BIN-PPI-Databases
Protein-Protein Interaction Databases
Keywords: IntAct, iRef,
Contents
This unit is under development. There is some contents here but it is incomplete and/or may change significantly: links may lead to nowhere, the contents is likely going to be rearranged, and objectives, deliverables etc. may be incomplete or missing. Do not work with this material until it is updated to "live" status.
Abstract
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This unit ...
Prerequisites
You need the following preparation before beginning this unit. If you are not familiar with this material from courses you took previously, you need to prepare yourself from other information sources:
- Biomolecules: The molecules of life; nucleic acids and amino acids; the genetic code; protein folding; post-translational modifications and protein biochemistry; membrane proteins; biological function.
You need to complete the following units before beginning this one:
Objectives
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Outcomes
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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 protein-protein interaction databases.
Data Sources
Interaction databases have similar problems as sequence databases: the need for standards for abstracting biological concepts into computable objects, data integrity, search and retrieval, and the metrics of comparison. There is however an added complication: interactions are rarely all-or-none, and the high-throughput experimental methods have large false-positive and false-negative rates. This makes it necessary to define confidence scores for interactions. On top of experimental methods, there are also a variety of methods for computational interaction prediction. However, even though the "gold standard" are careful, small-scale laboratory experiments, different curated efforts on the same experimental publication usually lead to different results - with as little as 42% overlap between databases being reported.
Currently, likely the best integrated protein-protein interaction database is IntAct, at the EBI, which besides curating interactions from the literature hosts interactions from the IMEx consortium, an extensive data-sharing agreement between a number of general and specialized source databases.
Task:
- Access IntAct and enter the UniProt ID for yeast Mbp1 P39678.
- Click on the "Graph" tab to load a network graph.
- Switch "Merge edges" off to show the reported edges for this interaction individually. Which protein pair has the most interactions? Does this make sense?
But then what?
If you are like me, you would now like to be able to link expression profiles, information about known complexes, GO annotations, knock-out phenotypes etc. etc. Too bad.
Further reading, links and resources
| Mora & Donaldson (2011) iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database. BMC Bioinformatics 12:455. (pmid: 22115179) |
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[ PubMed ] [ DOI ] BACKGROUND: The iRefIndex addresses the need to consolidate protein interaction data into a single uniform data resource. iRefR provides the user with access to this data source from an R environment. RESULTS: The iRefR package includes tools for selecting specific subsets of interest from the iRefIndex by criteria such as organism, source database, experimental method, protein accessions and publication identifier. Data may be converted between three representations (MITAB, edgeList and graph) for use with other R packages such as igraph, graph and RBGL.The user may choose between different methods for resolving redundancies in interaction data and how n-ary data is represented. In addition, we describe a function to identify binary interaction records that possibly represent protein complexes. We show that the user choice of data selection, redundancy resolution and n-ary data representation all have an impact on graphical analysis. CONCLUSIONS: The package allows the user to control how these issues are dealt with and communicate them via an R-script written using the iRefR package - this will facilitate communication of methods, reproducibility of network analyses and further modification and comparison of methods by researchers. |
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:
- 2017-08-05
Version:
- 0.1
Version history:
- 0.1 First stub
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