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

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. Introduction Recent examples of microbial tag sequencing experiments include the Human Microbiome Project(HMP Consortium, 2012) and a survey of the Arabidopsis root microbiome(Lundberg et al., 2012). The experimental protocol in such studies includes amplification by PCR followed by sequencing, which introduces errors in several ways. Amplification introduces substitution and gap errors (point errors) due to incorrect base pairing and polymerase slippage respectively(Turnbaugh et al., 2010). PCR chimeras form when an incomplete amplicon primes extension into a different biological template(Haas et al., 2011). Sequencing also introduces point errors due to substitutions (incorrect base calls) and gaps (omitted or spurious base calls). Contaminants from reagents and other sources can introduce spurious species(Edgar, 2013). Spurious species can also be introduced when reads are assigned to incorrect samples due to cross-talk, also known as tag switching or barcode switching(Carlsen et al., 2012). peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/081257 doi: bioRxiv preprint first posted online Oct. 15, 2016;