WO2012068949A1 - Classification method based on the metagenome 16s high variable region v6 - Google Patents

Abstract

Disclosed is a classification method for analyzing a microorganism population by using a Solexa sequencing platform to sequence the metagenome 16S rDNA high variable region V6.

Description

 Classification method based on metagenomic 16S hypervariable region V6

 The invention relates to the technical field of microbial gene sequencing analysis, in particular to a classification method based on the hypergenome 16S hypervariable region V6. Background technique

 In order to study the types of microbial populations in biological environments, conventional methods include: direct microbial culture, DGGE, Denaturing Gradient Gel Electrophoresis, end-restricted end-cut fragment length polymorphism (T- RFLP, Terminal Restriction Fragment Length Polymorphism, Fluorescence In Situ Hybridization, PCR (Polymerase Chain Reaction) for possible microbial species; however, these methods can only be revealed in the environment. A small part of the microbial species. If the metagenomic analysis can be carried out, a comprehensive catalogue of microbial species can be obtained by directly conducting genome research on the microbial population in the environment, which is helpful for the subsequent research and application of the microbial population.

 Because the sequence of 16S rRNA (RiboNucleic Acid) in prokaryotes is highly conserved, it can accurately indicate the phylogenetic relationship between bacteria; the size of 16S rRNA is about 1500bp ( ^JJit, Base Pair ). Containing information can reflect the evolutionary relationship of the biological world, easy to operate, suitable for all levels of taxon; so in the study of metagenomics, 16S region sequencing is the most commonly used clustering and classification methods. The traditional metagenomic sequencing is performed by Sanger technology sequencing 16S rRNA gene ( 16S rDNA ) to obtain a read length of at least 500 bp. This read length is long enough to assemble a nearly complete 16S rDNA sequence, helping us to accurately study each A sequence of species sources, but it is prone to chimerism, and the cost of sequencing is relatively high, time consuming and laborious.

With the newly developed sequencing technology and the gradual reduction in sequencing costs, the metagenomics Research has become more and more practical, and the technologies involved include Pyrosequencing, Solexa, etc. One of the main challenges for these revolutionary technologies is that they are too short to sequence each individual's 16S rDNA, so its sequencing information is not sufficient for us to accurately classify microorganisms.

 In summary, it is a technical problem to be solved in the art to provide a more accurate method for cluster analysis of microorganisms, which is convenient, fast, and low in cost. Summary of the invention

 One technical problem to be solved by the present invention is to provide a classification method based on the macrogen 16S hypervariable region V6, by performing solexa sequencing on the high variable region V6 region of 16S, and by short sequences of these 16S variable regions. Systematic classification can accurately reflect species abundance information on a low cost basis.

 One aspect of the present invention provides a classification method based on the hypergenome 16S hypervariable region V6, the method comprising: deoxyribonucleic acid DNA for extracting microorganisms; and high primer 16G ribosomal deoxyribonucleic acid rDNA Transformer V6 performs polymerase chain reaction PCR, labeling each sample; mixing PCR products of different samples; performing Solexa library construction on the mixed PCR products; The library of the variable region V6 is subjected to double-end pair-end sequencing to obtain a sequencing sequence; the sequencing sequence is screened to filter out low-quality sequencing sequences; based on the sequencing sequence, the relationship of the contig is used to the high-variable region V6 The full-length sequence is assembled; the sequencing sequence is assigned to the corresponding sample by the tag sequence; and the sequencing sequence is analyzed to achieve high-throughput classification of the microbial population by sequencing using the high variable region.

 In one embodiment of the method for classifying a macrogenome 16S hypervariable region V6 provided by the present invention, the method further comprises: performing sampling of the microbial population prior to extracting the deoxyribonucleic acid DNA of the microorganism.

In an embodiment of the method for classifying the macrogen 16S hypervariable region V6 provided by the present invention, the method further comprises: after classifying the sequencing sequence, The sequences of different degrees of difference were classified by the operational taxonomic unit OTU; based on the tag sequence and the sequencing sequence, the diversity analysis of the population diversity estimation Chaol algorithm and angiotensin converting enzyme ACE was performed.

 In one embodiment of the method for classifying the macrogenome 16S hypervariable region V6 provided by the present invention, after analyzing the diversity of the population diversity estimation Chaol algorithm and the angiotensin converting enzyme ACE, the diversity analysis of the output microbial population Figure and relative abundance map.

 In one embodiment of the method for classifying a macrogenome 16S hypervariable region V6 provided by the present invention, the step "polymerase chain reaction PCR is performed on the hypervariable region V6 of the metagenomic 16S ribosomal deoxyribonucleic acid rDNA by a primer, And labeling the sequence for each sample" further includes: using primers 967f: CNACGCGAAGAACCTTANC (Seq ID ΝΟ: 1) and 1406R: GACAGCCATGCANCACCT (Seq ID NO: 2) to replicate the 16S hypervariable region V6 region of the bacteria in the microbial population Fragment; tag sequence for each microbial sample, the tag sequence to the front of the 5, end of primer 967f, and 1⁄2 GT between the tag sequence and primer 967f.

 In one embodiment of the method for classifying a macrogenome 16S hypervariable region V6 provided by the present invention, the method further comprises: using polymerase chain reaction PCR for the hypervariable region V6 of archaea, using primer 958AR: AATTGGANTCAACGCCGG (Seq ID NO: 3) and 1048AR: CGRCGGCCATGCACCWC (Seq ID NO: 4).

 In one embodiment of the method for classifying a macrogenome 16S hypervariable region V6 provided by the present invention, the step of "mixing PCR products of different samples" further comprises: quantifying the concentration of the PCR product of the 16S high variable region V6 ; and mixed together in equimolar amounts.

In one embodiment of the method for classifying the macrogen 16S hypervariable region V6 provided by the present invention, the step of "building a library for the mixed PCR product by Solexa" further comprises: purifying the mixed product, and repairing the end, Add base at 3, end Base A, plus a double-end Pair-end sequencing adaptor; after the addition of the linker, the sample is purified; the purified sample is dissolved, and used as a template for polymerase chain reaction PCR amplification; and the polymerase chain The reaction PCR product was subjected to gel purification.

 In one embodiment of the method for classifying a macrogenome 16S hypervariable region V6 provided by the present invention, low quality data: a sequence that does not match the nearest primer, a sequence that is less than 50 1⁄2 pairs, or has at least one ambiguity The sequence of the base.

 In one embodiment of the method for classifying a macrogenome 16S hypervariable region V6 provided by the present invention, the step of "assembling the full-length sequence of the hypervariable region V6 by using the relationship of contigs" further comprises: employing a hypervariable region The PCR product 5 of V6, the first 75, 70, 65, 60 and 55 bases of the ends are overlapped for assembly; wherein, the standard of assembly is that a pair of sequences has an overlap length of more than 5 base pairs and less than 10% in the overlap region. The degree of mismatch.

 In one embodiment of the method for classifying a macrogenome 16S hypervariable region V6 provided by the present invention, the step of "classifying the sequence by sequencing" further comprises: comparing the sequencing sequences assigned to the corresponding sample to the existing 16S In the v6 database, high-throughput classification analysis of microbial populations is achieved by label sequencing using high variable regions, and the structure of the microbial population is studied.

 The present invention provides a high-throughput sequencing of microbial populations in a specific environment based on the method of classification of the metagenomic 16S hypervariable region V6, which combines the labeling technology with Solexa technology, which reduces labor and economics. The cost makes it easy to study the relationship between microbial community structure and health, environmental factors, and the like. DRAWINGS

 FIG. 1 is a flowchart of a method for classifying a hypergen group 16S hypervariable region V6 according to an embodiment of the present invention;

2 is a flow chart showing another embodiment of a classification method based on the metagenomic 16S hypervariable region V6 provided by the present invention; Figure 3 shows the number of OTUs in the case of a microbial population of 0.03 and 0.3 in different environments. detailed description

 The invention will now be described more fully hereinafter with reference to the accompanying drawings

 FIG. 1 is a flow chart showing a method for classifying a hypergen group 16S hypervariable region V6 according to an embodiment of the present invention.

 As shown in Fig. 1, the classification method flow 100 based on the metagenomic 16S hypervariable region V6 includes:

Step 102, extracting DNA DNA of the microorganism. Example, DNA of microorganisms using Ultraclean Soil DNA kit kit (MoBio, USA) extracted from the sample sediment ₀

 In step 104, the high variable region V6 of the macrogen 16S ribosomal deoxyribonucleic acid rDNA is introduced by a primer (there is a 20 1⁄2 pair of bp conserved regions at both ends of the region, and the intermediate variable region is about 60-90 bp). Polymerase chain reaction PCR and labeling sequences for each sample. For example, primers 967f: CNACGCGAAGAACCTTANC (Seq ID ΝΟ: 1 ) and 1406R: GACAGCCATGCANCACCT (Seq ID NO: 2) are used to replicate fragments of the 16S hypervariable region V6 region of the bacteria in the microbial population; and each microbial sample is tagged , the tag sequence is to the front of the 5' end of the primer 967f, and 1⁄2 GT (ie 1⁄2 G and T) is added between the tag sequence and the primer 967f. The tag sequence may be a bar code sequence consisting of 8 bases, and the tag sequence is designed to conform to certain rules, such as base content and number of bases, etc., in order to prevent tags from being mutually related due to individual sequencing errors and the like. Confusion, for example, can be found in the methods and principles disclosed in U.S. Patent Application No. US20100267043A1.

In one embodiment of the method for classifying the macrogenome 16S hypervariable region V6 provided by the present invention, for polymerase chain reaction PCR of the hypervariable region V6 of archaea, Primer 958AR was used: AATTGGANTCAACGCCGG (Seq ID NO: 3) and 1048AR: CGRCGGCCATGCACCWC (Seq ID NO: 4) „

 Step 106, mixing the PCR products of different samples. For example, the PCR product of the 16S high variable region V6 is quantified using a spectrophotometer (e.g., Nanodrop) and then mixed together in equimolar amounts.

 Step 108: Perform a Solexa database construction method on the mixed PCR product. For example, the mixed product is purified with QIAquick PCR purification Kit (Qiagen), end-repaired (ie, the viscous end of all DNA duplexes is blunt-ended by enzymatic reaction), plus "A", plus Pair-end linker (Pair -end library preparation kit, IUumina ); after the addition of the linker, the sample is purified; the purified sample is dissolved, and polymerase chain reaction PCR amplification (12 cycles) is used as a template; and (QIAquick gel extraction kit) , Qiagen) Gelatinization of the polymerase chain reaction PCR product (ie, spot electrophoresis, gelatinization at the DNA position, purification with a kit).

In step 110, the library of the high variable region V6 is subjected to pair-end sequencing using a Solexa sequencing tool (such as IUumina GA, illumina GA2, illumina Hiseq2000, illumina Hiseq 1000, etc.) to obtain the original sequencing sequence data. For example, sequencing is performed directly using the Illumina GA II (75 bp pair-end strategy). The Illumina genome analyzer is a new generation of high-throughput sequencer with low-cost sequencing and high data readout. Solexa sequencing costs one-tenth the cost of 454 sequencing with the same amount of sequencing. Moreover, the error rate is low (such as single-base sequencing error rate <10 ^{_ 5} ), sequencing is unbiased, and for the metagenomics, the abundance information of the species can be truly reflected.

 In step 112, the sequencing sequence data is screened to filter out low quality sequencing sequences. For example, the low quality sequencing sequence is selected from any of the following sequencing sequences: a sequencing sequence that does not match the nearest primer, a sequencing sequence of less than 50 bp, or a sequencing sequence that has at least one cryptogenic base.

Step 114: Perform a full-length sequence of the high variable region V6 by using the relationship of the contigs Assembly. For example, the PCR product 5 of the high variable region V6, the first 75, 70, 65, 60 or 55 base pairs bp of the ends are overlapped and assembled; wherein the assembly standard may be a pair of sequences having an overlap length of more than 5 bp. And a mismatch of less than 10% in the overlap area (ie, a match of more than 90%).

 Step 116, assigning the sequencing sequence to the corresponding sample by the tag sequence. Step 118: Perform high-throughput classification of the microbial population by performing classification analysis on the sequencing sequence to achieve sequencing using the high variable region. For example, the sequencing sequence assigned to the corresponding sample is compared to the database 16S v6 database refhvr-V6 by GAST software to achieve high-throughput classification analysis of the microbial population using high-variable region tag sequencing, and then to study the microorganism The structure of the group.

 The present invention provides a classification method based on the macrogen 16S hypervariable region V6, which is classified on the basis of the best matching by comparing the sequence of the 16S hypervariable region with the rRNA database. The classification method can provide information on the composition and diversification of the microbial population, and has the same technical effect as the 16S measurement full length in the microbial classification and the relative abundance of the measured population; in addition, the present invention can be found by using a large number of parallel sequencing. More rare microbial species.

 Further, since Solexa has a read length of about 75 bp, it has a large throughput and produces a large amount of data. This method has a good cost-effectiveness in exploring changes in the structure of microbial communities (including thin biological mites).

 Fig. 2 is a flow chart showing another embodiment of the classification method based on the metagenomic 16S hypervariable region V6 provided by the present invention.

 As shown in FIG. 2, the classification method 200 based on the metagenomic 16S hypervariable region V6 includes: Steps 201, 202-218, 219, and 220, wherein steps 202-218, 204, 206, and 208 can be performed separately from FIG. The same or similar technical contents of the steps 102-118 shown are for the sake of clarity, and the technical contents thereof will not be described herein.

As shown in Fig. 2, before step 202 "Extracting the DNA DNA of the microorganism", step 201, sampling of the microbial population is performed. For example, a precipitate is taken from a water such as a lake as a sample. After the step 218 "by classifying the sequence of the sequencing", step 219 is performed to classify the sequences of the different degrees of difference by the operational taxonomy unit (OTU). For example, using the V丄6.0 version of Mothur software (download at http://www.mothur.org/wiki/Main_Page), the GAST-OTU strategy is used to classify OTUs for sequences of different degrees of difference.

 Step 220: Based on the tag sequence and the sequencing sequence, the diversity analysis of Chaol and Angiotensin Converting Enzyme (ACE) was performed using Mothur. Canoco software.

 The present invention provides a classification method based on the macrogen 16S hypervariable region V6. Although the sequencing sequence of the 16S hypervariable region V6 measured by Solexa is short and does not contain sufficient evolutionary information to infer the systematic classification, the present invention utilizes Search software such as GAST, Mothur software, etc., achieve high-throughput classification analysis of microbial populations by tag sequencing using high variable regions by aligning the sequencing sequences of each sample into the database 16S v6 region database refhvr_V6. In summary, the use of Solexa sequencing technology to sequence microbial samples provides a good balance of throughput, cost, and effective classification. In addition, the sequencing technology used in the present invention combines tag sequences with greatly improved resolution, a single Run on Solexa (IUumina) produces 100-fold more sequencing sequences than 454. Therefore, it is possible to obtain a good classification effect only by sequencing the 16S rRNA V6 region in such a short length, and because of the combination of the labeling technique, the measured length is relatively short, so that it can be in a single Lane (IUumina high-throughput sequencer) The chip has 8 channels, each channel is called "lane", which is more expensive, which greatly saves the cost of sequencing each sample.

 Next, a specific embodiment of the classification method based on the metagenomic 16S hypervariable region V6 provided by the present invention will be described in detail.

 Step 1. Sampling the microbial population.

Specifically, extracts from Shenzhen-Beishan Reservoir sediments, Shenzhen-Xianhu Botanical Garden sediments, Shenzhen-Mangrove sediments, Shenzhen-Dameisha sediments, Shenzhen-Longganghe sediments, Shenzhen-Sewage treatment plant sediments , Shenzhen-Donghu Park sediments, A total of 65 samples.

 Step 2. Extract the DNA of the microbial sample.

 Specifically, all sediment DNA was extracted from fresh or deep frozen sediment samples using the Ultraclean Soil DNA kit (MoBio, USA).

 Step 3. Use specific primers for PCR amplification and add a sequence tag to each sample.

 Specifically, primers 967f: CNACGCGAAGAACCTTANC (Seq ID ΝΟ: 1 ) and 1406R: GACAGCCATGCANCACCT (Seq ID NO: 2) were used to replicate the 16S V6 region fragment of the bacterium in the microbial population. Since ^ needs to be mixed and sequenced for all microorganisms, a label sequence can be added to each sample. This sequence can be a modified base barcode sequence consisting of 8 bases, which is in front of the 5' end of primer 967f. A linker "GT" is added between the tag sequence (barcode sequence) and the primer 967f.

 In addition, for the polymerase chain reaction PCR product of the V6 region of archaea, primers 958AR: AATTGGANTCAACGCCGG (Seq ID NO: 3) and 1048AR: CGRCGGCCATGCACCWC (Seq ID NO: 4) can be used, followed by the same method for microorganisms. Sample plus barcode sequence and "GT" connector.

 Step 4. Mix the PCR products of the sample and use the optimized Solexa library for the mixed PCR products.

 Specifically, for the PCR product of the 16S V6 region to which the barcode label is attached, the concentration is quantified using a spectrophotometer Nanodrop, and then mixed in equimolar amounts. In this embodiment, a total of 65 samples of the PCR product of 52 bacteria V6 and the PCR product of V6 of 13 archaea are mixed together.

These mixed products were purified by kit: QIAquick PCR purification Kit (Qiagen), terminal repair, base A at the 3' end, plus paired-end Pair-end, 】 -end library preparation kit, Illumina ) „ After the fitting is completed, the sample is purified and eluted with 30 μΙ^ Solution, Elution buffer). The extracted solution was then used as a template for PCR amplification (12 cycles). The PCR product was gel purified using a QIAquick gel extraction kit (Qagen).

 Step 5. Solexa sequencing. Specifically, sequencing can be performed directly with Illumina GA II according to the manufacturer's Illumina instructions (75 bp pair-end strategy, ie, 75-base double-end sequencing), as shown in Table 1.

 Table 1 Sample Name - Label Sequence - Solexa reads

 Total reads specific sample name tag sequence note

 Number of reads Shenzhen - Beishan Reservoir samples amplified by bacteria v6 region primers

Beishan Reservoir — 1 AACGGCAA 76,019 30,309 Beishan Reservoir — 2 AAGGAACC 71,051 24,714 Beishan Reservoir — 3 AATTGCGC 60,441 23,558 Beishan Reservoir — 4 ACAGACTC 83,156 32,553 Beishan Reservoir — 5 ACTCAGAC 74,639 26,703 Beishan Reservoir — 6 CACTACTC 84,390 31,436 Beishan Reservoir — 7 CACTAGAC 77,874 28,372 Beishan Reservoir — 8 CAGTGTCA 74,475 30,909 Beishan Reservoir — 9 GGAAGCAT 38,817 15,630 Beishan Reservoir — 10 GTAGCATC 82,994 30,159 Beishan Reservoir — 11 GTCTTGAG 66,029 25,175 Beishan Reservoir — 12 GTTCCATC 54,770 21,399 Beishan Reservoir — 13 GTTCCTAC 47,365 19,466 Beishan Reservoir — 14 TGCTCATC 55,725 21,762 Beishan Reservoir — 15 TGGTTGCA 57,097 23,625 Beishan Reservoir — 16 TTATCCGC 29,660 13,013 Beishan Reservoir — 17 TTCGCCAT 36,927 15,879 Beishan Reservoir — 18 TTGCGGTA 38,178 19,324 Shenzhen-Donghu Park and Shenzhen-Mangrove samples are amplified by bacterial v6 region primers

Donghu Park 1 ACGATCGT Sampling at Donghu Park Lawn 85,018 28,387 Donghu Park 2 CAAGTGCT Sampling at Donghu Park Lawn 85,926 26,677 Donghu Park 3 TGTGAGAG Sampling at Donghu Park Lawn 74,697 23,953 Mangrove 1 TGCTCTAC Sampling around mangrove roots 57,215 20,566 Mangrove 2 GTTGGATC Sampling around the roots of mangrove trees 96,113 31,700 Shenzhen-Xianhu Botanical Garden, Shenzhen-Longgang River and Shenzhen-Sewage Treatment Plant samples were amplified by bacteria v6 region primers Xianhu Botanical Garden 1 CAGACTCA Sampling at Xianhu Botanical Garden Flowers 79,151 25,651 Xianhu Botanical Garden 2 CCATGGAT Sampling at Xianhu Botanical Garden, 81,335 26,120 Xianhu Botanical Garden 3 TTGCTACC Sampling at Xianhu Botanical Garden 65,935 21,413 Longgang River 1 GTACACGT Sampling at Longgang River Sludge 91,359 27,729 Longgang River 2 GTAGCTAC Sampling at Longgang River Sludge 75,548 23,583 Sewage Treatment Plant 1 GTCAGTCA Sampling at wastewater treatment plant wastewater inlet 70,169 23,649 Sewage treatment plant 2 GTGTACTG Sampling at wastewater treatment plant wastewater inlet 72,584 21,994 Above Shenzhen-Mangrove sample DNA Amplification with bacterial v6 region primer

Mangrove 1 a AATTGCCG with mangrove 1 sample DNA as template 75,016 26,436 Mangrove 1 b TTAAGGCC with mangrove 1 sample DNA as template 95,077 28,337 Mangrove 1 c TTGGTTCC with mangrove 1 sample DNA as template 66,169 20,403 above Shenzhen-Xianhu Botanical Garden Sample DNA is amplified by bacterial v6 region primers

Xianhu Botanical Garden 1— a AACCAACC Take the sample DNA of Xianhu Botanical Garden as template 100,127 28,292 Xianhu Botanical Garden 1— b CAGACAGA Take Xianhu Botanical Garden 1 sample DNA as template 70,732 21,892 Xianhu Botanical Garden 1—c CAGTGAGA to Xianhu Botanical Garden 1 Sample DNA As a template 104, 230 29,007 Xianhu Botanical Garden 1 - d CATCTCGT with Xianhu Botanical Garden 1 sample DNA as template 33,956 10,280 Xianhu Botanical Garden 1 - e GGTAGGAT with Xianhu Botanical Garden 1 sample DNA as template 37,598 10,168 Xianhu Botanical Garden 1 - f GTGTAGAG to Xianhu Botanical Garden 1 sample DNA as template 14,345 6,106 Xianhu Botanical Garden 1— g GTTGGTAC with Xianhu Botanical Garden 1 sample DNA as template 33,356 12,099 Xianhu Botanical Garden 1—h TGGAGTAG with Xianhu Botanical Garden 1 sample DNA as template 107,998 29,426 Xianhu Botanical Garden 1-i TGTGACTG takes the DNA of Xianhu Botanical Garden 1 as template 214,344 54,573 Xianhu Botanical Garden 1J GTCAGAGA takes Xiannan Botanical Garden 1 sample DNA as template 44,731 11,576 Xianhu Botanical Garden 1-k GTCTTCTG takes Xianhu Botanical Garden 1 sample DNA as template 44,912 11,195 Shenzhen-Da Bacterial samples coast sand v6 region primer

Dameisha 1 AACGCGTT 40,151 11,803 Dameisha-2 AAGCTTGC 77,856 24,389 Dameisha-3 ACAGAGAC 103,043 28,314 Dameisha-4 ACCTGATG 93,710 26,667 Dameisha-5 ACTCACTC 74,530 21,144 Dameisha-6 CAACGATC 99,482 30,508 Dameisha —7 CAACGTAC 104,545 29,222 Dameisha—8 CAAGTCGT 113,590 26,376 Shenzhen-Donghu Park, Shenzhen-Mangrove, Shenzhen-Xianhu Botanical Garden, Shenzhen-Longgang River and Shenzhen-Sewage Treatment Plant samples were amplified by bacterial v6 region primers

East Lake Park la GGATGGTA Take East Lake Park 1 sample DNA as template 16,274 3,450 East Lake Park 3 a TGGTTCGA Take East Lake Park 3 sample DNA as template 14,042 3,346 East Lake Park 3 b ACTGCAGT Take East Lake Park 3 sample DNA as template 7,949 2,008 Mangrove 1 a TGGAGATG Mangrove 1 sample DNA as template 27,490 6,453 mangrove 2 a TGCAACGT with mangrove 2 sample DNA as template 10,502 2,654 Xianhu Botanical Garden 1 a TGACTCTC with Xianhu Botanical Garden 1 sample DNA as template 49,096 11,083 Xianhu Botanical Garden 2 a GGATTACC Take the sample DNA of Xianhu Botanical Garden 2 as a template 7,625 2,527 Xianhu Botanical Garden 3 a CCAACCTT Take the sample DNA of Xianhu Botanical Garden 3 as template 2,447 959 Longgang River la TGACTGAC Take Longgang River 1 sample DNA as template 32,108 8,401 Longgang River 2 a ACCACATG with Longgang River 2 sample DNA as template 4,919 1,852 sewage treatment plant la ACCACTAG with sewage treatment plant 1 sample DNA as template 5,765 2,078 sewage treatment plant 2 a ACCTGTAG with sewage treatment plant 2 sample DNA as template 7,140 2,367 mangrove 2 b GGAACGTA uses mangrove 2 sample DNA as template 4,454 1,398 Step 6. After obtaining the original sequencing data, the low quality data is filtered out. Specifically, sequences that do not match the nearest primer, sequences less than 50 bp, or sequences with one or more different subtractions are removed, as shown in Table 2.

 Table 2 Metagenomic data

 *These data are used to find contigs with a length of 60bp (in case of 0 and 1 mismatch)

 Step 7. Assemble the full length sequence of V6 using the relationship of the contigs.

 Specifically, the sequence of the hypervariable region V6 is assembled by the reads overlap region of the Pair-end. The average length of the PCR product is 100 bp, and each tag sequence is 75 bp in length on both ends; since the mass of Solexa sequencing is gradually decreased at the 3, the end can be 5, the first 75, 70, 65, 60 Overlap with 55 bp to assemble the full length sequence of V6. The standard for a pair of sequence connections is an overlap length greater than 5 bp and a mismatch of less than 10% in the overlap region. The 1⁄2 reading (base calling) on the mismatched sites is dependent on the quality of the sequencing at both ends.

 Step 8. Correspond to the corresponding sample by the barcode tag sequence.

Step 9. Classify the microbial population in the sample, specifically, each The sequencing sequence of the sample was compared to the 16S v6 database refhvr-V6, and then the difference was calculated using the GSAT algorithm.

 Step 10: Perform OTU (operational taxonomic unit) classification, for example, using the GAST-OTU strategy (that is, using the GAST algorithm to calculate the OTU strategy) to classify OTUs for sequences of different degrees of difference. In this embodiment, more than 3.7 million tag sequences and 680,000 exact sequencing sequences (ie, perfectly matched) are obtained, which are classified into OTUs by software mothur (v丄6.0); among them, software mothur (v.1.6.0) The download path is http:〃 www.mothur.org/wiki/Main- Page.

 Step 11. Data analysis.

 Specifically, Chaol, Angiotensin Converting Enzyme (ACE) diversity analysis was performed using Mothur. Canoco (v4.5) software, as shown in Tables 3 and 4. Thereby, a diversity analysis map and a relative abundance map of the microbial population are obtained.

 Diversity assessment in specific environments

*These reads are from 60bp contigs (allowing 0 and 1 mismatches). When using accurate V6 tag sequences for classification, ACE and Chaol classifications show extremely rich species diversity in specific environments. Our data also supports Previous view: There are millions of bacteria per gram of soil. A complete combination of bar code labeling technology, Solexa run, can generate 100 million tag sequences, which will make it increasingly practical to explore bacterial diversity in the environment through sequencing. Analysis of genus and abundance genus common in specific sediments

*The number of citation data is derived from the number of citations in Google Scholar (2009.11.18); #NA stands for uncertainty or ^ is searched by Google Scholar.

 Figure 3 shows the number of OTUs in the case of microbial populations with different degrees of 0.03 and 0.3 in different environments.

 As shown in Figure 3, the sparse curve shows the OTUs of the Beishan Reservoir 4, the sediments of Xianhu Botanical Garden 1 and Dameisha 8 in the case of unique (the algorithm for evaluating the degree of difference) of 0.03 and 0.3. Quantity. The sediments of Beishan Reservoir have the largest species diversity and uniformity, and the microbial diversity of Dameisha seawater sediments is the lowest. In the classified structure of the level, the reservoir freshwater sediments show more distribution diversity than other environments. Studies show that about 27% of reservoir freshwater sediments, 20% of Donghu Park sediments, and 17% of Dameisha marine sediments. The sequence of objects has not previously been classified, indicating that there are more untapped rare species in freshwater environments.

 The present invention provides a high-throughput sequencing of a microbial population in a specific environment based on the metagenomic 16S hypervariable region V6 classification method using Solexa technology combined with a labeling technique. In a single Lane, we measured the Approximately 4 million 16S rRNA V6 tag sequences from 65 samples. In the specific Beishan Reservoir, Donghu Park, Mangrove and Dameisha seawater sediments, the number of different tag sequences is 257,001, 228,101, 144,295 and 137,997, with an estimated diversity of 1 million. Among them, the sediments of Beishan Reservoir have the highest species diversity and homogeneity. It can be seen that the method of classifying the microbial population by Solexa sequencing 16S rRNA v6 variable region is economical, which reduces the labor and economic cost, and makes the relationship between microbial community structure and health, environmental factors, etc. It becomes easy to work on. In addition, the number of reads, whether it is a total of reads or 0 mismatches, is higher than the number of previously reported sequencing 16S tag sequences. The number of 690,165 precision v6 tag sequences is approximately 630,000 higher than in the Ribosomal Database Project release 10.15 database.

With reference to the foregoing exemplary description of the invention, it will be apparent to those skilled in the art that the invention has the following advantages: The present invention provides a classification method based on the macrogen 16S hypervariable region V6, which only uses high-variable region v6 sequencing to classify microorganisms in a sample, and this method shows very much in classifying and measuring the relative abundance of the microbial population. Good results, even in the case where the variable region V6 region sequences differ from their nearest reference sequences, can achieve good results. The results showed that by analyzing the V6 variable region for microbial species analysis, not only the main microorganisms but also more rare microorganisms could be detected. By sequencing the V6 variable region of SSU rRNA, it was found that the diversity of microorganisms is not limited to the previous classification of Burkins by phenotype, and the microbial population is far more complex than imagined. In addition, in developing the diversity and relative abundance of microbial populations, a large number of parallel Solexa sequencing V6 variable region sequences have advantages over other techniques. Further research on sequencing of variable regions revealed many advantages over other sequencing, such as the relative level of microbial diversity, the length of the sequence, the density of homopolymers, the ability to recognize species levels, or the adaptation to different amplifications. The advantages of primers.

 The present invention provides a classification method based on the metagenomic 16S hypervariable region V6, and the V6 variable region Solexa sequencing can generate similar taxonomic and relative abundance values compared to conventional full-length SSU rRNA sequencing, but due to its short sequence The same run, which provides more samples of the reads, identifies more microbes, and costs less for each read than traditional full-length SSU rRNA sequencing. As technology advances to produce more Reads data and longer sequences, Solexa sequencing will provide a broader opportunity to sequence variable microbes for variable region sequencing, such as long sequencing, variable region applications, and a variety of variables. Combination of regions, or deeper sequencing depth. The biggest advantage of variable-zone tag sequencing is that it uses the advantages of a large number of parallel Solexa sequencing, which is several orders of magnitude deeper than the original, and promotes the broad diversity of microbial populations and rare organisms.

The description of the present invention has been presented for purposes of illustration and description. Many modifications and variations will be apparent to those skilled in the art. The functional modules and functional modules described in the present invention are divided only to explain the idea of the present invention, and those skilled in the art according to the present invention The teachings of the present invention and the needs of the actual application can freely change the division manner of the functional modules and their module configurations to achieve the same functions. The embodiments are chosen and described in order to better explain the principles and practical applications of the present invention, and The skilled artisan can understand the invention and thus design various embodiments with various modifications that are suitable for a particular use.

Claims

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A classification method based on a metagenomic 16S hypervariable region V6, characterized in that the method comprises:

 For the deoxyribonucleic acid DNA of the extracted microorganism, a polymerase chain reaction PCR is performed on the hypervariable region V6 of the metagenomic 16S ribosomal deoxyribonucleic acid rDNA by a primer to add a tag sequence to each sample;

 Mixing the PCR products of different samples;

 The Solexa database construction method is performed on the mixed PCR product;

 The library of the high variable region V6 was subjected to double-end pair-end sequencing using a Solexa sequencing tool to obtain a sequencing sequence;

 The sequencing sequence is screened to filter out low quality sequencing sequences;

 Assembling the full-length sequence of the hypervariable region V6 based on the relationship of the contigs based on the sequencing sequence;

 Assigning the sequencing sequence to a corresponding sample by a tag sequence;

 The high-throughput classification of the microbial population is achieved by classifying the sequencing sequences to achieve sequencing using the hypervariable regions.

The method according to claim 1, characterized in that the method further comprises: performing sampling of the microbial population before extracting the DNA DNA of the microorganism.

The method according to claim 1, wherein the method further comprises: performing, by classifying the sequencing sequence, performing classification of the classification sequence OTU on sequencing sequences of different degrees of difference;

 Based on the tag sequence and sequencing sequence, population diversity estimation Chaol algorithm and angiotensin-converting enzyme ACE diversity analysis were performed.

The method according to claim 3, wherein after the population diversity estimation Chaol algorithm and the angiotensin-converting enzyme ACE diversity analysis, the output micro-production Diversity analysis and relative abundance map of the population

The method according to claim 1, wherein the polymerase chain reaction PCR is performed on the hypervariable region V6 of the metagenomic 16S ribosomal deoxyribonucleic acid rDNA by a primer to label each sample. The sequence includes:

 Primer 967f: CNACGCGAAGAACCTTANC and 1406R: GACAGCCATGCANCACCT were used to replicate the 16S hypervariable region V6 region fragment of the bacteria in the microbial population;

 Each microbial sample is tagged, the tag sequence is placed in front of the 5' end of the primer 967f, and a base GT is added between the tag sequence and the primer 967f.

The method according to claim 5, wherein the method further comprises: for polymerase chain reaction PCR of the hypervariable region V6 of archaea, using primers 958AR: AATTGGANTCAACGCCGG and 1048AR: CGRCGGCCATGCACCWC.

7. The method of claim 1 wherein said mixing the PCR products of different samples comprises:

 The PCR product of the 16S high variable region V6 is subjected to concentration quantification; and mixed together in equimolar amounts.

The method according to claim 1, wherein the performing the Solexa database construction process on the mixed PCR product comprises:

 The mixed product is purified, end-repaired, base A is added at the 3' end, and a double-end Pair-end sequencing linker is added;

 After the joint is added, the sample is purified;

The purified sample is dissolved and used as a template for polymerase chain reaction PCR amplification; The polymerase chain reaction PCR product was subjected to gel purification.

9. The method of claim 1, wherein the low quality sequencing sequence comprises: a sequencing sequence that does not match the nearest primer, a sequencing sequence of less than 50 base pairs, or has at least one different basis. Sequencing sequence.

10. The method according to claim 1, wherein the assembling the full length sequence of the hypervariable region V6 based on the relationship of the contigs based on the sequencing sequence comprises: employing the hypervariable region The PCR product 5 of V6, the first 75, 70, 65, 60 and 55 base pairs of the ends are overlapped for assembly; wherein the assembly standard is that a pair of sequencing sequences has an overlap length of more than 5 pairs and less than 10% in the overlap region The degree of mismatch.

The method according to claim 1, wherein the classifying the sequence of the sequence comprises:

 The sequencing sequences assigned to the corresponding samples were aligned into an existing 16s v6 database to achieve high throughput classification of the microbial population using tag sequencing using high variable regions, thereby studying the structure of the microbial population.