Difference between revisions of "BIN-Data integration"
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<li><b>Time management</b>: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.</li> | <li><b>Time management</b>: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.</li> | ||
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<li><b>Journal</b>: Document your progress in your [[FND-Journal|Course Journal]]. Some tasks may ask you to include specific items in your journal. Don't overlook these.</li> | <li><b>Journal</b>: Document your progress in your [[FND-Journal|Course Journal]]. Some tasks may ask you to include specific items in your journal. Don't overlook these.</li> | ||
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<li><b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]].</li> | <li><b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]].</li> | ||
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This unit builds on material covered in the following prerequisite units:<br /> | This unit builds on material covered in the following prerequisite units:<br /> | ||
*[[BIN-EBI|BIN-EBI (Databases and services at the EBI)]] | *[[BIN-EBI|BIN-EBI (Databases and services at the EBI)]] | ||
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== Further reading, links and resources == | == Further reading, links and resources == | ||
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{{#pmid: 20652519}} | {{#pmid: 20652519}} | ||
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Revision as of 16:29, 24 September 2020
Data Integration
(Integration of biological data; Identifier mapping; Entrez; UniProt; BioMart. ID mapping service and match() function.)
Abstract:
Data integration is a challenging problem. This unit discusses the issues and how the large databases solve this with NCBI's Entrez system and the EBI's UniProt Knoledeg Base and BioMart System. R coding exercises put some technical issues in practice.
Objectives:
|
Outcomes:
|
Deliverables:
Prerequisites:
This unit builds on material covered in the following prerequisite units:
Evaluation
Evaluation: NA
Contents
Task:
- Read the introductory notes on concepts and approaches to data integration in bioinformatics.
Task:
- Visit the UniProt ID mapping service, enter
NP_010227
into the identifier field, select options from RefSeq Protein to UniProtKB and click Go. - Confirm that this retrieved the right identifier.
- Also note that you could have searched with a list of IDs, and downloaded the results, e.g. for further processing in R.
Task:
- Open RStudio and load the
ABC-units
R project. If you have loaded it before, choose File → Recent projects → ABC-Units. If you have not loaded it before, follow the instructions in the RPR-Introduction unit. - Choose Tools → Version Control → Pull Branches to fetch the most recent version of the project from its GitHub repository with all changes and bug fixes included.
- Type
init()
if requested. - Open the file
BIN-Data_integration.R
and follow the instructions.
Note: take care that you understand all of the code in the script. Evaluation in this course is cumulative and you may be asked to explain any part of code.
Task:
The biomartr
bioconductor package is a second-generation R interface to BioMart that extends the biomaRt
package. It has a good quick start introduction to "Functional Annotation".
- Navigate to https://cran.r-project.org/web/packages/biomartr/vignettes/Functional_Annotation.html
- Work through the tutorial.
Further reading, links and resources
Xie & Ahn (2010) Statistical methods for integrating multiple types of high-throughput data. Methods Mol Biol 620:511-29. (pmid: 20652519) |
[ PubMed ] [ DOI ] Large-scale sequencing, copy number, mRNA, and protein data have given great promise to the biomedical research, while posing great challenges to data management and data analysis. Integrating different types of high-throughput data from diverse sources can increase the statistical power of data analysis and provide deeper biological understanding. This chapter uses two biomedical research examples to illustrate why there is an urgent need to develop reliable and robust methods for integrating the heterogeneous data. We then introduce and review some recently developed statistical methods for integrative analysis for both statistical inference and classification purposes. Finally, we present some useful public access databases and program code to facilitate the integrative analysis in practice. |
Notes
About ...
Author:
- Boris Steipe <boris.steipe@utoronto.ca>
Created:
- 2017-08-05
Modified:
- 2020-09-24
Version:
- 1.1
Version history:
- 1.1 2020 Maintenance
- 1.0 First live version.
- 0.1 First stub
This copyrighted material is licensed under a Creative Commons Attribution 4.0 International License. Follow the link to learn more.