Bakta 1.8.0

2023-05-31 1369 words 7 minutes

Contents

Conda

See the ‘activating the conda environment’ section below to access this software.

Bakta 1.8.0: rapid & standardized annotation of bacterial genomes, MAGs & plasmids

Bakta is a tool for the rapid & standardized annotation of bacterial genomes and plasmids from both isolates and MAGs. It provides dbxref-rich and sORF-including annotations in machine-readable JSON & bioinformatics standard file formats for automatic downstream analysis.

Description

Comprehensive & taxonomy-independent database Bakta provides a large and taxonomy-independent database using UniProt’s entire UniRef protein sequence cluster universe. Thus, it achieves favourable annotations in terms of sensitivity and specificity along the broad continuum ranging from well-studied species to unknown genomes from MAGs.
Protein sequence identification Bakta exactly identifies known identical protein sequences (IPS) from RefSeq and UniProt allowing the fine-grained annotation of gene alleles (AMR) or closely related but distinct protein families. This is achieved via an alignment-free sequence identification (AFSI) approach using full-length MD5 protein sequence hash digests.
Fast This AFSI approach substantially accellerates the annotation process by avoiding computationally expensive homology searches for identified genes. Thus, Bakta can annotate a typical bacterial genome in 10 ±5 min on a laptop, plasmids in a couple of seconds/minutes.
Database cross-references Fostering the FAIR principles, Bakta exploits its AFSI approach to annotate CDS with database cross-references (dbxref) to RefSeq (WP_*), UniRef100 (UniRef100_*) and UniParc (UPI*). By doing so, IPS allow the surveillance of distinct gene alleles and streamlining comparative analysis as well as posterior (external) annotations of putative & hypothetical protein sequences which can be mapped back to existing CDS via these exact & stable identifiers (E. coli gene ymiA …more). Currently, Bakta identifies ~214.8 mio, ~199 mio and ~161 mio distinct protein sequences from UniParc, UniRef100 and RefSeq, respectively. Hence, for certain genomes, up to 99 % of all CDS can be identified this way, skipping computationally expensive sequence alignments.
FAIR annotations To provide standardized annotations adhearing to FAIR principles, Bakta utilizes a versioned custom annotation database comprising UniProt’s UniRef100 & UniRef90 protein clusters (FAIR -> DOI/DOI) enriched with dbxrefs (GO, COG, EC) and annotated by specialized niche databases. For each db version we provide a comprehensive log file of all imported sequences and annotations.
Small proteins / short open reading frames Bakta detects and annotates small proteins/short open reading frames (sORF) which are not predicted by tools like Prodigal.
Expert annotation systems To provide high quality annotations for certain proteins of higher interest, e.g. AMR & VF genes, Bakta includes & merges different expert annotation systems. Currently, Bakta uses NCBI’s AMRFinderPlus for AMR gene annotations as well as an generalized protein sequence expert system with distinct coverage, identity and priority values for each sequence, currenlty comprising the VFDB as well as NCBI’s BlastRules.
Comprehensive workflow Bakta annotates ncRNA cis-regulatory regions, oriC/oriV/oriT and assembly gaps as well as standard feature types: tRNA, tmRNA, rRNA, ncRNA genes, CRISPR, CDS and pseudogenes.
GFF3 & INSDC conform annotations Bakta writes GFF3 and INSDC-compliant (Genbank & EMBL) annotation files ready for submission (checked via GenomeTools GFF3Validator, table2asn_GFF and ENA Webin-CLI for GFF3 and EMBL file formats, respectively for representative genomes of all ESKAPE species).
Bacteria & plasmids only Bakta was designed to annotate bacteria (isolates & MAGs) and plasmids, only. This decision by design has been made in order to tweak the annotation process regarding tools, preferences & databases and to streamline further development & maintenance of the software.
Reasoning By annotating bacterial genomes in a standardized, taxonomy-independent, high-throughput and local manner, Bakta aims at a well-balanced tradeoff between fully featured but computationally demanding pipelines like PGAP and rapid highly customizable offline tools like Prokka. Indeed, Bakta is heavily inspired by Prokka (kudos to Torsten Seemann) and many command line options are compatible for the sake of interoperability and user convenience. Hence, if Bakta does not fit your needs, please consider trying Prokka.

Bakta Web

You can visualize bakta json files with this web viewer inside your browser.

https://bakta.computational.bio/viewer

Download the completed json annotation and upload it here to view your genome.

Activating the conda environment

Check out a node with qrsh and then:

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bash
source /local/cluster/conda-envs/envs/bakta/activate.sh

Using conda activate bakta

If your conda is set up as here, you can run conda activate bakta instead of the source line above as well.

To use over SGE, add the source line above to your shell script prior to the bakta commands.

Running over SGE

To use over SGE, include the source line above in a shell script prior to your commands, e.g.

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$ cat run_bakta.sh
#!/usr/bin/env bash
source /local/cluster/conda-envs/envs/bakta/activate.sh
bakta ...

And then run hqsub 'bash ./run_bakta.sh' -r sge.bakta ....

Using hqsub –conda

You can also run:

hqsub 'conda activate bakta; bakta ...' -r sge.bakta --conda ...

Without having to write a separate shell script.

Location, version, DB info

The database is on version 5.0.

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$ which bakta
/local/cluster/conda-envs/envs/bakta-1.8.0/bin/bakta
$ bakta --version
bakta 1.8.0
$ echo $BAKTA_DB
/nfs1/CGRB/databases/bakta/latest

Note: deepsig is included in this install:

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$ which deepsig
/local/cluster/conda-envs/envs/bakta-1.8.0/bin/deepsig

If you run into problems with deepsig, please let us know.

help message

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

Rapid & standardized annotation of bacterial genomes, MAGs & plasmids

positional arguments:
  <genome>              Genome sequences in (zipped) fasta format

Input / Output:
  --db DB, -d DB        Database path (default = <bakta_path>/db). Can also be
                        provided as BAKTA_DB environment variable.
  --min-contig-length MIN_CONTIG_LENGTH, -m MIN_CONTIG_LENGTH
                        Minimum contig size (default = 1; 200 in compliant mode)
  --prefix PREFIX, -p PREFIX
                        Prefix for output files
  --output OUTPUT, -o OUTPUT
                        Output directory (default = current working directory)
  --force, -f           Force overwriting existing output folder

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

Annotation:
  --complete            All sequences are complete replicons
                        (chromosome/plasmid[s])
  --prodigal-tf PRODIGAL_TF
                        Path to existing Prodigal training file to use for CDS
                        prediction
  --translation-table {11,4}
                        Translation table: 11/4 (default = 11)
  --gram {+,-,?}        Gram type for signal peptide predictions: +/-/? (default
                        = ?)
  --locus LOCUS         Locus prefix (default = 'contig')
  --locus-tag LOCUS_TAG
                        Locus tag prefix (default = autogenerated)
  --keep-contig-headers
                        Keep original contig headers
  --replicons REPLICONS, -r REPLICONS
                        Replicon information table (tsv/csv)
  --compliant           Force Genbank/ENA/DDJB compliance
  --proteins PROTEINS   Fasta file of trusted protein sequences for CDS
                        annotation
  --meta                Run in metagenome mode. This only affects CDS
                        prediction.

Workflow:
  --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-pseudo         Skip pseudogene detection & annotation
  --skip-sorf           Skip sORF detection & annotation
  --skip-gap            Skip gap detection & annotation
  --skip-ori            Skip oriC/oriT detection & annotation
  --skip-plot           Skip generation of circular genome plots

General:
  --help, -h            Show this help message and exit
  --verbose, -v         Print verbose information
  --debug               Run Bakta in debug mode. Temp data will not be removed.
  --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

Version: 1.8.0
DOI: 10.1099/mgen.0.000685
URL: github.com/oschwengers/bakta

Citation:
Schwengers O., Jelonek L., Dieckmann M. A., Beyvers S., Blom J., Goesmann A. (2021).
Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification.
Microbial Genomics, 7(11). https://doi.org/10.1099/mgen.0.000685

software ref: https://github.com/oschwengers/bakta
research ref: https://doi.org/10.1099/mgen.0.000685