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Bakta: rapid & comprehensive annotation of bacterial genomes & plasmids

Project description

License: GPL v3 PyPI - Python Version GitHub release PyPI PyPI - Status Conda Conda

Bakta: rapid & comprehensive annotation of bacterial genomes & plasmids




Bakta is an offline tool dedicated to the rapid & comprehensive annotation of bacteria & plasmids. It provides dbxref-rich and sORF-including annotations in machine-readble (JSON) & bioinformatics standard file formats for automatic downstream analysis.

The annotation of microbial genomes is a diverse task comprising the structural & functional annotation of different feature types with distinct overlapping characteristics. Existing local annotation pipelines cover a broad range of microbial taxa, e.g. bacteria, aerchaea, viruses. To streamline and foster the expansion of supported feature types, Bakta is strictly dedicated to the annotation of bacteria and plasmids. To standardize the annotation of bacterial sequences, Bakta uses a comprehensive annotation database based on UniProt's UniRef protein clusters enriched by cross-references and specialized niche databases.

Exact matches to known protein coding sequences (CDS), subsequently referred to as identical protein sequences (IPS) are identified via MD5 digests and annotated with database cross-references (dbxref) to:

  • RefSeq (WP_*)
  • UniRef100/UniRef90 (UniRef100_*/UniRef90_*)
  • UniParc (UPI*)

By doing so, IPS allow the surveillance of distinct gene alleles and streamline comparative analysis. Also, posterior (external) annotations of putative & hypothetical protein sequences can be mapped back to existing cds via these exact & stable identifiers (E. coli gene ymiA ...more). Unidentified remaining CDS are annotated via UniRef90 protein sequence clusters (PSC). PSC & IPS are enriched by pre-annotated and stored information (GO, COG, EC).

Next to standard feature types (tRNA, tmRNA, rRNA, ncRNA, CRISPR, CDS, gaps) Bakta also detects and annotates:

  • short ORFs (sORF) which are not predicted by tools like Prodigal
  • ncRNA regulatory regions distinct from ncRNA genes
  • origins of replication/transfer (oriC, oriV, oriT)

Bakta can annotate a typical bacterial genome within minutes and hence fits the niche between large & computationally-demanding (online) pipelines and rapid, highly-customizable offline tools like Prokka. If Bakta does not fit your needs, please consider using Prokka. The development of Bakta was highly inspired by Prokka and many command line options are mutually compatible for the sake of interoperability and user convenience.



Bakta accepts bacterial and plasmid assemblies (complete / draft) in (zipped) fasta format.

Further genome information and workflow customizations can be provided and set via a number of input parameters. For a full description, please have a look at the Usage section.

Most important parameters:

  • use a custom database location, e.g. a local instance for runtime improvements: --db
  • genome parameters: --min-contig-length, --complete
  • number of threads: --threads
  • locus information --locus, --locus-tag

Replicon meta data table:

To fine-tune the very details of each sequence in the input fasta file, Bakta accepts a replicon meta data table provided in tsv file format: --replicons <tsv-replicon-file>. Thus, for example, complete replicons within partially completed draft assemblies can be marked as such.

Table format:

original locus id new locus id type topology name
old id [new id / <empty>] [chromosome / plasmid / contig / <empty>] [circular / linear / <empty>] name

Thus, for each input sequence recognized via the original locus id a new locus id, the replicon type and the topology as well a name can be explicitly set.

Available short cuts:

  • chromosome: c
  • plasmid: p
  • circular: c
  • linear: l

<empty> values (- / ``) will be replaced by defaults. If new locus id is empty, a new contig name will be autogenerated.

Defaults: replicon type: contig topology: linear


original locus id new locus id type topology name
NODE_1 chrom chromosome circular -
NODE_2 p1 plasmid c pXYZ1
NODE_3 p2 p c pXYZ2
NODE_4 special-contig-name-xyz - -
NODE_5 `` - -


Bakta provides detailed information on each annotated feature in a standardized machine-readable JSON file. In addition, the following standard file formats are supported:

  • tsv: annotations as simple human readble tab separated values
  • GFF3: annotations in GFF3 format
  • GenBank: annotations in GenBank format
  • fna: replicons/contigs as FASTA
  • faa: CDS as FASTA



$ bakta --db ~/db genome.fasta

Expert: verbose output writing results to results directory with ecoli123 file prefix and eco634 locus tag using an existing prodigal training file, using additional replicon information and 8 threads:

$ bakta --db ~/db --verbose --output results/ --prefix ecoli123 --locus-tag eco634 --prodigal-tf --replicons replicon.tsv --threads 8 genome.fasta


Bakta can be installed via BioConda, Docker or Pip. To automatically install all required 3rd party dependencies, we highly encourage to use Conda. In all cases a mandatory db must be downloaded (-> Mandatory database)


$ conda install -c conda-forge -c bioconda -c defaults bakta


$ sudo docker pull oschwengers/bakta

$ sudo docker run oschwengers/bakta --help
$ --help


  1. install Bakta per pip
  2. install 3rd party binaries (-> Dependencies)
$ python3 -m pip install --user bakta


Bacta requires Biopython (>=1.72), Xopen (0.9) and the following 3rd party executables which must be installed & executable:

On Ubuntu/Debian/Mint you can install these via:

$ sudo apt install aragorn infernal prodigal diamond-aligner ncbi-blast+

tRNAscan-se must be installed manually as v2.0 is currently not yet available via standard Ubuntu packages.

Mandatory database

In all cases, Bakta requires a mandatory database which is publicly hosted at Zenodo: DOI Further information is provided below.

$ wget <XYZ>/db.tar.gz
$ tar -xzf db.tar.gz
$ rm db.tar.gz

The, the database path can be provided via the --db parameter:

$ bakta --db <db-path>

It's also possible to set a BAKTA_DB environment variable:

$ export BAKTA_DB=<db-path>

Additionally, for a system-wide setup, the database can be copied to the Bakta base directory:

$ cp -r db/ <bakta-installation-dir>

Annotation workflow


  1. tRNA genes: tRNAscan-SE 2.0
  2. tmRNA genes: Aragorn
  3. rRNA genes: Infernal vs. Rfam rRNA covariance models
  4. ncRNA genes: Infernal vs. Rfam ncRNA covariance models
  5. ncRNA regulatory regions: Infernal vs. Rfam ncRNA covariance models
  6. CRISPR arrays: PILER-CR

Bakta distinguishes ncRNA genes and (regulatory) regions in order to enable the distinct handling thereof during the annotation process, i.e. feature overlap detection. ncRNA gene types:

  • sRNA
  • antisense
  • ribozyme
  • antitoxin

ncRNA (regulatory) region types:

  • riboswitch
  • thermoregulator
  • leader
  • frameshift element

Coding sequences

The structural prediction is conducted via Prodigal and complemented by a custom detection of short open reading freames (sORF) < 30 aa.

To rapidly conduct a comprehensive annotation while also identifing known protein sequences with exact sequence matches, Bakta uses a comprehensive SQLite database comprising protein sequence digests and pre-annotations for millions of known protein sequences and clusters.

Conceptual terms:

  • UPS: unique protein sequences identified via length and MD5 sequence digests (100% coverage & 100% sequence identity)
  • IPS: identical protein sequences comprising representatives of UniProt's UniRef100 protein sequence clusters
  • PSC: protein sequences clusters comprising representatives of UniProt's UniRef90 protein sequence clusters


  1. Prediction via Prodigal
  2. Detection of UPSs via MD5 digests and lookup of related IPS and PCS
  3. Homology search of remainder via Diamond vs. PSC
  4. Combination of available IPS & PSC information favouring more specific annotations and avoiding redundancy

CDS without IPS or PSC hits will be marked as hypothetical. Additionally, all CDS without gene symbols or with product descriptions equal to hypothetical will be marked as hypothetical.

However, hypothetical CDS are included in the final annotation.


  1. Custom detection & extraction of sORF with amino acid lengths < 30 aa
  2. Filter via strict feature type-dependent overlap filters with annotated features
  3. Detection of UPS via MD5 hashes and lookup of related IPS
  4. Homology search of remainder via Diamond vs. seed sequences of an sORF subset of UniProt's UniRef90 PSC
  5. Exclude sORF without sufficient annotation information

sORF not identified via IPS or PSC will be discarded. Additionally, all sORF without gene symbols or with product descriptions equal to hypothetical will be discarded.

Due due to uncertain nature of sORF prediction, only those identified via IPS / PSC hits exhibiting proper gene symbols or product descriptions different from hypothetical will be included in the final annotation.


  1. Gaps: in-mem detection & annotation of sequence gaps
  2. oriC/oriV/oriT: Blast+ (blastn) vs. MOB-suite oriT & DoriC oriC/oriV sequences. Annotations of ori regions take into account overlapping Blast+ hits and are conducted based on a majority vote heuristic.


The Bakta database comprises a set of DNA & AA sequence databases as well as HMM & covariance models. In addition, at its core Bakta uses a compact SQLite db storing protein sequence digests, lengths, pre-annotations and dbxrefs of UPS, IPS and PSC from:

  • UPS: UniParc / UniProtKB
  • IPS: UniProt UniRef100
  • PSC: UniProt UniRef90

This allows the exact protein sequences identification via MD5 digests & sequence lengths as well as the rapid subsequent lookup of related information. IPS & PSC have been comprehensively pre-annotated integrating annotations & database dbxrefs from:

  • NCBI nonredundant proteins ('WP_*', exact matches)
  • NCBI COG db (80% coverage & 90% identity)
  • GO terms (via IPS/PSC SwissProt entries)
  • EC (via IPS/PSC SwissProt entries)
  • NCBI AMRFinderPlus (IPS exact matches, PSC HMM hits reaching trusted cutoffs)
  • ISFinder db (90% coverage & 99% identity)

Database (23 Gb zipped, 43 Gb unzipped) hosted at Zenodo: DOI



bakta --help
usage: bakta [--db DB] [--min-contig-length MIN_CONTIG_LENGTH]
             [--prefix PREFIX] [--output OUTPUT] [--genus GENUS]
             [--species SPECIES] [--strain STRAIN] [--plasmid PLASMID]
             [--prodigal-tf PRODIGAL_TF] [--translation-table {11,4}]
             [--complete] [--gram {+,-,?}] [--locus LOCUS]
             [--locus-tag LOCUS_TAG] [--keep-contig-headers]
             [--replicons REPLICONS] [--skip-trna] [--skip-tmrna]
             [--skip-rrna] [--skip-ncrna] [--skip-ncrna-region]
             [--skip-crispr] [--skip-cds] [--skip-sorf] [--skip-gap]
             [--skip-ori] [--help] [--verbose] [--threads THREADS]
             [--tmp-dir TMP_DIR] [--version] [--citation]

Comprehensive and rapid annotation of bacterial genomes.

positional arguments:
  <genome>              (Draft) genome in fasta format

Input / Output:
  --db DB, -d DB        Database path (default = <bakta_path>/db)
  --min-contig-length MIN_CONTIG_LENGTH, -m MIN_CONTIG_LENGTH
                        Minimum contig size (default = 1)
  --prefix PREFIX, -p PREFIX
                        Prefix for output files
  --output OUTPUT, -o OUTPUT
                        Output directory (default = current working directory)

  --genus GENUS         Genus name
  --species SPECIES     Species name
  --strain STRAIN       Strain name
  --plasmid PLASMID     Plasmid name

  --prodigal-tf PRODIGAL_TF
                        Path to existing Prodigal training file to use for CDS
  --translation-table {11,4}
                        Translation table to use: 11/4 (default = 11)
  --complete            Replicons (chromosome/plasmid[s]) are complete
  --gram {+,-,?}        Gram type: +/-/? (default = '?')
  --locus LOCUS         Locus prefix (instead of 'contig')
  --locus-tag LOCUS_TAG
                        Locus tag prefix
                        Keep original contig headers
  --replicons REPLICONS, -r REPLICONS
                        Replicon information table (TSV)

  --skip-trna           Skip tRNA detection & annotation
  --skip-tmrna          Skip tmRNA detection & annotation
  --skip-rrna           Skip rRNA detection & annotation
  --skip-ncrna          Skip ncRNA detection & annotation
  --skip-ncrna-region   Skip ncRNA region detection & annotation
  --skip-crispr         Skip CRISPR array detection & annotation
  --skip-cds            Skip CDS detection & annotation
  --skip-sorf           Skip sORF detection & annotation
  --skip-gap            Skip gap detection & annotation
  --skip-ori            Skip oriC/oriT detection & annotation

  --help, -h            Show this help message and exit
  --verbose, -v         Print verbose information
  --threads THREADS, -t THREADS
                        Number of threads to use (default = number of
                        available CPUs)
  --tmp-dir TMP_DIR     Location for temporary files (default = system
                        dependent auto detection)
  --version             show program's version number and exit
  --citation            Print citation


A manuscript is in preparation. To temporarily cite our work, please transitionally refer to:

Schwengers O., Goesmann A. (2020) Bakta: comprehensive annotation of bacterial genomes. GitHub

Bakta takes advantage of many publicly available databases. If you find any of the data used within Bakta useful, please also be sure to credit the primary source also:


  • Bakta is running too long without CPU load... why? Bakta takes advantage of an SQLite DB which results in high storage IO loads. If this DB is stored on a remote / network volume, the lookup of IPS/PSC annotations might take a long time. In these cases, please, consider moving the DB to a local volume/hard drive.

Issues and Feature Requests

If you run into any issues with Bakta, we'd be happy to hear about it! Please, execute bakta in verbose mode (-v) and do not hesitate to file an issue including as much information as possible:

  • a detailed description of the issue
  • command line output
  • log file (<prefix>.log)
  • result file (<prefix>.json) if possible
  • a reproducible example of the issue with an input file that you can share if possible

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