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Conference Paper: GLProbs: aligning multiple sequences adaptively
Title | GLProbs: aligning multiple sequences adaptively |
---|---|
Authors | |
Issue Date | 2013 |
Publisher | ACM. |
Citation | The 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB 2013), Washington, DC., 22-25 September 2013. In Conference Proceedings, 2013, p. 1-9 How to Cite? |
Abstract | This paper proposes a simple and effective approach to improve the accuracy of multiple sequence alignment. We use a natural measure to estimate the similarity of the input sequences, and based on this measure, we align the input sequences differently. For example, for inputs with high similarity, we consider the whole sequences and align them globally, while for those with moderately low similarity, we may ignore the flank regions and align locally. To test the effectiveness of this approach, we have implemented a multiple sequence alignment tool call GLProbs, and compares its performance with a dozen leading alignment tools on three benchmark alignment databases. Our results shows that GLProbs has the best accuracy for almost all testings. |
Persistent Identifier | http://hdl.handle.net/10722/189635 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ye, Y | en_US |
dc.contributor.author | Cheung, DWL | en_US |
dc.contributor.author | Wang, Y | en_US |
dc.contributor.author | Yiu, SM | en_US |
dc.contributor.author | Zhan, Q | en_US |
dc.contributor.author | Lam, TW | en_US |
dc.contributor.author | Ting, HF | - |
dc.date.accessioned | 2013-09-17T14:50:32Z | - |
dc.date.available | 2013-09-17T14:50:32Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB 2013), Washington, DC., 22-25 September 2013. In Conference Proceedings, 2013, p. 1-9 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/189635 | - |
dc.description.abstract | This paper proposes a simple and effective approach to improve the accuracy of multiple sequence alignment. We use a natural measure to estimate the similarity of the input sequences, and based on this measure, we align the input sequences differently. For example, for inputs with high similarity, we consider the whole sequences and align them globally, while for those with moderately low similarity, we may ignore the flank regions and align locally. To test the effectiveness of this approach, we have implemented a multiple sequence alignment tool call GLProbs, and compares its performance with a dozen leading alignment tools on three benchmark alignment databases. Our results shows that GLProbs has the best accuracy for almost all testings. | - |
dc.language | eng | en_US |
dc.publisher | ACM. | - |
dc.relation.ispartof | ACM BCB 2013 Conference Proceedings | en_US |
dc.title | GLProbs: aligning multiple sequences adaptively | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Ye, Y: ytye@cs.hku.hk | en_US |
dc.identifier.email | Cheung, DWL: dcheung@cs.hku.hk | en_US |
dc.identifier.email | Wang, Y: ydwang@hit.edu.cn | en_US |
dc.identifier.email | Yiu, SM: smyiu@cs.hku.hk | - |
dc.identifier.email | Zhan, Q: cyanzhan@gmail.com | - |
dc.identifier.email | Lam, TW: twlam@cs.hku.hk | - |
dc.identifier.email | Ting, HF: hfting@cs.hku.hk | - |
dc.identifier.authority | Cheung, DWL=rp00101 | en_US |
dc.identifier.authority | Yiu, SM=rp00207 | en_US |
dc.identifier.authority | Lam, TW=rp00135 | en_US |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.hkuros | 222866 | en_US |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 9 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 131023 | - |