MAPseq: Highly efficient k-mer search with confidence estimates, for rRNA sequence analysis

Abstract

Motivation: Ribosomal RNA profiling has become crucial to studying microbial communities, but meaningful taxonomic analysis and inter-comparison of such data are still hampered by technical limitations, between-study design variability and inconsistencies between taxonomies used. Results: Here we present MAPseq, a framework for reference-based rRNA sequence analysis that is up to 30% more accurate (F1=2 score) and up to one hundred times faster than existing solutions, providing in a single run multiple taxonomy classifications and hierarchical operational taxonomic unit mappings, for rRNA sequences in both amplicon and shotgun sequencing strategies, and for datasets of virtually any size.