UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing

Abstract

Abstract Amplicon sequencing of tags such as 16S and ITS ribosomal RNA is a popular method for investigating microbial populations. In such experiments, sequence errors caused by PCR and sequencing are difficult to distinguish from true biological variation. I describe UNOISE2, an updated version of the UNOISE algorithm for denoising (error-correcting) Illumina amplicon reads and show that it has comparable or better accuracy than DADA2.