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Updated: Jun 7, 2026
07:08
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
VARUN: discovering extensible motifs under saturation constraints.
Alberto Apostolico1, Matteo Comin, Laxmi Parida
1College of Computing, Georgia Institute of Technology, Atlanta, GA 30332-0280, USA. axa@dei.unipd.it
IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 30, 2010
Summary
This study introduces extensible motifs for biosequence analysis, combining pattern structure with statistical occurrence. This method efficiently discovers significant motifs in protein families, reducing candidate set size.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Discovering biosequence motifs is challenging due to model rigidity and numerous candidates, especially with wildcards.
- Extensible motifs with variable-length 'don't cares' exponentially increase search complexity.
Purpose of the Study:
- To introduce and study the concept of extensible motifs in biosequences.
- To develop a method that efficiently discovers overrepresented motifs by integrating pattern syntax and statistical occurrence.
Main Methods:
- Introduced the notion of extensible motifs combining syntactic specification and statistical measures.
- Utilized saturation conditions and monotonicity of probabilistic scores for parsimony.
- Developed and implemented a software suite named Varun for motif discovery.
Main Results:
- Demonstrated significant parsimony in generating and testing candidate motifs.
- Achieved faster extraction of surprising motifs with more manageable set sizes.
- Successfully applied the method to protein sequence families.
Conclusions:
- The proposed extensible motif discovery method offers computational efficiency and practical advantages.
- Saturation constraints effectively reduce the complexity of motif discovery in biosequences.
- The Varun software suite provides a robust tool for identifying biologically relevant motifs.
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