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Photo of Tobias Ambjörnsson

Tobias Ambjörnsson

Senior lecturer

Photo of Tobias Ambjörnsson

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


  • Albertas Dvirnas
  • Callum Stewart
  • Vilhelm Müller
  • Santosh Kumar Bikkarolla
  • Karolin Frykholm
  • Linus Sandegren
  • Erik Kristiansson
  • Fredrik Westerlund
  • Tobias Ambjörnsson

Summary, in English

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.


  • Computational Biology and Biological Physics - Has been reorganised

Publishing year









11 November

Document type

Journal article


Public Library of Science (PLoS)


  • Genetics
  • Bioinformatics (Computational Biology)
  • Other Physics Topics
  • Biophysics




  • ISSN: 1932-6203