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Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time

A fundamental operation in computational genomics is to reduce the input sequences to their constituent k-mers. For maximum performance of downstream applications it is important to store the k-mers in small space, while keeping the representation easy and efficient to use (i.e. without k-mer repeti...

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Detalles Bibliográficos
Autores principales: Schmidt, Sebastian, Alanko, Jarno N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949180/
https://www.ncbi.nlm.nih.gov/pubmed/36824947
http://dx.doi.org/10.21203/rs.3.rs-2581995/v1
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author Schmidt, Sebastian
Alanko, Jarno N.
author_facet Schmidt, Sebastian
Alanko, Jarno N.
author_sort Schmidt, Sebastian
collection PubMed
description A fundamental operation in computational genomics is to reduce the input sequences to their constituent k-mers. For maximum performance of downstream applications it is important to store the k-mers in small space, while keeping the representation easy and efficient to use (i.e. without k-mer repetitions and in plain text). Recently, heuristics were presented to compute a near-minimum such representation. We present an algorithm to compute a minimum representation in optimal (linear) time and use it to evaluate the existing heuristics. Our algorithm first constructs the de Bruijn graph in linear time and then uses a Eulerian-cycle-based algorithm to compute the minimum representation, in time linear in the size of the output.
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spelling pubmed-99491802023-02-24 Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time Schmidt, Sebastian Alanko, Jarno N. Res Sq Article A fundamental operation in computational genomics is to reduce the input sequences to their constituent k-mers. For maximum performance of downstream applications it is important to store the k-mers in small space, while keeping the representation easy and efficient to use (i.e. without k-mer repetitions and in plain text). Recently, heuristics were presented to compute a near-minimum such representation. We present an algorithm to compute a minimum representation in optimal (linear) time and use it to evaluate the existing heuristics. Our algorithm first constructs the de Bruijn graph in linear time and then uses a Eulerian-cycle-based algorithm to compute the minimum representation, in time linear in the size of the output. American Journal Experts 2023-02-16 /pmc/articles/PMC9949180/ /pubmed/36824947 http://dx.doi.org/10.21203/rs.3.rs-2581995/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. https://creativecommons.org/licenses/by/4.0/License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License (https://creativecommons.org/licenses/by/4.0/)
spellingShingle Article
Schmidt, Sebastian
Alanko, Jarno N.
Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
title Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
title_full Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
title_fullStr Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
title_full_unstemmed Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
title_short Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
title_sort eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949180/
https://www.ncbi.nlm.nih.gov/pubmed/36824947
http://dx.doi.org/10.21203/rs.3.rs-2581995/v1
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