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Title: Pangenome graph layout by Path-Guided Stochastic Gradient Descent
Abstract MotivationThe increasing availability of complete genomes demands for models to study genomic variability within entire populations. Pangenome graphs capture the full genomic similarity and diversity between multiple genomes. In order to understand them, we need to see them. For visualization, we need a human-readable graph layout: a graph embedding in low (e.g. two) dimensional depictions. Due to a pangenome graph’s potential excessive size, this is a significant challenge. ResultsIn response, we introduce a novel graph layout algorithm: the Path-Guided Stochastic Gradient Descent (PG-SGD). PG-SGD uses the genomes, represented in the pangenome graph as paths, as an embedded positional system to sample genomic distances between pairs of nodes. This avoids the quadratic cost seen in previous versions of graph drawing by SGD. We show that our implementation efficiently computes the low-dimensional layouts of gigabase-scale pangenome graphs, unveiling their biological features. Availability and implementationWe integrated PG-SGD in ODGI which is released as free software under the MIT open source license. Source code is available at https://github.com/pangenome/odgi.  more » « less
Award ID(s):
2118743 2118709
PAR ID:
10526352
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Editor(s):
Robinson, Peter
Publisher / Repository:
Oxford Journals
Date Published:
Journal Name:
Bioinformatics
Volume:
40
Issue:
7
ISSN:
1367-4811
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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