CN105631246A - Predicting method for analyzing required sequencing amount of microbial community structure - Google Patents

Abstract

The invention discloses a predicting method for analyzing a required sequencing amount of a microbial community structure. The method comprises the steps of fitting a regression relationship between a community distance caused by the insufficiency of a sequencing amount and the sequencing amount to obtain a regression equation between the community distance and the sequencing amount; correcting the regression equation through predicting the regression relationship between an error and the sequencing amount. With the increasing of the sequencing amount, the community distance caused by the insufficiency of the sequencing amount is decreased gradually; when the distance is close to zero, the similarity of a community structure obtained by repeatedly sampling for a plurality of times is close to 100 percent, and the community structure can represent microbial community composition in an environment. In such a way, through setting the community distance in the obtained linear equation to be zero, the required sequencing amount capable of reflecting the environment microbial community composition can be predicted relatively accurately according to the corrected regression equation.

Description

A kind of analyze the Forecasting Methodology of order-checking amount needed for biological community structure

Technical field:

The invention belongs to microbial ecology field, be specifically related to a kind of analyze the Forecasting Methodology of order-checking amount needed for biological community structure, particularly in the method predicting in ecological study that in environment, biological community structure resolves required minimum order-checking amount.

Background technology:

At present, the microbiologic population in various environment receives significant attention. Development along with analysis means, the degree of depth of the microbiologic population's composition analysis in same sample is constantly deepened by we, builds tens bacteria 16 S rRNA genes clones from the most junior one microbiologic population and carries out sequencing analysis and analyze several ten thousand even hundreds of thousands bar 16SrRNA gene orders by high throughput sequencing technologies up till now. This is greatly expanded us to the understanding of microbial diversity in environment. But, although high throughput sequencing technologies can be accomplished bacteria 16 S rRNA genes sequence up to a million in each sample is carried out sequencing analysis, and become the main flow means that current biological community structure resolves, it is contemplated that order-checking cost, most analyses are also in each sample carries out the degree of depth to tens0000 bacteria 16 S rRNA genes sequence up to ten thousand at present.

In natural environment, microorganism has high multiformity, estimates containing 10 in 1 gram of soil⁷-10¹¹Individual bacterial cell, several thousand even up to ten thousand kinds of antibacterials. These microorganisms distributed pole in group is uneven, and generally a few sociales occupy in microbiologic population more than the 80% of total cell number. It addition, do not have 1-15 not etc. usually used as molecular marker for resolving the bacteria 16 S rRNA genes of the bacterial quorum sensing copy number in genome. Factors above both increases the difficulty of the order-checking analyzing microbial community structures by 16SrRNA gene order. Although generally believing that the order-checking degree of depth is more deep at present, the parsing of biological community structure is more abundant, but how many 16SrRNA gene orders are carried out order-checking and can the species composition in analyzing microbial community it be not immediately clear typically by minimum needs, and this results in us and is difficult to judge whether the biological community structure obtained can truly reflect they situations under field conditions (factors) actually.

Summary of the invention:

Cannot judge at present to obtain the minimum order-checking amount having required for the microbiologic population representing meaning to overcome, it is an object of the invention to provide and a kind of analyze the Forecasting Methodology of order-checking amount needed for biological community structure, the method can by same sample is carried out minimum three times relatively low depth repeat check order Accurate Prediction to obtain the effective information of the microbiologic population's composition intending analyzing time required order-checking amount.

The Forecasting Methodology of order-checking amount needed for the analysis biological community structure of the present invention, it is characterised in that comprise the following steps:

One, correction function PS_b/ AS=a'log₁₀(PS_b) acquisition of a' and b' in+b'

A, selection are no less than 10 existing 16SrRNA gene sequencing informations and the microbiologic population close with the biological community structure habitat intending analysis, called after microbiologic population M₁��M₂��M₃��������M_n, n >=10, the 16SrRNA sequence number that each microbiologic population is contained is AS;

For above-mentioned M₁��M₂��M₃��������M_nMicrobiologic population, it is determined that be sampled obtaining 16SrRNA gene order group no less than 5 stochastic sampling degree of depth, be respectively designated as D₁��D₂��D₃��������D_nThe 16SrRNA gene order group of sequence number, n >=5, these 16SrRNA gene order groups meet following characteristics: the 16SrRNA gene order number that sequence quantity that the sequence quantity of the 16SrRNA gene order group of (1) this n group sequence number is different but maximum is minimum less than AS in selected microbiologic population, i.e. D₁��D₂��D₃�١�����D_n, and mix{D₁,D₂,D₃,����,D_n}��min{AS}; (2) D₁��D₂��D₃��������D_nThe 16SrRNA gene order group of sequence number, the 16SrRNA gene order group of each sequence number of each microbiologic population has the repetition sample of more than 3, i.e. D₁The 16SrRNA gene order group of sequence number has the repetition sample of more than 3, D₂The 16SrRNA gene order group of sequence number has the repetition sample of more than 3, and the rest may be inferred; (3) respectively from M₁��M₂��M₃��������M_nThe D that microbiologic population extracts₁��D₂��D₃��������D_nThe 16SrRNA gene order group of sequence number, their D₁��D₂��D₃��������D_nSequence number is consistent, 3 D that namely all microbiologic populations extract₁The sequence number of the 16SrRNA gene order group of sequence number is identical, is all D₁; 3 D that all microbiologic populations extract₂The sequence number of the 16SrRNA gene order group of sequence number is identical, is all D₂; The rest may be inferred;

B, under identical sampling depth conditions, calculate D in each microbiologic population respectively₁��D₂��D₃��������D_n3 extracted in the 16SrRNA gene order group of sequence number repeat the ecotone distance d of sample, then to the independent matching sequence number D of each microbiologic population₁��D₂��D₃��������D_n10 be dependent equation d=a log between the logarithmic function value at the end and ecotone distance d₁₀D+b, D described above are sequence number, it is thus achieved that a value in formula and b value;

C, make d=0, calculate the order-checking degree of depth PS of each microbiologic population prediction_b, i.e. equation d=a log₁₀D value during d=0 in D+b;

The order-checking degree of depth PS that d, each microbiologic population of comparison predict_bAnd the difference between AS, and by fit equation PS_b/ AS=a'log₁₀(PS_b)+b' obtain a' and b' value;

Two, minimum order-checking amount needed for prediction group

16SrRNA gene in the microbiologic population intending analysis is carried out the random PCR amplification of several times repetition, and checks order, obtain several data sets, from the data set of each order-checking, extract a respectively₁��a₂��a₃��������a_nThe sequence of sequence number, by a extracted in each data set₁Composition { a₁Data set, a₂Composition { a₂Data set, by that analogy, calculating the ecotone distance d between the data set of identical sequence number D respectively, described several times refer to more than 3 times, described a₁��a₂��a₃��������a_nSequence number meets a₁��a₂��a₃�١�����a_n, described n >=5;

According to the D obtained and corresponding d fit equation d=a log₁₀D+b, and obtain a value in formula and b value;

According to formula PS_a=PS_b/(PS_b/PS_a)��PS_b/(PS_b/ AS)=(10^-b/a)/(b'-a'b/a), substitute into a', b', a and b value, order-checking amount PS needed for calculating acquisition analysis biological community structure_a��

Regression relation between ecotone distance and order-checking amount that the present invention is caused because of order-checking amount deficiency by matching, it is thus achieved that regression equation between the two, and by the regression relation between forecast error and order-checking amount, this regression equation is corrected. Along with the increase of order-checking amount, the ecotone distance caused because of order-checking amount deficiency can taper into; When this distance is close to 0, repeatedly the similarity of the structure of community that repeated sampling obtains is just close to 100%, and this structure of community just can represent the microbiologic population's composition in environment. Therefore, by setting ecotone distance in the linear equation obtained as 0, can relatively accurately dope the order-checking amount that can reflect that environmental microorganism group composition is required according to the regression equation after correction.

The invention have the benefit that can by carrying out the sequencing analysis of three relatively low order-checking degree of depth to the microbiologic population intending analyzing, minimum order-checking amount required when just can predict this biological community structure according to sequencing result by the method for the present invention, simple to operation.

Accompanying drawing illustrates:

Fig. 1 is the rationale model of the present invention.

Fig. 2 is the order-checking amount D denary logarithm value multiple ecotone distance d dependency relation figure with same order-checking amount of 11,4 different microorganisms groups order-checking sample acquisition.

Fig. 3 is (A) and graph of a relation between order-checking amount predictive value and ratio and the predictive value of actual value needed for (B) after correcting before correction.

Detailed description of the invention:

Following example are further illustrating the present invention, rather than limitation of the present invention.

Embodiment 1:

As shown in Figure 1, the microbiologic population's spacing caused because of order-checking amount deficiency diminishes with the increase of order-checking amount, when the ecotone distance that same sample multiple repairing weld obtains is close to 0, repeatedly the similarity of the biological community structure that repeated sampling obtains is just close to 100%, then this biological community structure just can represent the microbiologic population's composition in environment. And there is d=a log because of between ecotone distance and order-checking amount that order-checking amount deficiency causes₁₀The dependency relation of D+b, as the ecotone distance d=0 caused because of order-checking amount deficiency, it was predicted that order-checking amount PS_b=10^-b/a��

The microbiologic population that the present embodiment is intended analyzing is the microbiologic population in contaminant rivers bed mud, is the L in table 1.

Analyze the Forecasting Methodology of order-checking amount needed for this biological community structure, comprise the following steps:

One, correction function PS_b/ AS=a'log₁₀(PS_b) acquisition of a' and b' in+b'

A, 11 existing 16SrRNA gene sequencing informations of selection and the microbiologic population close with the microbiologic population's L structure habitat intending analysis, called after microbiologic population A, B, C, D, E, F, G, H, I, J and K, the 16SrRNA sequence number that each microbiologic population is contained is AS, the 16SrRNA sequence number that each microbiologic population is contained as shown in table 1;

For mentioned microorganism group A, B, C, D, E, F, G, H, I, J and K, determine 100,500,1000,5000 and 9600 totally 5 sampling degree of depth carry out 3 sampling and obtain 16SrRNA gene order groups, namely extract respectively from microbiologic population A 3 containing 100 16SrRNA gene order groups, 3 containing 500 16SrRNA gene order groups, 3 containing 1000 16SrRNA gene order groups, 3 containing 5000 16SrRNA gene order groups and 3 samples containing 9600 16SrRNA gene order groups; Extract respectively from microbiologic population B 3 containing 100 16SrRNA gene order groups, 3 containing 500 16SrRNA gene order groups, 3 containing 1000 16SrRNA gene order groups, 3 containing 5000 16SrRNA gene order groups and 3 samples containing 9600 16SrRNA gene order groups, by that analogy.

B, respectively calculate in each microbiologic population A, B, C, D, E, F, G, H, I, J and K 3 containing 100 16SrRNA gene order groups, 3 containing 500 16SrRNA gene order groups, 3 containing 1000 16SrRNA gene order groups, 3 containing 5000 16SrRNA gene order groups and 3 containing in 9600 16SrRNA gene order groups 3 the ecotone distance d repeating samples, namely in calculating microbiologic population A 3 containing the ecotone distance d between 100 16SrRNA gene order groups₁, 3 containing the ecotone distance d between 500 16SrRNA gene order groups₂, 3 containing the ecotone distance d between 1000 16SrRNA gene order groups₃, 3 containing the ecotone distance d between 5000 16SrRNA gene order groups₄With 3 containing the ecotone distance d between 9600 16SrRNA gene order groups₅; Calculate in microbiologic population B 3 containing the ecotone distance d between 100 16SrRNA gene order groups₁, 3 containing the ecotone distance d between 500 16SrRNA gene order groups₂, 3 containing the ecotone distance d between 1000 16SrRNA gene order groups₃, 3 containing the ecotone distance d between 5000 16SrRNA gene order groups₄With 3 containing the ecotone distance d between 9600 16SrRNA gene order groups₅; Arrive microbiologic population K by that analogy. Then to the independent matching of each microbiologic population 3 containing the log in 100 16SrRNA gene order groups₁₀100 ecotone distance d corresponding thereto₁, 3 containing the log in 500 16SrRNA gene order groups₁₀500 ecotone distance d corresponding thereto₂, 3 containing the log in 1000 16SrRNA gene order groups₁₀1000 ecotone distance d corresponding thereto₃, 3 containing the log in 5000 16SrRNA gene order groups₁₀5000 ecotone distance d corresponding thereto₄With 3 containing the log in 9600 16SrRNA gene order groups₁₀9600 ecotone distance d corresponding thereto₅Between dependent equation d=a log₁₀D+b, wherein d (d₁��d₂��d₃��d₄��d₅) for ecotone distance, D is corresponding 16SrRNA gene order bar number 100,500,1000,5000 and 9600, it is thus achieved that a value in formula and b value, matching dependency relation figure is as shown in Figure 2.

C, make d=0, calculate the order-checking degree of depth (order-checking amount) PS of each microbiologic population prediction_b, i.e. equation d=a log₁₀D value during d=0 in D+b; PS in 11 samples_bAs shown in table 1, as it can be seen from table 1 the uncorrected PS that predicts the outcome_bIt is generally higher than actual result AS, and the ratio exceeding actual result that predicts the outcome increases, the PS and this predicts the outcome with the increase predicted the outcome_bAnd the ratio (prediction deviation) between actual result AS increases with the increase of this predictive value, there is PS_b/ AS=a'log₁₀(PS_b) linear relationship (Fig. 3) of+b'.

Relation between them follows following dependency relation:

PS_b/ AS=a'log₁₀(PS_b)+b'

Wherein AS is the actual 16SrRNA sequence number that microbiologic population is contained, and a' and b' is parameter, it is possible to obtained by this regression equation of matching.

The order-checking degree of depth PS that d, each microbiologic population of comparison predict_bAnd the difference between AS, and by fit equation PS_b/ AS=a'log₁₀(PS_b)+b' obtain a' and b' value, a' is 2.3985, b' is-8.698.

Two, minimum order-checking amount needed for prediction group

16SrRNA gene in the microbiologic population L intending analysis is carried out 3 times the random PCR amplification repeated, and carry out check order (sequence number is at about 10000), obtain 3 data set { I}, { II}, { III} data set, 100 sequences are extracted respectively respectively from the data set of each order-checking, article 500, sequence, article 1000, sequence, article 5000, sequence, article 9600,5 new data sets of Sequence composition (namely 5 new groups), namely from { I} data set extracting 100 sequences, article 500, sequence, article 1000, sequence, article 5000, sequence, article 9600,5 new data sets of Sequence composition, from { II} data set extracting 100 sequences, 500 sequences, 1000 sequences, 5000 sequences, 5 new data sets of 9600 Sequence composition, from { III} data set extracting 100 sequences, 500 sequences, 1000 sequences, 5000 sequences, 5 new data sets of 9600 Sequence composition. calculate 100 sequence (D respectively₁) data set between ecotone distance d₁, 500 sequence (D₂) data set between ecotone distance d₂, 1000 sequence (D₃) data set between ecotone distance d₃, 5000 sequence (D₄) data set between ecotone distance d₄With 9600 sequence (D₅) data set between ecotone distance d₅��

According to the sequence number D (D obtained₁��D₂��D₃��D₄��D₅) and corresponding d (d₁��d₂��d₃��d₄��d₅) fit equation d=a log₁₀D+b, and obtain a value in formula and b value.

Thus, microbiologic population L, its a value and b value respectively-0.1901 and 0.9627.

According to formula PS_a=PS_b/(PS_b/PS_a)��PS_b/(PS_b/ AS)=(10^-b/a)/(b ' a ' b/a), substitute into a ', b ', a and b value, order-checking amount PS needed for calculating acquisition analysis biological community structure_a. Result is as shown in table 1.

Order-checking amount PS needed for the analysis biological community structure of the 16SrRNA sequence number AS that the reality of microbiologic population L contains and calculating prediction_aAs shown in table 1. As it can be seen from table 1 the 16SrRNA sequence number AS that contains of the reality of the present embodiment predictive microbiology group L and calculate prediction analysis biological community structure needed for order-checking amount PS_aIt is more or less the same, PS_a/ AS is 0.88, thus illustrates that the result predicted according to the Forecasting Methodology of the present invention is more accurately.

Predictive value before 16SrRNA sequence number AS that the reality of 10 two microbiologic populations of table contains, correction, the predictive value PS after correction_aAnd PS_aThe ratio of/AS

Claims (1)

1. analyze the Forecasting Methodology of order-checking amount needed for biological community structure for one kind, it is characterised in that comprise the following steps:

One, correction function PS_b/ AS=a'log₁₀(PS_b) acquisition of a' and b' in+b'

A, selection are no less than 10 existing 16SrRNA gene sequencing informations and the microbiologic population close with the biological community structure habitat intending analysis, called after microbiologic population M₁��M₂��M₃��������M_n, n >=10, the 16SrRNA sequence number that each microbiologic population is contained is AS;

For above-mentioned M₁��M₂��M₃��������M_nMicrobiologic population, it is determined that be sampled obtaining 16SrRNA gene order group no less than 5 stochastic sampling degree of depth, be respectively designated as D₁��D₂��D₃��������D_nThe 16SrRNA gene order group of sequence number, n >=5, these 16SrRNA gene order groups meet following characteristics: the 16SrRNA gene order number that sequence quantity that the sequence quantity of the 16SrRNA gene order group of (1) this n group sequence number is different but maximum is minimum less than AS in selected microbiologic population, i.e. D₁��D₂��D₃�١�����D_n, and mix{D₁,D₂,D₃,����,D_n}��min{AS}; (2) D₁��D₂��D₃��������D_nThe 16SrRNA gene order group of sequence number, the 16SrRNA gene order group of each sequence number of each microbiologic population has the repetition sample of more than 3, i.e. D₁The 16SrRNA gene order group of sequence number has the repetition sample of more than 3, D₂The 16SrRNA gene order group of sequence number has the repetition sample of more than 3, and the rest may be inferred; (3) respectively from M₁��M₂��M₃��������M_nThe D that microbiologic population extracts₁��D₂��D₃��������D_nThe 16SrRNA gene order group of sequence number, their D₁��D₂��D₃��������D_nSequence number is consistent, 3 D that namely all microbiologic populations extract₁The sequence number of the 16SrRNA gene order group of sequence number is identical, is all D₁; 3 D that all microbiologic populations extract₂The sequence number of the 16SrRNA gene order group of sequence number is identical, is all D₂; The rest may be inferred;

B, under identical sampling depth conditions, calculate D in each microbiologic population respectively₁��D₂��D₃��������D_n3 extracted in the 16SrRNA gene order group of sequence number repeat the ecotone distance d of sample, then to the independent matching sequence number D of each microbiologic population₁��D₂��D₃����D_n10 be dependent equation d=a log between the logarithmic function value at the end and ecotone distance d₁₀D+b, D described above are sequence number, it is thus achieved that a value in formula and b value;

C, make d=0, calculate the order-checking degree of depth PS of each microbiologic population prediction_b, i.e. equation d=a log₁₀D value during d=0 in D+b;

The order-checking degree of depth PS that d, each microbiologic population of comparison predict_bAnd the difference between AS, and by fit equation PS_b/ AS=a'log₁₀(PS_b)+b' obtain a' and b' value;

Two, minimum order-checking amount needed for prediction group

16SrRNA gene in the microbiologic population intending analysis is carried out the random PCR amplification of several times repetition, and checks order, obtain several data sets, from the data set of each order-checking, extract a respectively₁��a₂��a₃��������a_nThe sequence of sequence number, by a extracted in each data set₁Composition { a₁Data set, a₂Composition { a₂Data set, by that analogy, calculating the ecotone distance d between the data set of identical sequence number D respectively, described several times refer to more than 3 times, described a₁��a₂��a₃��������a_nSequence number, meets a₁��a₂��a₃�١�����a_n, described n >=5;

According to the D obtained and corresponding d fit equation d=a log₁₀D+b, and obtain a value in formula and b value;

According to formula PS_a=PS_b/(PS_b/PS_a)��PS_b/(PS_b/ AS)=(10^-b/a)/(b'-a'b/a), substitute into a', b', a and b value, order-checking amount PS needed for calculating acquisition analysis biological community structure_a��