Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.
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Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.
Before using code patterns, verify installed versions match. If versions differ:
CLI: <tool> --version then <tool> --help to confirm flags
Python: pip show <package> then help(module.function) to check signatures
Annotation content tracks the database version, not just the binary: a Bakta full DB (schema v5+, ~30 GB zipped) and a Bakta light DB give different functional calls, and two runs months apart can differ purely from DB updates. Record the Bakta DB version in methods. Prokka's bundled databases are frozen (~2019-2021). If code throws an error, introspect the installed tool and adapt rather than retrying.
Prokaryotic Genome Annotation
"Annotate my bacterial genome" -> Call protein-coding genes, tRNAs/rRNAs/ncRNAs, and other features, then assign function by database identity, and emit submission-ready files.
The Single Most Important Modern Insight -- Gene Calling Is Near-Solved; Function Is Not, and Wrong Labels Propagate
For a typical isolate, Prodigal/Pyrodigal recovers >95-99% of true coding genes. The unsolved problems are the start codon (translation initiation site), the small/overlapping/recoded ORFs, and above all the function. Three consequences a postdoc must internalize:
A confidently wrong product name is worse than "hypothetical protein." Names assigned by loose homology transfer across distant lineages and self-amplify through databases - there is no mechanism to retract a correction once it spreads, so annotation accuracy has gone down, not up, as sequencing scaled (Salzberg 2019 Genome Biol 20:92). Bakta's design counters this: gene-level identity only via exact (MD5/UniRef100) match, falling back to cluster level, then "hypothetical" rather than guessing. 25-50% hypothetical is healthy for a non-model organism; near 0% means over-confident transfer.
Comparing gene counts across tools/versions/DBs is invalid. Tool agreement is "highly dependent on the organism of study" and biased toward model organisms (Dimonaco 2022 Bioinformatics 38:1198) - there is no universal best tool. Differences between two annotations are mostly tool artifacts, not biology. For any collection (pangenomics), re-annotate every assembly from FASTA with one pipeline + one pinned DB; merging published annotations inflates the accessory genome ~10× (Tonkin-Hill 2020 Genome Biol 21:180).
Annotation completeness ≠ assembly completeness. A fragmented/contaminated assembly produces truncated partial CDS at every contig break, inflated counts, and missing rRNA operons. Run CheckM2 before trusting any annotation QC number.
GenBank/RefSeq submission; best pseudogene/frameshift/selenoprotein handling
DFAST
Yes (DDBJ)
DFAST reference DBs + swappable refs
INSDC files
DDBJ submission; fast, flexible reference swap
RAST / BV-BRC
Yes
SEED subsystems
GenBank/GFF + subsystems
Subsystem/metabolic framing; web; not for direct INSDC submission
Prokka uses Aragorn (tRNA) + barrnap (rRNA); Bakta uses tRNAscan-SE 2.0 (tRNA, domain-specific) + Infernal/Rfam (rRNA/ncRNA) + PILER-CR (CRISPR arrays) + DeepSig (signal peptides). Bakta is deliberately conservative on naming, so it reports "hypothetical" where Prokka confidently (sometimes wrongly) names a gene - Bakta looking "less annotated" is it being honest.
Decision Tree by Scenario
Scenario
Recommended
Why
Routine isolate, reproducible
Bakta, full DB
standardized, versioned, current functional calls
Submitting to GenBank/RefSeq
PGAP
RefSeq re-annotates with PGAP regardless; best pseudogene handling
Submitting to DDBJ
DFAST
INSDC-ready, DDBJ-aligned
MAG / metagenome bin
Bakta --meta + CheckM2 gate
anonymous-mode calling; QC before trust
Mycoplasma / Mollicutes
Bakta --translation-table 4
UGA=Trp; table 11 splits genes at every UGA
Archaeon
Bakta + verify tRNAscan-SE archaeal model; PGAP if N-termini matter
leaderless mRNAs degrade Prodigal TIS
Inherited Prokka pangenome pipeline
pin Prokka version, or re-call all in Bakta
tool consistency vs current biology
Subsystem/metabolic view
RAST / BV-BRC
SEED subsystem categories
Genome not yet assembled / poor QC
-> genome-assembly/assembly-qc
fix assembly before annotating
AMR for clinical report
-> run AMRFinderPlus/CARD-RGI directly
--organism context, point mutations, partials matter
Bakta (Default)
# Database (record the version): full ~30 GB for publishable annotation; light for triage/CI
bakta_db download --output db/ --type full
bakta --db db/ --output bakta_out --prefix ecoli_k12 \
--genus Escherichia --species coli --strain K-12 \
--locus-tag ECK12 --gram - --complete --threads 16 \
assembly.fasta
Key flags: --translation-table {11,4,25} (default 11), --gram {+,-,?} (gates DeepSig signal-peptide calls; default ?), --complete (all sequences are finished replicons; enables oriC detection - do NOT use on draft contigs), --meta (metagenome/MAG mode), --compliant (enforce INSDC structure), --keep-contig-headers, --proteins <faa> (trusted-protein transfer). Set --genus/--species from a GTDB-Tk classification, not a guess. Primary outputs: .gff3, .gbff, .faa, .ffn, .fna, .tsv, plus .hypotheticals.tsv and .inference.tsv (open the inference column to ask why a product was assigned).
Use only for tool-chain consistency with an existing Prokka-based pangenome workflow, and pin the version. Its bundled databases are frozen: a gene family characterized after ~2019 is "hypothetical" in Prokka but named by current Bakta/PGAP, so the same gene flips core/accessory purely on DB vintage.
Coding Density and CDS Extraction with Python
Goal: Load Bakta/Prokka GFF3 into a queryable database and compute coding density, the first sanity number.
Approach: Build a gffutils database, sum CDS lengths, divide by genome length; flag values outside the expected band.
import gffutils
CODING_DENSITY_LOW = 0.85# <0.85 in a free-living bacterium suggests wrong table, fragmentation, or heavy pseudogenization
CODING_DENSITY_HIGH = 0.93# >0.93 suggests ORF over-calling (spurious short hypotheticals)defcoding_density(gff_file, genome_length):
db = gffutils.create_db(gff_file, ':memory:', merge_strategy='merge')
coding_bp = sum(c.end - c.start + 1for c in db.features_of_type('CDS'))
density = coding_bp / genome_length
if density < CODING_DENSITY_LOW or density > CODING_DENSITY_HIGH:
print(f'WARNING: coding density {density:.1%} outside expected 85-93%')
return density
Hard Cases the Caller Gets Wrong
Wrong genetic code is silent and looks like fragmentation. A Mycoplasma run under table 11 yields anomalously low coding density + many short "hypothetical" fragments because every internal UGA split a gene. Confirm the table from taxonomy (GTDB-Tk), never a guess. Table 4 (UGA=Trp, Mollicutes), table 25 (UGA=Gly, Gracilibacteria/SR1).
Pseudogene over-calling in reductive genomes is real biology.Mycobacterium leprae has ~1,116 pseudogenes vs ~1,604 intact CDS (Cole 2001 Nature 409:1007) - high pseudogene fraction in a symbiont/host-restricted pathogen (Rickettsia, Sodalis) is a lifestyle signal, not a defect. PGAP (ProSplign aligns through frameshifts, emits /pseudo) is the best automated arbiter.
Phase variation is not a pseudogene. A frameshift inside a homopolymer/SSR tract in a contingency locus (Neisseria ~65 candidate loci, Haemophilus, Campylobacter poly-G tracts) means the assembled cell was in the OFF phase - do NOT "correct" or polish it away. This confounds with ONT homopolymer indel error: a pseudogene spike is ambiguous between reductive biology, phase variation, and basecalling error; disambiguate with orthogonal (short-read/HiFi) data.
Programmed frameshifts make one gene look like two.prfB/RF2 (a +1 frameshift at a slippery CTTT + internal SD), dnaX (−1), and IS-element transposases encode one protein across two frames; naive callers emit two short ORFs. PGAP recognizes a curated set.
Small ORFs (<~50 aa) are systematically missed - callers impose a ~30 aa minimum because short ORFs arise by chance and coding statistics are unreliable there. E. coli K-12 was missing dozens of 16-50 aa proteins. Treat the gene count as a lower bound on the small proteome; for sORF biology use a dedicated caller (smORFer, smORFinder) and, ideally, Ribo-seq (the experimental arbiter of translation).
Archaea and Actinobacteria use leaderless mRNAs (no 5' UTR, no Shine-Dalgarno) - Prodigal's RBS-first scoring degrades TIS placement; GeneMarkS-2 (and PGAP) model leaderless transcription. Use tRNAscan-SE's archaeal model for archaeal intron-containing tRNAs.
Per-Method Failure Modes
Cross-tool gene-count comparison
Trigger: comparing counts from Bakta vs Prokka vs old RefSeq, or mixed-vintage records. Mechanism: tool/DB differences dominate biological differences. Symptom: "novel genes" or accessory-genome inflation. Fix: re-annotate every genome from FASTA with one pipeline + pinned DB.
Wrong translation table
Trigger: table 11 on a Mollicute. Mechanism: UGA read as stop. Symptom: low coding density, doubled short gene count, high hypothetical fraction. Fix:--translation-table 4; confirm from GTDB-Tk.
Trigger: reading the product column as ground truth. Mechanism: loose-homology transfer below ~40% identity is unreliable, especially for promiscuous folds. Symptom: a "histidine-kinase expansion" that is one over-called domain. Fix: open .inference.tsv; prefer InterPro/Pfam architecture over free-text product when they conflict.
Single-mode calling on a metagenome or tiny replicon
Trigger: Prodigal single mode on a MAG/plasmid/phage. Mechanism: self-training needs a full single-organism genome. Symptom: poor calls on short/mixed input. Fix: Bakta --meta (anonymous mode).
Missing signal peptides on macOS
Trigger: Bakta on macOS. Mechanism: DeepSig was dropped from the default mac conda env (~v1.9.4). Symptom: silently absent signal-peptide calls. Fix: verify the run log; run on Linux if SPs matter.
ONT homopolymer indels vs real decay vs phase variation
check homopolymer context; corroborate with short-read/HiFi
Submission rejected on locus tags
invented prefix
register the prefix via BioSample (3-12 alnum, starts with a letter)
References
Schwengers O, et al. 2021. Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microb Genom 7:000685.
Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068-2069.
Hyatt D, et al. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119.
Larralde M. 2022. Pyrodigal: Python bindings and interface to Prodigal. J Open Source Softw 7:4296.
Lomsadze A, et al. 2018. Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes (GeneMarkS-2). Genome Res 28:1079-1089.
Tatusova T, et al. 2016. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res 44:6614-6624.
Li W, et al. 2021. RefSeq: expanding the Prokaryotic Genome Annotation Pipeline reach with protein family model curation. Nucleic Acids Res 49:D1020-D1028.
Tanizawa Y, et al. 2018. DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication. Bioinformatics 34:1037-1039.
Chan PP, et al. 2021. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res 49:9077-9096.
Chklovski A, et al. 2023. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods 20:1203-1212.
Dimonaco NJ, et al. 2022. No one tool to rule them all: prokaryotic gene prediction tool annotations are highly dependent on the organism of study. Bioinformatics 38:1198-1207.
Tonkin-Hill G, et al. 2020. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol 21:180.
Salzberg SL. 2019. Next-generation genome annotation: we still struggle to get it right. Genome Biol 20:92.
Cole ST, et al. 2001. Massive gene decay in the leprosy bacillus. Nature 409:1007-1011.
Related Skills
functional-annotation - Add GO/KEGG/Pfam to hypothetical proteins with eggNOG-mapper/InterProScan
ncrna-annotation - Detailed ncRNA identification with Infernal/Rfam beyond the built-in callers
annotation-qc - CheckM2 completeness/contamination gate and gene-set sanity metrics
genome-assembly/assembly-qc - Assess assembly quality before annotation
genome-intervals/gtf-gff-handling - Parse and manipulate GFF3 output
comparative-genomics/pangenome-analysis - Uniform re-annotation before pangenome clustering
