Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach
Journal article, 2021

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.

Author

Albertas Dvirnas

Lund University

Callum L. Stewart

Lund University

King's College London

Vilhelm Müller

Chalmers, Biology and Biological Engineering, Chemical Biology

Santosh Kumar Bikarolla

Chalmers, Biology and Biological Engineering, Chemical Biology

Ulster University

Karolin Frykholm

Chalmers, Biology and Biological Engineering, Chemical Biology

L. Sandegren

Uppsala University

Erik Kristiansson

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Fredrik Westerlund

Chalmers, Biology and Biological Engineering, Chemical Biology

Tobias Ambjörnsson

Lund University

PLoS ONE

1932-6203 (ISSN)

Vol. 16 11 November e0259670

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Genetics

DOI

10.1371/journal.pone.0259670

PubMed

34739528

More information

Latest update

11/25/2021