selected in the present application, arCOG, KOG and COG databases are all based on expert manual annotation and have high reliability, KEGG and eggNOG databases have the advantages of full-species coverage, supporting large-scale data analysis and system biology analysis, and the five databases all adopt hierarchical functional classification, which is convenient for annotation and cross-study comparison. Finally, the information of bacterial, archaeal and fungal species covered by the database is obtained from the RefSeq database of microorganisms in NCBI, the sequences in NCBI RefSeq database have high quality and non-redundancy, cover a wide range of species and have detailed annotation, and the classification hierarchy is clear, which is convenient for obtaining the species information of protein sequences. Through screening and using of various databases, a dehalogenating microorganism functional genome database with high accuracy is constructed, which has high analysis efficiency and accuracy when applied to the analysis of dehalogenating microorganisms.

[0012] Further, in step (1), the dehalogenation reaction related genes include:

[0013] According to the microbial dehalogenation reaction process in the KEGG database, the dehalogenation reaction related genes are obtained through literature research; the dehalogenation reaction includes one or more of reduction dehalogenation, oxidation dehalogenation, hydrolysis dehalogenation, thiolysis dehalogenation, dehydrohalogenation, methyl transfer dehalogenation or intramolecular substitution reaction dehalogenation.

[0014] Further, the dehalogenation reaction related genes include:

[0015] reduction dehalogenation reaction related genes, oxidation dehalogenation reaction related genes, intramolecular substitution reaction related genes, hydrolysis dehalogenation reaction related genes, dehydrohalogenation reaction related genes, methyl transfer dehalogenation reaction related genes and thiolysis dehalogenation reaction related genes;

[0016] The reductive dehalogenation-related genes include cfrA, cprA, cprA5, dcaA, dcpA, dcrA, DebcprA, linD, pceA and vcrA;

[0017] The oxidative dehalogenation-related genes include bnz, bphA, bphB, bphC, bphD, cbdA, cbdB, cbdC, clcA, cpo, GLG, hadA, LIP, LPOA, mnp, pcpB, tcbC, tecA and tfdC;

[0018] The intramolecular substitution-related genes include hheA, hheB and hheC;

[0019] The hydrolytic dehalogenation-related genes include atzA, caa43, dcmA, dehH, dhaA, dhaAF, dhlA, dhlB, dhlC, dehI, dhmA, fac-dex, fcbB, hadL, HADs, linB and RPA1163;

[0020] The dehydrohalogenation-related genes include linA;

[0021] The methyltransfer dehalogenation-related genes include cmuA;

[0022] The thiolysis dehalogenation-related genes include bbdE, bbdI, gstA, gstB, GST, pcpC and TCHQD.

[0023] The present application is based on personal experience, and 59 dehalogenation-related genes are obtained through literature research. When combined with the foregoing construction method, the dehalogenation microorganism functional genome database constructed by the dehalogenation-related genes has relatively high comprehensiveness and accuracy.

[0024] Further, in step (2), the core database is constructed by the following method:

[0025] The seed sequence of the family to which the dehalogenation-related gene belongs is obtained based on the Swiss-Prot database in the Uniprot protein database; the homologous sequences obtained by aligning the seed sequence with the TrEMBL database are combined with the seed sequence to obtain a core database;

[0026] Preferably, the search for the seed sequence in the Swiss-Prot database is performed by gene name, and the seed sequence includes at least 150 amino acids. ClustalW is used for sequence alignment and development tree file generation during the search process;

[0027] Further preferably, in the TrEMBL database, the homologous sequence id is obtained by searching homologous sequences by Usearch with a homologous cutoff rate > 30%; based on the homologous sequence id, the sequence is extracted by Seqkit.

[0028] Further, in step (3), the homologous protein database comprises one or more of arCOG, KOG, COG, eggNOG, KEGG homologous protein database.

[0029] Preferably, the sequence id of the dehalogen functional gene family is obtained by aligning the homologous protein database using the blastp module of Diamond, and the alignment parameters include: E-value < 10-5, percentage of identical matches > 80, bitscore > 40; based on the sequence id, the sequence is extracted by Seqkit.

[0030] Further, in step (4), the Refseq database of microorganisms in the NCBI comprises the Refseq database of archaea, bacteria and fungi.

[0031] Preferably, the alignment parameters of the preliminary database include: E-value < 10-5, percentage of identical matches > 80.

[0032] Further preferably, the classification of each sequence is determined by TaxonKit to determine the complete classification level information.

[0033] More preferably, the de-redundancy is performed using CD-hit software with the parameter id = 1.0.

[0034] The present application further optimizes each process, and selects software that is stable and efficient for realizing the function, which helps to improve the comprehensiveness and accuracy of the dehalogen microorganism functional genome database constructed by the present application.

[0035] In a second aspect, the present application provides a dehalogen microorganism functional genome database, which is constructed by the aforementioned construction method.

[0036] In a third aspect, the present application provides the aforementioned dehalogen microorganism functional genome database for use in the identification or analysis of dehalogen microorganism functional communities.

[0037] In a fourth aspect, the present application provides a method for analyzing dehalogen microorganisms, comprising:

[0038] (1) extracting total DNA of a sample to be tested, and sequencing and quality control to obtain clean sequences;

[0039] (2) assembling the clean sequences to obtain contig sequences;

[0040] (3) predicting ORFs, clustering to obtain a non-redundant gene set;

[0041] (4) based on the contig sequences, obtaining non-redundant high-quality MAGs by binning, de-redundancy and quality control, and predicting ORFs of the non-redundant high-quality MAGs to obtain MAG protein sequences;

[0042] (5) aligning at least one of the clean sequences, the non-redundant gene set or the MAG protein sequences to the aforementioned dehalogenating microorganism functional genome database to obtain one or more of the following information in the sample to be tested:

[0043] i) composition of microbial dehalogenation functional genes,

[0044] ii) abundance of microbial dehalogenation functional genes,

[0045] iii) composition of dehalogenating microbiome,

[0046] iv) abundance of dehalogenating microbiome.

[0047] Further, the quality control in step (1) comprises:

[0048] using FastQC to check the sequencing quality, and using KneadData to remove adapter sequences, low-quality sequences and host sequences; and / or,

[0049] The assembly in step (2) comprises:

[0050] using Megahit to assemble the clean sequences, and filtering the contig sequences with a length less than 500 bp; and / or,

[0051] In step (3), using Prodigal to predict ORFs, and using CD-Hit to cluster; and / or,

[0052] In step (4), using metaWRAP encapsulating three binning methods of Maxbin2, metaBAT and CONCOCT to bin, using dRep to de-redundancy, and retaining non-redundant high-quality MAGs with completeness ≥ 70% and contamination ≤ 5%; and / or,

[0053] In step (5), Diamond blastx is used for alignment and abundance calculation of microbial dehalogen functional genes for the clean sequences, and Kraken2 is used for species annotation;

[0054] For the non-redundant gene set, Diamond blastx is used for alignment, Salmon is used for abundance calculation of microbial dehalogen functional genes, and Kraken2 and GTDB database are used for species annotation;

[0055] For the MAG protein sequence, Diamond blastp is used for alignment to obtain MAGs containing dehalogen functional genes, and GTDB-tk is used to align the MAGs containing dehalogen functional genes with the GTDBs database to obtain species information.

[0056] Further, in step (1), Trimmomactic in KneadData is used to remove adapter sequences and low-quality sequences, and Bowtie2 is used to remove host sequences.

[0057] Further, for the clean sequences, the sequence depth statistics obtained using the stats module of the Seqkit software are standardized to RPKM.

[0058] Further, for the non-redundant gene set, coverM is used to delete sequences with alignment sequence length <75% and nucleic acid identity <95%, then tpmean is used to calculate the average read depth and normalize according to the number of base pairs; the abundance information and species annotation information are combined to obtain the final microbial classification.

[0059] The present application has the following beneficial effects:

[0060] The present application constructs an accurate dehalogen functional gene database based on the analysis and search results of multiple databases, and based on the dehalogen functional gene database, a complete bioinformatics data analysis process of in-situ dehalogen microbial functional community is established by combining functional gene analysis and host information recognition, which can accurately and effectively analyze the dehalogen microbial functional community in the environment. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0062] Figure 1 is a schematic diagram of the method for constructing a dehalogenating microorganism functional genome database and further analyzing dehalogenating microorganisms according to Embodiment 1 of the present application.

[0063] Figure 2 is a comparison result of the accuracy, sensitivity, false negative rate and false positive rate of DehaloDB, KEGG, COG, KOG, eggNOG and arCOG databases according to Embodiment 2 of the present application. DETAILED DESCRIPTION

[0064] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0065] In the following examples, if not specifically mentioned, all the experimental methods are conventional methods in the art, for example, refer to the experimental manual in the art or the conditions suggested in the manufacturer's instruction.

[0066] In the following examples, if not specifically mentioned, all the experimental materials and reagents can be obtained from commercial channels.

[0067] Embodiment 1

[0068] The present application provides a method for constructing a dehalogenating microorganism functional genome database based on metagenomic samples of industrial wastewater treatment plants and further analyzing dehalogenating microorganisms (as shown in Figure 1 , which comprises the following processes:

[0069] 1. Construction of dehalogenating microorganism functional genome database

[0070] (1) According to literature research, all genes participating in seven dehalogenation reactions are summarized according to the microbial dehalogenation reaction processes provided in the KEGG database.

[0071] These dehalogenation reaction processes include reduction dehalogenation, oxidation dehalogenation, hydrolysis dehalogenation, thiolysis dehalogenation, dehydrohalogenation, methyl transfer dehalogenation and intramolecular substitution reaction dehalogenation.

[0072] The seven dehalogenation reactions obtained all genes include: reductive dehalogenation reactions: cfrA, cprA, cprA5, dcaA, dcpA, dcrA, DebcprA, linD, pceA, vcrA; oxidative dehalogenation reactions: bnz, bphA, bphB, bphC, bphD, cbdA, cbdB, cbdC, clcA, cpo, GLG, hadA, LIP, LPOA, mnp, pcpB, tcbC, tecA, tfdC; intramolecular substitution reactions: hheA, hheB, hheC; hydrolytic dehalogenation reactions: atzA, caa43, dcmA, dehH, dhaA, dhaAF, dhlA, dhlB, dhlC, dehI, dhmA, fac-dex, fcbB, hadL, HADs, linB, RPA1163; dehalogenation reactions: linA; methyltransfer dehalogenation reactions: cmuA; thiolysis dehalogenation reactions: bbdE, bbdI, gstA, gstB, GST, pcpC, TCHQD.

[0073] (2) Construct seed sequences: obtain the seed sequences of the gene families of all genes of the aforementioned seven dehalogenation reactions from the Swiss-Prot database in the Uniprot protein database, search by gene name, and the selected sequences include at least 150 amino acids, and ClustalW is used for sequence alignment during the search process and generate a phylogenetic tree file;

[0074] Compare the seed sequences with the TrEMBL database in the Uniprot protein database, search for homologous sequences of the seed sequences in TrEMBL using Usearch software, set the homologous cutoff rate id=30%, obtain a file containing the homologous sequence id, extract the homologous sequences according to the id through Seqkit software, and download the obtained homologous sequences to merge with the seed sequences to obtain a core database (Core database).

[0075] (3) Compare the core database with the existing homologous protein databases arCOG, KOG, COG, eggNOG, KEGG, and the sequence alignment software used is the blastp module of Diamond, and the alignment parameters are set to meet E-value<10 -5 , percentage of identical matches>80, bitscore>40 to filter the sequences to obtain sequence ids belonging to the dehalogenation functional gene family, then use Seqkit again to extract the sequences and merge with the core database to form a preliminary database (pre-full database).

[0076] (4) The preliminary database is compared to the Refseq database of microorganisms in NCBI (E-value <10 -5 , percentage of identical matches >80), the taxonomic summary of the preliminary database is classified, the coverage of dehalogen functional species is determined, and TaxonKit is used to determine the complete classification level information of each sequence. Homologous sequences are identified, extracted, and merged to generate a complete database (Full database).

[0077] (5) The obtained sequences are subjected to sequence redundancy removal using CD-hit software with parameter id=1.0 to form a complete database (DehaloDB).

[0078] 2. Sample collection and pretreatment

[0079] Different types of industrial wastewater treatment plants in an industrial park were selected as research objects, and influent, biological unit sludge-water mixture, and secondary sedimentation tank effluent samples were collected. DNA in the water samples was filtered by 0.22 μm mixed cellulose ester filter membranes (Millipore, Germany). After 3-4 filter membranes were obtained from each sample point, they were stored in a-80℃ refrigerator for DNA extraction and sequencing.

[0080] 3. DNA extraction and metagenomic sequencing

[0081] The 0.22 μm filter membranes were cut into 2-3 mm squares and transferred to DNA extraction tubes of a DNA extraction kit. DNA extraction was performed using a PowerSoil DNA Isolation Kit (Mo Bio, USA), and the specific operation was described in the operating instructions of the kit. The qualified DNA samples were used for library construction. The qualified library was subjected to PE150 sequencing using an Illumina HiSeq 2500 high-throughput sequencing platform to obtain raw sequencing sequences (Raw reads).

[0082] 4. Metagenomic data analysis

[0083] 4.1 Metagenomic sequence quality control, assembly, and binning

[0084] (1) First, the overall quality of raw reads was checked using FastQC (v0.11.9) software, and the quality control of sequences was performed using the KneadData pipeline, including the removal of double-end adapter sequences and low-quality sequences by Trimmomactic (v0.39) software (parameter settings: SLIDINGWINDOW:4:20 MINLEN:50) and the removal of human host contamination by Bowtie2 (v2.3.6) software.

[0085] (2) Clean reads were de novo assembled using the software MEGAHIT (v1.2.9) (default parameter settings), and contigs were obtained. Contigs with a length of 500 bp or more were screened for subsequent analysis.

[0086] (3) The software Prodigal (v2.6.3) was used to predict ORFs for contigs (parameter settings: -p meta), and the software CD-Hit (v4.8.1) was used to cluster the obtained ORFs (parameter settings: -aS 0.9 -c 0.95), obtaining a non-redundant gene set. The software Salmon (v1.4.0) was used to calculate the reads count of each gene and further standardize it to obtain the TPM value, which was used to represent the abundance information of the gene in each sample.

[0087] (4) Based on the quality-controlled clean reads and assembled contigs, the software metaWRAP (v1.3.2) (which encapsulates three binning methods: Maxbin2, metaBAT, and CONCOCT) was used to extract and purify the microbial genome sketch (MAGs) in each sample, and the gene sequences of the MAGs were predicted. The threshold for screening MAGs was set to contamination ≤5% and completeness ≥70%, and the software dRep (v3.5.0) was used to remove redundancy from the obtained MAGs (parameter settings: -sa 0.95, -nc 0.30).

[0088] 4.2 Annotation of metagenomic sequencing data by DehaloDB database:

[0089] (1) Short sequence annotation and abundance calculation: The blastx algorithm of the Diamond software was used to align the double-end sequences to the constructed DehaloDB database, with the specific parameter settings being E-value <10 -10, percentage of identical matches >80, the sequence depth statistics were obtained using the stats module of Seqkit software, and the relative abundance of dehalogenase genes was standardized to RPKM (transcripts per million mapped reads per kilobase). The obtained sequences were annotated with species using Kraken2 (using the Kraken2 full database or GTDB database).

[0090] (2) Assembly sequence annotation and abundance calculation: The protein sequences of the non-redundant gene set were aligned with the DehaloDB database using the blastp algorithm of the Diamond software, and the parameters selected were the same as described in (1). Species annotation was performed using Kraken2; coverM was used to delete sequences with alignment lengths <75% and nucleic acid identity <95%, and then tpmean in coverM was used to calculate the average read depth and standardize it according to the number of base pairs. The abundance information was combined with the species annotation information to obtain the final classification.

[0091] (3) Genome sequence annotation and abundance calculation: The obtained MAGs were aligned with the DehaloDB database using the blastp algorithm of the Diamond software, and the parameters selected were the same as in (1) above. GTDB-tk was used to align them with the GTDBs database to obtain species information. coverM was used to calculate the relative abundance and coverage of the MAGs.

[0092] Example 2

[0093] The present application constructed an artificial protein sequence dataset, which collected 400 protein sequences from the NCBI database, including 150 microbial dehalogenase functional protein sequences, 50 non-target homologous gene protein sequences, and 200 protein sequences completely unrelated to dehalogenation reactions. The artificial dataset was used to calculate the false-positive rate (false-positive rate) and false-negative rate (false-negative rate), and the sequence was incorrectly annotated as a false positive for a functional gene, and the sequence was not annotated but actually had a target functional gene, which was considered as a false negative. Diamond was used to search the artificial dataset in COG, arCOG, KOG, and DehaloDB, with E value ≤10 -5 , sequence identity >40%, eggNOG-mapper was used to search in the eggNOG database, with E value ≤10 -5 , KofamKOALA, a functional annotation tool provided by the KEGG database, was used to search the artificial dataset in the KEGG database, with E value ≤10 -5 , in order to compare the annotation accuracy of these databases.

[0094] Results as shown in Table 2 Figure 2 As shown in Table 2: the present application evaluates DehaloDB, KEGG, COG, KOG, eggNOG and arCOG databases using artificial dataset. The sensitivity evaluation results show that DehaloDB database correctly annotates all sequences belonging to dehalogenase functional gene family into dehalogenase functional gene family; the accuracy results show that almost all dehalogenase functional sequences are annotated into correct dehalogenase functional gene family, and other non-dehalogenase functional genes and non-target homologous gene sequences are not annotated (accuracy is 98.3%).

[0095] The public ortholog database is not as good as DehaloDB in dehalogenase functional gene annotation, among which the annotation accuracy of eggNOG database is the highest in public ortholog databases, but it is less than 50%, only 47.23%, and has 50.67% false negative; the sensitivity of COG database is very high (75.33%), but many homologous sequences are misjudged as dehalogenase functional genes because of its very high false positive rate. The sensitivity and accuracy of KOG and arCOG sequences of fungi and archaea to dehalogenase function are very low. Therefore, DehaloDB provided in embodiment 1 of the present application has higher sensitivity and accuracy in microbial dehalogenase functional gene analysis than other ortholog databases.

[0096] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for constructing a dehalogenase microbial functional genome database, characterized by, Comprising: (1) According to the microbial dehalogenation reaction process in KEGG database, the dehalogenation reaction related genes are obtained through literature research; The dehalogenation reaction includes one or more of reduction dehalogenation, oxidative dehalogenation, hydrolytic dehalogenation, thiolysis dehalogenation, dehydrohalogenation, methyl transfer dehalogenation or intramolecular substitution reaction dehalogenation (2) The seed sequences of the dehalogenation reaction related gene family are obtained based on the Swiss-Prot database in Uniprot protein database; the homologous sequences obtained by aligning the seed sequences with TrEMBL database are combined with the seed sequences to obtain a core database; in step (2), the search of the seed sequences in the Swiss-Prot database is carried out by gene name, and the seed sequences include at least 150 amino acids, and ClustalW is used for sequence alignment and development tree file generation during the search process; In the TrEMBL database, the homologous sequence id is obtained by searching the homologous sequence by Usearch, and the homologous cutoff rate is > 30%; based on the homologous sequence id, the sequence is extracted by Seqkit; the sequence id of the dehalogen functional gene family is obtained by using the blastp module of Diamond to align the homologous protein database, and the alignment parameters include: E-value < 10 -5 , percentage of identical matches > 80, bitscore > 40; based on the sequence id, the sequence is extracted by Seqkit; (3) The core database is aligned with homologous protein databases arCOG, KOG, COG, eggNOG and KEGG, and the sequences of the dehalogenation functional gene family obtained by alignment are combined with the core database to obtain a preliminary database; (4) aligning the preliminary database to the Refseq database of microorganisms in NCBI to obtain a complete database through classification, merging of homologous sequences and de-redundancy; in step (4), the Refseq database of microorganisms in NCBI includes the Refseq databases of archaea, bacteria and fungi; the alignment parameters of the preliminary database include: E-value < 10 -5 , percentage of identical matches > 80; the complete classification level information of each sequence is determined through classification by TaxonKit; de-redundancy is performed using CD-hit software with the parameter id = 1.

0.

2. The construction method of claim 1, wherein, The dehalogenation reaction related genes include: reduction dehalogenation reaction related genes, oxidative dehalogenation reaction related genes, intramolecular substitution reaction related genes, hydrolytic dehalogenation reaction related genes, dehydrohalogenation reaction related genes, methyl transfer dehalogenation reaction related genes and thiolysis dehalogenation reaction related genes; The reduction dehalogenation reaction related genes include: cfrA, cprA, cprA5, dcaA, dcpA, dcrA, DebcprA, linD, pceA and vcrA; The oxidative dehalogenation reaction related genes include: bnz, bphA, bphB, bphC, bphD, cbdA, cbdB, cbdC, clcA, cpo, GLG, hadA, LIP, LPOA, mnp, pcpB, tcbC, tecA and tfdC; The intramolecular substitution reaction related genes include: hheA, hheB and hheC; The hydrolytic dehalogenation reaction related genes include: atzA, caa43, dcmA, dehH, dhaA, dhaAF, dhlA, dhlB, dhlC, dehI, dhmA, fac-dex, fcbB, hadL, HADs, linB and RPA1163; The dehydrohalogenation reaction related genes include: linA; The methyl transfer dehalogenation reaction related genes include: cmuA; The thiolysis dehalogenation reaction related genes include: bbdE, bbdI, gstA, gstB, GST, pcpC and TCHQD.

3. A dehalogenating microbial functional genome database, characterized by, The dehalogenation microbial functional genome database is constructed by the construction method of any one of claims 1-2.

4. The dehalogenation microbial functional genome database of claim 3 is used in the identification or analysis of dehalogenation microbial functional community.

5. A method for analyzing dehalogenating microorganisms, characterized by, Comprising: (1) Extracting total DNA of the sample to be tested, and performing sequencing and quality control to obtain clean sequences; (2) Assembling the clean sequences to obtain contig sequences; (3) Predicting ORFs, clustering to obtain a non-redundant gene set; (4) Based on the contig sequences, obtaining non-redundant high-quality MAGs by binning, de-redundancy and quality control, and predicting ORFs of the non-redundant high-quality MAGs to obtain MAG protein sequences; (5) Aligning at least one of the clean sequences, the non-redundant gene set or the MAG protein sequences to the dehalogenating microorganism functional genome database of claim 3 to obtain one or more of the following information in the sample to be tested: i) composition of microbial dehalogenation functional genes, ii) abundance of microbial dehalogenation functional genes, iii) dehalogenating microbial composition, iv) dehalogenating microbial abundance.

6. The method of claim 5, wherein, The quality control in step (1) includes: using FastQC to check the sequencing quality, using Kneaddata to remove adapter sequences, low-quality sequences and host sequences; and / or, The assembly in step (2) includes: using Megahit to assemble the clean sequences, and filtering the overlapping sequences with a length less than 500 bp; and / or, In step (3), using Prodigal to predict ORFs, and using CD-Hit to cluster; and / or, In step (4), using metaWRAP encapsulating three binning methods of Maxbin2, metaBAT and CONCOCT to bin, using dRep to de-redundancy, and retaining non-redundant high-quality MAGs with completeness ≥ 70% and contamination ≤ 5%; and / or, In step (5), for the clean sequences, Diamond blastx is used for alignment and abundance calculation of microbial dehalogenation functional genes, and Kraken2 is used for species annotation; For the non-redundant gene set, Diamond blastx is used for alignment, Salmon is used for abundance calculation of microbial dehalogenation functional genes, and Kraken2 and GTDB database are used for species annotation; For the MAG protein sequence, Diamond blastp is used for alignment to obtain MAGs containing dehalogenation functional genes, and GTDB-tk is used to align the MAGs containing dehalogenation functional genes with GTDBs database to obtain species information.

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