CN110010203B - Interactive dynamic QTL analysis system and method based on biological cloud platform - Google Patents
Interactive dynamic QTL analysis system and method based on biological cloud platform Download PDFInfo
- Publication number
- CN110010203B CN110010203B CN201910249550.XA CN201910249550A CN110010203B CN 110010203 B CN110010203 B CN 110010203B CN 201910249550 A CN201910249550 A CN 201910249550A CN 110010203 B CN110010203 B CN 110010203B
- Authority
- CN
- China
- Prior art keywords
- analysis
- qtl
- data
- key
- interval
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses an interactive dynamic QTL analysis system and method based on a biological cloud platform, wherein the system comprises: the system comprises an interactive analysis display module, an interactive molecular intelligent screening module and an interactive dynamic QTL analysis module; the interactive analysis display module is used for importing and analyzing the phenotype data to obtain first processing data; the interactive molecular intelligent screening module is used for carrying out marker screening and filtering on the genotype data to obtain second processing data; and the interactive dynamic QTL analysis module is used for carrying out QTL analysis on the first processing data and the second processing data. The system provided by the invention has high integration level and is convenient for users to use, and the phenotype data, the genotype data and the QTL analysis can be used for completing related analysis contents in batches by one-key parameter setting, so that the analysis threshold is reduced and the user experience is improved.
Description
Technical Field
The invention relates to the field of biology, in particular to an interactive dynamic QTL analysis system and method based on a biological cloud platform.
Background
Quantitative Trait Loci (QTL) refers to the position of a gene controlling quantitative traits in a genome, analyzes the relationship between a whole genome DNA molecular marker and a quantitative trait phenotype value, detects the presence of the QTL, locates the QTL on a genetic map, and determines the genetic distance between the genetic marker and the QTL. The second generation sequencing-based high-density genetic map and QTL positioning become one of the common methods for functional gene mining of crops, livestock and aquatic products at present.
The rapid development of molecular markers and high-throughput sequencing technologies has become an important tool for QTL analysis and research, and bioinformatics technology of sequencing off-machine data has increasingly prominent role in processing the generated huge molecular biological information. How to utilize the biological information technology to mine valuable core information from mass data has become a core problem of scientific researchers in the field. Data output of a traditional biological information analysis result is displayed as a static problem report, data display is inconvenient for convenient analysis and key data mining, in order to obtain a better research effect, scientific researchers are often required to master a biological information analysis method to correct a static result, and the process is time-consuming and labor-consuming. The sequencing requirement is continuously increased, and the sequencing requirement is inconsistent with the fact that the data analysis result is inconvenient for scientific research personnel to analyze, and in order to overcome the problem, one-key parameter modification and dynamic analysis report become hot methods for solving the problem.
Content of application
In order to solve the technical problems, the invention provides an interactive dynamic QTL analysis system and method based on a biological cloud platform, the system is high in integration level and convenient for users to use, and the phenotype data, the genotype data and the QTL analysis can be completed in batch by one-key parameter setting, so that the analysis threshold is reduced, and the user experience is improved.
In order to achieve the object, the invention provides an interactive dynamic QTL analysis system based on a biological cloud platform, comprising: the system comprises an interactive analysis display module, an interactive molecular intelligent screening module and an interactive dynamic QTL analysis module;
the interactive analysis display module is used for importing and analyzing the phenotype data to obtain first processing data;
the interactive molecular intelligent screening module is used for carrying out marker screening and filtering on the genotype data to obtain second processing data;
and the interactive dynamic QTL analysis module is used for carrying out QTL analysis on the first processed data and the second processed data.
Optionally, importing and analyzing the phenotype data comprises one or more of: performing one-click phenotype distribution analysis and one-click phenotype parameter analysis on the phenotype data;
optionally, the marker screening filter comprises one or more of the following: selecting the marker deletion rate in a one-key self-defining mode, selecting the minimum marker genetic distance in a one-key self-defining mode and selecting the minimum marker physical distance in a one-key self-defining mode;
QTL analysis includes one or more of the following ways: one-key QTL analysis software selection, one-key mode selection, one-key parameter selection, one-key QTL analysis overall result display and one-key QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval
In order to achieve the object of the present invention, the present invention also provides a biological cloud platform-based interactive dynamic QTL analysis method, including:
step 1, importing and analyzing the form data to obtain first processing data, and executing step 3;
step 2, performing marker screening and filtering on the genotype data to obtain second processing data, and executing step 3;
step 3, QTL analysis is carried out on the first processed data and the second processed data to obtain a first analysis result, and step 4 is executed;
step 4, judging whether the threshold value of the first analysis result meets the expectation; if yes, executing step 5; if not, executing step 6;
step 5, analyzing and reading the first analysis result and deriving a personalized analysis report;
and 6, redefining a QTL interval threshold value, and executing the step 3.
Optionally, importing and analyzing the phenotype data comprises one or more of: performing one-click phenotype distribution analysis and one-click phenotype parameter analysis on the phenotype data;
optionally, the marker screening filter comprises one or more of the following: selecting the marker deletion rate in a one-key self-defining mode, selecting the minimum marker genetic distance in a one-key self-defining mode and selecting the minimum marker physical distance in a one-key self-defining mode;
QTL analysis includes one or more of the following ways: one-key QTL analysis software selection, one-key mode selection, one-key parameter selection, one-key QTL analysis overall result display and one-key QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval;
the redefinition of the QTL interval threshold comprises one or more of the following ways: parameter selection, QTL interval function enrichment analysis, QTL analysis overall result display and QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval.
In order to achieve the aim, the invention provides an interactive dynamic QTL analysis method based on a biological cloud platform, which comprises the following steps:
step 1, importing and analyzing the form data to obtain a first processing result; executing the step 3;
step 2, performing marker screening and filtering on the genotype data to obtain a second processing result; executing the step 3;
step 3, QTL analysis is carried out on the first processed data and the second processed data to obtain a first analysis result; executing the step 4;
and 4, processing the first analysis result by adopting a sequence tool.
Optionally, importing and analyzing the form data comprises one or more of the following: performing one-click phenotype distribution analysis and one-click phenotype parameter analysis on the phenotype data;
optionally, the marker screening filter comprises one or more of the following: selecting the marker deletion rate in a one-key self-defining mode, selecting the minimum marker genetic distance in a one-key self-defining mode and selecting the minimum marker physical distance in a one-key self-defining mode;
QTL analysis includes one or more of the following ways: one-key QTL analysis software selection, one-key mode selection, one-key parameter selection, one-key QTL analysis overall result display and one-key QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval;
the sequence tool includes one or more of the following ways: a genome browser, Blast comparison and primer design; wherein, the genome browser comprises: and (3) displaying the gene structure, the mutation information of the parents and the sequence download in the interval.
Optionally, a step 5 is further included between steps 3 and 4;
and 5, redefining the QTL interval threshold.
Optionally, the QTL interval threshold redefinition comprises one or more of the following ways: parameter selection, function enrichment analysis of QTL interval, integral result display of QTL analysis and function enrichment analysis of QTL interval genes; the parameters specifically include: threshold, LOD confidence interval.
The interactive dynamic QTL analysis system and method based on the biological cloud platform have the advantages that the system integration level is high, the system is convenient for users to use, the phenotype data, the genotype data and the QTL analysis can be completed in batch by one-key parameter setting, the analysis threshold is lowered, and the user experience is improved.
Drawings
FIG. 1 is a schematic structural diagram of an interactive dynamic QTL analysis system based on a biological cloud platform provided by an embodiment of the invention;
FIG. 2 is a schematic structural diagram of an interactive dynamic QTL analysis system based on a biological cloud platform provided by an embodiment of the invention;
FIG. 3 is a schematic flowchart of a method for interactive dynamic QTL analysis based on a biological cloud platform according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of another method for interactive dynamic QTL analysis based on a biological cloud platform according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
As shown in fig. 1-2, the embodiment of the present invention provides a biological cloud platform-based interactive dynamic QTL analysis system, which includes but is not limited to: the system comprises an interactive analysis display module, an interactive molecular intelligent screening module and an interactive dynamic QTL analysis module;
the interactive analysis display module is used for importing and analyzing the phenotype data to obtain first processing data;
the interactive molecular intelligent screening module is used for carrying out marker screening and filtering on the genotype data to obtain second processing data;
and the interactive dynamic QTL analysis module is used for carrying out QTL analysis on the first processing data and the second processing data.
Alternatively, the first processed data may be phenotype data, and the second processed data may be genotype data.
Optionally, the importing and analyzing includes, but is not limited to, one or more of the following: one-click phenotypic distribution analysis and one-click phenotypic parameter analysis; in particular, but not limited to, a one-click phenotype distribution analysis unit and a one-click phenotype parameter analysis unit are arranged in the interactive analysis display module.
Optionally, marker screening filters include, but are not limited to, one or more of the following: selecting the marker deletion rate in a one-key self-defining mode, selecting the minimum marker genetic distance in a one-key self-defining mode and selecting the minimum marker physical distance in a one-key self-defining mode; specifically, but not limited to, a one-key custom selection marker deletion rate unit, a one-key custom selection minimum marker genetic distance unit and a one-key custom selection minimum marker physical distance unit are arranged in the interactive molecular intelligent screening module.
Optionally, QTL analysis includes, but is not limited to, one or more of the following: one-key QTL analysis software selection, one-key mode selection, one-key parameter selection, one-key QTL analysis overall result display and one-key QTL interval gene function enrichment analysis; wherein the parameters specifically include but are not limited to: threshold, LOD confidence interval.
The interactive analysis display module is specifically used for realizing the functions of importing and analyzing the phenotype data for a user through one-click phenotype distribution analysis, one-click phenotype parameter analysis and the like, such as a genetic breeding phenotype data automatic analysis and result visualization display module. Wherein the phenotypic distribution analysis includes, but is not limited to: the analysis of the phenotype distribution frequency histogram, the fitting line, the phenotype distribution box line graph and the phenotype distribution correlation heat map can be realized by a user by self-defining and selecting a plurality of system phenotype values after uploading data. Wherein phenotypic parameter analysis includes, but is not limited to: selecting parameters such as a missing value processing method, an outlier processing method, data processing for years (including but not limited to averaging or BLUP breeding value) in a self-defined mode; meanwhile, the graph and the font of the result obtained by analysis can be modified and downloaded in one key mode.
The interactive molecular intelligent screening module specifically selects parameters of the marker screening such as genetic map marker filtering through a one-key type user-defined selection marker deletion rate, a one-key type user-defined selection minimum marker genetic distance, a one-key type user-defined selection minimum marker physical distance and the like, so that marker controllability is ensured, impurity markers are removed, genetic map marker filtering is realized, meanwhile, the system can realize marker information result visualization, and a genetic map schematic diagram and all sample genotype information are obtained.
An interactive dynamic QTL analysis module is developed based on R/QTL, and can be used for customizing parameters (including but not limited to LOD threshold selection, replacement test times, replacement test thresholds, confidence intervals and the like) by a user, automatically and simultaneously extracting QTL interval gene information and carrying out GO/KEGG enrichment analysis; for example, in the form of a webpage picture, a user can interactively view QTL positioning results on the same map and compare a plurality of character positioning results; the system uses Javascript to interact with the user, in the QTL interaction analysis module, the user can select corresponding analysis according to the own needs, including but not limited to LOD (LOD) map (character + chromosome), QTL interval and related gene analysis, threshold value set by QTL detection of each character, and the like, and can download the file formats of png or svg and the like of the corresponding analysis display map to the local.
Preferably, the system may be based on an existing biological cloud platform; besides the three modules, the system can also comprise various modules corresponding to accessory functions such as blast comparison, primer design, a genome browser (which can display the gene structure in an interval, the mutation information of parents and sequence downloading) and the like.
The invention has at least the following three advantages:
(1) the system has high integration level: various data can be stored in the system for a long time and can be used at any time when logging in, and a user of the system can upload newly collected phenotype data at any time and can store the phenotype data for a long time.
(2) The system can be analyzed in one key: various data, QTL analysis and other multiple analysis contents can be subjected to one-key batch related analysis, analysis work which can be completed only by professional biological information analysts in the past can be realized, and after a system user uploads data, experiment data mining and analysis can be realized by checking corresponding analysis options and self-defined parameters of the system, and a personalized analysis result is generated.
(3) Besides the above two points, the system can also comprise corresponding modules such as input and output file descriptions, analysis parameter descriptions, operation video courses of the system and the like, thereby being convenient for users to use.
As shown in fig. 3, an embodiment of the present application further provides a biological cloud platform-based interactive dynamic QTL analysis method, including:
step S301, importing and analyzing the form data to obtain first processing data, and executing step S303;
optionally, the importing and analyzing comprises one or more of the following: one-click phenotype distribution analysis and one-click phenotype parameter analysis;
step S302, performing marker screening and filtering on the genotype data to obtain second processing data, and executing step S303;
optionally, the marker screening filter comprises one or more of the following: one-button self-defined selection marker deletion rate, one-button self-defined selection minimum marker genetic distance, one-button self-defined selection minimum marker physical distance and the like;
step S303, QTL analysis is carried out on the first processing data and the second processing data to obtain a first analysis result, and step S304 is executed;
optionally, the QTL analysis comprises one or more of the following: one-key QTL analysis software selection, one-key mode selection, one-key parameter selection, one-key QTL analysis overall result display and one-key QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval;
step S304, judging whether the threshold value of the first analysis result meets expectations or not; if yes, go to step S305; otherwise, executing step S306;
step S305, further performing analysis interpretation: analyzing and reading the first analysis result, and deriving a personalized analysis report;
step S306, redefining a QTL interval threshold value, and executing step S303;
optionally, the QTL interval threshold redefinition comprises one or more of the following ways: parameter selection, QTL interval function enrichment analysis, QTL analysis overall result display and QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval.
According to the embodiment, the interactive dynamic QTL system analysis process comprises the following steps: and importing and analyzing sample phenotype data, filtering genetic map markers, carrying out QTL analysis to obtain whether a result judgment threshold is met, carrying out one-click parameter selection on a system if the result judgment threshold is not met, changing the QTL interval threshold such as the QTL interval threshold, redefining until an expected result is obtained, completing experimental data mining and further analysis, and finally, deriving a personalized analysis report.
As shown in fig. 4, an embodiment of the present application further provides a biological cloud platform-based interactive dynamic QTL analysis method, including:
step 401, importing and analyzing the form data to obtain a first processing result; step 403 is executed;
optionally, the importing and analyzing comprises one or more of the following: one-click phenotype distribution analysis, one-click phenotype parameter analysis and the like;
wherein the phenotypic distribution analysis includes, but is not limited to: the analysis of the phenotype distribution frequency histogram, the fitting line, the phenotype distribution box line graph and the phenotype distribution correlation heat map can be realized by a user by self-defining and selecting a plurality of system phenotype values after uploading data. Wherein phenotypic parameter analysis includes, but is not limited to: selecting parameters such as a missing value processing method, an outlier processing method, data processing for years (including but not limited to averaging or BLUP breeding value) in a self-defined mode; meanwhile, the graph and the font of the result obtained by analysis can be modified and downloaded in one key mode; additionally, marker screening filtration may further comprise: custom screening, BLUP analysis, etc.;
step 402, performing marker screening and filtering on the genotype data to obtain a second processing result; step 403 is executed;
optionally, the marker screening filter comprises one or more of the following: selecting the marker deletion rate in a one-key self-defining mode, selecting the minimum marker genetic distance in a one-key self-defining mode and selecting the minimum marker physical distance in a one-key self-defining mode;
the marker filtering parameters such as genetic map marker filtering parameters are screened and filtered through markers such as one-button self-defined selection marker deletion rate, one-button self-defined selection minimum marker genetic distance, one-button self-defined selection minimum marker physical distance and the like, the marker controllability is ensured, impurity markers are removed, the genetic map marker filtering is realized, meanwhile, the system can realize the visualization of marker information results, and a genetic map schematic diagram and all sample genotype information are obtained; wherein the visualization of the marking information result specifically comprises type statistics, a map and the like; additionally, marker screening filtration may further comprise: group types and custom screening;
optionally, the QTL analysis comprises one or more of the following: one-key QTL analysis software selection, one-key mode selection, one-key parameter selection, one-key QTL analysis overall result display and one-key QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval;
step 403, performing QTL analysis on the first processed data and the second processed data to obtain a first analysis result; step 404 is executed;
step 404, processing the first analysis result by using a sequence tool.
Optionally, the sequence tool comprises one or more of the following: a genome browser, Blast comparison and primer design; wherein, the genome browser comprises: and (3) displaying the gene structure, the mutation information of the parents and the sequence download in the interval.
Optionally, a step 405 is further included between steps 403 and 404;
step 405, redefining the QTL interval threshold.
Optionally, the QTL interval threshold redefinition comprises one or more of the following ways: parameter selection, QTL interval function enrichment analysis, QTL analysis overall result display and QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval.
The above embodiment shows that the present invention has at least the following three advantages:
(1) the system has high integration level: various data can be stored in the system for a long time and can be used at any time when logging in, and a user of the system can upload newly collected phenotype data at any time and can store the phenotype data for a long time.
(2) The system can be analyzed in one key: various data, QTL analysis and other multiple analysis contents can be subjected to one-key batch related analysis, analysis work which can be completed only by professional biological information analysts in the past can be realized, and after a system user uploads data, experiment data mining and analysis can be realized by checking corresponding analysis options and self-defined parameters of the system, and a personalized analysis result is generated.
(3) Besides the above two points, the system can also comprise corresponding modules such as input and output file descriptions, analysis parameter descriptions, operation video courses of the system and the like, thereby being convenient for users to use.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.
Claims (5)
1. An interactive dynamic QTL analysis system based on a biological cloud platform, which is characterized by comprising: the system comprises an interactive analysis display module, an interactive molecular intelligent screening module and an interactive dynamic QTL analysis module;
the interactive analysis display module is used for importing and analyzing the phenotype data to obtain first processing data;
the interactive molecular intelligent screening module is used for carrying out marker screening and filtering on the genotype data to obtain second processing data;
the interactive dynamic QTL analysis module is used for carrying out QTL analysis on the first processing data and the second processing data;
wherein, the importing and analyzing of the form data comprises one or more of the following modes: performing one-click phenotype distribution analysis and one-click phenotype parameter analysis on the phenotype data; the one-click phenotypic distribution analysis comprises a phenotypic distribution frequency histogram and a fit line, a phenotypic distribution box line graph, a phenotypic distribution correlation heat map; the one-click phenotypic parameter analysis comprises a missing value processing method, an outlier processing method and a multi-year and multi-place data processing method; the data processing method for the multi-year and multi-place comprises averaging or BLUP breeding value;
marker screening filtration includes one or more of the following ways: selecting the marker deletion rate in a one-key self-defining mode, selecting the minimum marker genetic distance in a one-key self-defining mode and selecting the minimum marker physical distance in a one-key self-defining mode;
QTL analysis includes one or more of the following ways: one-key QTL analysis software selection, one-key mode selection, one-key parameter selection, one-key QTL analysis overall result display and one-key QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval;
the interactive analysis display module is also used for carrying out one-click modification and downloading on the graph and the font of the first processing data;
the interactive molecular intelligent screening module is also used for filtering parameters of genetic map label filtration and visualizing the result of label information, and obtaining a genetic map schematic diagram and genotype information of all samples;
the interactive dynamic QTL analysis module is also used for setting custom parameters, automatically and simultaneously completing extraction of QTL interval gene information, GO/KEGG enrichment analysis, interactively checking QTL positioning results on the same map in a web page picture mode, comparing a plurality of character positioning results, setting custom analysis and downloading a corresponding analysis display map to the local; the self-defined parameters comprise LOD threshold value selection, replacement test times, replacement test threshold values and confidence intervals; the user-defined analysis comprises LOD (loss of tolerance) maps of characters and chromosomes, QTL (quantitative trait locus) interval and related gene analysis and threshold values set by QTL detection of all characters;
the system also comprises various modules corresponding to blast comparison, primer design, a genome browser, input and output file descriptions, analysis parameter descriptions and a system operation video course; the corresponding modules of the genome browser comprise corresponding modules capable of displaying gene structures, parent mutation information and sequence downloads in intervals.
2. A biological cloud platform-based interactive dynamic QTL analysis method, using the biological cloud platform-based interactive dynamic QTL analysis system of claim 1, the method comprising:
step 1, importing and analyzing the form data to obtain first processing data, and executing step 3;
step 2, performing marker screening and filtering on the genotype data to obtain second processing data, and executing step 3;
step 3, QTL analysis is carried out on the first processed data and the second processed data to obtain a first analysis result, and step 4 is executed;
step 4, judging whether the threshold value of the first analysis result meets the expectation; if yes, executing step 5; if not, executing the step 6;
step 5, analyzing and reading the first analysis result and deriving a personalized analysis report;
step 6, redefining a QTL interval threshold value, and executing the step 3;
wherein the importing and analyzing of the phenotype data comprises one or more of the following ways: performing one-click phenotype distribution analysis and one-click phenotype parameter analysis on the phenotype data;
marker screening filtration includes one or more of the following: selecting the marker deletion rate in a one-key self-defining mode, selecting the minimum marker genetic distance in a one-key self-defining mode and selecting the minimum marker physical distance in a one-key self-defining mode;
QTL analysis includes one or more of the following ways: one-key QTL analysis software selection, one-key mode selection, one-key parameter selection, one-key QTL analysis overall result display and one-key QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval;
the redefinition of the QTL interval threshold comprises one or more of the following ways: parameter selection, QTL interval function enrichment analysis, QTL analysis overall result display and QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval.
3. A biological cloud platform-based interactive dynamic QTL analysis method, using the biological cloud platform-based interactive dynamic QTL analysis system of claim 1, the method comprising:
step 1, importing and analyzing the form data to obtain a first processing result; executing the step 3;
step 2, performing marker screening and filtering on the genotype data to obtain a second processing result; executing the step 3;
step 3, QTL analysis is carried out on the first processed data and the second processed data to obtain a first analysis result; executing the step 4;
step 4, processing the first analysis result by using a sequence tool;
wherein, the introduction and analysis of the phenotype data comprises one or more of the following modes: performing one-click phenotype distribution analysis and one-click phenotype parameter analysis on the phenotype data;
marker screening filtration includes one or more of the following ways: selecting the marker deletion rate in a one-key self-defining mode, selecting the minimum marker genetic distance in a one-key self-defining mode and selecting the minimum marker physical distance in a one-key self-defining mode;
QTL analysis includes one or more of the following ways: QTL analysis software selection, mode selection, parameter selection, QTL analysis overall result display and function enrichment analysis of QTL interval genes; the parameters specifically include: threshold, LOD confidence interval;
the sequence tool includes one or more of the following ways: a genome browser, Blast comparison and primer design; wherein, the genome browser comprises: and (3) displaying the gene structure, the mutation information of the parents and the sequence download in the interval.
4. The method of claim 3,
a step 5 is also included between the steps 3 and 4;
and 5, redefining the QTL interval threshold.
5. The method of claim 4,
the redefinition of the QTL interval threshold comprises one or more of the following ways: parameter selection, QTL interval function enrichment analysis, QTL analysis overall result display and QTL interval gene function enrichment analysis; the parameters specifically include: threshold, LOD confidence interval.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910249550.XA CN110010203B (en) | 2019-03-29 | 2019-03-29 | Interactive dynamic QTL analysis system and method based on biological cloud platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910249550.XA CN110010203B (en) | 2019-03-29 | 2019-03-29 | Interactive dynamic QTL analysis system and method based on biological cloud platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110010203A CN110010203A (en) | 2019-07-12 |
| CN110010203B true CN110010203B (en) | 2022-05-27 |
Family
ID=67168901
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910249550.XA Active CN110010203B (en) | 2019-03-29 | 2019-03-29 | Interactive dynamic QTL analysis system and method based on biological cloud platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110010203B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111276190B (en) * | 2020-01-07 | 2023-09-12 | 广州基迪奥生物科技有限公司 | Dynamic interactive enrichment analysis method and system based on biological cloud platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001088086A2 (en) * | 2000-04-19 | 2001-11-22 | Multiqtl Ltd. | System and method for mapping of multiple trait complexes in multiple environments |
| CN104573409A (en) * | 2015-01-04 | 2015-04-29 | 杭州和壹基因科技有限公司 | Gene mapping multi-inspection method |
| CN107368704A (en) * | 2017-07-21 | 2017-11-21 | 上海桑格信息技术有限公司 | The interactive analysis system and method for the transcriptome project for having reference gene group based on cloud computing platform |
| CN107516022A (en) * | 2016-06-17 | 2017-12-26 | 北京光大隆泰科技有限责任公司 | The data processing method and system of phenotype genes type based on discrete interrelated decision tree |
| CN109448788A (en) * | 2018-10-24 | 2019-03-08 | 广州基迪奥生物科技有限公司 | On-line analysis platform architecture of microbiology of genomics and bioinformatics |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8170805B2 (en) * | 2009-02-06 | 2012-05-01 | Syngenta Participations Ag | Method for selecting statistically validated candidate genes |
-
2019
- 2019-03-29 CN CN201910249550.XA patent/CN110010203B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001088086A2 (en) * | 2000-04-19 | 2001-11-22 | Multiqtl Ltd. | System and method for mapping of multiple trait complexes in multiple environments |
| CN104573409A (en) * | 2015-01-04 | 2015-04-29 | 杭州和壹基因科技有限公司 | Gene mapping multi-inspection method |
| CN107516022A (en) * | 2016-06-17 | 2017-12-26 | 北京光大隆泰科技有限责任公司 | The data processing method and system of phenotype genes type based on discrete interrelated decision tree |
| CN107368704A (en) * | 2017-07-21 | 2017-11-21 | 上海桑格信息技术有限公司 | The interactive analysis system and method for the transcriptome project for having reference gene group based on cloud computing platform |
| CN109448788A (en) * | 2018-10-24 | 2019-03-08 | 广州基迪奥生物科技有限公司 | On-line analysis platform architecture of microbiology of genomics and bioinformatics |
Non-Patent Citations (3)
| Title |
|---|
| A shorter tour of R/qtl;Karl W Broman;《R/url：A QTL mapping environment》;20121126;1-8 * |
| QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations;Lei Meng1, Huihui Li1, Luyan Zhang1, Jiankang Wang;《the Crop Journal》;20150223;1-15 * |
| 灭霸有无限宝石，我有无限遗传图谱https://www.omicshare.com/forum/thread-4447-1-1.html;基迪奥-李泽标;《omicshare》;20181107;1-7 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110010203A (en) | 2019-07-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109196123B (en) | SNP molecular marker combination for rice genotyping and application thereof | |
| CN109597767B (en) | Genetic variation-based fuzzy test case generation method and system | |
| CN107209814A (en) | For making structure variation and the visual system and method for phase information | |
| CN107944228A (en) | A kind of method for visualizing of gene sequencing variant sites | |
| CN110010203B (en) | Interactive dynamic QTL analysis system and method based on biological cloud platform | |
| CN104673884A (en) | Method of developing polymorphic EST-SSR marker by utilizing complete genome and EST data | |
| CN108376210B (en) | Genome information assisted breeding method II-breeding parent selection method based on whole genome SNP favorable haplotype mining | |
| CN108256291A (en) | It is a kind of to generate the method with higher confidence level detection in Gene Mutation result | |
| KR101539737B1 (en) | Methodology for improving efficiency of marker-assisted backcrossing using genome sequence and molecular marker | |
| CN114317769A (en) | Auxiliary breeding method based on high-throughput SNP site screening | |
| CN117778585A (en) | Large yellow croaker 55K liquid phase chip and application thereof | |
| CN112233722A (en) | Method for identifying variety, and method and device for constructing prediction model thereof | |
| CN116004898A (en) | Peanut 40K liquid-phase SNP chip PeannitGBTS 40K and application thereof | |
| CN112837746B (en) | Probe design method and positioning method for wheat exon sequencing gene positioning | |
| CN110310699A (en) | The analysis tool and application of target gene sequence are excavated based on whole genome sequence | |
| KR101911307B1 (en) | Method for selecting and utilizing tag-SNP for discriminating haplotype in gene unit | |
| CN111091870B (en) | Method and system for controlling quality of gene mutation site | |
| CN117095747B (en) | Method for detecting group inversion or transposon endpoint genotype based on linear ubiquitin genome and artificial intelligence model | |
| JP3878503B2 (en) | Nucleotide sequencing method | |
| CN108363906A (en) | The establishment of collection of illustrative plates OsMS-IVMap1.0 is integrated in the variation of rice multisample | |
| CN113178231B (en) | Cononsus sequence statistical analysis and visualization method based on second-generation sequencing technology | |
| CN113380323B (en) | Sanger sequencing peak image interception identification method and system, computer equipment and storage medium | |
| CN115188414A (en) | SNP genotype coding method based on genome annotation | |
| CN117153248B (en) | Gene region variation detection and visualization method and system based on pan genome | |
| CN111128297B (en) | Preparation method of gene chip |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |