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Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning

Abdulkadir Celikkanat, Andres R. Masegosa, Thomas D. Nielsen

TL;DR

This paper revisit k-mer-based representations of genomes and provides a theoretical analysis of their use in representation learning, and proposes a lightweight and scalable model for performing metagenomic binning at the genome read level, relying only on the k-mer compositions of the DNA fragments.

Abstract

Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures of DNA fragments from biological samples with the aim of determining their microbial compositions. In this paper, we revisit k-mer-based representations of genomes and provide a theoretical analysis of their use in representation learning. Based on the analysis, we propose a lightweight and scalable model for performing metagenomic binning at the genome read level, relying only on the k-mer compositions of the DNA fragments. We compare the model to recent genome foundation models and demonstrate that while the models are comparable in performance, the proposed model is significantly more effective in terms of scalability, a crucial aspect for performing metagenomic binning of real-world datasets.

Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning

TL;DR

This paper revisit k-mer-based representations of genomes and provides a theoretical analysis of their use in representation learning, and proposes a lightweight and scalable model for performing metagenomic binning at the genome read level, relying only on the k-mer compositions of the DNA fragments.

Abstract

Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures of DNA fragments from biological samples with the aim of determining their microbial compositions. In this paper, we revisit k-mer-based representations of genomes and provide a theoretical analysis of their use in representation learning. Based on the analysis, we propose a lightweight and scalable model for performing metagenomic binning at the genome read level, relying only on the k-mer compositions of the DNA fragments. We compare the model to recent genome foundation models and demonstrate that while the models are comparable in performance, the proposed model is significantly more effective in terms of scalability, a crucial aspect for performing metagenomic binning of real-world datasets.

Paper Structure

This paper contains 14 sections, 6 theorems, 12 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Backgroud and Related Works
  3. $k$-mer embeddings
  4. $\mathbf{k}$-mers are a powerful representation of reads
  5. Linear read embeddings
  6. Non-linear read embeddings
  7. Experiments
  8. Metagenomics Binning Task
  9. Ablation Study
  10. Conclusion
  11. Appendix
  12. Theoretical Analysis
  13. Experiments
  14. Notation and Symbols

Key Result

Theorem 3.1

Let $\mathbf{r}$ be a read of length $\ell$. There exists no other distinct read having the same $k$-mer profile if and only if it does not satisfy any of the following conditions:

Figures (5)

  • Figure 1: Number of parameters of the different evaluated models ($\log_{10}$-scale).
  • Figure 2: Illustration of the non-linear $k$-mer embedding approach described in Section \ref{['subsec:nonlinear:embedding']}.
  • Figure 3: Evaluation of the models on multiple datasets for the metagenomic binning task. Each color represents the number of clusters within a specific $F_1$ score range, and the number of clusters with the highest quality is highlighted in dark blue.
  • Figure 4: Influence of parameter $k$ on the Ours(kmer-$l_1$) model across different datasets.
  • Figure 5: Influence of dimension size $(d)$ on the Ours(nl) model across different datasets.

Theorems & Definitions (12)

  • Theorem 3.1
  • proof
  • Proposition 3.2
  • proof
  • Lemma A.1
  • proof
  • Corollary A.2
  • proof
  • Theorem A.3
  • proof
  • ...and 2 more