EPIK: Precise and scalable evolutionary placement with informative $k$-mers - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Bioinformatics Year : 2023

EPIK: Precise and scalable evolutionary placement with informative $k$-mers


Motivation: Phylogenetic placement enables phylogenetic analysis of massive collections of newly sequenced DNA, when de novo tree inference is too unreliable or inefficient. Assuming that a high-quality reference tree is available, the idea is to seek the correct placement of the new sequences in that tree. Recently, alignment-free approaches to phylogenetic placement have emerged, both to circumvent the need to align the new sequences and to avoid the calculations that typically follow the alignment step. A promising approach is based on the inference of k-mers that can be potentially related to the reference sequences, also called phylo-k-mers. However, its usage is limited by the time and memory-consuming stage of reference data preprocessing and the large numbers of k-mers to consider. Results: We suggest a filtering method for selecting informative phylo-k-mers based on mutual information, which can significantly improve the efficiency of placement, at the cost of a small loss in placement accuracy. This method is implemented in IPK, a new tool for computing phylo-k-mers that significantly outperforms the software previously available. We also present EPIK, a new software for phylogenetic placement, supporting filtered phylo-k-mer databases. Our experiments on real-world data show that EPIK is the fastest phylogenetic placement tool available, when placing hundreds of thousands and millions of queries while still providing accurate placements. Availability and Implementation: IPK and EPIK are freely available at https://github.com/phylo42/IPK and https://github.com/phylo42/EPIK. Both are implemented in C ++ and Python and supported on Linux and MacOS. Supplementary information Supplementary data are available at Bioinformatics online.
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Dates and versions

lirmm-04308461 , version 1 (27-11-2023)





Nikolai Romashchenko, Benjamin Linard, Fabio Pardi, Eric Rivals. EPIK: Precise and scalable evolutionary placement with informative $k$-mers. Bioinformatics, 2023, 39 (12), pp.btad692. ⟨10.1093/bioinformatics/btad692⟩. ⟨lirmm-04308461⟩
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