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G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era

A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before th...

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Autores principales: Piña, Johan S., Orozco-Arias, Simon, Tobón-Orozco, Nicolas, Camargo-Forero, Leonardo, Tabares-Soto, Reinel, Guyot, Romain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871978/
https://www.ncbi.nlm.nih.gov/pubmed/36703866
http://dx.doi.org/10.1177/11769343221150585
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author Piña, Johan S.
Orozco-Arias, Simon
Tobón-Orozco, Nicolas
Camargo-Forero, Leonardo
Tabares-Soto, Reinel
Guyot, Romain
author_facet Piña, Johan S.
Orozco-Arias, Simon
Tobón-Orozco, Nicolas
Camargo-Forero, Leonardo
Tabares-Soto, Reinel
Guyot, Romain
author_sort Piña, Johan S.
collection PubMed
description A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking.
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spelling pubmed-98719782023-01-25 G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era Piña, Johan S. Orozco-Arias, Simon Tobón-Orozco, Nicolas Camargo-Forero, Leonardo Tabares-Soto, Reinel Guyot, Romain Evol Bioinform Online Original Research A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking. SAGE Publications 2023-01-20 /pmc/articles/PMC9871978/ /pubmed/36703866 http://dx.doi.org/10.1177/11769343221150585 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page(https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Piña, Johan S.
Orozco-Arias, Simon
Tobón-Orozco, Nicolas
Camargo-Forero, Leonardo
Tabares-Soto, Reinel
Guyot, Romain
G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era
title G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era
title_full G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era
title_fullStr G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era
title_full_unstemmed G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era
title_short G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era
title_sort g-saip: graphical sequence alignment through parallel programming in the post-genomic era
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871978/
https://www.ncbi.nlm.nih.gov/pubmed/36703866
http://dx.doi.org/10.1177/11769343221150585
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