Inference of Phylogenetic Trees Directly From Raw Sequencing Reads Using Read2Tree

Authors

David Dylus
Adrian Altenhoff
Sina Majidian
Fritz J Sedlazeck
Christophe Dessimoz

Publication Date

1-1-2024

Journal

Nature Biotechnology

DOI

10.1038/s41587-023-01753-4

PMID

37081138

PMCID

PMC10791578 DO

PubMedCentral® Posted Date

4-20-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Animals, Phylogeny, Sequence Analysis, Genomics, Phylogeny, Genome informatics, Phylogenetics, Comparative genomics

Abstract

Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy. In a benchmark encompassing a broad variety of datasets, Read2Tree is 10-100 times faster than assembly-based approaches and in most cases more accurate-the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale.

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