Cargando…
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...
Autores principales: | , , , , , |
---|---|
Lenguaje: | eng |
Publicado: |
2023
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.1038/s41598-023-41086-5 http://cds.cern.ch/record/2875198 |
_version_ | 1780978887430242304 |
---|---|
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. |
id | cern-2875198 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2023 |
record_format | invenio |
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 |
work_keys_str_mv | AT kosoglukindbusra abiologicalsequencecomparisonalgorithmusingquantumcomputers AT loredorobert abiologicalsequencecomparisonalgorithmusingquantumcomputers AT grossimichele abiologicalsequencecomparisonalgorithmusingquantumcomputers AT berneckerchristian abiologicalsequencecomparisonalgorithmusingquantumcomputers AT burksjodym abiologicalsequencecomparisonalgorithmusingquantumcomputers AT buchkremerrudiger abiologicalsequencecomparisonalgorithmusingquantumcomputers AT kosoglukindbusra biologicalsequencecomparisonalgorithmusingquantumcomputers AT loredorobert biologicalsequencecomparisonalgorithmusingquantumcomputers AT grossimichele biologicalsequencecomparisonalgorithmusingquantumcomputers AT berneckerchristian biologicalsequencecomparisonalgorithmusingquantumcomputers AT burksjodym biologicalsequencecomparisonalgorithmusingquantumcomputers AT buchkremerrudiger biologicalsequencecomparisonalgorithmusingquantumcomputers |