| name | bioconductor-dada2 |
| description | The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, an |
| when_to_use | Use when: 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.. Not for: 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 p |
| user-invocable | false |
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
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