AmpliCI: a high-resolution model-based approach for denoising Illumina amplicon data.

Bioinformatics
Xiyu Peng, Karin S Dorman

Abstract

Next-generation amplicon sequencing is a powerful tool for investigating microbial communities. A main challenge is to distinguish true biological variants from errors caused by amplification and sequencing. In traditional analyses, 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 more frequent sequences, because they ignore the sequencing quality information. We 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 considers the 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. AmpliCI has better performance than three popular denoising methods...Continue Reading

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Methods Mentioned

BETA
amplicon sequencing
PCR
Illumina sequencing

Software Mentioned

Extreme
LOESS
DADA2
GNU
Deblur
UNOISE3
UCHIME3
UPARSE
Miseq
USEARCH

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