dada2

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 1.40.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: Rcpp
• Imports: Biostrings, ggplot2, reshape2, ShortRead, RcppParallel, IRanges, XVector, BiocGenerics
• System requirements: GNU make
• Install: BiocManager::install("dada2")

When to Use

• Resolving exact Amplicon Sequence Variants (ASVs) from high-throughput amplicon sequencing data using the dada algorithm.
• Filtering and trimming raw paired-end or single-end FASTQ files using filterAndTrim.
• Modeling and learning sequencing error rates directly from the data using learnErrors.
• Removing chimeric sequences from merged sequence tables using removeBimeraDenovo.

When NOT to Use

• For shotgun metagenomic sequencing data (use MetaPhlAn or HUMAnN instead).
• For clustering sequences based on a fixed 97% identity threshold (use vsearch or UCLUST instead).
• For downstream ecological statistics and visualization of ASV tables (use phyloseq instead).

Data Requirements

• Input Format: Demultiplexed FASTQ files (unzipped or gzipped). One file per sample for single-end, or two files per sample for paired-end.
• Quality Scores: Raw reads must contain quality scores.
• Primers: Sequences must have non-biological bases (like PCR primers) removed prior to ASV inference.

Key Parameters

• truncLen: Truncate reads after this many bases. Reads shorter than this are discarded. Crucial for removing low-quality tails.
• maxEE: After truncation, reads with higher than this number of expected errors are discarded (e.g., maxEE=2).
• trimLeft: The number of nucleotides to remove from the start of each read (useful for removing primers).
• maxN: Maximum number of ambiguous nucleotides allowed. DADA2 requires maxN=0.
• multithread: If TRUE, enables parallel processing to speed up computation.
• err: The error model generated by learnErrors, passed into the dada function.

Best Practices

• Inspect the quality profiles of forward and reverse reads using plotQualityProfile before setting truncLen parameters.
• Learn error rates using learnErrors on at least a subset of your data to ensure accurate sample inference.
• Maintain a suitable overlap (>20nts) between forward and reverse reads after trimming to ensure mergePairs succeeds.
• Perform chimera removal using removeBimeraDenovo on the combined sequence table created by makeSequenceTable.

Common Pitfalls

• Failing to merge paired reads: Truncating reverse reads too aggressively can prevent them from overlapping with forward reads, causing mergePairs to fail. Fix this by ensuring truncLen leaves enough sequence for a >20nt overlap.
• Leaving primers in the sequences: Non-biological nucleotides distort the error model. Fix this by using trimLeft in filterAndTrim or an external tool to remove primers.
• Reads dropped due to Ns: DADA2 cannot process sequences with ambiguous bases. Fix this by ensuring maxN=0 is set in filterAndTrim.

Alternatives

• phyloseq: Used for downstream analysis, visualization, and integration of the ASV tables produced by DADA2.
• vsearch: An alternative for traditional OTU clustering based on sequence identity thresholds.
• deblur: An alternative sub-operational taxonomic unit (sOTU) algorithm that uses a static error model.

Citations

• Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. (2016). "DADA2: High-resolution sample inference from amplicon data." Nature Methods, 13(7), 581-583.
• Callahan BJ, McMurdie PJ, Holmes SP. (2017). "Exact sequence variants should replace operational taxonomic units in marker-gene data analysis." ISME Journal, 11(12), 2639-2643.

References

• Homepage: https://bioconductor.org/packages/dada2
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/dada2/inst/doc/dada2-intro.html

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