DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors

SUMMARY: DCAlign is a new alignment method able to cope with the conservation and the co-evolution signals that characterize the columns of multiple sequence alignments of homologous sequences. However, the pre-processing steps required to align a candidate sequence are computationally demanding. We...

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Detalles Bibliográficos
Autores principales: Muntoni, Anna Paola, Pagnani, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491954/
https://www.ncbi.nlm.nih.gov/pubmed/37647658
http://dx.doi.org/10.1093/bioinformatics/btad537
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author Muntoni, Anna Paola
Pagnani, Andrea
author_facet Muntoni, Anna Paola
Pagnani, Andrea
author_sort Muntoni, Anna Paola
collection PubMed
description SUMMARY: DCAlign is a new alignment method able to cope with the conservation and the co-evolution signals that characterize the columns of multiple sequence alignments of homologous sequences. However, the pre-processing steps required to align a candidate sequence are computationally demanding. We show in v1.0 how to dramatically reduce the overall computing time by including an empirical prior over an informative set of variables mirroring the presence of insertions and deletions. AVAILABILITY AND IMPLEMENTATION: DCAlign v1.0 is implemented in Julia and it is fully available at https://github.com/infernet-h2020/DCAlign.
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spelling pubmed-104919542023-09-10 DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors Muntoni, Anna Paola Pagnani, Andrea Bioinformatics Applications Note SUMMARY: DCAlign is a new alignment method able to cope with the conservation and the co-evolution signals that characterize the columns of multiple sequence alignments of homologous sequences. However, the pre-processing steps required to align a candidate sequence are computationally demanding. We show in v1.0 how to dramatically reduce the overall computing time by including an empirical prior over an informative set of variables mirroring the presence of insertions and deletions. AVAILABILITY AND IMPLEMENTATION: DCAlign v1.0 is implemented in Julia and it is fully available at https://github.com/infernet-h2020/DCAlign. Oxford University Press 2023-08-30 /pmc/articles/PMC10491954/ /pubmed/37647658 http://dx.doi.org/10.1093/bioinformatics/btad537 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Muntoni, Anna Paola
Pagnani, Andrea
DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors
title DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors
title_full DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors
title_fullStr DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors
title_full_unstemmed DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors
title_short DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors
title_sort dcalign v1.0: aligning biological sequences using co-evolution models and informed priors
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491954/
https://www.ncbi.nlm.nih.gov/pubmed/37647658
http://dx.doi.org/10.1093/bioinformatics/btad537
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