Difference between revisions of "BIN-PPI-Analysis"

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:# The R code for this unit contains at its end a short task. Put your submission for this task on your page.
 
:# The R code for this unit contains at its end a short task. Put your submission for this task on your page.
 
:# When you are done with everything, add the following category tag '''to the end of page''':
 
:# When you are done with everything, add the following category tag '''to the end of page''':
  ::<code><nowiki>[[Category:EVAL-BIN-PPI-Analysis]]</nowiki></code>.
+
::<code><nowiki>[[Category:EVAL-BIN-PPI-Analysis]]</nowiki></code>.
  
 
Once the page has been saved with this tag, it is considered "submitted".
 
Once the page has been saved with this tag, it is considered "submitted".
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:# Create a new page on the student Wiki as a subpage of your User Page. Develop your question there.
 
:# Create a new page on the student Wiki as a subpage of your User Page. Develop your question there.
 
:# WWhen you are done with everything, add the following category tag '''to the end of page''':
 
:# WWhen you are done with everything, add the following category tag '''to the end of page''':
  ::<code><nowiki>[[Category:EVAL-BIN-PPI-Analysis]]</nowiki></code>.
+
::<code><nowiki>[[Category:EVAL-BIN-PPI-Analysis]]</nowiki></code>.
  
 
Once the page has been saved with this tag, it is considered "submitted".
 
Once the page has been saved with this tag, it is considered "submitted".

Revision as of 03:21, 3 November 2018

PPI Analysis

(Analysis of PPIs; Totality, context; date- and party hubs; dynamics; Cytoscape)


 


Abstract:

This unit contains a brief introduction to the analysis of biological networks.


Objectives:
This unit will ...

  • ... introduce concepts of PPI network analysis;
  • ... demonstrate this in practice with a sample human interaction network.

Outcomes:
After working through this unit you ...

  • ... can load and analyze a network from STRING data;
  • ... can annotate selected nodes using BioMART.

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.

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.
  • Metabolism: Enzymatic catalysis and control; reaction sequences and pathways; chemiosmotic coupling; catabolic- and anabolic pathways.
  • Organelles: Compartmentalization, organelles and structures of the cell; the extracellular matrix.

This unit builds on material covered in the following prerequisite units:


 



 



 


Evaluation

This learning unit can be evaluated for a maximum of 6 marks. If you want to submit tasks for this unit for credit you have the following options. If you have any questions about these options, discuss on the mailing list.

Task submission option
  1. Create a new page on the student Wiki as a subpage of your User Page.
  2. The R code for this unit contains at its end a short task. Put your submission for this task on your page.
  3. When you are done with everything, add the following category tag to the end of page:
[[Category:EVAL-BIN-PPI-Analysis]].

Once the page has been saved with this tag, it is considered "submitted". Do not change your submission after this tag has been added. The page will be marked and the category tag will be removed by the instructor.

Quiz option
Open the signup-page for the quiz for this unit (linked from here) and add your name. Your name must be signed up by 12:00 of the day of the Quiz to ensure copies of the quiz are available for all participants.
Quizzes will be written in class, back-to-back if there is more than one quiz scheduled. We may begin at any time. We will have an open-ended Q&A session before the quiz. You can't take the quiz if you are not present in class when the question sheets are handed out, so don't be late. Once all scheduled quizzes are written, we will discuss and mark them. You will mark your own quiz. All marking must be done with a red pen - so you must bring a red pen to class in order to participate. The mark you give yourself may be revised by the instructor after spot-checking quizzes. If this is necessary, you will be notified. You must mark your quiz correctly and honestly - don't get into trouble with academic integrity rules: it will be an academic offence if you mark questions as correct that were discussed in class and should have been marked incorrect. When in doubt, ask.


Contents

Task:


 

Task:

 
  • Open RStudio and load the ABC-units R project. If you have loaded it before, choose FileRecent projectsABC-Units. If you have not loaded it before, follow the instructions in the RPR-Introduction unit.
  • Choose ToolsVersion ControlPull 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-PPI-Analysis.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.


 


 

Optional: Cytoscape

 
This is all that is required. There is optional material below that you may find interesting.


 

If you work a lot with interaction networks, sooner or later you will come across Cytoscape. It is more or less the standard among "professional" systems biologists. But it is not an online tool. Cytoscape was originally written in Trey Ideker's lab at the Institue for Systems Biology; it is now a thriving, open-source community project for the development of a biology-oriented network display and analysis tools.

Kohl et al. (2011) Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol 696:291-303. (pmid: 21063955)

PubMed ] [ DOI ] Substantial progress has been made in the field of "omics" research (e.g., Genomics, Transcriptomics, Proteomics, and Metabolomics), leading to a vast amount of biological data. In order to represent large biological data sets in an easily interpretable manner, this information is frequently visualized as graphs, i.e., a set of nodes and edges. Nodes are representations of biological molecules and edges connect the nodes depicting some kind of relationship. Obviously, there is a high demand for computer-based assistance for both visualization and analysis of biological data, which are often heterogeneous and retrieved from different sources. This chapter focuses on software tools that assist in visual exploration and analysis of biological networks. Global requirements for such programs are discussed. Utilization of visualization software is exemplified using the widely used Cytoscape tool. Additional information about the use of Cytoscape is provided in the Notes section. Furthermore, special features of alternative software tools are highlighted in order to assist researchers in the choice of an adequate program for their specific requirements.

Task:

  • Navigate to the Cytoscape homepage and inform yourself what the program does and how to install it. There are many tutorials online available. But this is software that needs to be downloaded, and installed and it definitively has a learning curve.


Self-evaluation

Notes

Further reading, links and resources

von Mering et al. (2005) STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res 33:D433-7. (pmid: 15608232)

PubMed ] [ DOI ] A full description of a protein's function requires knowledge of all partner proteins with which it specifically associates. From a functional perspective, 'association' can mean direct physical binding, but can also mean indirect interaction such as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive, yet quality-controlled collection of protein-protein associations for a large number of organisms. The associations are derived from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable. STRING currently holds 730,000 proteins in 180 fully sequenced organisms, and is available at http://string.embl.de/.


 




 

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:

2017-11-09

Version:

1.0

Version history:

  • 1.0 First live version
  • 0.1 First stub

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