ViruSurf

an integrated database to investigate viral sequences

verfasst von

Arif Canakoglu, Pietro Pinoli, Anna Bernasconi, Tommaso Alfonsi, Damianos p Melidis, Stefano Ceri

Abstract

ViruSurf, available at gmql.eu/virusurf/, is a large public database of viral sequences and integrated and curated metadata from heterogeneous sources (RefSeq, GenBank, COG-UK and NMDC); it also exposes computed nucleotide and amino acid variants, called from original sequences. A GISAID-specific ViruSurf database, available at gmql.eu/virusurf gisaid/, offers a subset of these functionalities. Given the current pandemic outbreak, SARS-CoV-2 data are collected from the four sources; but ViruSurf contains other virus species harmful to humans, including SARS-CoV, MERS-CoV, Ebola and Dengue. The database is centered on sequences, described from their biological, technological and organizational dimensions. In addition, the analytical dimension characterizes the sequence in terms of its annotations and variants. The web interface enables expressing complex search queries in a simple way; arbitrary search queries can freely combine conditions on attributes from the four dimensions, extracting the resulting sequences. Several example queries on the database confirm and possibly improve results from recent research papers; results can be recomputed over time and upon selected populations. Effective search over large and curated sequence data may enable faster responses to future threats that could arise from new viruses.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Politecnico di Milano

Typ

Artikel

Journal

Nucleic Acids Research

Band

Seiten

D817-D824

ISSN

0301-5610

Publikationsdatum

08.01.2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

ASJC Scopus Sachgebiete

Genetik

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1093/nar/gkaa846 (Zugang: Offen)

BibTeX

@article{ca11031611fb4ded93e11fabc013df2f,
title = "ViruSurf: an integrated database to investigate viral sequences",
abstract = "ViruSurf, available at http://gmql.eu/virusurf/, is a large public database of viral sequences and integrated and curated metadata from heterogeneous sources (RefSeq, GenBank, COG-UK and NMDC); it also exposes computed nucleotide and amino acid variants, called from original sequences. A GISAID-specific ViruSurf database, available at http://gmql.eu/virusurf gisaid/, offers a subset of these functionalities. Given the current pandemic outbreak, SARS-CoV-2 data are collected from the four sources; but ViruSurf contains other virus species harmful to humans, including SARS-CoV, MERS-CoV, Ebola and Dengue. The database is centered on sequences, described from their biological, technological and organizational dimensions. In addition, the analytical dimension characterizes the sequence in terms of its annotations and variants. The web interface enables expressing complex search queries in a simple way; arbitrary search queries can freely combine conditions on attributes from the four dimensions, extracting the resulting sequences. Several example queries on the database confirm and possibly improve results from recent research papers; results can be recomputed over time and upon selected populations. Effective search over large and curated sequence data may enable faster responses to future threats that could arise from new viruses.",
author = "Arif Canakoglu and Pietro Pinoli and Anna Bernasconi and Tommaso Alfonsi and Damianos p Melidis and Stefano Ceri",
note = "Funding Information: The authors would like to thank Ilaria Capua, Matteo Chiara, Ana Conesa, Luca Ferretti, Alice Fusaro, Ruba Khalaf, Susanna Lamers, Stefania Leopardi, Francesca Mari, Carla Mavian, Graziano Pesole, Alessandra Renieri, Anna Sandionigi, Stephen Tsui, Limsoon Wong and Federico Zambelli for their contribution to requirements elicitation and for inspiring future developments of this research. The authors are grateful to the GISAID organization for the data sharing agreement that allowed the development of the GISAID-specific version of ViruSurf. The authors also acknowledge the depositions of worldwide laboratories to GenBank, COG-UK and NMDC. Finally, we acknowledge the support from Amazon Machine Learning Research Award 'Data-driven Machine and Deep Learning for Genomics'. H2020 European Research Council [693174]; H2020 European Institute of Innovation and Technology [20663]. Funding for open access charge: H2020 European Research Council [693174].",
year = "2021",
month = jan,
day = "8",
doi = "10.1093/nar/gkaa846",
language = "English",
volume = "49",
pages = "D817--D824",
journal = "Nucleic Acids Research",
issn = "0301-5610",
publisher = "Oxford University Press",
number = "D1",
}