WASTER: Practical de novo Phylogenomics from Low-coverage Short Reads

Area of Science:

• Genomics
• Bioinformatics
• Evolutionary Biology

Background:

• Affordable whole-genome sequencing drives large-scale phylogenomic projects.
• Traditional species tree inference is limited by high sequencing, alignment, and computational costs.

Purpose of the Study:

• Introduce WASTER, a novel de novo tool for shallow phylogeny inference from short-read sequences.
• Address the challenges of cost and computational demand in species tree inference.

Main Methods:

• WASTER utilizes a k-mer based approach to identify variable sites, bypassing genome assembly and alignment.
• Employs simulations and real data validation for phylogenetic reconstruction.

Main Results:

• WASTER achieves accuracy comparable to alignment-based methods, even at low sequencing depths (1.5X).
• Demonstrates substantially higher accuracy than other alignment-free methods.
• Successfully reconstructs phylogenies for eukaryotic species using real data.

Conclusions:

• WASTER offers a fast, efficient solution for phylogeny estimation, especially with low-coverage data or when assembly/alignment is challenging.
• Provides a method for generating guide trees for alignment algorithms.
• Reduces sequencing and computational costs in phylogenomic projects.