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A biological sequence comparison algorithm using quantum computers

Genetic information is encoded in a linear sequence of nucleotides, represented by letters ranging from thousands to billions. Mutations refer to changes in the DNA or RNA nucleotide sequence. Thus, mutation detection is vital in all areas of biology and medicine. Careful monitoring of virulence-enh...

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Autores principales: Kösoglu-Kind, Büsra, Loredo, Robert, Grossi, Michele, Bernecker, Christian, Burks, Jody M., Buchkremer, Rudiger
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:http://cds.cern.ch/record/2855976
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author Kösoglu-Kind, Büsra
Loredo, Robert
Grossi, Michele
Bernecker, Christian
Burks, Jody M.
Buchkremer, Rudiger
author_facet Kösoglu-Kind, Büsra
Loredo, Robert
Grossi, Michele
Bernecker, Christian
Burks, Jody M.
Buchkremer, Rudiger
author_sort Kösoglu-Kind, Büsra
collection CERN
description Genetic information is encoded in a linear sequence of nucleotides, represented by letters ranging from thousands to billions. Mutations refer to changes in the DNA or RNA nucleotide sequence. Thus, mutation detection is vital in all areas of biology and medicine. Careful monitoring of virulence-enhancing mutations is essential. However, an enormous amount of classical computing power is required to analyze genetic sequences of this size. Inspired by human perception of vision and pixel representation of images on quantum computers, we leverage these techniques to implement a pairwise sequence analysis. The methodology has a potential advantage over classical approaches and can be further applied to identify mutations and other modifications in genetic sequences. We present a method to display and analyze the similarity between two genome sequences on a quantum computer where a similarity score is calculated to determine the similarity between nucleotides.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
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spelling cern-28559762023-09-20T06:39:57Zhttp://cds.cern.ch/record/2855976engKösoglu-Kind, BüsraLoredo, RobertGrossi, MicheleBernecker, ChristianBurks, Jody M.Buchkremer, RudigerA biological sequence comparison algorithm using quantum computersq-bio.GNquant-phGeneral Theoretical PhysicsGenetic information is encoded in a linear sequence of nucleotides, represented by letters ranging from thousands to billions. Mutations refer to changes in the DNA or RNA nucleotide sequence. Thus, mutation detection is vital in all areas of biology and medicine. Careful monitoring of virulence-enhancing mutations is essential. However, an enormous amount of classical computing power is required to analyze genetic sequences of this size. Inspired by human perception of vision and pixel representation of images on quantum computers, we leverage these techniques to implement a pairwise sequence analysis. The methodology has a potential advantage over classical approaches and can be further applied to identify mutations and other modifications in genetic sequences. We present a method to display and analyze the similarity between two genome sequences on a quantum computer where a similarity score is calculated to determine the similarity between nucleotides.arXiv:2303.13608oai:cds.cern.ch:28559762023-03-23
spellingShingle q-bio.GN
quant-ph
General Theoretical Physics
Kösoglu-Kind, Büsra
Loredo, Robert
Grossi, Michele
Bernecker, Christian
Burks, Jody M.
Buchkremer, Rudiger
A biological sequence comparison algorithm using quantum computers
title A biological sequence comparison algorithm using quantum computers
title_full A biological sequence comparison algorithm using quantum computers
title_fullStr A biological sequence comparison algorithm using quantum computers
title_full_unstemmed A biological sequence comparison algorithm using quantum computers
title_short A biological sequence comparison algorithm using quantum computers
title_sort biological sequence comparison algorithm using quantum computers
topic q-bio.GN
quant-ph
General Theoretical Physics
url http://cds.cern.ch/record/2855976
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