SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine-learning method

Authors: de Bernardi Schneider, Adriano ;

Su, Michelle;

Hinrichs, Angie S.;

Wang, Jade C.;

Amin, Helly;

Bell, John I.;

Wadford, Debra A;

O’Toole, Áine;

Scher, Emily;

Perry, Marc D.;

Turakhia, Yatish;

de Maio, Nicola;

Hughes, Scott;

Corbett-Detig, Russ

Source: Virus Evolution

Full text

: https://doi.org/10.1093/ve/vead085

Abstract

With the rapid spread and evolution of SARS-CoV-2, the ability to monitor its transmission and distinguish among viral lineages is critical for pandemic response efforts. The most commonly used software for the lineage assignment of newly isolated SARS-CoV-2 genomes is pangolin, which offers two methods of assignment, pangoLEARN and pUShER. PangoLEARN rapidly assigns lineages using a machine-learning algorithm, while pUShER performs a phylogenetic placement to identify the lineage corresponding to a newly sequenced genome. In a preliminary study, we observed that pangoLEARN (decision tree model), while substantially faster than pUShER, offered less consistency across different versions of pangolin v3. Here, we expand upon this analysis to include v3 and v4 of pangolin, which moved the default algorithm for lineage assignment from pangoLEARN in v3 to pUShER in v4, and perform a thorough analysis confirming that pUShER is not only more stable across versions but also more accurate. Our findings suggest that future lineage assignment algorithms for various pathogens should consider the value of phylogenetic placement.

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This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Cite as

de Bernardi Schneider, A., Su, M., Hinrichs, A., Wang, J., Amin, H., Bell, J., Wadford, D., O’Toole, Á., Scher, E., Perry, M., Turakhia, Y., de Maio, N., Hughes, S. & Corbett-Detig, R. 2024, 'SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine-learning method', Virus Evolution, 10(1), article no: vead085. https://doi.org/10.1093/ve/vead085

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DOI: https://doi.org/10.1093/ve/vead085

Repository URI: https://www.research.ed.ac.uk/en/publications/bbcf0bb4-c38a-4570-a032-28cea5c868c0