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

Dvirnas, Albertas LU ; Stewart, Callum ; Müller, Vilhelm ; Bikkarolla, Santosh Kumar ; Frykholm, Karolin ; Sandegren, Linus ; Kristiansson, Erik ; Westerlund, Fredrik and Ambjörnsson, Tobias LU (2021) In PLoS ONE 16(11 November).
Abstract

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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
16
issue
11 November
article number
e0259670
publisher
Public Library of Science (PLoS)
external identifiers
  • scopus:85118993476
  • pmid:34739528
ISSN
1932-6203
DOI
10.1371/journal.pone.0259670
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2021 Dvirnas et al.
id
56ca934a-033c-4789-b9fe-8abda0c88a04
date added to LUP
2021-12-03 14:28:10
date last changed
2024-06-15 22:07:14
@article{56ca934a-033c-4789-b9fe-8abda0c88a04,
  abstract     = {{<p>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.</p>}},
  author       = {{Dvirnas, Albertas and Stewart, Callum and Müller, Vilhelm and Bikkarolla, Santosh Kumar and Frykholm, Karolin and Sandegren, Linus and Kristiansson, Erik and Westerlund, Fredrik and Ambjörnsson, Tobias}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  number       = {{11 November}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{Detection of structural variations in densely-labelled optical DNA barcodes : A hidden Markov model approach}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0259670}},
  doi          = {{10.1371/journal.pone.0259670}},
  volume       = {{16}},
  year         = {{2021}},
}