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A biological sequence comparison algorithm using quantum computers

Genetic information is encoded as linear sequences of nucleotides, represented by letters ranging from thousands to billions. Differences between sequences are identified through comparative approaches like sequence analysis, where variations can occur at the individual nucleotide level or collectiv...

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Autores principales: Kösoglu-Kind, Büsra, Loredo, Robert, Grossi, Michele, Bernecker, Christian, Burks, Jody M, Buchkremer, Rüdiger
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:https://dx.doi.org/10.1038/s41598-023-41086-5
http://cds.cern.ch/record/2875198
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author Kösoglu-Kind, Büsra
Loredo, Robert
Grossi, Michele
Bernecker, Christian
Burks, Jody M
Buchkremer, Rüdiger
author_facet Kösoglu-Kind, Büsra
Loredo, Robert
Grossi, Michele
Bernecker, Christian
Burks, Jody M
Buchkremer, Rüdiger
author_sort Kösoglu-Kind, Büsra
collection CERN
description Genetic information is encoded as linear sequences of nucleotides, represented by letters ranging from thousands to billions. Differences between sequences are identified through comparative approaches like sequence analysis, where variations can occur at the individual nucleotide level or collectively due to various phenomena such as recombination or deletion. Detecting these sequence differences is vital for understanding biology and medicine, but the complexity and size of genomic data require substantial classical computing power. Inspired by human visual perception and pixel representation on quantum computers, we leverage these techniques to implement pairwise sequence analysis. Our method utilizes the Flexible Representation of Quantum Images (FRQI) framework, enabling comparisons at a fine granularity to single letters or amino acids within gene sequences. This novel approach enhances accuracy and resolution, surpassing traditional methods by capturing subtle genetic variations with precision. In summary, our approach offers algorithmic advantages, including reduced time complexity, improved space efficiency, and accurate sequence comparisons. The novelty lies in applying the FRQI algorithm to compare quantum images in genome sequencing, allowing for examination at the individual letter or amino acid level. This breakthrough holds promise for advancing biological data analysis and enables a more comprehensive understanding of genetic information.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
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spelling cern-28751982023-10-10T22:59:46Zdoi:10.1038/s41598-023-41086-5http://cds.cern.ch/record/2875198engKösoglu-Kind, BüsraLoredo, RobertGrossi, MicheleBernecker, ChristianBurks, Jody MBuchkremer, RüdigerA biological sequence comparison algorithm using quantum computersQuantum TechnologyGenetic information is encoded as linear sequences of nucleotides, represented by letters ranging from thousands to billions. Differences between sequences are identified through comparative approaches like sequence analysis, where variations can occur at the individual nucleotide level or collectively due to various phenomena such as recombination or deletion. Detecting these sequence differences is vital for understanding biology and medicine, but the complexity and size of genomic data require substantial classical computing power. Inspired by human visual perception and pixel representation on quantum computers, we leverage these techniques to implement pairwise sequence analysis. Our method utilizes the Flexible Representation of Quantum Images (FRQI) framework, enabling comparisons at a fine granularity to single letters or amino acids within gene sequences. This novel approach enhances accuracy and resolution, surpassing traditional methods by capturing subtle genetic variations with precision. In summary, our approach offers algorithmic advantages, including reduced time complexity, improved space efficiency, and accurate sequence comparisons. The novelty lies in applying the FRQI algorithm to compare quantum images in genome sequencing, allowing for examination at the individual letter or amino acid level. This breakthrough holds promise for advancing biological data analysis and enables a more comprehensive understanding of genetic information.oai:cds.cern.ch:28751982023
spellingShingle Quantum Technology
Kösoglu-Kind, Büsra
Loredo, Robert
Grossi, Michele
Bernecker, Christian
Burks, Jody M
Buchkremer, Rüdiger
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 Quantum Technology
url https://dx.doi.org/10.1038/s41598-023-41086-5
http://cds.cern.ch/record/2875198
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