@inproceedings{afe4a3fb0aac4d5dbb23e9de46995858,
title = "GLProbs: Aligning multiple sequences adaptively",
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 glob- Ally, 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 called GLProbs, and compared its performance with a dozen leading alignment tools on three benchmark alignment databases. Our results show that GL- Probs has the best accuracy for almost all testings.",
keywords = "Hidden Markov model, Multiple sequence alignment, Phylogenetic analysis, Progressive alignment, Secondary structure prediction",
author = "Yongtao Ye and Cheung, \{D. W.\} and Yadong Wang and Yiu, \{Siu Ming\} and Qing Zhan and Lam, \{Tak Wah\} and Ting, \{Hing Fung\}",
year = "2013",
doi = "10.1145/2506583.2506611",
language = "英语",
isbn = "9781450324342",
series = "2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013",
pages = "152--160",
booktitle = "2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013",
note = "2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013 ; Conference date: 22-09-2013 Through 25-09-2013",
}