IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0259670.html
   My bibliography  Save this article

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

Author

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

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 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.

Suggested Citation

  • Albertas Dvirnas & Callum Stewart & Vilhelm Müller & Santosh Kumar Bikkarolla & Karolin Frykholm & Linus Sandegren & Erik Kristiansson & Fredrik Westerlund & Tobias Ambjörnsson, 2021. "Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-15, November.
  • Handle: RePEc:plo:pone00:0259670
    DOI: 10.1371/journal.pone.0259670
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259670
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0259670&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0259670?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Richard Redon & Shumpei Ishikawa & Karen R. Fitch & Lars Feuk & George H. Perry & T. Daniel Andrews & Heike Fiegler & Michael H. Shapero & Andrew R. Carson & Wenwei Chen & Eun Kyung Cho & Stephanie Da, 2006. "Global variation in copy number in the human genome," Nature, Nature, vol. 444(7118), pages 444-454, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yichen Henry Liu & Can Luo & Staunton G. Golding & Jacob B. Ioffe & Xin Maizie Zhou, 2024. "Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    2. Yu Chen & Amy Y. Wang & Courtney A. Barkley & Yixin Zhang & Xinyang Zhao & Min Gao & Mick D. Edmonds & Zechen Chong, 2023. "Deciphering the exact breakpoints of structural variations using long sequencing reads with DeBreak," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Jae Eun Lee & Jung Hye Sung & Daniel Sarpong & Jimmy T. Efird & Paul B. Tchounwou & Elizabeth Ofili & Keith Norris, 2018. "Knowledge Management for Fostering Biostatistical Collaboration within a Research Network: The RTRN Case Study," IJERPH, MDPI, vol. 15(11), pages 1-13, November.
    4. Yang Guo & Shuzhen Wang & A. K. Alvi Haque & Xiguo Yuan, 2022. "WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
    5. repec:jss:jstsof:40:i12 is not listed on IDEAS

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0259670. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.