Difference between revisions of "Module 2: How well does sequence similarity predict similarity in binding specificity?"

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* Key claim of the paper: "Similarity in sequence of binding motifs of PDZ domains can be used to identify 16 distinct specificity classes."  
 
* Key claim of the paper: "Similarity in sequence of binding motifs of PDZ domains can be used to identify 16 distinct specificity classes."  
  
* Data and Code
+
* Data and Instructions
 +
 
 +
**Download all the raw data needed for this course at: xxx
 +
**Try opening the LOLA software (see instruction in README.txt).
 +
**Make sure R is installed in your computer
 +
**Make sure you have Java
 
    
 
    
 
* Schedule:  
 
* Schedule:  
  
 
**H1: General intro to the paper/motivation
 
**H1: General intro to the paper/motivation
**H2-3: Write code to import the data and start computing similarity
+
**H2-3: Write code to import the data and compute the Position Weight Matrics
  
 
**H4-6: Write code to compute and compare different types of similarity values and do the clustering.
 
**H4-6: Write code to compute and compare different types of similarity values and do the clustering.
  
**H7: Use a visualization software to look at the data.
+
**H7: Use the LOLA visualization software to look at the data.
 
**H8: Start writing the report
 
**H8: Start writing the report
 
**H9: Broader view on the use and challenges with similarity metrics between biological objects  
 
**H9: Broader view on the use and challenges with similarity metrics between biological objects  

Revision as of 16:07, 15 October 2015

  • Title: "How well does sequence similarity predict similarity in binding specificity?"
  • Paper to be examined: “A Specificity Map for the PDZ Domain Family”, PLoS Biol 6(9): e239 [1]
  • Key claim of the paper: "Similarity in sequence of binding motifs of PDZ domains can be used to identify 16 distinct specificity classes."
  • Data and Instructions
    • Download all the raw data needed for this course at: xxx
    • Try opening the LOLA software (see instruction in README.txt).
    • Make sure R is installed in your computer
    • Make sure you have Java
  • Schedule:
    • H1: General intro to the paper/motivation
    • H2-3: Write code to import the data and compute the Position Weight Matrics
    • H4-6: Write code to compute and compare different types of similarity values and do the clustering.
    • H7: Use the LOLA visualization software to look at the data.
    • H8: Start writing the report
    • H9: Broader view on the use and challenges with similarity metrics between biological objects
  • Key bioinformatics concept of this module:
    • Similarity measures between biological objects (here protein sequence and binding specificity)
    • Clustering