A toolbox for visualization of sequencing coverage signal

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Аннотация

Whole genome sequencing data allow access not only to information about genetic variation, but also provide an opportunity to evaluate the overall genome stability. Sequencing coverage signal considered as the number of fragments alligned to a given region within the genome can be used as a trustworthy source of data both on discovery of genomic rearrangements and the current state of whole genome sequencing as well as on precision of structural variant predictions by computational algorithms. The latter is of utmost importance as conflicting data on gene rearrangement events obtained by tools for finding gene rearrangements often appear. However, until recently, validation of predicted variants may present a significant challenge mainly due to the lack of information sources that may assist researchers with direct work with coverage signals and signal visualization with high precision. The present study proposes Sequence COverage ProfilEs (SCOPE), a prototype toolset that includes databases, web-interface and a series of programs for the processing of sequencing data, visualizing and storing of signal coverage profiles. The computer platform and interface is equipped with open-source software, supports local host deployment and allows users to process and analyze their own sequencing data.

Авторлар туралы

I. Bezdvornykh

St. Petersburg State University

St. Petersburg, Russia

N. Cherkasov

St. Petersburg State University

St. Petersburg, Russia

A. Kanapin

St. Petersburg State University

St. Petersburg, Russia

A. Samsonova

St. Petersburg State University

Email: a.samsonova@spbu.ru
St. Petersburg, Russia

Әдебиет тізімі

  1. A. Abyzov, et al., Genome Res., 21 (6), 974 (2011).
  2. S. Kosugi, et al., Genome Biol., 20 (1), 117 (2019).
  3. Z. Liu, et al., Genome Biol., 23 (1), 68 (2022).
  4. M. Mahmoud, et al., Genome Biol., 20 (1), 1 (2019).
  5. A. Kuzniar, J. Maassen, S. Verhoeven, et al., PeerJ, 18, e8214 (2020). doi: 10.7717/peerj.821
  6. J. M. Zook, et al., Sci. Data, 3, 160025 (2016).
  7. J. M. Zook, et al., Nat. Biotechnol., 32 (3), 246 (2014).
  8. A. Shumate, et al., Genome Biol., 1 (2020).
  9. M. J. P. Chaisson, et al., Nat.Commun., 10 (1), 1 (2019).
  10. L. M. Chapman, et al., PLoS Comput. Biol. 16 (6), e1007933-20 (2020).
  11. I. Bezdvornykh, A. Kanapin, and A. Samsonova, In Abst. Book of the Thirteenth Int. Multiconf. on Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) (2022), p. 762.
  12. J. O. Korbel and P. J. Campbell, Cell, 152 (6), 1226 (2013).
  13. A. Aguilera and T. Garda-Muse, Annu. Rev. Genetics, 47 (1), 1 (2013).
  14. B. S. Pedersen and A. R. Quinlan, Bioinformatics, 34 (5), 867 (2018).

© Russian Academy of Sciences, 2023

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