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PASTA for proteins

SUMMARY: PASTA is a multiple sequence method that uses divide-and-conquer plus iteration to enable base alignment methods to scale with high accuracy to large sequence datasets. By default, PASTA included MAFFT L-INS-i; our new extension of PASTA enables the use of MAFFT G-INS-i, MAFFT Homologs, CON...

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Detalles Bibliográficos
Autores principales: Collins, Kodi, Warnow, Tandy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223367/
https://www.ncbi.nlm.nih.gov/pubmed/29931282
http://dx.doi.org/10.1093/bioinformatics/bty495
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author Collins, Kodi
Warnow, Tandy
author_facet Collins, Kodi
Warnow, Tandy
author_sort Collins, Kodi
collection PubMed
description SUMMARY: PASTA is a multiple sequence method that uses divide-and-conquer plus iteration to enable base alignment methods to scale with high accuracy to large sequence datasets. By default, PASTA included MAFFT L-INS-i; our new extension of PASTA enables the use of MAFFT G-INS-i, MAFFT Homologs, CONTRAlign and ProbCons. We analyzed the performance of each base method and PASTA using these base methods on 224 datasets from BAliBASE 4 with at least 50 sequences. We show that PASTA enables the most accurate base methods to scale to larger datasets at reduced computational effort, and generally improves alignment and tree accuracy on the largest BAliBASE datasets. AVAILABILITY AND IMPLEMENTATION: PASTA is available at https://github.com/kodicollins/pasta and has also been integrated into the original PASTA repository at https://github.com/smirarab/pasta. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-62233672018-11-14 PASTA for proteins Collins, Kodi Warnow, Tandy Bioinformatics Applications Notes SUMMARY: PASTA is a multiple sequence method that uses divide-and-conquer plus iteration to enable base alignment methods to scale with high accuracy to large sequence datasets. By default, PASTA included MAFFT L-INS-i; our new extension of PASTA enables the use of MAFFT G-INS-i, MAFFT Homologs, CONTRAlign and ProbCons. We analyzed the performance of each base method and PASTA using these base methods on 224 datasets from BAliBASE 4 with at least 50 sequences. We show that PASTA enables the most accurate base methods to scale to larger datasets at reduced computational effort, and generally improves alignment and tree accuracy on the largest BAliBASE datasets. AVAILABILITY AND IMPLEMENTATION: PASTA is available at https://github.com/kodicollins/pasta and has also been integrated into the original PASTA repository at https://github.com/smirarab/pasta. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-11-15 2018-06-19 /pmc/articles/PMC6223367/ /pubmed/29931282 http://dx.doi.org/10.1093/bioinformatics/bty495 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Collins, Kodi
Warnow, Tandy
PASTA for proteins
title PASTA for proteins
title_full PASTA for proteins
title_fullStr PASTA for proteins
title_full_unstemmed PASTA for proteins
title_short PASTA for proteins
title_sort pasta for proteins
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223367/
https://www.ncbi.nlm.nih.gov/pubmed/29931282
http://dx.doi.org/10.1093/bioinformatics/bty495
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