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Updated: Dec 25, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Binning unassembled short reads based on k-mer abundance covariance using sparse coding.

Olexiy Kyrgyzov1, Vincent Prost1,2, Stéphane Gazut2

  • 1Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université Paris-Saclay, 2 rue Gaston Crémieux, 91057 Evry, France.

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Summary

This study introduces a scalable pre-assembly binning method for recovering microbial genomes from metagenomes. The novel approach, using sparse dictionary learning, successfully identifies low-abundance genomes without prior assembly, complementing existing methods.

Keywords:
human microbiomemetagenomicssequence binningsparse coding

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Metagenome assembly is computationally intensive and can miss low-abundance genomes.
  • Existing sequence-binning techniques often rely on prior metagenome assembly.
  • Large-scale metagenomic datasets present computational challenges for assembly.

Purpose of the Study:

  • To develop a scalable pre-assembly binning scheme for microbial genome recovery.
  • To enable the recovery of low-abundance genomes from complex metagenomes.
  • To leverage sparse dictionary learning for read-level binning.

Main Methods:

  • A pre-assembly binning scheme operating on unassembled short reads.
  • Application of sparse dictionary learning and elastic-net regularization.
  • Joint analysis of microbiomes from the LifeLines DEEP population cohort (n = 1,135).

Main Results:

  • Recovery of hundreds of metagenome-assembled genomes, including very low-abundance ones.
  • Demonstration of read-level binning at scale using sparse coding techniques.
  • Observed read enrichment across six orders of magnitude in relative abundance.

Conclusions:

  • Sparse coding enables scalable read-level binning for microbial genome recovery.
  • Bin-first strategies can complement assembly-first protocols by targeting distinct genome segregation.
  • The method effectively recovers genomes with low relative abundance.