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K2R: Tinted de Bruijn graphs implementation for efficient read extraction from sequencing datasets.

Léa Vandamme1, Bastien Cazaux1, Antoine Limasset1

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Summary
This summary is machine-generated.

We introduce Tinted de Bruijn graphs and K2R, a new method for efficiently identifying k-mers in raw sequencing reads. This approach offers a scalable solution for de novo analysis in genomics.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Biological sequence analysis often depends on reference genomes, but their accurate assembly is challenging.
  • De novo analysis directly from raw sequencing reads is frequently more practical for biological sequence analysis.
  • Identifying reads containing specific k-mers is crucial for various genomic applications like genotyping, profiling, and assembly.

Purpose of the Study:

  • To develop a scalable and efficient method for identifying reads containing a given k-mer directly from raw sequencing data.
  • To address the resource-intensive nature of read-level k-mer identification in large datasets.
  • To introduce Tinted de Bruijn graphs as a novel approach to this problem.

Main Methods:

  • Introduced Tinted de Bruijn graphs, a variation of colored de Bruijn graphs where each read is a unique source.
  • Developed K2R, a scalable index that efficiently implements the Tinted de Bruijn graph model.
  • Benchmarked K2R against leading methods like Short Read Connector, Fulgor, Movi, and Themisto.

Main Results:

  • K2R demonstrated efficient indexing of large human datasets (T2T) with high ONT coverage (126X).
  • The tool completed indexing in under 9 hours, utilizing a peak memory of 61 GB RAM.
  • K2R's performance was evaluated in terms of index size, memory usage, throughput, and construction time.

Conclusions:

  • Tinted de Bruijn graphs and the K2R index make read-level k-mer identification tractable and efficient.
  • K2R provides a scalable solution for de novo genomic analysis, outperforming existing methods in benchmarks.
  • The K2R tool is open-source, implemented in C++, and available for broader use in the research community.