Genome Comparison on Succinct Colored de Bruijn Graphs

Ramos, Lucas P.; Louza, Felipe A.; Telles, Guilherme P.

doi:10.1007/978-3-031-20643-6_12

Cited by 1 publication

(1 citation statement)

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“…An efficient way to represent a kmer set from sequencing datasets is through the use of a de Bruijn graph [10], which retains, to a certain extent, the original ordering of kmers. Kmer sets have found widespread application in various domains, including quantification [39], dataset comparison [34], phylogeny [23], assembly [6, 5], compression [8], correction [20], and alignment [22, 12]. The advent of short-read sequencing technologies has particularly emphasized the extensive utility of the de Bruijn graph as a convenient and effective structure.…”

Section: Introductionmentioning

confidence: 99%

Tinted de Bruijn Graphs for efficient read extraction from sequencing datasets

Vandamme,

Cazaux,

Limasset

2024

Preprint

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The study of biological sequences often relies on using reference genomes, yet achieving accurate assemblies remains challenging. Consequently, de novo analysis directly from raw reads, without pre-processing, is frequently more practical. We identify a very commonly shared need across various applications: identifying reads containing a specific kmer in a dataset. This kmer-to-reads association would be pivotal in multiple contexts, including genotyping, bacterial strain resolution, profiling, data compression, error correction or assembly. While this challenge appears similar to the extensively researched colored de Bruijn graph problem, resolving it at the read level would be prohibitively resource-intensive in practical applications. In this work, we demonstrate its tractable resolution by leveraging certain assumptions for sequencing dataset indexing. To tackle this challenge, we introduce the Tinted de Bruijn Graph concept, an altered version of the colored de Bruijn graph where each read within a sequencing dataset represents a unique source. We developed K2R, a highly scalable index that implement such search efficiently within this framework. K2Rs performance, in terms of index size, memory footprint, throughput, and construction time, is benchmarked against leading methods, including hashing techniques (e.g., Short Read Connector) and full-text indexing (e.g., Spumoni and Movi), across various datasets. K2R consistently outperforms contemporary solutions in most metrics and is the only tool capable of scaling to larger datasets. To prove K2R scalability we indexed two human datasets of the T2T consortium: the 126X coverage ONT dataset was indexed in 18hours using 19GB of RAM for a final index of 9.5GB and the 56X coverage HiFi dataset was constructed in 90 minutes using 5Gb of RAM for a final index of 207Mb. The K2R index, developed in C++, is open source and available on Github github.com/LeaVandamme/K2R.

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