CN111951888B - Beef fatty acid composition prediction method, system and storage medium - Google Patents
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- 235000015278 beef Nutrition 0.000 title claims abstract description 95
- 239000000203 mixture Substances 0.000 title claims abstract description 80
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000003860 storage Methods 0.000 title claims abstract description 19
- 238000012216 screening Methods 0.000 claims abstract description 30
- 238000012070 whole genome sequencing analysis Methods 0.000 claims abstract description 9
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- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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Abstract
The invention discloses a beef fatty acid composition prediction method, a beef fatty acid composition prediction system and a storage medium, wherein the method comprises the following steps: performing genome-wide sequencing on a target to be detected to obtain SNPs loci of the target to be detected; screening effective SNPs sites from the SNPs sites of the target to be detected according to preset SNPs sites; inputting the effective SNPs sites into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected. According to the embodiment of the invention, the SNPs sites of the target to be detected are obtained through a whole genome sequencing technology, the effective SNPs sites are screened out from the SNPs sites of the target to be detected, and the effective SNPs sites are input into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected, so that the beef fatty acid composition detection efficiency is greatly improved compared with the existing gas chromatography. The invention can be widely applied to the biotechnology field.
Description
Technical Field
The invention relates to the technical field of biology, in particular to a beef fatty acid composition prediction method, a beef fatty acid composition prediction system and a beef fatty acid composition storage medium.
Background
In recent years, with the improvement of the life quality of people and the change of consumption concepts, high-quality beef products are more and more popular with consumers. Fatty acid is an indispensable nutrient for human body, and the composition and content of beef fatty acid are important indexes for measuring the meat quality characteristics of beef and are also important factors for determining the selection and purchase of consumers. Beef with proper proportion of saturated fatty acid, monounsaturated fatty acid and polyunsaturated fatty acid is rich in succulent, good in flavor and rich in nutrition. Therefore, the beef fatty acid composition is scientifically and reasonably predicted, the enterprises can be helped to fully exert the production potential, and the economic benefit is improved.
Beef fatty acid composition is one of the important indexes for beef value assessment, and is currently mainly measured by gas chromatography. The gas chromatography can only measure beef fatty acid at a specific time point, and can only be measured after beef slaughtering and sampling, and the detection cost, the technology complexity, the measurement time length and the great limitation of the beef fatty acid composition by using the gas chromatography are high, so that the detection efficiency of the beef fatty acid composition is low.
Disclosure of Invention
In view of the above, the present invention aims to provide a beef fatty acid composition prediction method, a beef fatty acid composition prediction system and a beef fatty acid composition storage medium, so as to improve the detection efficiency of beef fatty acid composition.
The first technical scheme adopted by the invention is as follows:
a method for predicting the fatty acid composition of beef comprising:
performing genome-wide sequencing on a target to be detected to obtain SNPs loci of the target to be detected;
screening effective SNPs sites from the SNPs sites of the target to be detected according to preset SNPs sites;
inputting the effective SNPs sites into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected.
Further, the method for determining the preset SNPs loci comprises the following steps:
constructing a sample set, wherein the sample set comprises SNPs (single nucleotide polymorphisms) sites of a sample and fatty acid compositions of the sample;
calculating correlation coefficients of the SNPs loci of the sample and the fatty acid composition of the sample according to the SNPs loci of the sample and the fatty acid composition of the sample;
screening SNPs sites of the sample according to the correlation coefficient to obtain screened SNPs sites;
and obtaining preset SNPs sites according to the SNPs sites after screening and the fatty acid composition of the sample.
Further, the correlation coefficient is a pearson correlation coefficient.
Further, the preset SNPs loci are sparse representations of the screened SNPs loci.
Further, the beef fatty acid prediction model includes an input layer, a first hidden layer, a second hidden layer, and an output layer.
Further, the first hidden layer is followed by a ReLU activation function, and the second hidden layer is followed by a ReLU activation function.
Further, the loss function of the beef fatty acid prediction model is an L2 norm.
The second technical scheme adopted by the invention is as follows:
a beef fatty acid composition prediction system comprising:
the sequencing module is used for carrying out whole genome sequencing on a target to be detected and obtaining SNPs loci of the target to be detected;
the screening module is used for screening effective SNPs sites from the SNPs sites of the target to be detected according to preset SNPs sites;
and the prediction module is used for inputting the effective SNPs sites into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected.
The third technical scheme adopted by the invention is as follows:
a beef fatty acid composition prediction system comprising:
a memory for storing a program;
and the processor is used for loading the program to execute the beef fatty acid composition prediction method.
The fourth technical scheme adopted by the invention is as follows:
a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of predicting the composition of beef fatty acids.
According to the embodiment of the invention, the SNPs sites of the target to be detected are obtained through a whole genome sequencing technology, the effective SNPs sites are screened out from the SNPs sites of the target to be detected, and the effective SNPs sites are input into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected, so that the beef fatty acid composition detection efficiency is greatly improved compared with the existing gas chromatography.
Drawings
Fig. 1 is a flowchart of a beef fatty acid composition prediction method according to an embodiment of the invention.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention.
The embodiment of the invention provides a beef fatty acid composition prediction method, which comprises the following steps of:
s100, carrying out whole genome sequencing on a target to be detected, and obtaining SNPs loci of the target to be detected;
s200, screening effective SNPs sites from SNPs sites of the target to be detected according to preset SNPs sites;
s300, inputting the effective SNPs sites into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected.
Specifically, when beef fatty acid is predicted for a cow with unknown fatty acid composition, the corresponding beef fatty acid composition can be obtained by collecting the whole genome DNA of the cow and sequencing, selecting effective SNPs sites from the obtained SNPs and sending the SNPs sites to a beef fatty acid prediction model.
The target to be detected is cattle to be detected for the fatty acid composition of the beef.
Whole genome sequencing is a novel gene detection technique that enables analysis of whole sequences of genes from blood or saliva, prediction or analysis of individual characteristics. Because the beef fatty acid composition has strong correlation with the genetic resources of the cattle, the beef fatty acid composition can be predicted to a certain extent by acquiring SNPs loci of the cattle through a whole genome sequencing technology.
SNPs, also called single nucleotide diversity, refer to DNA sequence polymorphisms at the genomic level that result from variation of a single nucleotide.
The preset SNPs sites refer to SNPs sites with higher correlation with beef fatty acid composition in the sample set.
The effective SNPs sites refer to SNPs sites with higher correlation with beef fatty acid composition in the target to be detected.
The beef fatty acid prediction model is a neural network model after training is completed, the input of the neural network model is SNPs locus data, the output of the neural network model is corresponding to the beef fatty acid composition, and the effective SNPs locus of the target to be detected is input into the beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected.
Beef fatty acid composition refers to the composition and content of beef fatty acids, and the fatty acid composition in beef, such as the content of various saturated fatty acids and the content of various unsaturated fatty acids in beef, is generally analyzed by a gas chromatograph in the prior art.
In some embodiments, the method for determining the preset SNPs sites comprises:
constructing a sample set, wherein the sample set comprises SNPs (single nucleotide polymorphisms) sites of a sample and fatty acid compositions of the sample;
calculating correlation coefficients of the SNPs loci of the sample and the fatty acid composition of the sample according to the SNPs loci of the sample and the fatty acid composition of the sample;
screening SNPs sites of the sample according to the correlation coefficient to obtain screened SNPs sites;
and obtaining preset SNPs sites according to the SNPs sites after screening and the fatty acid composition of the sample.
Specifically, beef fatty acid composition has strong correlation with bovine genes. In order to be able to predict beef fatty acid composition, a sample set is required to be constructed that is needed to solve the prediction model. An example of constructing a sample set is as follows:
slaughtering the same group of N-headed cattle of 24 months old, measuring the longus dorsi fatty acid component by using a gas chromatography method, and obtaining the SNPs loci of the whole genome by using a high-density chip. Assume that q fatty acids and p are detected together 0 The SNPs sites, construct pairs of samples:i=1, 2, …, N (N. Gtoreq.200) wherein ∈>And y i ＝[y i,1 ,y i,2 ,…,y i,q ] T Are column vectors. y is ik A value representing the kth component of fatty acids of the ith calf, i.e. k=1, 2, … q; />The j-th SNPs loci detected by the i-th cattle are shown. According to the nature of SNPs, < >>The value is {0,1,2}, p is shared 0 The SNPs sites, i.e., j=1, 2, … p 0 . The superscript 0 indicates the original gene signature. Since the number of SNPs loci detected by each population is different, p is used 0 The number of SNPs sites actually detected is shown.
The fatty acid composition of beef is determined by a small number of genes, so that most of SNPs loci detected by whole genome sequencing are irrelevant to the fatty acid composition of beef. To accelerate the screening, it is necessary to calculate the correlation of each SNPs site with beef fatty acid, and to remove SNPs of lesser correlation. At present, SNPs locus values are described through numerical values, dimensional differences exist between the SNPs locus values and the fatty acid composition of beef, and correlation coefficients are used for solving the correlation. After the correlation coefficient is calculated, SNPs loci with larger phase relation number can be taken as SNPs loci after screening.
For the SNPs loci after screening, the preset SNPs loci can be obtained through subsequent further screening.
In some embodiments, the correlation coefficient is a pearson correlation coefficient.
Specifically, the pearson correlation coefficient is used to calculate the correlation coefficient between each snp site and beef fatty acid, and the absolute value is taken when calculating the correlation coefficient because some snp sites can inhibit the formation of beef fatty acid, namely, the correlation coefficient is negatively correlated.Defining the correlation coefficient r of the jth SNPs locus and the kth component of beef fatty acid jk The method comprises the following steps:
wherein,,mean value of the jth SNPs locus value of N-headed cattle; />The average value of the k-th beef fatty acid component of N-headed cattle is shown.
Defining the j-th SNPs locus and the related coefficient r of the whole beef fatty acid j For its correlation coefficient r with all fatty acid components jk Maximum value of (r), i.e. r j ＝max{r jk },k＝1,2,…,q。
According to the correlation coefficient r j Ordering SNPs loci, and taking the front p with larger front correlation coefficient 1 The SNPs sites are used as the primary screening result, and the characteristics of the primary screening SNPs sites of the ith cattle are recorded as follows
In some embodiments, the predetermined SNPs sites are sparse representations of the screened SNPs sites.
Specifically, the accuracy of screening using correlation coefficients is low, so the threshold p 1 Cannot be set too small. Can take p 1 =1000. Using a larger threshold p 1 More ineffective SNPs sites are left in the rest SNPs sites, and further screening is needed. Further screening can obtain preset SNPs loci.
The preset SNPs sites can be obtained using a sparse representation algorithm, examples of further screening are as follows:
wherein W is a parameter to be solved, and is a q×p 1 Is a matrix of (a); II W II 1 This means that the parameter W is sparsely normalized so that most of the elements in the parameter W tend to be 0. The zero term in the parameter W indicates that the corresponding SNPs sites contribute 0 to the beef fatty acid, and the non-zero term corresponds to the effective SNPs sites. Specifically, the non-zero term in row m of W indicates that the corresponding SNPs sites are associated with the mth fatty acid. Lambda is an artificial setting parameter, and the degree of regularization by balance is usually 1.0 to 5.0. The bigger the lambda value is, the solved parameter W * The more sparse, the more non-zero terms.
The balance factor lambda is reasonably set, and the number of non-zero items of the parameter W can be controlled. The parameter W obtained by solving * Generating a p by column summation 1 Row vectors of dimensions. The non-zero entries in the row vector correspond to valid SNPs sites, i.e., preset SNPs sites. Assuming such a site has p 2 The effective SNPs locus of the ith cattle is marked as follows
The SNPs loci after screening can be further screened through a sparse representation algorithm, so that preset SNPs loci are obtained.
In some embodiments, the beef fatty acid prediction model includes an input layer, a first hidden layer, a second hidden layer, and an output layer.
Specifically, after two snp site screens, the effective snp sites will be very few, typically no more than 100. At this time, the corresponding beef fatty acid composition was regressed by neural network according to the effective SNPs sites screened. Constructing training sample sets asi＝1,2,…,N。
The neural network comprises an input layer, two hidden layers and an output layer, wherein the node number of the input layer can be p 2 The number of nodes of the first hidden layer may be 50, the firstThe number of nodes of the two hidden layers can be 10, and the number of nodes of the output layer can be q.
From the above example it is possible to obtain: the input layer is the effective SNPs locus value of each cow, and p is shared 2 And each. The parameters of the first hidden layer are marked asThe parameters of the second layer are noted as Θ 2 ∈R 10×50 The parameter of the output layer is Θ 3 ∈R q×10 。
In some embodiments, the first hidden layer is followed by a ReLU activation function and the second hidden layer is followed by a ReLU activation function.
Specifically, the activation function is a function that runs on neurons of an artificial neural network, responsible for non-linearizing the output of the neurons. By adding a ReLU activation function after the first hidden layer and the second hidden layer, the problem of gradient disappearance can be overcome, and the training speed can be increased.
In some embodiments, the loss function of the beef fatty acid prediction model is an L2 norm.
Specifically, the overall parameter of the neural network is Θ, i.e., Θ= { Θ 1 ,Θ 2 ,Θ 3 }. The whole neural network is represented by a function f, the parameter is Θ, and an L2 norm is adopted as a loss function:
wherein,,i＝1,2,…,N，X 2 representing a set of effective SNPs loci for N-headed cattle; y= { Y i I=1, 2, …, N, Y represents the fatty acid component of N-headed cattle.
The embodiment of the invention also provides a beef fatty acid composition prediction system, which comprises the following steps:
the sequencing module is used for carrying out whole genome sequencing on a target to be detected and obtaining SNPs loci of the target to be detected;
the screening module is used for screening effective SNPs sites from the SNPs sites of the target to be detected according to preset SNPs sites;
and the prediction module is used for inputting the effective SNPs sites into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected.
Specifically, the content in the above method embodiment is applicable to the system embodiment, and the functions specifically implemented by the system embodiment are the same as those of the above method embodiment, and the beneficial effects achieved by the method embodiment are the same as those achieved by the above method embodiment.
The layers, modules, units, and/or platforms, etc. included in the system may be implemented or embodied by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer readable storage medium configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, in accordance with the methods and drawings described in the specific embodiments. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
The embodiment of the invention also provides a beef fatty acid composition prediction system, which comprises the following steps:
a memory for storing a program;
and the processor is used for loading the program to execute the beef fatty acid composition prediction method.
In particular, the system may be implemented in any type of computing platform operatively connected to a suitable computing platform, including but not limited to a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and so forth. The data processing flows corresponding to the execution of the layers, modules, units, and/or platforms included in the system of the present invention may be implemented in machine readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and/or write storage medium, RAM, ROM, etc., so that it may be read by a programmable computer, which when read by a computer, may be used to configure and operate the computer to perform the processes described herein. Further, the machine readable code, or portions thereof, may be transmitted over a wired or wireless network. When such media includes instructions or programs that, in conjunction with a microprocessor or other data processor, implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media. The invention also includes the computer itself when programmed according to the methods and techniques of the present invention.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, realizes the beef fatty acid composition prediction method.
In particular, the storage medium stores processor-executable instructions for performing, when executed by a processor, the steps of an interactive information processing method according to any one of the above-described method embodiments. For the storage medium, it may include high-speed random access memory, but may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. It can be seen that the content in the above method embodiment is applicable to the present storage medium embodiment, and the specific functions of the present storage medium embodiment are the same as those of the above method embodiment, and the achieved beneficial effects are the same as those of the above method embodiment.
It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could also be termed a second element, and, similarly, a second element could also be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
The present invention is not limited to the above embodiments, but can be modified, equivalent, improved, etc. by the same means to achieve the technical effects of the present invention, which are included in the spirit and principle of the present invention. Various modifications and variations are possible in the technical solution and/or in the embodiments within the scope of the invention.
Claims (9)
1. A method for predicting the fatty acid composition of beef, comprising:
performing genome-wide sequencing on a target to be detected to obtain SNPs loci of the target to be detected;
screening effective SNPs sites from the SNPs sites of the target to be detected according to preset SNPs sites;
inputting the effective SNPs sites into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected;
the method for determining the preset SNPs loci comprises the following steps:
constructing a sample set, wherein the sample set comprises SNPs (single nucleotide polymorphisms) sites of a sample and fatty acid compositions of the sample;
calculating correlation coefficients of the SNPs loci of the sample and the fatty acid composition of the sample according to the SNPs loci of the sample and the fatty acid composition of the sample;
screening SNPs sites of the sample according to the correlation coefficient to obtain screened SNPs sites;
and obtaining preset SNPs sites according to the SNPs sites after screening and the fatty acid composition of the sample.
2. The method for predicting the fatty acid composition of beef according to claim 1, wherein the correlation coefficient is pearson correlation coefficient.
3. The method for predicting the fatty acid composition of beef according to claim 1, wherein the preset SNPs are sparse representations of the SNPs after screening.
4. The method of claim 1, wherein the beef fatty acid composition prediction model comprises an input layer, a first hidden layer, a second hidden layer, and an output layer.
5. The method of claim 4, wherein the first concealing layer is followed by a ReLU activation function and the second concealing layer is followed by a ReLU activation function.
6. The method of claim 1, wherein the beef fatty acid predictive model has a loss function of L2 norm.
7. A beef fatty acid composition prediction system, comprising:
the sequencing module is used for carrying out whole genome sequencing on a target to be detected and obtaining SNPs loci of the target to be detected;
the screening module is used for screening effective SNPs sites from the SNPs sites of the target to be detected according to preset SNPs sites;
the prediction module is used for inputting the effective SNPs sites into a beef fatty acid prediction model to obtain the beef fatty acid composition of the target to be detected;
the method for determining the preset SNPs loci comprises the following steps:
constructing a sample set, wherein the sample set comprises SNPs (single nucleotide polymorphisms) sites of a sample and fatty acid compositions of the sample;
calculating correlation coefficients of the SNPs loci of the sample and the fatty acid composition of the sample according to the SNPs loci of the sample and the fatty acid composition of the sample;
screening SNPs sites of the sample according to the correlation coefficient to obtain screened SNPs sites;
and obtaining preset SNPs sites according to the SNPs sites after screening and the fatty acid composition of the sample.
8. A beef fatty acid composition prediction system, comprising:
a memory for storing a program;
a processor for loading the program to perform a beef fatty acid composition prediction method according to any one of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements a beef fatty acid composition prediction method according to any one of claims 1-6.
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| 人肌球蛋白7A基因非同义单核苷酸多态性位点潜在致聋突变的预测分析;王全;鲁雅洁;曹新;;生物技术通讯(第06期);全文 * |
| 西门塔尔牛饱和脂肪酸含量的低密度芯片基因组预测;齐欣;张静静;樊惠中;李娟;胡鑫;刘飞;朱波;高雪;陈燕;张路培;高会江;李俊雅;;畜牧兽医学报(第08期);全文 * |
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