Efficient dynamic variation graphs.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
29 01 2021
29 01 2021
Historique:
received:
21
04
2020
revised:
20
06
2020
accepted:
09
07
2020
pubmed:
12
10
2020
medline:
10
8
2021
entrez:
11
10
2020
Statut:
ppublish
Résumé
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. Here, we present a stack of two C++ libraries, libbdsg and libhandlegraph, which use a simple, field-proven interface, designed to expose elementary features of these graphs while preventing common graph manipulation mistakes. The libraries also provide a Python binding. Using a diverse collection of pangenome graphs, we demonstrate that these tools allow for efficient construction and manipulation of large genome graphs with dense variation. For instance, the speed and memory usage are up to an order of magnitude better than the prior graph implementation in the VG toolkit, which has now transitioned to using libbdsg's implementations. libhandlegraph and libbdsg are available under an MIT License from https://github.com/vgteam/libhandlegraph and https://github.com/vgteam/libbdsg.
Identifiants
pubmed: 33040146
pii: 5872523
doi: 10.1093/bioinformatics/btaa640
pmc: PMC7850124
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5139-5144Subventions
Organisme : NHLBI NIH HHS
ID : U01 HL137183
Pays : United States
Organisme : Federal Ministry for Economic Affairs and Energy of Germany
Organisme : W. M. Keck Foundation
ID : DT06172015
Organisme : NHGRI NIH HHS
ID : T32 HG008345
Pays : United States
Organisme : Central Innovation Programme
Organisme : NHGRI NIH HHS
ID : R01 HG010485
Pays : United States
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.