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Bioinformatics. 2017 Dec 1;33(23):3808-3810. doi: 10.1093/bioinformatics/btx517.

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

Author information

1
Department of Molecular Life Sciences, and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.

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 (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.

Contact:

mering@imls.uzh.ch.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28961926
PMCID:
PMC5860325
DOI:
10.1093/bioinformatics/btx517
[Indexed for MEDLINE]
Free PMC Article

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