APPLES: Fast Distance Based Phylogenetic Placement
research in computational molecular biology(2019)
摘要
Phylogenetic placement consists of adding a query species onto an existing phylogeny and has increasing relevance as sequence datasets continue to grow in size and diversity. Placement is useful for updating existing phylogenies and for identifying samples taxonomically using (meta-)barcoding or metagenomics. Maximum likelihood (ML) methods of phylogenetic placement exist, but these methods are not scalable to trees with many thousands of leaves. They also rely on assembled and aligned sequences for the reference tree and the query and thus cannot analyze unassembled reads used recently in applications such as genome skimming. Here, we introduce APPLES, a distance-based method of phylogenetic placement that improves on ML by more than an order of magnitude in speed and memory and comes very close to ML in accuracy. APPLES has better accuracy than ML for placing on trees with thousands of species and can place on trees with a hundred thousands species where ML cannot run. Finally, APPLES can accurately identify samples without assembled sequences for the reference or the query using k-mer-based distances, a scenario that ML cannot handle.
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关键词
Phylogenetic placement,Distance-based methods,Genome-skimming
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