DADA2: High-resolution sample inference from Illumina amplicon data.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• High-throughput sequencing generates vast amounts of data, but is prone to errors.
• Accurate variant identification is crucial for microbiome research.

Purpose of the Study:

• To introduce DADA2, an open-source software for accurate amplicon sequence variant inference.
• To demonstrate DADA2's superior performance in error correction compared to existing methods.
• To apply DADA2 for novel variant discovery in a human microbiome study.

Main Methods:

• DADA2 models and corrects Illumina sequencing errors.
• It infers exact sample sequences and resolves single-nucleotide differences.
• Performance was evaluated using mock communities and real-world vaginal microbiome samples.

Main Results:

• DADA2 identified more true variants and fewer spurious sequences than other methods in mock communities.
• Application to vaginal samples revealed previously undetected Lactobacillus crispatus variants.
• The software precisely resolves sequence differences as small as 1 nucleotide.

Conclusions:

• DADA2 offers a powerful and accurate solution for amplicon error correction.
• It enhances the discovery of microbial diversity, including subtle variants.
• The open-source package facilitates advanced microbiome analysis.