Efficient dynamic variation graphs

Author:

Eizenga Jordan M12ORCID,Novak Adam M12ORCID,Kobayashi Emily13ORCID,Villani Flavia45ORCID,Cisar Cecilia12ORCID,Heumos Simon6ORCID,Hickey Glenn1,Colonna Vincenza4,Paten Benedict12,Garrison Erik12

Affiliation:

1. Genomics Institute, Santa Cruz, CA 95064, USA

2. Biomolecular Engineering and Bioinformatics, University of California Santa Cruz, Santa Cruz, CA 95064, USA

3. Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA 92093, USA

4. Institute of Genetics and Biophysics, Consiglio Nazionale di Ricerche, Naples 80131, Italy

5. Biotecnologie Mediche, Università degli Studi di Napoli Federico II, Naples 80138,Italy

6. Quantitative Biology Center (QBiC), University of Tübingen, Tübingen 72076, Germany

Abstract

Abstract Motivation 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. Results 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. Availability and implementation libhandlegraph and libbdsg are available under an MIT License from https://github.com/vgteam/libhandlegraph and https://github.com/vgteam/libbdsg.

Funder

National Institutes of Health

W. M. Keck Foundation

Central Innovation Programme

Federal Ministry for Economic Affairs and Energy of Germany

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference12 articles.

1. Hash tables with pseudorandom global order;Brehm;INFOCOMP J. Comput. Sci,2019

2. Multi-platform discovery of haplotype-resolved structural variation in human genomes;Chaisson;Nat. Commun,2019

3. Computational pan-genomics: status, promises and challenges;Brief. Bioinf,2016

4. Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery

5. A graph-based approach to diploid genome assembly;Garg;Bioinformatics,2018

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