Local Structural Alignment of RNA with Affine Gap Model
Author Information
Author(s): King-Fung Wong, Wing-Yan Cheung, Lam Tak-Wah, Yiu Siu-Ming
Primary Institution: Department of Computer Science, The University of Hong Kong
Hypothesis
Can local structural alignment with an affine gap penalty model identify non-coding RNA families more effectively than existing methods?
Conclusion
The study demonstrates that local structural alignment with an affine gap model can more effectively identify real ncRNA family members compared to global alignment or local alignment without a gap penalty model.
Supporting Evidence
- The algorithm was evaluated using real data from the Rfam database.
- The local structural alignment algorithm with affine gap model showed a 7.2% miss rate compared to 53.9% for Gotohscan.
- The study found that considering local structural alignment can distinguish real members from false hits more effectively.
Takeaway
The researchers created a new way to compare RNA sequences that helps find important RNA types better than older methods.
Methodology
A dynamic programming algorithm was developed to compute optimal local structural alignment using an affine gap penalty model.
Limitations
The study has not yet explored different types of gap penalty models or the effects of varying gap penalty parameters.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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