Difference between revisions of "Module 2: How well does sequence similarity predict similarity in binding specificity?"
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* Data and Instructions | * Data and Instructions | ||
− | **Download all the raw data needed for this module at: | + | **Download all the raw data needed for this module at: [[Media:Module2.zip]] |
**Try opening the LOLA software (see instruction in README.txt). | **Try opening the LOLA software (see instruction in README.txt). | ||
**Make sure R is installed in your computer | **Make sure R is installed in your computer | ||
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− | * back to [[UNIL MSc course: "Case studies in bioinformatics | + | * back to [[UNIL MSc course: "Case studies in bioinformatics 2016"]] |
Latest revision as of 13:09, 16 November 2016
- 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 module at: Media:Module2.zip
- 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 Matrices (PWM).
- 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 in biology, and other applications of the mathematical tools developed in this paper.
- Key bioinformatics concept of this module:
- Similarity measures between biological objects (here protein sequence and binding specificity)
- Clustering