Analysis of computational approaches for motif discovery
2006
Analysis of Computational Approaches for Motif Discovery
publication
Evidence: moderate
Author Information
Author(s): Li Nan, Tompa Martin
Primary Institution: University of Washington
Hypothesis
What makes a particular dataset hard to solve in motif discovery?
Conclusion
The study identifies key features that affect the performance of motif discovery tools and suggests improvements in objective functions.
Supporting Evidence
- The study categorizes objective functions used by existing tools and evaluates their effectiveness.
- It identifies features of datasets that make them challenging for motif discovery tools.
- The research suggests a new objective function that incorporates conservation information.
Takeaway
This study looks at different computer programs that find patterns in DNA and suggests ways to make them better at their job.
Methodology
The study involved assessing 13 computational tools on datasets from four species and analyzing the effectiveness of various objective functions.
Limitations
The study's findings may not apply to datasets with more deviation from the tested conditions.
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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