@article{fdi:010089484,
  title = {{G}-{SAIP} : graphical sequence alignment through parallel programming in the post-genomic era},
  author = {{P}ina, {J}. {S}. and {O}rozco-{A}rias, {S}. and {T}obon-{O}rozco, {N}. and {C}amargo-{F}orero, {L}. and {T}abares-{S}oto, {R}. and {G}uyot, {R}omain},
  editor = {},
  language = {{ENG}},
  abstract = {{A} common task in bioinformatics is to compare {DNA} sequences to identify similarities between organisms at the sequence level. {A}n approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze {DNA} or protein alignments. {D}ot-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. {H}igh-{P}erformance {C}omputing ({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. {H}ere, we present {G}-{SAIP} ({G}raphical {S}equence {A}lignment in {P}arallel), a software capable of spawning multiple distributed processes on {CPU}s, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68x 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.},
  keywords = {{G}-{SAIP} ; {HPC} ; bioinformatics ; dot-plots ; graphical alignments ; post-genomic era},
  booktitle = {},
  journal = {{E}volutionary {B}ioinformatics},
  volume = {19},
  numero = {},
  pages = {11769343221150585 [10 p.]},
  ISSN = {1176-9343},
  year = {2023},
  DOI = {10.1177/11769343221150585},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089484},
}