Mandrake: visualizing microbial population structure by embedding millions of genomes into a low-dimensional representation

In less than a decade, population genomics of microbes has progressed from the effort of sequencing dozens of strains to thousands, or even tens of thousands of strains in a single study. There are now hundreds of thousands of genomes available even for a single bacterial species, and the number of...

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Veröffentlicht in:Philosophical transactions of the Royal Society of London. Series B. Biological sciences 2022-10, Vol.377 (1861), p.20210237-20210237
Hauptverfasser: Lees, John A., Tonkin-Hill, Gerry, Yang, Zhirong, Corander, Jukka
Format: Artikel
Sprache:eng
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Zusammenfassung:In less than a decade, population genomics of microbes has progressed from the effort of sequencing dozens of strains to thousands, or even tens of thousands of strains in a single study. There are now hundreds of thousands of genomes available even for a single bacterial species, and the number of genomes is expected to continue to increase at an accelerated pace given the advances in sequencing technology and widespread genomic surveillance initiatives. This explosion of data calls for innovative methods to enable rapid exploration of the structure of a population based on different data modalities, such as multiple sequence alignments, assemblies and estimates of gene content across different genomes. Here, we present Mandrake, an efficient implementation of a dimensional reduction method tailored for the needs of large-scale population genomics. Mandrake is capable of visualizing population structure from millions of whole genomes, and we illustrate its usefulness with several datasets representing major pathogens. Our method is freely available both as an analysis pipeline ( https://github.com/johnlees/mandrake ) and as a browser-based interactive application ( https://gtonkinhill.github.io/mandrake-web/ ). This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’.
ISSN:0962-8436
1471-2970
DOI:10.1098/rstb.2021.0237