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https://dx.doi.org/10.48550/ar...
Article . 2024
License: CC BY
Data sources: Datacite
DBLP
Preprint . 2024
Data sources: DBLP
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Lock-free de Bruijn graph

Authors: Daniel Górniak; Robert Nowak 0002;

Lock-free de Bruijn graph

Abstract

De Bruijn graph is one of the most important data structures used in de-novo genome assembly algorithms, especially for NGS data. There is a growing need for parallel data structures and algorithms due to the increasing number of cores in modern computers. The assembly task is an indispensable step in sequencing genomes of new organisms and studying structural genomic changes. In recent years, the dynamic development of next-generation sequencing (NGS) methods raises hopes for making whole-genome sequencing a fast and reliable tool used, for example, in medical diagnostics. However, this is hampered by the slowness and computational requirements of the current processing algorithms, which raises the need to develop more efficient algorithms. One possible approach, still little explored, is the use of quantum computing. We created the lock-free version of the de Bruijn graph, as well as a lock-free algorithm to build such graph from reads. Our algorithm and data structures are developed to use parallel threads of execution and do not use mutexes or other locking mechanisms, instead, we used only compare-and-swap instruction and other atomic operations. It makes our algorithm very fast and efficiently scaling. The presented article depicts the new lock-free de Bruijn graph data structure with a graph build algorithm. We developed a C++ library and tested its performance to depict its high speed and scalability compared to other available tools.

Keywords

Genomics (q-bio.GN), FOS: Computer and information sciences, H.4, Computer Science - Distributed, Parallel, and Cluster Computing, FOS: Biological sciences, Quantitative Biology - Genomics, Distributed, Parallel, and Cluster Computing (cs.DC)

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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!
0
Average
Average
Average
Green