AmpliCI: A High-resolution Model-Based Approach for Denoising Illumina Amplicon Data

MotivationNext-generation amplicon sequencing is a powerful tool for investigating microbial communities. One main challenge is to distinguish true biological variants from errors caused by PCR and sequencing. In the traditional analysis pipeline, such errors are eliminated by clustering reads within a sequence similarity threshold, usually 97%, and constructing operational taxonomic units, but the arbitrary threshold leads to low resolution and high false positive rates. Recently developed "denoising" methods have proven able to resolve single-nucleotide amplicon variants, but they still miss low frequency sequences, especially those near abundant variants, because they ignore the sequencing quality information. ResultsWe introduce AmpliCI, a reference-free, model-based method for rapidly resolving the number, abundance and identity of error-free sequences in massive Illumina amplicon datasets. AmpliCI takes into account quality information and allows the data, not an arbitrary threshold or an external database, to drive conclusions. AmpliCI estimates a finite mixture model, using a greedy strategy to gradually select error-free sequences and approximately maximize the likelihood. We show that AmpliCI is superior to three popular denoising methods, with acceptable computation time and memory usage. AvailabilitySource code available at https://github.com/DormanLab/AmpliCI