Difference between revisions of "FND-CSC-Data models"
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| − | Data Models | + | Relational Data Models |
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| − | ... | + | Computational work with data often begins with data modeling: parsing facts about the world into a set of entities, their attributes, and their relationships. These are usually represented in a "relational data model". This unit introduces the concept, discusses pitfalls in creating such models and how they can be addressed, and practices designing and evaluating datamodels. |
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=== Objectives === | === Objectives === | ||
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| − | ... | + | This unit will ... |
| + | * Introduce the concept of relational datamodels and the terms we use to describe them; | ||
| + | * Provide a template that you can use for modelling; | ||
| + | * Present problems for you to think through in designing your own data models. | ||
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=== Outcomes === | === Outcomes === | ||
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| − | ... | + | After working through this unit you ... |
| + | * can interpret and critically evaluate an ERD diagram; | ||
| + | * have practiced building your own datamodels; | ||
| + | * have participated in the discussion and improvement of three models motivated by common bioinformatics tasks. | ||
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{{Task|1= | {{Task|1= | ||
* Read the introductory notes on {{ABC-PDF|FND-CSC-Data_models|data models}}. | * Read the introductory notes on {{ABC-PDF|FND-CSC-Data_models|data models}}. | ||
| + | |||
| + | In the PDF notes, a protein data model is developed. You can access a sketch of the data model [ https://docs.google.com/drawings/d/1uupNvz18_FYFwyyVPebTM0CUxcJCPDQuxuIJGpjWQWg '''here'''], save it, and edit it. (Or you can use any other drawing tool.) To practice data modeling, think about and try modeling extensions to the "'''proteinDB'''" model: | ||
| + | |||
| + | ::*Some of the proteins that you might want to store are transcription factors. A transcription factor has a canonical binding site sequence, and there are sequences it is actually has been observed to bind to. The actual binding instances in specific locations may have genes associated with them, which encode proteins. You might come up with other facts that are important too. | ||
| + | ::* A "systems model" would group together a number of proteins to a system such as "G1/S checkpoint control", "cell-wall repair", "acid/base homeostasis" etc.: i.e. a set of proteins that collaborate towards a common goal. Within that system a protein performs one or more functions, that may be associated with specific states of the protein (like translocation, or post-translational modification). Proteins may be structurally part of any number of systems. | ||
| + | ::* A protein-protein interaction database stores interaction information. Interactions may be observed by a number of different experimental methods and thus several different interactions may be reported for the same protein pair. Moreover, there may be meta-information, such as a confidence score that evaluates whether two proteins functionally interact with each other in the cell, rather than the observation being an experimental artefact. Some of the interactions may be between a protein and a complex, or between two complexes and can't be further resolved. Some interactions may be permanent, and some may be transient, i.e. depend on particular conditions. | ||
| + | |||
| + | *Sketch each of these datamodels on your own. Think about the principles that were discussed in the introduction. You will probably start by listing the entities first, then the attributes of the entities, then the relationships that you need to represent the facts. Don't forget the cardinalities. | ||
| + | |||
| + | *If you are the first to do so, post your model on the mailing list and share. In the ensuing discussion we would like to evaluate the design decisions, provide feedback, and iterate improvements. We also may be curious about which tool people used for sketching and whether that was convenient. | ||
| + | |||
}} | }} | ||
Revision as of 14:35, 22 September 2017
Relational Data Models
Keywords: Relational data models - what, why, how
Contents
Abstract
Computational work with data often begins with data modeling: parsing facts about the world into a set of entities, their attributes, and their relationships. These are usually represented in a "relational data model". This unit introduces the concept, discusses pitfalls in creating such models and how they can be addressed, and practices designing and evaluating datamodels.
This unit ...
Prerequisites
You need to complete the following units before beginning this one:
Objectives
This unit will ...
- Introduce the concept of relational datamodels and the terms we use to describe them;
- Provide a template that you can use for modelling;
- Present problems for you to think through in designing your own data models.
Outcomes
After working through this unit you ...
- can interpret and critically evaluate an ERD diagram;
- have practiced building your own datamodels;
- have participated in the discussion and improvement of three models motivated by common bioinformatics tasks.
Deliverables
- Time management: 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.
- Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
- Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.
Evaluation
Evaluation: NA
- This unit is not evaluated for course marks.
Contents
Task:
- Read the introductory notes on data models.
In the PDF notes, a protein data model is developed. You can access a sketch of the data model [ https://docs.google.com/drawings/d/1uupNvz18_FYFwyyVPebTM0CUxcJCPDQuxuIJGpjWQWg here], save it, and edit it. (Or you can use any other drawing tool.) To practice data modeling, think about and try modeling extensions to the "proteinDB" model:
- Some of the proteins that you might want to store are transcription factors. A transcription factor has a canonical binding site sequence, and there are sequences it is actually has been observed to bind to. The actual binding instances in specific locations may have genes associated with them, which encode proteins. You might come up with other facts that are important too.
- A "systems model" would group together a number of proteins to a system such as "G1/S checkpoint control", "cell-wall repair", "acid/base homeostasis" etc.: i.e. a set of proteins that collaborate towards a common goal. Within that system a protein performs one or more functions, that may be associated with specific states of the protein (like translocation, or post-translational modification). Proteins may be structurally part of any number of systems.
- A protein-protein interaction database stores interaction information. Interactions may be observed by a number of different experimental methods and thus several different interactions may be reported for the same protein pair. Moreover, there may be meta-information, such as a confidence score that evaluates whether two proteins functionally interact with each other in the cell, rather than the observation being an experimental artefact. Some of the interactions may be between a protein and a complex, or between two complexes and can't be further resolved. Some interactions may be permanent, and some may be transient, i.e. depend on particular conditions.
- Sketch each of these datamodels on your own. Think about the principles that were discussed in the introduction. You will probably start by listing the entities first, then the attributes of the entities, then the relationships that you need to represent the facts. Don't forget the cardinalities.
- If you are the first to do so, post your model on the mailing list and share. In the ensuing discussion we would like to evaluate the design decisions, provide feedback, and iterate improvements. We also may be curious about which tool people used for sketching and whether that was convenient.
Further reading, links and resources
Notes
Self-evaluation
If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.
About ...
Author:
- Boris Steipe <boris.steipe@utoronto.ca>
Created:
- 2017-08-05
Modified:
- 2017-09-21
Version:
- 1.0
Version history:
- 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.
