CN110019155A - MicroRNA group disturbance of data platform - Google Patents
MicroRNA group disturbance of data platform Download PDFInfo
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- CN110019155A CN110019155A CN201710937493.5A CN201710937493A CN110019155A CN 110019155 A CN110019155 A CN 110019155A CN 201710937493 A CN201710937493 A CN 201710937493A CN 110019155 A CN110019155 A CN 110019155A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F16/248—Presentation of query results
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
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Abstract
The invention discloses a kind of microRNA group disturbance of data platforms, should include forecasting tool module, publication module and external service module.MicroRNA group disturbance of data platform of the present invention, the negatively correlated of data can be expressed by microRNA to calculate, the potential association between disease and drug is predicted, accurately therapeutic agent is predicted for more microRNA related diseases, realizes treating the same disease with different methods and treating different diseases with same method；By the similarity calculation to the relevant various disease cance high-expression gene of microRNA, the association between various disease is established, is realized to the understanding of complex disease pathogenesis, diagnosis, prediction and medicament research and development；By the calculating to the relevant various disease cance high-expression gene similarity of microRNA, the association between different compounds is established, finds me-too and me-better similar compound.It is final to realize the increasingly automated, intelligent of compound prediction, realize old medicine newly use, the optimization and theory innovation of new drug development, clinical treatment.
Description
Technical field
The present invention relates to a kind of biomedicine technical fields, are based especially on " the disease-medicine of microRNA group data
Object " is associated with platform.
Background technique
MicroRNA(miRNA) be current biological informatics research hotspot, it is that a kind of important non-coding is small
RNA molecule, function are mainly expressed in post-transcriptional level controlling gene, and each microRNA probably can control dozens of gene
Expression, and the expression also regulation by multiple microRNA of each gene, are a set of completely new expression and regulation mechanisms.Largely
Research shows that the occurrence and development of a variety of diseases such as the unconventionality expression of microRNA and tumour are closely related, many microRNA's
Expression in expression and normal cell has apparent difference, some up-regulations, some downwards.MicroRNA and disease
Correlation is unquestionable, and people have carried out relevant research.Have several microRNA exceptions and disease phase at present
The foundation of information database is closed, but wherein or manual Arranging Literatures obtain, only with the data of simple correlation information, or only
It concentrates in certain a kind of tumor type.Up to the present, most researchs are only absorbed in correlation of the microRNA with disease, also
None is exclusively based on " disease-drug " association platform of microRNA group data.
In addition, Gene Expression Omnibus(GEO) etc. microRNA data in biomolecule information databases and day
It is all to increase, in face of the microRNA group data of magnanimity, these data how are explained and analyzed, potential rule is therefrom excavated,
It was found that " disease-drug " is potentially associated with, new treatment thoughts and scheme are provided for refractory diseases such as cancers, are urgently to be resolved
The problem of.
Summary of the invention
The present invention has built microRNA group disturbance of data platform for the prior art, can calculate and disease and drug
Relevant high expression microRNA predicts the potential association between disease and drug by the negative correlation of gene expression data,
Accurately therapeutic agent is predicted for more microRNA related diseases, optimizes disease treatment scheme.
Present invention provide the technical scheme that a kind of microRNA group disturbance of data platform, including forecasting tool module,
Publication module and external service module, in which:
Forecasting tool module is mainly established connection with MySQL database using Python programming language and is operated, and passes through
Data cleansing, cance high-expression gene calculate, association analysis step constructs " disease-drug " correlation model；It is main to include 2 analyses
Tool, one is disease and drug association analysis tool, the other is high expression microRNA analysis tool；Search result is with two
A table is presented: the relevance score of a table display retrieval disease or drug；Another table shows hit results
In the frequency that occurs of various diseases or drug；
Publication module main presentation is correlative theses questions record that researcher is delivered using the platform data, i.e. link is complete
Text focuses on display the research emphasis of this paper below the paper questions record in the form of a label and is worth in terms of using for reference study；
External service module is mainly that researcher provides the service of special data processing and analysis, and registration user can be to institute
It states platform and uploads problem, these problems, which summarize homogeneous classification, is transferred to corresponding backstage, after background process provides solution
The platform can be timely feedbacked to user；
Forecasting tool module in the present invention, publication module and external service module these three modules complement each other, and interdepend.
If the research achievement publication that researcher is worked it out using forecasting tool is delivered, the document delivered can all be embodied in publication
In module；External service module can specifically show what service platform will provide the user with, these services need user to mention
For what data, platform can provide the user with what data and charging standard etc., service provided by external service module
Forecasting tool is based on to realize.
The disturbance platform its can complete following function:
(1) Series description information is obtained, building Series obtains number (Accesion) dictionary: the GUI carried by database
Interface downloads Series description information relevant to microRNA, and the content of downloading includes sequence required for down-stream is developed
Number Accession；No. Accession in program extraction Series description information is write using Python, Series is established and obtains
The number of taking dictionary is stored in Accession.csv file；
(2) it obtains Series gene expression information: the corresponding network address of MINiML file being inferred to according to acquisition Accesion, so
After obtain corresponding url, download MINiML file；
(3) the MINiML compressed file downloaded is directed to using Python write corresponding gunzip, it is automatic uninterrupted
Decompressing compressed file；
(4) it after the completion of decompressing, is extracted by the data obtained to decompression, obtains the same series Series difference sample number
According to source-information, be stored in group.txt file；And the gene expression values in different sample datas are merged and generate one
Matrix is stored in matrix.txt file；
(5) analytical calculation is carried out to the data that (4) obtain by the program write, obtains up-regulation cance high-expression gene and turns down table under
Up to gene, it is respectively stored in up.csv and down.csv file；Analysis is calculated, provide " ... have found * * up-regulation base
The information of cause, * * down-regulated gene ", calculated result can be stored in up.csv and down.csv file；
(6) series Series obtained in (5) is matched with the serial Series in (2), decompression obtains what each matched
Platform information corresponding to serial Series；Then the gene symbol information in platform Platform information is selected, by gene
Symbol is matched with the data that (5) obtain, and the data matched are respectively stored in up_symbol.csv and down_
In symbol.csv file；
(7) association analysis: the negatively correlated of data is expressed by disease and drug micro and is calculated, is predicted between disease and drug
Potential association.
The microRNA group disturbance of data platform, wherein negatively correlated calculate is Jaccard similarity factor
Method: given two set A, B, Jaccard coefficients are defined as the size of A and B intersection and the ratio of A and the size of B union, right
This operation is done in the up-regulation of the down-regulated gene, the down-regulated gene and drug of disease of the up-regulation gene and drug of disease respectively, finally
The coefficient value calculated is used to predict the intensity of correlation, and value more high correlation is stronger, predicts between disease and drug
Potential association, if it is the effect mutually inhibited that disease and drug, which are that negative correlativing relation turned out, vice versa.
The invention has the following advantages:
MicroRNA group disturbance of data platform provided by the invention can express the negatively correlated of data by microRNA and count
It calculates, predicts the potential association between disease and drug, predict accurately medicine for more microRNA related diseases
Object realizes treating the same disease with different methods and treating different diseases with same method；By the similarity calculation to the relevant various disease cance high-expression gene of microRNA,
The association between various disease is established, is realized to the understanding of complex disease pathogenesis, diagnosis, prediction and medicament research and development；Pass through
The association between different compounds is established in calculating to the relevant various disease cance high-expression gene similarity of microRNA, discovery
Me-too and me-better similar compound.It is final to realize the increasingly automated, intelligent of compound prediction, realize that old medicine is new
With, the optimization and theory innovation of new drug development, clinical treatment.
Specific embodiment
A kind of microRNA group disturbance of data platform, including forecasting tool module, publication module and externally service mould
Block, in which:
Forecasting tool module is mainly established connection with MySQL database using Python programming language and is operated, and passes through
Data cleansing, cance high-expression gene calculate, association analysis step constructs " disease-drug " correlation model；It is main to include 2 analyses
Tool, one is disease and drug association analysis tool, the other is high expression microRNA analysis tool；Search result is with two
A table is presented: the relevance score of a table display retrieval disease or drug；Another table shows hit results
In the frequency that occurs of various diseases or drug；
Publication module main presentation is correlative theses questions record that researcher is delivered using the platform data, i.e. link is complete
Text focuses on display the research emphasis of this paper below the paper questions record in the form of a label and is worth in terms of using for reference study,
Such as: test progress；Data generate；Data analysis；Analysis method；Data integration；Data standard；Marker generates；Software is opened
Hair etc.；
External service module is mainly that researcher provides the service of special data processing and analysis, and registration user can be to institute
It states platform and uploads problem, these problems, which summarize homogeneous classification, is transferred to corresponding backstage, after background process provides solution
The platform can be timely feedbacked to user；
The disturbance platform its can complete following function:
(1) Series description information is obtained, building Series obtains number (Accesion) dictionary: the GUI carried by database
Interface downloads Series description information relevant to microRNA, and the content of downloading includes sequence required for down-stream is developed
Number Accession；No. Accession in program extraction Series description information is write using Python, Series is established and obtains
The number of taking dictionary is stored in Accession.csv file；
(2) it obtains Series gene expression information: the corresponding network address of MINiML file being inferred to according to acquisition Accesion, so
After obtain corresponding url, download MINiML file, MINiML file is XML format file, includes all platforms
(Platform), the partial data of sample (Sample) and serial (Series) information；
(3) the MINiML compressed file downloaded is directed to using Python write corresponding gunzip, it is automatic uninterrupted
Decompressing compressed file；
(4) it after the completion of decompressing, is extracted by the data obtained to decompression, obtains the same series Series difference sample number
According to source-information, be stored in group.txt file；And the gene expression values in different sample datas are merged and generate one
Matrix is stored in matrix.txt file；
(5) analytical calculation is carried out to the data that (4) obtain by the program write, obtains up-regulation cance high-expression gene and turns down table under
Up to gene, it is respectively stored in up.csv and down.csv file；Analysis is calculated, provide " ... have found * * up-regulation base
The information of cause, * * down-regulated gene ", calculated result can be stored in up.csv and down.csv file；
(6) series Series obtained in (5) is matched with the serial Series in (2), decompression obtains what each matched
Platform information corresponding to serial Series；Then the gene symbol information in platform Platform information is selected, by gene
Symbol is matched with the data that (5) obtain, and the data matched are respectively stored in up_symbol.csv and down_
In symbol.csv file；
(7) association analysis: the negatively correlated of data is expressed by disease and drug micro and is calculated, is predicted between disease and drug
Potential association.
Disease can cause the up-regulation or downward of certain gene expression abundance information, and drug also can cause certain gene expressions rich
The up-regulation or downward of information are spent, enables to up-regulation gene abundance to lower then we need to find a kind of drug, allows and lower base
Because abundance raises, here it is so-called negative correlation.What we to be looked for is exactly that negatively correlated strongest this drug is final as us
Prediction drug.Negative correlation, which is calculated, can give with many algorithms wherein having a kind of algorithm is exactly Jaccard similarity factor
Fixed two set A, B, Jaccard coefficients are defined as the size of A and B intersection and the ratio of A and the size of B union.But we
Used to have some differences with original Jaccard similarity factor, we are the downwards of the up-regulation gene and drug to disease
This operation is done in the up-regulation of gene, the down-regulated gene of disease and drug respectively, the coefficient value finally calculated, for predicting phase
The intensity of closing property, value more high correlation are stronger.The potential association between disease and drug is predicted, if disease is negative with drug
It is the effect mutually inhibited that correlativity, which has turned out, and vice versa.Judgment criteria is the value of Jaccard similarity factor,
The value of Jaccard similarity factor more high correlation is stronger.Now the platform is utilized it is predicted that scheme out.
Claims (2)
1. a kind of microRNA group disturbance of data platform, including forecasting tool module, publication module and external service module,
Wherein:
Forecasting tool module is mainly established connection with MySQL database using Python programming language and is operated, and passes through
Data cleansing, cance high-expression gene calculate, association analysis step constructs " disease-drug " correlation model；It is main to include 2 analyses
Tool, one is disease and drug association analysis tool, the other is high expression microRNA analysis tool；Search result is with two
A table is presented: the relevance score of a table display retrieval disease or drug；Another table shows hit results
In the frequency that occurs of various diseases or drug；
Publication module main presentation is correlative theses questions record that researcher is delivered using the platform data, i.e. link is complete
Text focuses on display the research emphasis of this paper below the paper questions record in the form of a label and is worth in terms of using for reference study；
External service module is mainly that researcher provides the service of special data processing and analysis, and registration user can be to institute
It states platform and uploads problem, these problems, which summarize homogeneous classification, is transferred to corresponding backstage, after background process provides solution
The platform can be timely feedbacked to user；
The disturbance platform its can complete following function:
(1) Series description information is obtained, building Series obtains number (Accesion) dictionary: the GUI carried by database
Interface downloads Series description information relevant to microRNA, and the content of downloading includes sequence required for down-stream is developed
Number Accession；No. Accession in program extraction Series description information is write using Python, Series is established and obtains
The number of taking dictionary is stored in Accession.csv file；
(2) it obtains Series gene expression information: the corresponding network address of MINiML file being inferred to according to acquisition Accesion, so
After obtain corresponding url, download MINiML file；
(3) the MINiML compressed file downloaded is directed to using Python write corresponding gunzip, it is automatic uninterrupted
Decompressing compressed file；
(4) it after the completion of decompressing, is extracted by the data obtained to decompression, obtains the same series Series difference sample number
According to source-information, be stored in group.txt file；And the gene expression values in different sample datas are merged and generate one
Matrix is stored in matrix.txt file；
(5) analytical calculation is carried out to the data that (4) obtain by the program write, obtains up-regulation cance high-expression gene and turns down table under
Up to gene, it is respectively stored in up.csv and down.csv file；Analysis is calculated, provide " ... have found * * up-regulation base
The information of cause, * * down-regulated gene ", calculated result can be stored in up.csv and down.csv file；
(6) series Series obtained in (5) is matched with the serial Series in (2), decompression obtains what each matched
Platform information corresponding to serial Series；Then the gene symbol information in platform Platform information is selected, by gene
Symbol is matched with the data that (5) obtain, and the data matched are respectively stored in up_symbol.csv and down_
In symbol.csv file；
(7) association analysis: the negatively correlated of data is expressed by disease and drug microRNA and is calculated, disease and drug are predicted
Between potential association.
2. microRNA group disturbance of data platform according to claim 1, it is characterised in that: the negative correlation, which calculates, is
Jaccard similarity factor algorithm: the size and A and B that given two set A, B, Jaccard coefficients are defined as A and B intersection are simultaneously
The ratio of the size of collection, the up-regulation point of the down-regulated gene, the down-regulated gene and drug of disease of up-regulation gene and drug to disease
This operation is not done, and the coefficient value finally calculated is used to predict the intensity of correlation, and value more high correlation is stronger, predicts
Potential association between disease and drug, if it is the effect mutually inhibited that disease and drug, which are that negative correlativing relation turned out,
Vice versa.
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