CN105567846A - Kit for detecting bacteria DNAs in faeces and application thereof in colorectal cancer diagnosis - Google Patents
Kit for detecting bacteria DNAs in faeces and application thereof in colorectal cancer diagnosis Download PDFInfo
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Abstract
The invention discloses a kit for detecting bacteria DNAs in faeces and the application thereof in colorectal cancer screening, diagnosis or auxiliary diagnosis. Bacteria DNAs in faeces are extracted and sequenced to obtain the species and abundance of bacteria, and colorectal cancer diagnosis is conducted based on the abundance of various bacteria. Compared with existing noninvasive colorectal cancer screen methods which have already been used clinically or for patent application, the kit has the advantages that the kit is completely noninvasive, colorectal cancer can be predicted and diagnosed more accurately, and the ROC area under the curve can be as high as 0.994.
Description
Technical field
The present invention relates to medical diagnosis on disease field, be specifically related to a kind of detect DNA of bacteria in ight soil test kit and application in the examination of large bowel cancer, diagnosis or auxiliary diagnosis.
Background technology
Large bowel cancer is number three in all tumor incidences.At present the biopsy of large intestine mirror is depended on to the diagnosis of large bowel cancer.But the biopsy of large intestine mirror is for there being the inspection of wound property, can not on a large scale for Mass screening.Desirable tumor screening index should have higher Sensitivity and Specificity, is woundless testing as far as possible simultaneously.In recent years development of scientific research prompting PATIENTS WITH LARGE BOWEL and normal healthy people intestinal microflora distribution on there were significant differences, intestinal microflora may take part in developing of large bowel cancer.If only analyzed with the ight soil of experimenter, just can determine whether to suffer from large bowel cancer, just can carry out examination to general population well, thus accomplish early to find early treatment to large bowel cancer.Therefore, find the method for the non-invasive Screening Diagnosis large bowel cancer of a kind of energy, the PATIENTS WITH LARGE BOWEL in large discovery crowd possibly can be use up as early as possible, thus greatly increase the lifetime of crowd, reduce the medical expense of patient.At present, the method of the large bowel cancer of examination non-invasively applied is clinically mainly: 1) occult blood test (FOBT): FOBT is screening for colorectal method the most frequently used at present, and being also unique one has perspective randomized controlled trial to prove effective screening method.Colorectal Carcinoma or large adenoma can ooze out a small amount of blood usually, and blood enters in stool and is discharged.Occult blood test just can detect a small amount of blood ingredient in stool.But be non-specific performance with blood in ight soil, therefore the diagnosis of FOBT to this disease does not have specificity.And single fecal sample can not detect blood sometimes.2) S-CEA CEA measures: nineteen sixty-five finds the method detecting CEA examination large bowel cancer in serum.In Serum In Patients With Colorectal Carcinoma, can CEA be detected, this is a kind of glycoprotein, often comes across in sera of patients with malignant tumors, not the specificity related antigen of large bowel cancer, therefore change of serum C EA mensuration does not have specificity to the diagnosis of this disease.But using measured by radioimmunoassay CEA, doing quantitative dynamically observation, to judging that the surgical effect of large bowel cancer has the certain significance with detection postoperative recurrence.After tumour being excised completely in large bowel cancer underwent operative, change of serum C EA then declines gradually; If recurrence, can raise again once again.But the susceptibility of CEA is lower than 35% in invasive tumor, it can not be used for detecting Early cancer, and specificity is inadequate, has the rising of CEA level in much pathology.3) also find CA19-9 antigen recently, CA19-9 is a kind of tumor marker of glycoprotein analog, and in the serum of Infusion in Patients with Digestive, content obviously raises.Change of serum C A-19-9 concentration and Duke are proportionate by stages, and along with upgrading concentration by stages raises, therefore the size of concentration is for judging that the prognosis of large bowel cancer has clinical value.But the positive rate of Virus monitory CA19-9 is lower, only has 22.39%, divide interim at each Duke, serum is identical with CEA with the positive rate difference condition of CA19-9 in tissue.DukeA ~ DukeC is interim, is all starkly lower than postoperative tissue by the positive rate of Virus monitory CA19-9, and especially DukeA phase patient, the result detecting change of serum C A19-9 is negative.CA19-9 is worth not high to the early diagnosis of large bowel cancer, but has important clinical significance to human colorectal observation of curative effect and recurrence monitoring.4) form that CA50 combines with fat or lipoprotein is present in cytolemma, and belong to sphingoglycolipid class marker, be the marker of pancreas and large bowel cancer, specificity is inadequate.
Summary of the invention
Because the above-mentioned defect of prior art, the invention provides a kind of detect DNA of bacteria in ight soil test kit and application in the examination of large bowel cancer, diagnosis or auxiliary diagnosis, technical scheme is as follows:
The invention provides a kind of test kit detecting DNA of bacteria in ight soil, test kit comprise with the V1-V3 variable region of 16SrDNA design and with 5 '-454A, B joint-special primer-3 ' general fusion primer
27F5′-AGAGTTTGATCCTGGCTCAG-3′
533R5′-TTACCGCGGCTGCTGGCAC-3′，
Mentioned reagent box obtains operational taxonomic unit by 16SrDNA order-checking, without the comparison and merging with known microorganisms kind, can directly adopt Bayes DMNB text algorithm forecast colorectal cancer.
Present invention also offers the application of mentioned reagent box in the examination of large bowel cancer, diagnosis or auxiliary diagnosis.
Present invention also offers a kind of detect DNA of bacteria in ight soil the examination of test kit at large bowel cancer, application in diagnosis or auxiliary diagnosis, comprise the following steps:
Step 1, provide the faecal samples taking from experimenter;
Step 2, the extracting of fecal bacteria genome is carried out to the faecal samples in step 1;
Step 3, to design and synthesize with 5 '-454A, B joint-special primer-3 with the V1-V3 variable region of 16SrDNA ' general fusion primer
27F5′-AGAGTTTGATCCTGGCTCAG-3′
533R5′-TTACCGCGGCTGCTGGCAC-3′，
Pcr amplification is carried out to the fecal bacteria genome obtained in step 2 and order-checking acquisition sequencing data;
The sequencing data obtained in step 4, optimization step 3 obtains majorizing sequence;
Step 5, the majorizing sequence obtained in step 4 is carried out OTU cluster analysis, OTU (OperationalTaxonomicUnits, operational taxonomic unit) be in phylogenetics research or population genetic study, for the ease of analyzing, artificial to some taxon (strains, kind, belong to, grouping etc.) the same mark that arranges, in analysis of biological information, in general, each sequence that order-checking obtains is from a bacterium, understand the bacterial classification in a sample sequencing result, the information of number such as Pseudomonas, just need to carry out categorizing operation (cluster) to sequence, pass through categorizing operation, sequence is divided according to similarity each other and is classified as many groups, a group is exactly an OTU, according to the similarity of specifying such as 96%, 97% or 98%, OTU division is carried out to all sequences and carries out bioinformation statistical study,
Step 6, the one in Bayes netted algorithm, Bayes DMNB text algorithm or random forests algorithm is adopted to analyze the OTU obtained in step 5;
Step 7, by sorter predict experimenter whether be large bowel cancer classification.
Further, the sequence measurement used in above-mentioned steps 3 is mushroom 16s sequencing or genomic dna sequencing.
Further, in above-mentioned steps 4, the sequencing data obtained in step 3 is obtained ordered sequence by elementary removal of impurities, more accurately impurity elimination, be optimized sequence.
Further, in above-mentioned steps 5, majorizing sequence is carried out OTU cluster analysis by the similarity according to 97%.
Further, in above-mentioned steps 6, Bayes DMNB text algorithm is adopted to analyze the OUT obtained in step 5.
Further, for the OTU cluster analysis in above-mentioned application, can forecast colorectal cancer better than the OTU result after merging by the OTU result that do not merge after classification.After OTU cluster, meeting and the contrast of SILVA database, certain OTU which kind of bacterium corresponding, what likely several OTU was as corresponding in OTU1, OTU188, OTU19657 is all that same bacterium is as Fusobacterium nucleatum, merging refers to and these OTU is summarized as one, does not merge and is exactly or calculates by three variablees.
Further, large bowel cancer is colorectal carcinoma and/or the rectum cancer.
A first aspect of the present invention is the fact based on not recognizing in the past, and namely the stool sample namely taking from general population can be used as the sample of large bowel cancer screening.More specifically, the present invention points out can well predict whether sample is large bowel cancer classification by the Bayes DMNB text algorithm in WEKA platform first time.More specifically, the present invention points out the OTU result after with the OTU result ratio merging do not merged can predict whether be large bowel cancer classification better first time.Determine whether that large bowel cancer classification has some advantages based on fecal bacteria kind and abundance feature.Be applied at present compared with clinical non-invasive examination large bowel cancer method, by the specificity of fecal bacteria kind and abundance signatures to predict large bowel cancer and susceptibility far away higher than additive method.We find the intestines bacterium OTU data do not merged with the analysis of Bayes DMNB text algorithm, the AUC of its forecast colorectal cancer up to 0.994, far away higher than the AUC0.633 of occult blood test.The AUC of netted Bayes and random forest classification method also more than 0.9, higher than occult blood test, but lower than the Bayes DMNB text algorithm that we find.Show there is good Sensitivity and Specificity by fecal bacteria kind and abundance feature to forecast colorectal cancer.Therefore, carry out examination large bowel cancer by ight soil intestines bacterium gene order-checking result, the Sensitivity and Specificity of diagnosis can be improved widely, reduce rate of missed diagnosis widely, can well contribute to judging that experimenter is the need of further colonoscopy.
Below with reference to accompanying drawing, the invention will be further described, to absolutely prove object of the present invention, technical characteristic and technique effect.
Accompanying drawing explanation
Fig. 1 shows order-checking in preferred embodiment of the present invention and statistical study schema;
Fig. 2 shows the accuracy rate of the netted algorithm of Bayes, Bayes DMNB text algorithm and random forests algorithm three kinds of algorithm predicts crowd A (N=141 example) inner large bowel cancer classification, represents by ROC area under curve (AUC);
Fig. 3 a shows can forecast colorectal cancer more accurately than the OTU result merged by the OTU result of (merging) before merging after the OTU of crowd A classification; Fig. 3 b shows Bayes DMNB text algorithm and utilizes the OTU result of crowd A to merge the ROC graphic representation of the two groups of data difference forecast colorectal cancer classifications in front and back; The maximum of AUC derive from OTU result merge before Bayes DMNB text algorithm, reach 0.994, the AUC after merging is 0.943;
Fig. 4 shows the ROC graphic representation that the netted algorithm of Bayes (AUC=0.93) and random forests algorithm (AUC=0.94) predict large bowel cancer classification in crowd A, and the accuracy rate of occult blood test in contrast.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described further.
1, patient and tissue samples
1) crowd A: the stool sample of row electronics TC and Colorectal Neoplasm Operation during Renji Hospital Attached to Medical College of Shanghai Jiaotong Univ. January 1 to 30 days July in 2012 in 2012.
Inclusive criteria is:
(1) age >=50 years old;
(2) have the normal Colon Movement rhythm and pace of moving things, stool no more than 1 day 2 times, is no less than 2 days 1 time;
(3) TC is operated according to standard specifications by veteran doctor, and it is abundant to move back the mirror time.
Exclusion standard is:
(1) previously Colorectal Adenomas, large bowel cancer, irritable bowel syndrome (IBS), ulcerative colitis (UC) or Crohn's disease (CD) medical history is had;
(2) previously there is operation on digestive tract history, comprise stomach, biliary tract, enteron aisle etc.;
(3) heredity large bowel cancer high risk population, refers to from following various diseases family member: gastroenteric tumor family history; Familial adenomatous polyposis (FAP), cancer in hereditary nonpolyposis colorectal cancer (HNPCC) and P-J syndrome etc.;
(4) with cardiovascular systems, endocrine system, blood system and disease of immune system;
(5) with liver and kidney dysfunction, liver cirrhosis and portal hypertension gastopathy;
The medicine of the NSAID (non-steroidal anti-inflammatory drug) such as microbiotic, acetylsalicylic acid (NSAIDs), scorching corticosteroids medicine, short digestive tract power reinforcing medicine and adjustment intestinal microflora was applied in (6) 6 months;
Systematic radiotherapy or chemotherapy was accepted in (7) 6 months.
We will meet above-mentioned condition, be that large bowel cancer person includes colorectal cancer group (CRC group) in through Overall Colonoscope and proved by pathology, and confirm that Non Apparent Abnormality shower includes Normal group (negativecontrol, NC) group in through intestines mirror.Collect stool sample 141 example altogether, comprising NC group 99 example and CRC group 42 example.
2, fecal bacteria genome extracting
Use OMEGA ight soil genome DNA extracting reagent kit (E.Z.N.A.StoolDNAKit):
(1) granulated glass sphere (glassbeads) taking 200mg, in 2ml centrifuge tube, adds 200mg faecal samples, then adds 700 μ l damping fluid SP1, with top speed vortex 3 ~ 5min, and abundant sample dissolution;
(2) add 100 μ lDS damping fluid vortexs to mix;
(3) 70 DEG C of water-bath incubation 10min, during this period vortex 2 times;
(4) centrifugal 3,000rpm × 3min under room temperature, getting 400 μ l supernatant liquors in the new 2ml centrifuge tube adds 500 μ lSP2, and vortex fully mixes;
(5) ice bath 5min, 4 DEG C are centrifugal, 14,000rpm × 3min, and transfer supernatant is in a new 2ml centrifuge tube and add 0.7 times of volume isopropanol, put upside down mixing 20 ~ 30 times, place 1h for-20 DEG C;
(6) 4 DEG C centrifugal, and 14,000rpm × 10min, carefully takes out supernatant liquor, does not get precipitation, centrifuge tube is upside down in 1min on thieving paper and blots liquid;
(7) add 200 μ l elution buffer vortex 10s, 70 DEG C of incubation 10min, precipitate with dissolving DNA;
(8) 100 μ lHTR reagent (Reagent) are added, vortex 10s (note: HTRReagent mixes before the use completely);
(9) room temperature places 2min, centrifugal 14,000rpm × 2min, and shift supernatant liquor completely in new 2ml centrifuge tube, add isopyknic buffer B L, vortex mixes;
(10) upper step all samples is added in HibindDNA post (HibindDNAColumn), centrifugal 14,000rpm × 1min, remove filtered liquid and re-use collection tube;
(11) add 500 μ l buffer B L in HibindDNAColumn, centrifugal 14,000rpm × 1min, remove filtered liquid and be placed in new 2ml collection tube;
(12) add 700 μ l cleaning buffer solutions (diluting with dehydrated alcohol in proportion in advance), centrifugal 14,000rpm × 1min, remove filtered liquid and be placed in new 2ml collection tube;
(13) previous step is repeated;
(14) centrifugal 14, the 000rpm × 2min of void column under room temperature, to remove residual ethanol;
(15) HibindDNAColumn is moved in 1.5ml centrifuge tube, add 30 μ l elution buffers in filtering membrane center, 70 DEG C of incubation 10min;
(16) centrifugal 14,000rpm × 1min eluted dna.
3, DNA concentration determination
From the DNA sample of extracting, get 1 μ l, survey the ratio of concentration and OD260/OD280 with NANODROP2000 instrument, ratio 1.8 ~ 2.0 continue on for subsequent experimental.All DNA sample are placed in-20 DEG C of preservations, for subsequent PCR amplification and the order-checking of high-throughput flora.
4, pcr amplification and qualification
1) design and synthesize with 5 '-454A, B joint-special primer-3 with V1-V3 variable region, designated area ' general fusion primer, A tip side for order-checking end, need tag, B tip side primer can share.Bacterial 16 S rRNA primer sequence is:
27F5’-AGAGTTTGATCCTGGCTCAG-3’
533R5’-TTACCGCGGCTGCTGGCAC-3’
2) PCR amplification system
PCR adopts TransGen:TransStartFastPfuDNAPolymerase, 20 μ l reaction systems,
The single reaction consumption of reagent:
Mend distilled water to 20 μ l
3) pcr amplification product qualification and recovery
(1) 2% agarose gel electrophoresis detects PCR primer, 3 μ l electrophoresis, and all samples PCR primer object stripe size is correct, and concentration is suitable, can carry out next step experiment;
(2) all sample is tested according to formal experiment condition, the repetition of 3, each sample, uses 2% agarose gel electrophoresis by after same sample mixing;
(3) use the AxyPrepDNA gel of AXYGEN company to reclaim test kit and cut glue recovery PCR primer, Tris.HCL wash-out.
4) TBS-380 is quantitative
Typical curve: y=0.4931xR2=0.9990
5, Roche454GSFLX order-checking and baseline results analysis (schema as shown in Figure 1)
1) original sequence data statistics:
In order-checking experiment, usually adopt the method for multiple sample parallel order-checking, i.e. multiple sample mix order-checking.In order to distinguish sample, the sequence in each sample all introduces barcode (barcode) sequence label that a segment mark shows its samples sources information.If in the row that check order not containing barcode sequence label, then cannot determine its samples sources, and then cause subsequent bio information errors or interrogatory.Therefore, only when containing complete barcode sequence label in original series, this sequence is just recognized as ordered sequence.Barcode and front primer sequence remove by the analysis of the present embodiment, and carry out data and length distribution statistics to the ordered sequence after process.
Statistics is as follows:
The data statistics of table 1 ordered sequence
| Ordered sequence | Sample number | Total sequence number | Total bases (bp) | Mean length (bp) |
| 16s | 141 | 2905689 | 1190617453 | 409 |
2) majorizing sequence data statistics
Under normal circumstances, ordered sequence can be directly used in subsequent bio bioinformatics analysis.If but need to obtain more high quality and more accurately analysis of biological information result, then tackle ordered sequence and carry out impurity elimination.In experimentation, order-checking product may contain non-specific amplification fragment, utilizes Auele Specific Primer information to be removed; During order-checking, the tip mass of long sequence can reduce, and by detecting 3 ' end connector and primer, can remove the inferior quality sequence of tail of sequence; The too short sequence of fuzzy base (ambiguous), single base height iteron (homologous) and length may be contained in sequence, include these sequences in analyst coverage and can reduce analysis quality, therefore pruning, remove (trim) this partial sequence, the majorizing sequence for precisely analyzing can being obtained; Finally need to reject the sequence of base average quality lower than reference value.
Concrete analysis step:
(1) rear primer and joint sequence (adaptor), polybase base N, polyA/T tail and the inferior quality base of sequence end is removed;
(2) the barcode label of above-mentioned (1) gained sequence and front primer sequence is removed;
(3) abandon be shorter in length than 200bp, fuzzy base number is greater than 0, sequence average quality lower than 25 sequence.Data statistics result is as follows:
Table 2 majorizing sequence is added up
| Majorizing sequence | Sample number | Total sequence number | Total bases (bp) | Mean length (bp) |
| 16s | 141 | 2296326 | 1004126216 | 437 |
3) OTU cluster analysis
The key step of OUT cluster analysis is as follows:
A) extract non repetitive sequence, the completely the same sequence of base is tumor-necrosis factor glycoproteins, reduces and analyzes pilot process redundant computation amount;
B) with aligned (16S/18S, SSU) the rrna sequence library comparison in SILVA database (http://www.arb-silva.de/);
C) use UCHIME method to detect and remove chimeric (Chimeric) sequence;
D) uncorrected (uncorrectedpairwise) distance in pairs between the sequence after comparison alignment is calculated;
E) contiguous method cluster is farthest used to generate OTU.
Use software: mothur with reference to network address: http://www.mothur.org/wiki/Main_Page
4) Taxonomic analysis
In analytical procedure before, by the similarity of sequence according to the based composition of himself, divide and be grouped in each OTU.When carrying out Taxonomic analysis, first, each high-quality sequence all being compared with SILVA database (latest edition), finding out the kind information that its most close and confidence level reaches more than 80%.Afterwards, all sequences in each OTU is carried out analogy, find out the kind information of the not homotactic nearest ancestors in same OTU.Finally, by the outcome record that obtains in form document.Do like this and when the quantity of information that reservation most probable is many, the accuracy drawing information can be guaranteed.
Taxonomic analysis method: Species estimation is carried out to OTU according to the reference sequences in SILVA database;
Use software: mothur.
6, predict whether be large bowel cancer classification by algorithms of different in supervised classification method by analyzing sorted OTU information.
1) screen during above-mentioned OTU shows and be categorized as the OTU planted, convert CSV or ARFF form (required by WEKA software) to;
2) in WEKA software, above table is imported, at sorter interface (classify) interface, netted Bayes/random forest selected by sorter, cross validation multiple selects 10, categorizing selection group, namely be whether large bowel cancer for classification group, click and run, then can occur that corresponding AUC is equivalent in interface on the right.Its simple procedure for 141 cases and correspondingly intestines bacterium information be divided into 10 parts, go training pattern as netted Bayesian model with 9 partial datas wherein, allow the kind of bacterium in model analysis large bowel cancer and normal people and quantative attribute, then according to this feature, in remaining a part of case, which is PATIENTS WITH LARGE BOWEL to allow model go to infer, which is normal people, sees the accuracy rate of mode inference.Model can run 10 times, until the every part in 10 parts is all predicted.
More than describe preferred embodiment of the present invention in detail.Should be appreciated that the ordinary skill of this area just design according to the present invention can make many modifications and variations without the need to creative work.Therefore, all technician in the art, all should by the determined protection domain of claims under this invention's idea on the basis of existing technology by the available technical scheme of logical analysis, reasoning, or a limited experiment.
Claims (10)
1. detect a test kit for DNA of bacteria in ight soil, it is characterized in that, described test kit comprises primer:
27F5′-AGAGTTTGATCCTGGCTCAG-3′
533R5′-TTACCGCGGCTGCTGGCAC-3′；
Described test kit obtains operational taxonomic unit by 16SrDNA order-checking, without the comparison and merging with known microorganisms kind, can directly adopt Bayes DMNB text algorithm forecast colorectal cancer.
2. the application of test kit according to claim 1 in the examination of large bowel cancer, diagnosis or auxiliary diagnosis.
3. application according to claim 2, is characterized in that, described large bowel cancer is colorectal carcinoma and/or the rectum cancer.
4. detect the application in the examination of test kit at large bowel cancer of DNA of bacteria in ight soil, diagnosis or auxiliary diagnosis, it is characterized in that, described application comprises the following steps:
Step 1, provide the faecal samples taking from experimenter;
Step 2, the extracting of fecal bacteria genome is carried out to the described faecal samples in step 1;
Step 3, with primer:
27F5′-AGAGTTTGATCCTGGCTCAG-3′
533R5′-TTACCGCGGCTGCTGGCAC-3′，
Pcr amplification is carried out to the described fecal bacteria genome obtained in step 2 and order-checking acquisition sequencing data;
The described sequencing data obtained in step 4, optimization step 3 obtains majorizing sequence;
Step 5, the described majorizing sequence obtained in step 4 is carried out OTU cluster analysis;
Step 6, the one in the netted algorithm of Bayes, Bayes DMNB text algorithm or random forests algorithm is adopted to analyze to the OTU that obtains in step 5;
Step 7, predict whether described experimenter is large bowel cancer classification by sorter.
5. application according to claim 4, is characterized in that, the sequence measurement used in described step 3 is mushroom 16SrDNA sequencing or genomic dna sequencing.
6. application according to claim 4, is characterized in that, in described step 4, the described sequencing data obtained in described step 3 is obtained ordered sequence by elementary removal of impurities, more accurately impurity elimination, obtains described majorizing sequence.
7. application according to claim 4, is characterized in that, in described step 5, described majorizing sequence is carried out OTU cluster analysis by the similarity according to 97%.
8. application according to claim 4, is characterized in that, in described step 6, adopts Bayes DMNB text algorithm to analyze the OUT obtained in described step 5.
9. application according to claim 4, is characterized in that, described OTU cluster analysis, can forecast colorectal cancer better than the OTU result after merging by the OTU result that do not merge after classification.
10. according to the application in claim 4-9 described in any one, it is characterized in that, described large bowel cancer is colorectal carcinoma and/or the rectum cancer.
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| CN108690864A (en) * | 2017-10-31 | 2018-10-23 | 中山大学 | Flora stable state evaluation method and the application in colorectal cancer screening in a kind of fecal sample |
| CN111833963A (en) * | 2019-05-07 | 2020-10-27 | 中国科学院北京基因组研究所 | cfDNA classification method, device and application |
| WO2020224504A1 (en) * | 2019-05-07 | 2020-11-12 | 中国科学院北京基因组研究所 | Cfdna classification method, apparatus and application |
| CN112080565A (en) * | 2019-06-14 | 2020-12-15 | 韩书文 | Colorectal cancer-related prediction system, electronic device, and storage medium |
| CN111833965A (en) * | 2019-11-08 | 2020-10-27 | 中国科学院北京基因组研究所 | Urinary sediment genomic DNA classification method, device and application |
| WO2021088653A1 (en) * | 2019-11-08 | 2021-05-14 | 中国科学院北京基因组研究所(国家生物信息中心) | Method and device for classification of urine sediment genomic dna, and use of urine sediment genomic dna |
| CN112852804A (en) * | 2021-02-07 | 2021-05-28 | 湖南大学 | Universal bacterial target and screening method and application thereof |
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