MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis
Bioinformatics, 2017•academic.oup.com
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 (F ½ score) and up to one hundred times
faster than existing solutions, providing in a single run multiple taxonomy classifications and …
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 (F ½ score) and up to one hundred times
faster than existing solutions, providing in a single run multiple taxonomy classifications and …
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 (F½ 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.
Availability and implementation
Source code and binaries are freely available at https://github.com/jfmrod/mapseq
Supplementary information
Supplementary data are available at Bioinformatics online.
Oxford University Press