Efficient dynamic variation graphs | Zendy

Jordan M. Eizenga | Zendy; Adam M. Novak | Zendy; Emily Kobayashi | Zendy; Flavia Villani | Zendy; Cecilia Cisar | Zendy; Simon Heumos | Zendy; Glenn Hickey | Zendy; Vincenza Colonna | Zendy; Benedict Paten | Zendy; Erik Garrison | Zendy

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Efficient dynamic variation graphs

Author(s) -

Jordan M. Eizenga,

Adam M. Novak,

Emily Kobayashi,

Flavia Villani,

Cecilia Cisar,

Simon Heumos,

Glenn Hickey,

Vincenza Colonna,

Benedict Paten,

Erik Garrison

Publication year - 2020

Publication title -

bioinformatics

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa640

Subject(s) - mit license , computer science , python (programming language) , implementation , license , computational genomics , call graph , theoretical computer science , graph , genomics , software , programming language , genome , biology , operating system , biochemistry , gene

Pangenomics is a growing field within computational genomics. Many pangenomic analyses use bidirected sequence graphs as their core data model. However, implementing and correctly using this data model can be difficult, and the scale of pangenomic datasets can be challenging to work at. These challenges have impeded progress in this field.

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