CN107273715A - A kind of detection method and device - Google Patents
A kind of detection method and device Download PDFInfo
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- CN107273715A CN107273715A CN201710326413.2A CN201710326413A CN107273715A CN 107273715 A CN107273715 A CN 107273715A CN 201710326413 A CN201710326413 A CN 201710326413A CN 107273715 A CN107273715 A CN 107273715A
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Abstract
The present invention is applied to molecular biosciences information detection technology field there is provided a kind of detection method and device, and methods described includes：Obtain and the result data that high-flux sequence is obtained is carried out to sample；According to the result data, the Making by Probability Sets of the pollution rate of the sample is counted using bayes method；The Making by Probability Sets obtained according to statistics determines the level of pollution of the sample.Technical scheme realizes the accurate detection to the sample contamination level of high-flux sequence data, it is to avoid difficulty and uncertainty that sample contamination control is brought are carried out before sequencing, the accuracy rate of post analysis result is greatly improved.
Description
Technical field
The invention belongs to molecular biosciences information detection technology field, more particularly to a kind of detection method and device.
Background technology
When carrying out hereditary disease genetic test using high-flux sequence, a large amount of short long sequences of reading can be produced to carry out deoxidation core
Ribosomal ribonucleic acid (deoxyribonucleic acid, DNA) is sequenced and detection in Gene Mutation, but in the process, cross pollution is outstanding
It is that influence of the cross pollution to high-flux sequence is larger between sample, even a small amount of pollution can cause testing result to be forbidden
Really, there is the error result of false positive or false negative.
In existing high-flux sequence flow, intersection is reduced often through the experiment flow and sample process of early stage dirty
The generation of dye, but control cross pollution difficulty is larger in this way, and with uncertainty, due to sequencing depth and meter
The limitation of calculation method, it is also no to determine at present still not to the direct cross pollution detection method of high-flux sequence data
The method of high-flux sequence sample cross contamination during amount analysis hereditary disease genetic test.
The content of the invention
In view of this, in the prior art can not be right with solution the embodiments of the invention provide a kind of detection method and device
The problem of high-flux sequence data carry out sample contamination detection.
The first aspect of the embodiment of the present invention provides a kind of detection method, including：
Obtain and the result data that high-flux sequence is obtained is carried out to sample；
According to the result data, the Making by Probability Sets of the pollution rate of the sample is counted using bayes method；
The Making by Probability Sets obtained according to statistics determines the level of pollution of the sample.
The second aspect of the embodiment of the present invention provides a kind of detection means, including：
Acquisition module, the result data that high-flux sequence is obtained is carried out to sample for obtaining；
Statistical module, for according to the result data, counted using bayes method the sample pollution rate it is general
Rate set；
Determining module, the Making by Probability Sets for being obtained according to statistics determines the level of pollution of the sample.
The beneficial effect that the embodiment of the present invention exists compared with prior art is：Obtain and sample progress high-flux sequence is obtained
The result data arrived, the Making by Probability Sets of the pollution rate of bayes method statistical sample is used according to result data, and general according to this
Rate set determines the level of pollution of sample, realizes the accurate detection to the sample contamination level of high-flux sequence data, it is to avoid
Difficulty and uncertainty that sample contamination control is brought are carried out before sequencing, the accurate of post analysis result is greatly improved
Rate.
Brief description of the drawings
Technical scheme in order to illustrate the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
In required for the accompanying drawing that uses be briefly described, it should be apparent that, drawings in the following description are only some of the present invention
Embodiment, for those of ordinary skill in the art, without having to pay creative labor, can also be according to these
Accompanying drawing obtains other accompanying drawings.
Fig. 1 is a kind of implementation process figure for detection method that the embodiment of the present invention one is provided；
Fig. 2 is a kind of implementation process figure for detection method that the embodiment of the present invention two is provided；
Fig. 3 is a kind of structured flowchart for detection means that the embodiment of the present invention three is provided；
Fig. 4 is a kind of structured flowchart for detection means that the embodiment of the present invention four is provided.
Embodiment
In describing below, in order to illustrate rather than in order to limit, it is proposed that such as tool of particular system structure, technology etc
Body details, thoroughly to understand the embodiment of the present invention.However, it will be clear to one skilled in the art that there is no these specific
The present invention can also be realized in the other embodiments of details.In other situations, omit to well-known system, device, electricity
Road and the detailed description of method, in case unnecessary details hinders description of the invention.
In order to illustrate technical solutions according to the invention, illustrated below by specific embodiment.
Embodiment one：
Fig. 1 is a kind of flow chart for detection method that the embodiment of the present invention one is provided, the executive agent of the embodiment of the present invention
For computing device, it can be specifically computer, server etc., and a kind of detection method of Fig. 1 examples can specifically include step
S101 is to step S103, and details are as follows：
S101, acquisition carry out the result data that high-flux sequence is obtained to sample.
High-flux sequence (High-throughput sequencing) is also known as " next generation " sequencing technologies (" Next-
Generation " sequencing technology), so that once parallel sequence can be carried out to millions of DNA moleculars to hundreds of thousands
Row are determined and general reading is long shorter for mark.
Specifically, by carrying out high-flux sequence to sample, one group of result data is obtained.
S102, according to result data, using the Making by Probability Sets of the pollution rate of bayes method statistical sample.
Bayes method (Bayesian Analysis) provides a kind of method for calculating and assuming probability, and this method is
Based on the assumption that prior probability, given assume that the lower probability for observing different pieces of information and the data observed are drawn in itself.
It will be integrated on the prior information of unknown parameter and sample information, posterior information drawn further according to Bayesian formula, and according to rear
Test the information inference unknown parameter.
Specifically, the result data obtained according to step S101, to the pollution rate of sample, this unknown parameter infers one
Prior probability, and the sample data of the prior probability and high-flux sequence is integrated, sample is drawn according to Bayesian formula
Pollution rate posterior probability, and the corresponding posterior probability of a variety of possible values of the pollution rate of sample is constituted into Making by Probability Sets,
In the Making by Probability Sets, the corresponding probability of a variety of possible pollution rates of sample is contained.
S103, the Making by Probability Sets obtained according to statistics determine the level of pollution of sample.
Specifically, according to the Making by Probability Sets of the pollution rate of the obtained samples of step S102, it will be met in the Making by Probability Sets
The corresponding pollution rate of probability of preparatory condition is defined as the level of pollution of sample.
Preparatory condition can be to being averaged that the probable value progress progress arithmetic mean more than default probability threshold value is obtained
Probable value, can also the maximum probable value of probable value, can also be most probable value of equal probabilities value occurrence number etc., specifically
According to being configured the need for practical application, it is not limited herein.
In the present embodiment, obtain and the result data that high-flux sequence is obtained is carried out to sample, shellfish is used according to result data
The Making by Probability Sets of the pollution rate of this method statistic sample of leaf, and the level of pollution of sample is determined according to the Making by Probability Sets, realize
Accurate detection to the sample contamination level of high-flux sequence data, it is to avoid carry out what sample contamination control was brought before sequencing
Difficulty and uncertainty, greatly improve the accuracy rate of post analysis result.
Embodiment two：
Fig. 2 is a kind of flow chart for detection method that the embodiment of the present invention two is provided, the executive agent of the embodiment of the present invention
For computing device, it can be specifically computer, server etc., and a kind of detection method of Fig. 2 examples can specifically include step
S201 is to step S206, and details are as follows：
S201, acquisition carry out the result data that high-flux sequence is obtained to sample.
High-flux sequence is also known as " next generation " sequencing technologies, with can be once parallel to hundreds of thousands to millions of DNA moleculars
Carry out sequencing and general reading is long shorter for mark.
Specifically, by carrying out high-flux sequence to sample, one group of result data is obtained.
S202, the pollution rate for enumerating according to fixed step size sample.
Specifically, the span of the pollution rate of sample is between 0 to 1, i.e., more than or equal to 0 and less than or equal to 1.
Fixed step size could be arranged to 0.01, but be not limited to this, with specific reference to practical application the need for be configured, this
Place is not limited.
Assuming that so that fixed step size is 0.1 as an example, the pollution rate for enumerating sample according to the fixed step size can obtain 11 dirts
The value of dye rate, be respectively：0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9 and 1.
It is understood that the value of fixed step size is smaller, the value quantity of the pollution rate of sample is more, the sample finally given
This level of pollution is more accurate.
S203, the probability according to each pollution rate of formula (1) calculating：
Wherein, α is pollution rate, and F (α) is the probability of pollution rate, and S is the quantity in the mutational site in result data, R be
The quantity of high-flux sequence sequence on each site, G is sample genotype, and g is pollution genotype, bijFor in i-th site
The base observed in j-th of high-flux sequence sequence, af is the gene frequency of sample population, and P represents probability, P (bij|
G) prior probability for the base that j-th of high-flux sequence sequence in i-th of site occurs in reference gene group is represented.
Specifically, the value of each pollution rate included to step S202, each pollution rate is calculated according to formula (1)
Probability.
Site refers to the position of a gene or mark on chromosome.
Base includes adenine (ADENINE, A), and thymidine (THYMINE, T), guanine (GUANINE, G) and born of the same parents are phonetic
Four kinds of pyridine (CYTOSINE, C), DNA is that, by the helical structure of four kinds of base compositions, base exists in the form of base-pair, base
To being that the base being mutually matched for a pair is connected by hydrogen bond.
It should be noted that four kinds of bases can form n base-pair, including B1, B2, B3 ..., Bn, sample genotype G
Belong to take N number of base-pair, i.e. G ∈ { B1, B2, B3 ..., Bn }, g ∈ { B1, B2, B3 ..., Bn } with pollution genotype g.
Gene frequency is allele percentage shared in the specific gene seat of sample population, for showing sample
The diversity of gene in this population.
Further, there is base in reference gene group in j-th of high-flux sequence sequence for calculating i-th of site
Prior probability P (bij| when g), reference gene group deviation is introduced, i.e., probable deviation is set to prior probability, for adjusting and inferring
Prior probability, so as to improve the accuracy of the probability calculation to pollution rate.
Specific deviation probability can be configured according to the situation of practical application, be not limited herein.For example, probability is inclined
Difference could be arranged to 0.025, i.e., will refer to the prior probability of the base occurred in reference gene group from 0.5 lifting to 0.525
The prior probability for the base not occurred in genome drops to 0.475 from 0.5.
S204, the probability composition Making by Probability Sets by each pollution rate.
Specifically, the probability of the obtained each pollution rates of step S203 is constituted into Making by Probability Sets.
For example, so that fixed step size is 0.1 as an example, obtaining the probability of 11 pollution rates, this 11 probability are constituted into probability set
Close.
S205, the probability for comparing each pollution rate in Making by Probability Sets, sample is defined as by the corresponding pollution rate of maximum probability
Level of pollution.
Specifically, the Making by Probability Sets obtained according to step S204, the maximum probability of select probability value in the Making by Probability Sets
Corresponding pollution rate is defined as the level of pollution of sample.
For example, by fixed step size be 0.1 exemplified by, obtained Making by Probability Sets for 0.1,0,0.2,0.3,0.15,0.4,0.3,
0.25,0.32,0.2,0.24 }, the most probable value in the Making by Probability Sets is 0.4, then calculates the maximum probability using formula (1)
The pollution rate of value is the level of pollution of sample.
S206, the level of pollution according to sample, the confidence level of assay data.
Specifically, the level of pollution determined according to step S205, examines the credible of the obtained result data of high-flux sequence
Degree.
If the level of pollution that the method for inspection of confidence level can be specifically sample exceedes default pollution threshold, knot is assert
Fruit data are insincere, if the level of pollution of sample is not less than default pollution threshold, assert that result data is credible.For example, false
If result data is shown as positive, if the level of pollution of sample exceedes default pollution threshold, confirm that the result data is shown
The positive be false positive.
In the present embodiment, obtain and the result data that high-flux sequence is obtained is carried out to sample, then arranged according to fixed step size
The pollution rate of sample is lifted, the probability of each pollution rate is calculated each pollution rate according to formula (1), and introduce when calculating probability
Reference gene group deviation, probable deviation is set to prior probability, and the probability for calculating obtained each pollution rate is constituted into probability set
Close, compare the probability of each pollution rate in the Making by Probability Sets, the corresponding pollution rate of maximum probability is defined as to the contaminant water of sample
It is flat, realize the accurate detection to the sample contamination level of high-flux sequence data, it is to avoid sample contamination is carried out before sequencing
The difficulty and uncertainty brought are controlled, the accuracy rate of post analysis result is greatly improved；Also, according to the contaminant water of sample
The confidence level of flat assay data, helps to exclude due to error detection result caused by sample contamination, improves high pass and measures
The reliability and precision of sequence data processing, enabling more accurately determine hereditary disease gene mutation using high-flux sequence,
False positive site is effectively excluded, so that high-flux sequence is preferably used for into scientific research and clinical practice.
Embodiment three：
Fig. 3 is a kind of structural representation for detection means that the embodiment of the present invention three is provided, and for convenience of description, is only shown
The part related to the embodiment of the present invention.A kind of detection means of Fig. 3 examples can be the detection that previous embodiment one is provided
The executive agent of method.A kind of detection means of Fig. 3 examples includes：Acquisition module 31, statistical module 32 and determining module 33, respectively
Functional module describes in detail as follows：
Acquisition module 31, the result data that high-flux sequence is obtained is carried out to sample for obtaining；
Statistical module 32, for the result data obtained according to acquisition module 31, using bayes method statistical sample
The Making by Probability Sets of pollution rate；
Determining module 33, for counting the level of pollution that obtained Making by Probability Sets determines sample according to statistical module 32.
Each module realizes the process of respective function in a kind of detection means that the present embodiment is provided, and specifically refers to earlier figures
The description of 1 illustrated embodiment, here is omitted.
It was found from a kind of detection means of above-mentioned Fig. 3 examples, in the present embodiment, obtain and sample progress high-flux sequence is obtained
The result data arrived, the Making by Probability Sets of the pollution rate of bayes method statistical sample is used according to result data, and general according to this
Rate set determines the level of pollution of sample, realizes the accurate detection to the sample contamination level of high-flux sequence data, it is to avoid
Difficulty and uncertainty that sample contamination control is brought are carried out before sequencing, the accurate of post analysis result is greatly improved
Rate.
Example IV：
Fig. 4 is a kind of structural representation for detection means that the embodiment of the present invention four is provided, and for convenience of description, is only shown
The part related to the embodiment of the present invention.A kind of detection means of Fig. 4 examples can be the detection that previous embodiment one is provided
The executive agent of method.A kind of detection means of Fig. 4 examples includes：Acquisition module 41, statistical module 42 and determining module 43, respectively
Functional module describes in detail as follows：
Acquisition module 41, the result data that high-flux sequence is obtained is carried out to sample for obtaining；
Statistical module 42, for the result data obtained according to acquisition module 41, using bayes method statistical sample
The Making by Probability Sets of pollution rate；
Determining module 43, for counting the level of pollution that obtained Making by Probability Sets determines sample according to statistical module 42.
Further, statistical module 42 includes：
Submodule 421 is enumerated, the pollution rate for enumerating sample according to fixed step size；
Calculating sub module 422, the probability for each pollution rate that submodule 421 is obtained is enumerated for being calculated according to formula (2)：
Wherein, α is to enumerate the pollution rate that submodule 421 is obtained, and F (α) is the probability of pollution rate, and S is in result data
The quantity in mutational site, R is the quantity of the high-flux sequence sequence on each site, and G is sample genotype, and g is pollution gene
Type, bijFor the base observed in j-th of high-flux sequence sequence in i-th of site, af is the allele of sample population
Frequency, P represents probability, P (bij| g) j-th of high-flux sequence sequence in i-th of site of expression occurs in reference gene group
The prior probability of base；
Submodule 423 is combined, the probability of each pollution rate for calculating sub module 422 to be obtained constitutes the probability set
Close.
Further, calculating sub module 422 is additionally operable to：
Probable deviation is set to prior probability.
Further, it is determined that module 43 includes：
The probability of each pollution rate in submodule 431, the Making by Probability Sets obtained for comparison combination submodule 423 is selected,
The corresponding pollution rate of maximum probability is defined as to the level of pollution of sample.
Further, the device also includes：
Inspection module 44, for determining level of pollution, the confidence level of assay data according to selection submodule 431.
Each module realizes the process of respective function in a kind of detection means that the present embodiment is provided, and specifically refers to earlier figures
The description of 4 illustrated embodiments, here is omitted.
It was found from a kind of detection means of above-mentioned Fig. 4 examples, in the present embodiment, obtain and sample progress high-flux sequence is obtained
The result data arrived, then enumerates the pollution rate of sample according to fixed step size, calculates each according to formula (2) to each pollution rate
The probability of pollution rate, and reference gene group deviation is introduced when calculating probability, probable deviation is set to prior probability, will be calculated
The probability composition Making by Probability Sets of each pollution rate arrived, compares the probability of each pollution rate in the Making by Probability Sets, by maximum probability
Corresponding pollution rate is defined as the level of pollution of sample, realizes the accurate inspection to the sample contamination level of high-flux sequence data
Survey, it is to avoid difficulty and uncertainty that sample contamination control is brought are carried out before sequencing, post analysis result is greatly improved
Accuracy rate；Also, according to the confidence level of the level of pollution assay data of sample, help to exclude because sample contamination is led
The error detection result of cause, improves the reliability and precision of high-flux sequence data processing, enabling utilize high-flux sequence
More accurate determination hereditary disease gene mutation, effectively excludes false positive site, so that high-flux sequence is preferably used for into section
Grind and clinical practice.
It should be understood that the size of the sequence number of each step is not meant to the priority of execution sequence, each process in above-described embodiment
Execution sequence should determine that the implementation process without tackling the embodiment of the present invention constitutes any limit with its function and internal logic
It is fixed.
It is apparent to those skilled in the art that, for convenience of description and succinctly, only with above-mentioned each work(
Energy unit, the division progress of module are for example, in practical application, as needed can distribute above-mentioned functions by different
Functional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completion
The all or part of function of description.Each functional unit, module in embodiment can be integrated in a processing unit, also may be used
To be that unit is individually physically present, can also two or more units it is integrated in a unit, it is above-mentioned integrated
Unit can both be realized in the form of hardware, it would however also be possible to employ the form of SFU software functional unit is realized.In addition, each function list
Member, the specific name of module are also only to facilitate mutually differentiation, is not limited to the protection domain of the application.Said system
The specific work process of middle unit, module, may be referred to the corresponding process in preceding method embodiment, will not be repeated here.
Those of ordinary skill in the art are it is to be appreciated that the list of each example described with reference to the embodiments described herein
Member and algorithm steps, can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
Performed with hardware or software mode, depending on the application-specific and design constraint of technical scheme.Professional and technical personnel
Described function can be realized using distinct methods to each specific application, but this realization is it is not considered that exceed
The scope of the present invention.
In embodiment provided by the present invention, it should be understood that disclosed apparatus and method, others can be passed through
Mode is realized.For example, system embodiment described above is only schematical, for example, the division of the module or unit,
It is only a kind of division of logic function, there can be other dividing mode when actually realizing, such as multiple units or component can be with
With reference to or be desirably integrated into another system, or some features can be ignored, or not perform.It is another, it is shown or discussed
Coupling each other or direct-coupling or communication connection can be by some interfaces, the INDIRECT COUPLING of device or unit or
Communication connection, can be electrical, machinery or other forms.
The unit illustrated as separating component can be or may not be it is physically separate, it is aobvious as unit
The part shown can be or may not be physical location, you can with positioned at a place, or can also be distributed to multiple
On NE.Some or all of unit therein can be selected to realize the mesh of this embodiment scheme according to the actual needs
's.
In addition, each functional unit in each embodiment of the invention can be integrated in a processing unit, can also
That unit is individually physically present, can also two or more units it is integrated in a unit.Above-mentioned integrated list
Member can both be realized in the form of hardware, it would however also be possible to employ the form of SFU software functional unit is realized.
If the integrated unit is realized using in the form of SFU software functional unit and as independent production marketing or used
When, it can be stored in a computer read/write memory medium.Understood based on such, the technical scheme of the embodiment of the present invention
The part substantially contributed in other words to prior art or all or part of the technical scheme can be with software products
Form embody, the computer software product is stored in a storage medium, including some instructions are to cause one
Computer equipment (can be personal computer, server, or network equipment etc.) or processor (processor) perform this hair
The all or part of step of each embodiment methods described of bright embodiment.And foregoing storage medium includes：USB flash disk, mobile hard disk,
Read-only storage (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic
Dish or CD etc. are various can be with the medium of store program codes.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although with reference to foregoing reality
Example is applied the present invention is described in detail, it will be understood by those within the art that：It still can be to foregoing each
Technical scheme described in embodiment is modified, or carries out equivalent substitution to which part technical characteristic；And these are changed
Or replace, the essence of appropriate technical solution is departed from the spirit and scope of various embodiments of the present invention technical scheme, all should
Within protection scope of the present invention.
Claims (10)
1. a kind of detection method, it is characterised in that the detection method includes：
Obtain and the result data that high-flux sequence is obtained is carried out to sample；
According to the result data, the Making by Probability Sets of the pollution rate of the sample is counted using bayes method；
The Making by Probability Sets obtained according to statistics determines the level of pollution of the sample.
2. detection method as claimed in claim 1, it is characterised in that described according to the result data, using Bayes side
The Making by Probability Sets that method counts the pollution rate of the sample includes：
The pollution rate of the sample is enumerated according to fixed step size；
The probability of each pollution rate is calculated according to equation below：
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Wherein, α be the pollution rate, F (α) be the pollution rate probability, S be the result data in mutational site number
Amount, R is the quantity of the high-flux sequence sequence on each site, and G is sample genotype, and g is pollution genotype, bijFor
The base observed in j-th of high-flux sequence sequence in i-th of site, af is the gene frequency of the sample population, P
Represent probability, P (bij| g) represent the base that j-th of high-flux sequence sequence in i-th of site occurs in reference gene group
Prior probability；
The probability of each pollution rate is constituted into the Making by Probability Sets.
3. detection method as claimed in claim 2, it is characterised in that the detection method also includes：
Probable deviation is set to the prior probability.
4. detection method as claimed in claim 2, it is characterised in that the Making by Probability Sets obtained according to statistics is determined
The level of pollution of the sample includes：
Compare the probability of each pollution rate in the Making by Probability Sets, the corresponding pollution rate of maximum probability is defined as the sample
This level of pollution.
5. the detection method as described in any one of Claims 1-4, it is characterised in that the probability set that the statistics is obtained
Close after the level of pollution for determining the sample, the detection method also includes：
According to the level of pollution, the confidence level of the result data is examined.
6. a kind of detection means, it is characterised in that the detection means includes：
Acquisition module, the result data that high-flux sequence is obtained is carried out to sample for obtaining；
Statistical module, for according to the result data, the probability set of the pollution rate of the sample to be counted using bayes method
Close；
Determining module, the Making by Probability Sets for being obtained according to statistics determines the level of pollution of the sample.
7. detection means as claimed in claim 6, it is characterised in that the statistical module includes：
Submodule is enumerated, the pollution rate for enumerating the sample according to fixed step size；
Calculating sub module, the probability for calculating each pollution rate according to equation below：
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<mo>(</mo>
<mi>&alpha;</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Pi;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>S</mi>
</munderover>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>G</mi>
<mi>i</mi>
</mrow>
</munder>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>g</mi>
<mi>i</mi>
</mrow>
</munder>
<mo>{</mo>
<munderover>
<mo>&Pi;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>R</mi>
</munderover>
<mrow>
<mo>(</mo>
<mo>(</mo>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>&alpha;</mi>
</mrow>
<mo>)</mo>
<mi>P</mi>
<mo>(</mo>
<mrow>
<msub>
<mi>b</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>|</mo>
<mi>G</mi>
</mrow>
<mo>)</mo>
<mo>+</mo>
<mi>&alpha;</mi>
<mi>P</mi>
<mo>(</mo>
<mrow>
<msub>
<mi>b</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>|</mo>
<mi>g</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>}</mo>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>G</mi>
<mo>|</mo>
<mi>a</mi>
<mi>f</mi>
<mo>)</mo>
</mrow>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>g</mi>
<mo>|</mo>
<mi>a</mi>
<mi>f</mi>
<mo>)</mo>
</mrow>
</mrow>
Wherein, α be the pollution rate, F (α) be the pollution rate probability, S be the result data in mutational site number
Amount, R is the quantity of the high-flux sequence sequence on each site, and G is sample genotype, and g is pollution genotype, bijFor
The base observed in j-th of high-flux sequence sequence in i-th of site, af is the gene frequency of the sample population, P
Represent probability, P (bij| g) represent the base that j-th of high-flux sequence sequence in i-th of site occurs in reference gene group
Prior probability；
Submodule is combined, for the probability of each pollution rate to be constituted into the Making by Probability Sets.
8. detection means as claimed in claim 7, it is characterised in that the calculating sub module is additionally operable to：
Probable deviation is set to the prior probability.
9. detection means as claimed in claim 7, it is characterised in that the determining module includes：
Select submodule, the probability for comparing each pollution rate in the Making by Probability Sets, by the corresponding dirt of maximum probability
Dye rate is defined as the level of pollution of the sample.
10. the detection means as described in any one of claim 6 to 9, it is characterised in that described device also includes：
Inspection module, for according to the level of pollution, examining the confidence level of the result data.
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| CN107273715B (en) * | 2017-05-10 | 2020-03-17 | 安吉康尔（深圳）科技有限公司 | Detection method and device |
| CN111341383A (en) * | 2020-03-17 | 2020-06-26 | 安吉康尔（深圳）科技有限公司 | Method, device and storage medium for detecting copy number variation |
| CN117436532A (en) * | 2023-12-21 | 2024-01-23 | 中用科技有限公司 | Root cause analysis method for gaseous molecular pollutants in clean room |
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| CN103114150A (en) * | 2013-03-11 | 2013-05-22 | 上海美吉生物医药科技有限公司 | Single nucleotide polymorphism site identification method based on digestion library-establishing and sequencing and bayesian statistics |
| WO2015164432A1 (en) * | 2014-04-21 | 2015-10-29 | Natera, Inc. | Detecting mutations and ploidy in chromosomal segments |
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| CN103114150A (en) * | 2013-03-11 | 2013-05-22 | 上海美吉生物医药科技有限公司 | Single nucleotide polymorphism site identification method based on digestion library-establishing and sequencing and bayesian statistics |
| WO2015164432A1 (en) * | 2014-04-21 | 2015-10-29 | Natera, Inc. | Detecting mutations and ploidy in chromosomal segments |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107273715B (en) * | 2017-05-10 | 2020-03-17 | 安吉康尔（深圳）科技有限公司 | Detection method and device |
| CN111341383A (en) * | 2020-03-17 | 2020-06-26 | 安吉康尔（深圳）科技有限公司 | Method, device and storage medium for detecting copy number variation |
| CN111341383B (en) * | 2020-03-17 | 2021-06-29 | 安吉康尔（深圳）科技有限公司 | Method, device and storage medium for detecting copy number variation |
| CN117436532A (en) * | 2023-12-21 | 2024-01-23 | 中用科技有限公司 | Root cause analysis method for gaseous molecular pollutants in clean room |
| CN117436532B (en) * | 2023-12-21 | 2024-03-22 | 中用科技有限公司 | Root cause analysis method for gaseous molecular pollutants in clean room |
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