Abstract
BackgroundPredicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps.ResultsIn this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem.ConclusionsBased on an experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.
Highlights
Predicting new non-coding RNAs of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure
Our result We propose a local structural alignment algorithm with affine gap model which assumes the secondary structure of the query is known while that of the target sequence is unknown
We evaluate the resulting algorithm and show that considering local structural alignment with affine gap model can improve the effectiveness of locating non-coding RNAs (ncRNAs) for the families in which members may have variable size of hairpins, loops or stems when compared to using global alignment [10], local alignment without gap penalty model and Gotohscan [8]
Summary
Predicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps. A non-coding RNA (ncRNA) is a RNA molecule that does not translate into proteins. Large discoveries of ncRNAs and their families show the possibilities that ncRNAs may be as diverse as protein molecules [7]. Identifying ncRNAs is an important problem in biological study. It is time consuming and there is no effective method to identify ncRNAs in a laboratory, predicting ncRNAs based on known ncRNAs using comparative computational approach is one of the promising directions to identify potential candidates for further verification
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