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
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Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/5d51b3569c7f4932b048a6015027f32e
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spelling oai:doaj.org-article:5d51b3569c7f4932b048a6015027f32e2021-12-02T20:06:01ZDetection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach.1932-620310.1371/journal.pone.0259670https://doaj.org/article/5d51b3569c7f4932b048a6015027f32e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259670https://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, p e0259670 (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/5d51b3569c7f4932b048a6015027f32e
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