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Article . 2020 . Peer-reviewed
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https://doi.org/10.1109/dcc.20...
Article . 2015 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2014
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Article . 2014
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Variable-Order de Bruijn Graphs

Authors: Christina Boucher 0001; Alexander Bowe; Travis Gagie; Simon J. Puglisi; Kunihiko Sadakane;

Variable-Order de Bruijn Graphs

Abstract

The de Bruijn graph $G_K$ of a set of strings $S$ is a key data structure in genome assembly that represents overlaps between all the $K$-length substrings of $S$. Construction and navigation of the graph is a space and time bottleneck in practice and the main hurdle for assembling large, eukaryote genomes. This problem is compounded by the fact that state-of-the-art assemblers do not build the de Bruijn graph for a single order (value of $K$) but for multiple values of $K$. More precisely, they build $d$ de Bruijn graphs, each with a specific order, i.e., $G_{K_1}, G_{K_2}, ..., G_{K_d}$. Although, this paradigm increases the quality of the assembly produced, it increases the memory by a factor of $d$ in most cases. In this paper, we show how to augment a succinct de Bruijn graph representation by Bowe et al. (Proc. WABI, 2012) to support new operations that let us change order on the fly, effectively representing all de Bruijn graphs of order up to some maximum $K$ in a single data structure. Our experiments show our variable-order de Bruijn graph only modestly increases space usage, construction time, and navigation time compared to a single order graph.

Conference submission, 10 pages, +minor corrections

Keywords

Genomics (q-bio.GN), FOS: Computer and information sciences, FOS: Biological sciences, Computer Science - Data Structures and Algorithms, Quantitative Biology - Genomics, Data Structures and Algorithms (cs.DS), Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM)

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
38
Top 10%
Top 10%
Top 10%
Green