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PnpProbs: a better multiple sequence alignment tool by better handling of guide trees

BACKGROUND: This paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to con...

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Autores principales: Ye, Yongtao, Lam, Tak-Wah, Ting, Hing-Fung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009527/
https://www.ncbi.nlm.nih.gov/pubmed/27585754
http://dx.doi.org/10.1186/s12859-016-1121-7
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author Ye, Yongtao
Lam, Tak-Wah
Ting, Hing-Fung
author_facet Ye, Yongtao
Lam, Tak-Wah
Ting, Hing-Fung
author_sort Ye, Yongtao
collection PubMed
description BACKGROUND: This paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment; it first estimates the input’s discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment. RESULTS: To evaluate PnpProbs, we have compared it with thirteen other popular MSA tools, and PnpProbs has the best alignment scores in all but one test. We have also used it for phylogenetic analysis, and found that the phylogenetic trees constructed from PnpProbs’ alignments are closest to the model trees. CONCLUSIONS: By combining the strength of the progressive and non-progressive alignment methods, we have developed an MSA tool called PnpProbs. We have compared PnpProbs with thirteen other popular MSA tools and our results showed that our tool usually constructed the best alignments.
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spelling pubmed-50095272016-09-08 PnpProbs: a better multiple sequence alignment tool by better handling of guide trees Ye, Yongtao Lam, Tak-Wah Ting, Hing-Fung BMC Bioinformatics Research BACKGROUND: This paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment; it first estimates the input’s discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment. RESULTS: To evaluate PnpProbs, we have compared it with thirteen other popular MSA tools, and PnpProbs has the best alignment scores in all but one test. We have also used it for phylogenetic analysis, and found that the phylogenetic trees constructed from PnpProbs’ alignments are closest to the model trees. CONCLUSIONS: By combining the strength of the progressive and non-progressive alignment methods, we have developed an MSA tool called PnpProbs. We have compared PnpProbs with thirteen other popular MSA tools and our results showed that our tool usually constructed the best alignments. BioMed Central 2016-08-31 /pmc/articles/PMC5009527/ /pubmed/27585754 http://dx.doi.org/10.1186/s12859-016-1121-7 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Ye, Yongtao
Lam, Tak-Wah
Ting, Hing-Fung
PnpProbs: a better multiple sequence alignment tool by better handling of guide trees
title PnpProbs: a better multiple sequence alignment tool by better handling of guide trees
title_full PnpProbs: a better multiple sequence alignment tool by better handling of guide trees
title_fullStr PnpProbs: a better multiple sequence alignment tool by better handling of guide trees
title_full_unstemmed PnpProbs: a better multiple sequence alignment tool by better handling of guide trees
title_short PnpProbs: a better multiple sequence alignment tool by better handling of guide trees
title_sort pnpprobs: a better multiple sequence alignment tool by better handling of guide trees
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009527/
https://www.ncbi.nlm.nih.gov/pubmed/27585754
http://dx.doi.org/10.1186/s12859-016-1121-7
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