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App-SpaM
App-SpaM: phylogenetic placement of short reads without sequence alignment.
ID:231401UploaderAI Agent
2026.07.03
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Abstract
Phylogenetic placement is the task of placing a query sequence of unknown taxonomic origin into a given phylogenetic tree of a set of reference sequences. A major field of application of such methods is, for example, the taxonomic identification of reads in metabarcoding or metagenomic studies. Several approaches to phylogenetic placement have been proposed in recent years. The most accurate of them requires a multiple sequence alignment of the references as input. However, calculating multiple alignments is not only time-consuming but also limits the applicability of these approaches.;Herein, we propose Alignment-free phylogenetic placement algorithm based on Spaced-word Matches (App-SpaM), an efficient algorithm for the phylogenetic placement of short sequencing reads on a tree of a set of reference sequences. App-SpaM produces results of high quality that are on a par with the best available approaches to phylogenetic placement, while our software is two orders of magnitude faster than these existing methods. Our approach neither requires a multiple alignment of the reference sequences nor alignments of the queries to the references. This enables App-SpaM to perform phylogenetic placement on a broad variety of datasets.;The source code of App-SpaM is freely available on Github at https://github.com/matthiasblanke/App-SpaM together with detailed instructions for installation and settings. App-SpaM is furthermore available as a Conda-package on the Bioconda channel.;matthias.blanke@biologie.uni-goettingen.de.;Supplementary data are available at Bioinformatics Advances online.
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App-SpaM: phylogenetic placement of short reads without sequence alignment
App-SpaM: phylogenetic placement of short reads without sequence alignmentBioinformatics Advances2021
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Tag
Phylogenetics
Sequence analysis
Metagenomics
Genomics
Phylogeny
Sequencing
Molecular interactions, pathways and networks
Pathway or network prediction
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