Robust expansion of phylogeny for fast-growing genome sequence data

Yongtao Ye, Marcus H Shum, Joseph L Tsui, Guangchuang Yu, David K Smith, Huachen Zhu, Joseph T Wu, Yi Guan and Tommy Tsan-Yuk Lam

PLOS Computational Biology, 2024, vol. 20, issue 2, 1-22

Abstract: Massive sequencing of SARS-CoV-2 genomes has urged novel methods that employ existing phylogenies to add new samples efficiently instead of de novo inference. ‘TIPars’ was developed for such challenge integrating parsimony analysis with pre-computed ancestral sequences. It took about 21 seconds to insert 100 SARS-CoV-2 genomes into a 100k-taxa reference tree using 1.4 gigabytes. Benchmarking on four datasets, TIPars achieved the highest accuracy for phylogenies of moderately similar sequences. For highly similar and divergent scenarios, fully parsimony-based and likelihood-based phylogenetic placement methods performed the best respectively while TIPars was the second best. TIPars accomplished efficient and accurate expansion of phylogenies of both similar and divergent sequences, which would have broad biological applications beyond SARS-CoV-2. TIPars is accessible from https://tipars.hku.hk/ and source codes are available at https://github.com/id-bioinfo/TIPars.Author summary: Since the beginning of the COVID-19 pandemic, over 15 million SARS-CoV-2 genome sequences have been made publicly available. As sequencing cost decreases, the rate of genome sequencing is expected to greatly increase in the future and will generate numerous sequences where conventional de novo phylogenetic inference may no longer be suitable. TIPars allows rapid and memory-efficient expansion of phylogeny at high accuracy. This enables real-time monitoring of pathogen transmission during a pandemic using large-scale global phylogenetic analysis such as the ever-increasing SARS-CoV-2 genome sequences. We believe that the development of next-generation phylogenetic methods is imperative for analysing enormous, fast-growing genome sequence datasets to gain critical evolutionary insights that, as evident in this pandemic, have real-world applications.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011871

DOI: 10.1371/journal.pcbi.1011871

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