bioRxivBioinformatics
DADA2: High resolution sample inference from amplicon data
Benjamin J Callahan, Paul J McMurdie, Michael J Rosen, Andrew W Han, Amy J Johnson, Susan P Holmes
Abstract
Microbial communities are commonly characterized by amplifying and sequencing target genes, but errors limit the precision of amplicon sequencing. We present DADA2, a software package that models and corrects amplicon errors. DADA2 identified more real variants than other methods in Illumina-sequenced mock communities, some differing by a single nucleotide, while outputting fewer spurious sequences. DADA2 analysis of vaginal samples revealed a diversity of Lactobacillus crispatus strains undetected by OTU methods.
- organism: human
- assay: metagenomics
- research focus: clustering
- research focus: variant calling
- approach: deep learning
- approach: statistical model
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