Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach

Large-scale genomic alterations play an important role in disease, gene expression, and chromosome evolution. Optical DNA mapping (ODM), commonly categorized into sparsely-labelled ODM and densely-labelled ODM, provides sequence-specific continuous intensity profiles (DNA barcodes) along single DNA...

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Autores principales:	Albertas Dvirnas, Callum Stewart, Vilhelm Müller, Santosh Kumar Bikkarolla, Karolin Frykholm, Linus Sandegren, Erik Kristiansson, Fredrik Westerlund, Tobias Ambjörnsson
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f736f01fc2a246668e1812109bd1bf44
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spelling	oai:doaj.org-article:f736f01fc2a246668e1812109bd1bf442021-11-11T08:14:53ZDetection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach1932-6203https://doaj.org/article/f736f01fc2a246668e1812109bd1bf442021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570516/?tool=EBIhttps://doaj.org/toc/1932-6203Large-scale genomic alterations play an important role in disease, gene expression, and chromosome evolution. Optical DNA mapping (ODM), commonly categorized into sparsely-labelled ODM and densely-labelled ODM, provides sequence-specific continuous intensity profiles (DNA barcodes) along single DNA molecules and is a technique well-suited for detecting such alterations. For sparsely-labelled barcodes, the possibility to detect large genomic alterations has been investigated extensively, while densely-labelled barcodes have not received as much attention. In this work, we introduce HMMSV, a hidden Markov model (HMM) based algorithm for detecting structural variations (SVs) directly in densely-labelled barcodes without access to sequence information. We evaluate our approach using simulated data-sets with 5 different types of SVs, and combinations thereof, and demonstrate that the method reaches a true positive rate greater than 80% for randomly generated barcodes with single variations of size 25 kilobases (kb). Increasing the length of the SV further leads to larger true positive rates. For a real data-set with experimental barcodes on bacterial plasmids, we successfully detect matching barcode pairs and SVs without any particular assumption of the types of SVs present. Instead, our method effectively goes through all possible combinations of SVs. Since ODM works on length scales typically not reachable with other techniques, our methodology is a promising tool for identifying arbitrary combinations of genomic alterations.Albertas DvirnasCallum StewartVilhelm MüllerSantosh Kumar BikkarollaKarolin FrykholmLinus SandegrenErik KristianssonFredrik WesterlundTobias AmbjörnssonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Albertas Dvirnas Callum Stewart Vilhelm Müller Santosh Kumar Bikkarolla Karolin Frykholm Linus Sandegren Erik Kristiansson Fredrik Westerlund Tobias Ambjörnsson Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach
description	Large-scale genomic alterations play an important role in disease, gene expression, and chromosome evolution. Optical DNA mapping (ODM), commonly categorized into sparsely-labelled ODM and densely-labelled ODM, provides sequence-specific continuous intensity profiles (DNA barcodes) along single DNA molecules and is a technique well-suited for detecting such alterations. For sparsely-labelled barcodes, the possibility to detect large genomic alterations has been investigated extensively, while densely-labelled barcodes have not received as much attention. In this work, we introduce HMMSV, a hidden Markov model (HMM) based algorithm for detecting structural variations (SVs) directly in densely-labelled barcodes without access to sequence information. We evaluate our approach using simulated data-sets with 5 different types of SVs, and combinations thereof, and demonstrate that the method reaches a true positive rate greater than 80% for randomly generated barcodes with single variations of size 25 kilobases (kb). Increasing the length of the SV further leads to larger true positive rates. For a real data-set with experimental barcodes on bacterial plasmids, we successfully detect matching barcode pairs and SVs without any particular assumption of the types of SVs present. Instead, our method effectively goes through all possible combinations of SVs. Since ODM works on length scales typically not reachable with other techniques, our methodology is a promising tool for identifying arbitrary combinations of genomic alterations.
format	article
author	Albertas Dvirnas Callum Stewart Vilhelm Müller Santosh Kumar Bikkarolla Karolin Frykholm Linus Sandegren Erik Kristiansson Fredrik Westerlund Tobias Ambjörnsson
author_facet	Albertas Dvirnas Callum Stewart Vilhelm Müller Santosh Kumar Bikkarolla Karolin Frykholm Linus Sandegren Erik Kristiansson Fredrik Westerlund Tobias Ambjörnsson
author_sort	Albertas Dvirnas
title	Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach
title_short	Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach
title_full	Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach
title_fullStr	Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach
title_full_unstemmed	Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach
title_sort	detection of structural variations in densely-labelled optical dna barcodes: a hidden markov model approach
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f736f01fc2a246668e1812109bd1bf44
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Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach

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