A toolbox for visualization of sequencing coverage signal
- Authors: Bezdvornykh I.V1, Cherkasov N.A1, Kanapin A.A1, Samsonova A.A1
- 
							Affiliations: 
							- St. Petersburg State University
 
- Issue: Vol 68, No 2 (2023)
- Pages: 263-267
- Section: Articles
- URL: https://rjeid.com/0006-3029/article/view/673553
- DOI: https://doi.org/10.31857/S0006302923020072
- EDN: https://elibrary.ru/CAPGJL
- ID: 673553
Cite item
Abstract
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.
			                Keywords
About the authors
I. V Bezdvornykh
St. Petersburg State UniversitySt. Petersburg, Russia
N. A Cherkasov
St. Petersburg State UniversitySt. Petersburg, Russia
A. A Kanapin
St. Petersburg State UniversitySt. Petersburg, Russia
A. A Samsonova
St. Petersburg State University
														Email: a.samsonova@spbu.ru
				                					                																			                												                								St. Petersburg, Russia						
References
- A. Abyzov, et al., Genome Res., 21 (6), 974 (2011).
- S. Kosugi, et al., Genome Biol., 20 (1), 117 (2019).
- Z. Liu, et al., Genome Biol., 23 (1), 68 (2022).
- M. Mahmoud, et al., Genome Biol., 20 (1), 1 (2019).
- A. Kuzniar, J. Maassen, S. Verhoeven, et al., PeerJ, 18, e8214 (2020). doi: 10.7717/peerj.821
- J. M. Zook, et al., Sci. Data, 3, 160025 (2016).
- J. M. Zook, et al., Nat. Biotechnol., 32 (3), 246 (2014).
- A. Shumate, et al., Genome Biol., 1 (2020).
- M. J. P. Chaisson, et al., Nat.Commun., 10 (1), 1 (2019).
- L. M. Chapman, et al., PLoS Comput. Biol. 16 (6), e1007933-20 (2020).
- 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.
- J. O. Korbel and P. J. Campbell, Cell, 152 (6), 1226 (2013).
- A. Aguilera and T. Garda-Muse, Annu. Rev. Genetics, 47 (1), 1 (2013).
- B. S. Pedersen and A. R. Quinlan, Bioinformatics, 34 (5), 867 (2018).
Supplementary files
 
				
			 
					 
						 
						 
						 
						 
									

 
  
  
  Email this article
			Email this article 

 Open Access
		                                Open Access Access granted
						Access granted Subscription or Fee Access
		                                							Subscription or Fee Access
		                                					