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Updated: Jan 15, 2026
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
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Machine Learning Enables Alignment-Free Distance Calculation and Phylogenetic Placement Using k-Mer Frequencies
Eleonora Rachtman1, Yueyu Jiang1, Siavash Mirarab1
1Department of Electrical and Computer Engineering, UC San Diego, San Diego, California, USA.
Molecular Ecology Resources
|October 13, 2025
Summary
We introduce kf2vec, a novel method for phylogenetic placement of long DNA sequences using k-mer frequencies and deep learning. This approach simplifies analyses by avoiding sequence alignment and accurately identifies taxonomic labels for new genomic samples.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Phylogenetics is crucial for ecological studies, particularly for assigning taxonomic labels to unknown sequences.
- Phylogenetic placement methods are well-developed for short DNA fragments but less so for longer sequences like genomes or contigs.
- Placing long sequences offers more phylogenetic signal but presents challenges in homology detection and computational load.
Purpose of the Study:
- To develop and evaluate a novel phylogenetic placement method for long DNA sequences.
- To address the limitations of existing methods in handling large genomic data and computational complexity.
- To improve the accuracy and efficiency of taxonomic identification for long DNA sequences.
Main Methods:
- A new method, kf2vec, utilizes k-mer frequencies to measure distances between long query sequences and reference genomes.
- kf2vec employs a deep neural network trained to estimate phylogenetic distances from k-mer frequency vectors, bypassing the need for sequence alignment.
- The method is applicable to any genomic region and does not require marker genes, simplifying bioinformatics pipelines.
Main Results:
- kf2vec demonstrates superior performance compared to existing k-mer-based approaches in distance calculation.
- The method achieves accurate phylogenetic placement and taxonomic identification of new samples, including assembled genomes, contigs, and long reads.
- kf2vec effectively handles the challenges associated with analyzing longer DNA sequences.
Conclusions:
- kf2vec offers a robust and efficient solution for the phylogenetic placement of long DNA sequences.
- The alignment-free nature and deep learning approach of kf2vec simplify genomic analysis and enhance taxonomic identification accuracy.
- This method advances the application of phylogenetics in ecological and genomic studies involving large sequence data.
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