WildSpan: mining structured motifs from protein sequences
2011
WildSpan: A New Algorithm for Mining Protein Sequence Motifs
Sample size: 217
publication
10 minutes
Evidence: high
Author Information
Author(s): Hsu Chen-Ming, Chen Chien-Yu, Liu Baw-Jhiune
Primary Institution: Ching Yun University
Hypothesis
Can the WildSpan algorithm efficiently discover W-patterns in protein sequences?
Conclusion
WildSpan is effective in discovering functional signatures of proteins directly from sequences.
Supporting Evidence
- WildSpan successfully found at least one motif containing five blocks for 217 query proteins.
- W-patterns discovered by WildSpan showed a clustering propensity of 92.7% in three-dimensional space.
- WildSpan outperformed existing methods in identifying functional regions of proteins.
Takeaway
WildSpan is a tool that helps scientists find important patterns in protein sequences, even when the important parts are far apart.
Methodology
The WildSpan algorithm uses a two-phase approach to discover W-patterns by mining rigid-gapped blocks and connecting them with flexible gaps.
Limitations
The algorithm may struggle to find patterns that match all input sequences in a single run.
Digital Object Identifier (DOI)
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