16S Amplicon Profiling and Prokka Genome Annotation

A 16S amplicon table describes marker-gene sequences recovered from a mixed sample. Prokka annotates an assembled genome. Choose the analysis from the material you have and the question you need to answer; one workflow cannot substitute for the other.

16S rRNA amplicon profiling

Primers amplify a selected region of the 16S rRNA gene from sample DNA. Conserved stretches support primer binding; sequence variation within the amplified region helps distinguish some taxa. Results depend on primers, read quality, denoising, and reference database. Different taxa can share a marker sequence, so species-level labels are not always resolvable.

DADA2 models sequencing errors to infer amplicon sequence variants (ASVs). An ASV is an inferred sequence variant after denoising, not proof that each sequence represents a distinct organism or species. Historical OTU workflows cluster reads by a chosen similarity threshold; 97% is not a universal species boundary. Record method and parameters when comparing studies.

A QIIME 2 paired-read example

This command expects an existing QIIME 2 artifact of type SampleData[PairedEndSequencesWithQuality], already demultiplexed by sample; it does not import FASTQ files or create sample metadata. Inspect the artifact with qiime tools peek demux.qza and view per-cycle quality with qiime demux summarize --i-data demux.qza --o-visualization demux-summary.qzv, then open it with qiime tools view demux-summary.qzv. Choose truncation lengths from those plots and the target region, retaining sufficient paired-read overlap. The example produces a frequency table, representative sequences, and denoising statistics; compare input and retained read counts rather than treating file creation as success.

qiime dada2 denoise-paired \
  --i-demultiplexed-seqs demux.qza \
  --p-trunc-len-f 250 \
  --p-trunc-len-r 250 \
  --o-table table.qza \
  --o-representative-sequences rep-seqs.qza \
  --o-denoising-stats denoising-stats.qza

Match the QIIME 2 installation and DADA2 plugin to the documentation for the installed release. The 2024.10 reference below is archived; current QIIME 2 documentation points users to the amplicon distribution documentation.

Annotating an assembled genome with Prokka

Prokka predicts genomic features on supplied contigs and assigns annotations using its software and databases. Start with an assembled genome in FASTA format, not raw reads, a feature table, or a 16S-only amplicon. Assembly contamination, fragmentation, and incorrect taxonomic or genetic-code assumptions can affect predictions and labels.

Prokka's maintainer now recommends considering Bakta for new pipelines because Prokka is no longer maintained. These commands explain the existing Prokka workflow; check maintenance and database status before choosing it for new production work.

Installation and a basic run

Upstream documentation gives a Conda installation example through Bioconda. These commands assume Conda is installed and the environment can access the required packages and databases; record prokka --version with your results. Confirm the executable and configured databases before running. --listdb lists configured databases; if database files are missing or a run fails during setup, follow the upstream database setup instructions (including indexing where applicable) before proceeding.

conda create -n prokka bioconda::prokka
conda activate prokka
prokka --version
prokka --listdb

For an assembled FASTA called assembled_contigs.fasta, first check that it is non-empty, has one or more FASTA records, and each header has a non-empty identifier. This lightweight check catches malformed input, not contamination, completeness, or assembly quality:

test -s assembled_contigs.fasta && grep -q '^>[^[:space:]]' assembled_contigs.fasta && grep -qv '^>' assembled_contigs.fasta && echo "FASTA structure looks plausible"

prokka --outdir my_annotation --prefix isolate_01 assembled_contigs.fasta

On a completed run, confirm that my_annotation/isolate_01.gff, my_annotation/isolate_01.txt, and my_annotation/isolate_01.log exist, then inspect the log for a completion summary and the text summary for feature counts. Missing files, a non-zero exit status, or database/setup errors mean stop and resolve the failure before interpreting annotations. Counts are run-specific; there is no universal expected feature count. Output files do not validate the assembly or biological interpretation.

Select output for the next step

Using a curated protein reference

When a suitable curated reference exists, --proteins can prioritize annotations from a protein FASTA. This does not establish function experimentally or make every transferred name correct. Record reference source and version, and inspect matches and conflicts. Consult the upstream version-specific option documentation before using other reference formats or expecting gene/EC-number metadata.

prokka --proteins trusted_reference.faa \
  --outdir custom_annotation \
  --prefix isolate_01 \
  assembled_contigs.fasta

Interpretation and quality checks

• For 16S, record primer/target region, read-processing choices, denoising statistics, and taxonomy database/version. Check read loss during filtering or paired-read merging.
• For Prokka, retain assembly provenance and software/database versions. Inspect contig quality, feature counts, logs, and selected annotations against independent evidence where available.
• Marker-based labels and automated genome annotations alone do not establish phenotype, pathogenicity, or clinical significance. Treat weak or database-dependent assignments as hypotheses for review.