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Metagenomic profiling is challenging in part because of the highly unevensampling of the tree of life by genome sequencing projects and the limitationsimposed by performing phylogenetic inference at fixed taxonomic ranks. Wepresent the algorithm MetaPalette which uses long $k$-mer sizes ($k=30, 50$) tofit a $k$-mer "palette" of a given sample to the $k$-mer palette of referenceorganisms. By modeling the $k$-mer palettes of unknown organisms, the methodalso gives an indication of the presence, abundance, and evolutionaryrelatedness of novel organisms present in the sample. The method returns atraditional, fixed-rank taxonomic profile which is shown on independentlysimulated data to be one of the most accurate to date. Tree figures are alsoreturned that quantify the relatedness of novel organisms to referencesequences and the accuracy of such figures is demonstrated on simulatedspike-ins and a metagenomic soil sample. The software implementing MetaPaletteis available at: https://github.com/dkoslicki/MetaPalette. Pre-traineddatabases are included for Archaea, Bacteria, Eukaryota, and viruses.

MetaPalette: A $k$-mer painting approach for metagenomic taxonomic profiling and quantification of novel strain variation