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

TL;DR

MetaPalette is a novel $k$-mer based algorithm that accurately profiles metagenomic samples, detects novel organisms, and estimates their relatedness to known species, overcoming limitations of fixed taxonomic ranks.

Contribution

It introduces a $k$-mer palette fitting approach for improved taxonomic profiling and novel organism detection in metagenomics.

Findings

High accuracy in taxonomic profiling demonstrated on simulated data.

Effective identification of novel organisms and their relatedness.

Available software with pre-trained databases for diverse domains.

Abstract

Metagenomic profiling is challenging in part because of the highly uneven sampling of the tree of life by genome sequencing projects and the limitations imposed by performing phylogenetic inference at fixed taxonomic ranks. We present the algorithm MetaPalette which uses long $k$-mer sizes ($k=30, 50$) to fit a $k$-mer "palette" of a given sample to the $k$-mer palette of reference organisms. By modeling the $k$-mer palettes of unknown organisms, the method also gives an indication of the presence, abundance, and evolutionary relatedness of novel organisms present in the sample. The method returns a traditional, fixed-rank taxonomic profile which is shown on independently simulated data to be one of the most accurate to date. Tree figures are also returned that quantify the relatedness of novel organisms to reference sequences and the accuracy of such figures is demonstrated on simulated spike-ins and a metagenomic soil sample. The software implementing MetaPalette is available at: https://github.com/dkoslicki/MetaPalette. Pre-trained databases are included for Archaea, Bacteria, Eukaryota, and viruses.