PhyloMagnet : fast and accurate screening of short-read meta-omics data using gene-centric phylogenetics

Motivation: Metagenomic and metatranscriptomic sequencing have become increasingly popular tools for producing massive amounts of short-read data, often used for the reconstruction of draft genomes or the detection of (active) genes in microbial communities. Unfortunately, sequence assemblies of such datasets generally remain a computationally challenging task. Frequently, researchers are only interested in a specific group of organisms or genes; yet, the assembly of multiple datasets only to identify candidate sequences for a specific question is sometimes prohibitively slow, forcing researchers to select a subset of available datasets to address their question. Here, we present PhyloMagnet, a workflow to screen meta-omics datasets for taxa and genes of interest using genecentric assembly and phylogenetic placement of sequences. Results: Using PhyloMagnet, we could identify up to 87% of the genera in an in vitro mock community with variable abundances, while the false positive predictions per single gene tree ranged from 0 to 23%. When applied to a group of metagenomes for which a set of metagenome assembled genomes (MAGs) have been published, we could detect the majority of the taxonomic labels that the MAGs had been annotated with. In a metatranscriptomic setting, the phylogenetic placement of assembled contigs corresponds to that of transcripts obtained from transcriptome assembly