Rate variation and recurrent sequence errors in pandemic-scale phylogenetics.

Area of Science:

• Genomics
• Evolutionary Biology
• Computational Biology

Background:

• Phylogenetic analyses of pathogen genomes are crucial for understanding evolution and transmission, especially during pandemics like COVID-19.
• Recent methods enable pandemic-scale phylogenetic inference, analyzing millions of genomes.
• Homoplasies from recurrent mutations and errors introduce uncertainty and bias in phylogenetic reconstruction.

Purpose of the Study:

• To develop algorithms and models that enhance the computational performance and accuracy of pandemic-scale phylogenetics.
• To address challenges posed by mutation rate variation and recurrent sequence errors in large genomic datasets.

Main Methods:

• Developed novel algorithms and models for phylogenetic inference.
• Incorporated methods to identify and account for mutation rate variation.
• Implemented strategies to detect and correct recurrent sequence errors.

Main Results:

• Achieved substantial improvements in computational performance and accuracy for pandemic-scale phylogenetics.
• Reconstructed a reliable and publicly available sequence alignment and phylogenetic tree.
• The analysis included over 2 million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes.

Conclusions:

• The developed methods provide a more accurate and computationally efficient approach to phylogenetics for large-scale genomic data.
• The reconstructed phylogenetic tree offers insights into the evolutionary history and global spread of SARS-CoV-2 up to February 2023.
• This work enhances our ability to track and understand pathogen evolution during global health crises.