Abstract
Background: Clustering of unannotated transcripts is an important task to identify novel families of noncoding RNAs (ncRNAs). Several hierarchical clustering methods have been developed using similarity measures based on the scores of structural alignment. However, the high computational cost of exact structural alignment requires these methods to employ approximate algorithms. Such heuristics degrade the quality of clustering results, especially when the similarity among family members is not detectable at the primary sequence level.Results: We describe a new similarity measure for the hierarchical clustering of ncRNAs. The idea is that the reliability of approximate algorithms can be improved by utilizing the information of suboptimal solutions in their dynamic programming frameworks. We approximate structural alignment in a more simplified manner than the existing methods. Instead, our method utilizes all possible sequence alignments and all possible secondary structures, whereas the existing methods only use one optimal sequence alignment and one optimal secondary structure. We demonstrate that this strategy can achieve the best balance between the computational cost and the quality of the clustering. In particular, our method can keep its high performance even when the sequence identity of family members is less than 60%.Conclusions: Our method enables fast and accurate clustering of ncRNAs. The software is available for download at http://bpla-kernel.dna.bio.keio.ac.jp/clustering/.
| Original language | English |
|---|---|
| Article number | S48 |
| Journal | BMC Bioinformatics |
| Volume | 12 |
| Issue number | SUPPL. 1 |
| DOIs | |
| Publication status | Published - 2011 Feb 15 |
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ASJC Scopus subject areas
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Applied Mathematics
- Structural Biology
Cite this
Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures. / Saito, Yutaka; Sato, Kengo; Sakakibara, Yasubumi.
In: BMC Bioinformatics, Vol. 12, No. SUPPL. 1, S48, 15.02.2011.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures
AU - Saito, Yutaka
AU - Sato, Kengo
AU - Sakakibara, Yasubumi
PY - 2011/2/15
Y1 - 2011/2/15
N2 - Background: Clustering of unannotated transcripts is an important task to identify novel families of noncoding RNAs (ncRNAs). Several hierarchical clustering methods have been developed using similarity measures based on the scores of structural alignment. However, the high computational cost of exact structural alignment requires these methods to employ approximate algorithms. Such heuristics degrade the quality of clustering results, especially when the similarity among family members is not detectable at the primary sequence level.Results: We describe a new similarity measure for the hierarchical clustering of ncRNAs. The idea is that the reliability of approximate algorithms can be improved by utilizing the information of suboptimal solutions in their dynamic programming frameworks. We approximate structural alignment in a more simplified manner than the existing methods. Instead, our method utilizes all possible sequence alignments and all possible secondary structures, whereas the existing methods only use one optimal sequence alignment and one optimal secondary structure. We demonstrate that this strategy can achieve the best balance between the computational cost and the quality of the clustering. In particular, our method can keep its high performance even when the sequence identity of family members is less than 60%.Conclusions: Our method enables fast and accurate clustering of ncRNAs. The software is available for download at http://bpla-kernel.dna.bio.keio.ac.jp/clustering/.
AB - Background: Clustering of unannotated transcripts is an important task to identify novel families of noncoding RNAs (ncRNAs). Several hierarchical clustering methods have been developed using similarity measures based on the scores of structural alignment. However, the high computational cost of exact structural alignment requires these methods to employ approximate algorithms. Such heuristics degrade the quality of clustering results, especially when the similarity among family members is not detectable at the primary sequence level.Results: We describe a new similarity measure for the hierarchical clustering of ncRNAs. The idea is that the reliability of approximate algorithms can be improved by utilizing the information of suboptimal solutions in their dynamic programming frameworks. We approximate structural alignment in a more simplified manner than the existing methods. Instead, our method utilizes all possible sequence alignments and all possible secondary structures, whereas the existing methods only use one optimal sequence alignment and one optimal secondary structure. We demonstrate that this strategy can achieve the best balance between the computational cost and the quality of the clustering. In particular, our method can keep its high performance even when the sequence identity of family members is less than 60%.Conclusions: Our method enables fast and accurate clustering of ncRNAs. The software is available for download at http://bpla-kernel.dna.bio.keio.ac.jp/clustering/.
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U2 - 10.1186/1471-2105-12-S1-S48
DO - 10.1186/1471-2105-12-S1-S48
M3 - Article
VL - 12
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
IS - SUPPL. 1
M1 - S48
ER -