Design of optimal labeling patterns for optical genome mapping via information theory.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
03 10 2023
03 10 2023
Historique:
received:
05
06
2023
revised:
31
08
2023
accepted:
26
09
2023
medline:
11
10
2023
pubmed:
28
9
2023
entrez:
27
9
2023
Statut:
ppublish
Résumé
Optical genome mapping (OGM) is a technique that extracts partial genomic information from optically imaged and linearized DNA fragments containing fluorescently labeled short sequence patterns. This information can be used for various genomic analyses and applications, such as the detection of structural variations and copy-number variations, epigenomic profiling, and microbial species identification. Currently, the choice of labeled patterns is based on the available biochemical methods and is not necessarily optimized for the application. In this work, we develop a model of OGM based on information theory, which enables the design of optimal labeling patterns for specific applications and target organism genomes. We validated the model through experimental OGM on human DNA and simulations on bacterial DNA. Our model predicts up to 10-fold improved accuracy by optimal choice of labeling patterns, which may guide future development of OGM biochemical labeling methods and significantly improve its accuracy and yield for applications such as epigenomic profiling and cultivation-free pathogen identification in clinical samples. https://github.com/yevgenin/PatternCode.
Identifiants
pubmed: 37758248
pii: 7284110
doi: 10.1093/bioinformatics/btad601
pmc: PMC10563147
pii:
doi:
Substances chimiques
DNA
9007-49-2
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : European Research Council
ID : 802567
Pays : International
Organisme : European Research Council
ID : 817811
Pays : International
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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