WASTER: Practical de novo phylogenomics from low-coverage short reads

Area of Science:

• Phylogenomics
• Computational Biology
• Genomics

Background:

• Affordable whole-genome sequencing fuels large-scale phylogenomic projects.
• Traditional species tree inference is computationally intensive and requires high-quality data.
• Existing methods face challenges with cost, data requirements, and computational demands.

Purpose of the Study:

• Introduce WASTER, a novel de novo tool for species tree inference from short-read sequences.
• Address limitations of traditional methods by circumventing genome assembly and alignment.
• Provide a cost-effective and efficient solution for phylogenomic analyses.

Main Methods:

• Developed WASTER, a k-mer based tool for identifying variable sites directly from short-read sequences.
• Utilized simulations to assess WASTER's accuracy compared to traditional and other alignment-free methods.
• Validated WASTER's performance on real eukaryotic species data with low-depth sequencing.

Main Results:

• WASTER achieves accuracy comparable to alignment-based methods, even at low sequencing depths.
• Demonstrated substantially higher accuracy than existing alignment-free methods.
• Successfully reconstructed phylogenies from eukaryotic species data with as low as 1.5X sequencing depth.

Conclusions:

• WASTER offers a fast and efficient solution for phylogeny estimation, particularly when assembly or alignment is challenging or biased.
• The tool reduces sequencing and computational costs for phylogenomic projects.
• WASTER can generate guide trees for tree-based alignment algorithms.