Title | ||
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ChainKnot: a comparative H-type pseudoknot prediction tool using multiple ab initio folding tools |
Abstract | ||
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Pseudoknot is an important structural motif in many types of ncRNAs. However, the accuracy of pseudoknot derivation is still not satisfactory even for simple pseudoknotted structures and short sequences. In this work, we design and implement an effective pipeline, ChainKnot, for deriving secondary structures containing recursive H-type pseudoknots from two or multiple ncRNA sequences. ChainKnot solves the consensus structure derivation problem using an extended maximum-weighted chain algorithm. In addition, ChainKnot tests a new strategy that extracts structural elements from the optimal and sub-optimal predictions of multiple ab initio pseudoknot prediction tools. The experimental results on over five hundreds of pseudoknot-containing ncRNAs demonstrate that extracting stems from the output of ab initio tools significantly increases the performance of the prediction pipeline compared to using base-pairing probability matrices. Our approach achieves better sensitivity, PPV, and F-score than the state-of-the-art pseudoknot prediction tools on recursive H-type pseudoknots. The source code of ChainKnot is available at http://sourceforge.net/projects/chainknot |
Year | DOI | Venue |
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2013 | 10.1145/2506583.2506626 | BCB |
Keywords | Field | DocType |
pseudoknot derivation,sub-optimal prediction,recursive h-type pseudoknots,ab initio tool,multiple ab initio pseudoknot,consensus structure derivation problem,comparative h-type pseudoknot prediction,effective pipeline,multiple ab initio folding,state-of-the-art pseudoknot prediction tool,prediction pipeline,prediction tool,pseudoknot | Pseudoknot,Computer science,Matrix (mathematics),Source code,Rna folding,Theoretical computer science,Rna secondary structure prediction,Bioinformatics,Ab initio,Recursion | Conference |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jikai Lei | 1 | 26 | 3.64 |
Prapaporn Techa-Angkoon | 2 | 2 | 2.08 |
Yanni Sun | 3 | 219 | 21.16 |
Rujira Achawanantakun | 4 | 18 | 1.63 |