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ODGI: understanding pangenome graphs

View ORCID ProfileAndrea Guarracino, View ORCID ProfileSimon Heumos, View ORCID ProfileSven Nahnsen, View ORCID ProfilePjotr Prins, View ORCID ProfileErik Garrison
doi: https://doi.org/10.1101/2021.11.10.467921
Andrea Guarracino
1Genomics Research Centre, Human Technopole, Milan, Italy
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Simon Heumos
2Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany, 72076
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Sven Nahnsen
2Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany, 72076
3Biomedical Data Science, Dept. of Computer Science, University of Tübingen, Tübingen, Germany, 72076
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Pjotr Prins
4University of Tennessee Health Science Center, Memphis, TN, USA
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Erik Garrison
4University of Tennessee Health Science Center, Memphis, TN, USA
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  • For correspondence: erik.garrison@gmail.com
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Abstract

Motivation Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is difficult as it is not well-supported by existing tools. Hence, fast and versatile software is required to ask advanced questions to such data in an efficient way.

Results We wrote ODGI, a novel suite of tools that implements scalable algorithms and has an efficient in-memory representation of DNA variation graphs. ODGI includes tools for detecting complex regions, extracting loci, removing artifacts, exploratory analysis, manipulation, validation, and visualization. Its fast parallel execution facilitates routine pangenomic tasks, as well as pipelines that can quickly answer complex biological questions of gigabase-scale pangenome graphs.

Availability ODGI is published as free software under the MIT open source license. Source code can be downloaded from https://github.com/pangenome/odgi and documentation is available at https://odgi.readthedocs.io. ODGI can be installed via Bioconda https://bioconda.github.io/recipes/odgi/README.html or GNU Guix https://github.com/ekg/guix-genomics/blob/master/odgi.scm.

Contact egarris5{at}uthsc.edu

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/pangenome/odgi-paper

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 11, 2021.
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ODGI: understanding pangenome graphs
Andrea Guarracino, Simon Heumos, Sven Nahnsen, Pjotr Prins, Erik Garrison
bioRxiv 2021.11.10.467921; doi: https://doi.org/10.1101/2021.11.10.467921
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ODGI: understanding pangenome graphs
Andrea Guarracino, Simon Heumos, Sven Nahnsen, Pjotr Prins, Erik Garrison
bioRxiv 2021.11.10.467921; doi: https://doi.org/10.1101/2021.11.10.467921

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