bakta-genome-annotation
Annotate bacterial and archaeal genomes and plasmids with Bakta's Prodigal/HMM/diamond pipeline. Identifies CDS, ncRNA, tRNA, rRNA, tmRNA, sORFs, CRISPR arrays, oriC/oriV/oriT, and gaps against a curated UniRef-derived database. Produces NCBI-compatible GFF3, GenBank, EMBL, JSON, FASTA, TSV, and a circular genome plot. Use Prokka for legacy pipelines or non-bacterial kingdoms; PGAP for NCBI GenBank submission.
npx skills add BioTender-max/awesome-bio-agent-skills --skill bakta-genome-annotation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Annotates bacterial and archaeal genomes and plasmids with Bakta's pipeline, identifying CDS, ncRNA, tRNA, rRNA, tmRNA, sORFs, CRISPR arrays, oriC/oriV/oriT, and gaps against a curated UniRef-derived database. Produces GFF3, GenBank, EMBL, JSON, FASTA, TSV, and a circular genome plot. Advises using Prokka for legacy/non-bacterial kingdoms and PGAP for GenBank submission.
How it works
Bakta runs a Prodigal-based CDS predictor, non-coding RNA tools, CRISPR detectors, and a tiered DIAMOND/HMM search against a UniRef-derived database to assign gene names, EC numbers, GO terms, and other annotations. Outputs include GFF3/GenBank/EMBL/INSDC FASTA formats, a JSON summary, and a Circos-style plot. The workflow includes steps to install Bakta and the database, prepare the input FASTA, run bakta with parameters such as --db, --output, --genus, --species, --strain, --locus-tag, --threads, and --keep-contig-headers, and parse the generated JSON/TSV files for downstream analysis.
When to use it
- Annotating bacterial or archaeal genome assemblies with NCBI-compatible locus tags and product names
- Annotating plasmids or circular replicons with --plasmid and --complete
- Producing JSON outputs for parsing without GenBank/GFF3 detours
- Generating a circular genome plot via bakta_plot
- Annotating MAGs with --meta
- Using Prokka for viral/mitochondrial kingdoms or legacy pipelines; PGAP for GenBank submission
- Faster runs with regularly updated UniRef-derived databases and an integrated JSON summary
What it can touch
- Command: bakta
- Database: db/bakta_db_light (or full) via bakta_db download
- Output files in the annotation/ directory (e.g., E_coli_K12.json, E_coli_K12.gff3, E_coli_K12.tsv, E_coli_K12.png, etc.)
- Tools mentioned: bakta_plot, python (for JSON/CSV parsing), Biopython, pandas, matplotlib (parsing steps)
Caveats
- Requires Bakta ≥ 1.9 and dependencies: Python 3.8+, Prodigal, tRNAscan-SE, Aragorn, Barrnap, Infernal, DIAMOND, HMMER3, PILER-CR, BLAST+, AMRFinderPlus
- Database download is a one-time operation; light (~3 GB) or full (~70 GB)
- Outputs may vary with input quality and chosen database
# Bakta Genome Annotation ## Overview Bakta is a command-line pipeline for rapid, standardized annotation of bacterial and archaeal genomes and plasmids. It combines Prodigal for CDS prediction, tRNAscan-SE/Aragorn/Barrnap/Infernal for non-coding RNA, PILER-CR/PILERCR for CRISPR detection, and a tiered DIAMOND/HMM search against a curated UniRef100 + IPS/UPS database to assign gene names, EC numbers, GO terms, and COG categories. Bakta produces NCBI-compatible outputs (GFF3, GenBank, EMBL, INSDC-formatted FASTA, plus a JSON summary and a circular Circos plot) for a typical 5 Mb genome in 5–15 minutes on 8 CPUs. ## When to Use - Annotating bacterial or archaeal genome assemblies (Illumina, PacBio, Nanopore) with NCBI-compatible locus tags and product names - Annotating plasmids and other circular replicons separately with `--plasmid` and `--complete` flags - Producing JSON-structured annotation outputs that can be parsed without GenBank or GFF3 detours - Generating a publication-ready circular genome plot via the bundled `bakta_plot` command - Annotating MAGs (metagenome-assembled genomes) with `--meta` to disable Prodigal training - Use **Prokka** instead when you need viral/mitoch
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Step 1: Install Bakta and Download the Database
- Step 2: Prepare the Input Assembly
- Step 3: Run Standard Bakta Annotation
- Step 4: Parse the JSON Summary
- Step 5: Parse the TSV Feature Table
- Step 6: Render the Circular Genome Plot
- Step 7: Compute Annotation Quality Statistics
- Step 8: Batch Annotation Across Multiple Genomes
- Key Parameters
Install Bakta via conda/mamba (recommended) mamba install -c conda-forge -c bioconda bakta Verify installation bakta --version bakta 1.9.4 Download the light database (~3 GB, faster, fewer functional hits) bakta_db download --output db/ --type light Or full database (~70 GB, comprehensive UniRef100 coverage) bakta_db download --output db/ --type full Install Python parsing dependencies
What does the bakta-genome-annotation skill do?
Annotate bacterial and archaeal genomes and plasmids with Bakta's Prodigal/HMM/diamond pipeline. Identifies CDS, ncRNA, tRNA, rRNA, tmRNA, sORFs, CRISPR arrays, oriC/oriV/oriT, and gaps against a curated UniRef-derived database. Produces NCBI-compatible GFF3, GenBank, EMBL, JSON, FASTA, TSV, and a circular genome plot. Use Prokka for legacy pipelines or non-bacterial kingdoms; PGAP for NCBI GenBank submission.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill bakta-genome-annotation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.