DFAST is a flexible and customizable pipeline for prokaryotic genome annotation as well as data submission to the INSDC. It is originally developed as the background engine for the DFAST web service and is also available as a stand-alone command-line tool.
The stand-alone version of DFAST is also refered to as DFAST-core to differentiate it from its on-line version.
For inquiry and request, please contact us at dfast @ nig.ac.jp
.
- Overview
- Installation
- How to run
- Default workflow
- Options
- Software distribution
- Trouble shoot
- Citation
- Easy install
DFAST is implemented in Python and runs on Mac and Linux. No additional modules are required other than BioPython. It comes with external binaries for the default workflow. - Flexible and customizable
You can customize the pipeline as you like by specifying parameters, gene prediction tools, and reference databases in the configuraition file. Each of annotation processes is defined as a Python module with common interfaces, which facilitates future development and incorporation of new tools. - Fast and rich annotation
DFAST can annotate a typical-sized bacterial genome within several minutes. In addition to the conventional homology search, it features unique functions such as orthologous gene assignment between reference genomes, pseudo/frameshifted gene prediction, and conserved domain search. - INSDC submission
As its name suggested, DFAST is intended to support rapid genome submission to the INSDC, especially through DDBJ. DFAST generates submission files for DDBJ Mass Submission System (MSS) as well as .tbl and .fsa file for GenBank tbl2asn.
- Python (3.4- or 2.7)
DFAST is developed in Python 3.6 and runs both on Python 3.4 or later and Python 2.7. - BioPython package
You can install this with the Python package management toolpip
:If(sudo) pip install biopython
pip
is not available, please follow the instruction of BioPython. - 'futures' and 'six' packages (required only on Python 2.7)
DFAST uses theconcurrent.futures
module for multiprocessing and thesix
module for compatibility with Python 2 and 3. To run on Python 2.7, you need to install them:(sudo) pip install futures six
- Perl and Java
Some of the external programs called from DFAST depend on Perl or Java. Basically, they work with the pre-installed versions on your system.
For RedHat/CentOS/Fedora, the Time::Piece module should be installed:sudo yum install perl-Time-Piece
Available from our GitHub repository.
- Via git command (recommended)
git clone https://github.com/nigyta/dfast_core.git cd dfast_core # Hereafter, we call this directory $DFAST_APP_ROOT
- Download the distribution
Download the DFAST distribution from GitHub Releases, then unarchive it.wget https://github.com/nigyta/dfast_core/archive/x.x.x.tar.gz tar xvfz x.x.x.tar.gz # Files will be uncompressed into dfast_core-x.x.x direcotory cd dfast_core-x.x.x # Hereafter, we call this directory $DFAST_APP_ROOT
For your convenience, add $DFAST_APP_ROOT to your PATH
.
export PATH=$DFAST_APP_ROOT:$PATH
After downloading/cloning the source code, prepare reference databases using the bundled utility script.
- Default protein database
File downloading and database indexing for GHOSTX and BLASTP will be performed.
python scripts/file_downloader.py --protein dfast
- HMMer and RPS-BLAST databases (this may take time)
DFAST default workflow requires COG database for RPS-BLAST and TIGRFAM database for hmmerscan.
python scripts/file_downloader.py --cdd Cog --hmm TIGR
- See help for more information.
python scripts/file_downloader.py -h
-
Help
Make sure that $DFAST_APP_ROOT has been added to yourPATH
.dfast -h
or by specifying the Python interpreter,
python $DFAST_APP_ROOT/dfast -h
-
Test run
dfast --config $DFAST_APP_ROOT/example/test_config.py
This minimum workflow includes CDS prediction and database search against the default protein database using the GHOSTX aligner. The result will be generated in
RESULT_TEST
dierctory.
If not working properly, please check if the default database is installed. Normally, it finishes within a minute. -
Basic usage
dfast --genome path/to/your_genome.fna
This invokes the DFAST pipeline with the default workflow defined in $DFAST_APP_ROOT/dfc/default_config.py. DFAST accepts a FASTA-formatted genome sequence file as a query.
-
Advanced usage
By providing command line options, you can override the default settings described in the configuration file.dfast --genome your_genome.fna --organism "Escherichia coli" --strain "str. xxx" \ --locus_tag_prefix ECXXX --minimum_length 200 --references EC_ref_genome.gbk \ --aligner blastp --out OUT_ECXXX
'locus tag prefix' is required if you want your genome to be submitted to the INSDC (use
--locus_tag_prefix
option). DFAST generates DDBJ and GenBank submission files. For more information, please refer to INSDC submission. If you set--references
option, OrthoSearch (orthologous gene assignment) is enabled, which conducts all-against-all protein alignments between given reference genomes to infer orthologous genes.
--aligner blastp
will let DFAST use BLASTP for protein alignments instead of default GHOSTX.These optional values can be specified in a configuration file, saving you from providing them as command line options. See the following step.
-
More advanced usage: Creating your own workflow
An easy way to do this is to copy and edit the default configuration file, which is located in $DFAST_APP_ROOT/dfc/default_config.py. The configuration file is a self-explanatory Python script, in which the workflow is defined using basic Python objects like lists and dictionaries.You can call your original configuration file with the
--config
option.dfast --genome your_genome.fna --config your_config.py
DFAST default annotation workflow accepts a genomic FASTA file (draft or complete) as an input and includes following processes. Read Workflow to learn more.
The following tools are run in parallel to predict biological features (e.g. CDSs and RNAs). After that, partial and overlapping features will be cleaned up.
- CDS prediction (MetaGeneAnnotator)
- rRNA prediction (Barrnap)
- tRNA/tmRNA prediction (Aragorn)
- CRISPR prediction (CRT)
- Assembly gaps within sequences
- OrthoSearch (Optional. Set
--references
option to enable this.) - DBsearch using the Ghostx aligner against the DFAST default database
- PseudoGeneDetection (internal stop codons and frameshifts)
- HMMscan against the profile HMM database of TIGRFAM
- CDDsearch against COG database from NCBI Conserved Domain Database
- Sequence and annotation data in GFF3 and GenBank format
- Sequence data in FASTA format
- Statistics for genome sequences and annotated features
- DDBJ and GenBank submission files
Basic options:
-g PATH, --genome PATH
Genomic FASTA file
-o PATH, --out PATH Output directory (default:OUT)
-c PATH, --config PATH
Configuration file (default config will be used if not
specified)
--organism STR Organism name
--strain STR Strain name
Genome settings:
--complete BOOL Treat the query as a complete genome. Not required
unless you need INSDC submission files.
[t|f(=default)]
--use_original_name BOOL
Use original sequence names in a query FASTA file
[t|f(=default)]
--sort_sequence BOOL Sort sequences by length [t(=default)|f]
--minimum_length INT Minimum sequence length (default:200)
--fix_origin Rotate/flip the chromosome so that the dnaA gene comes
first. (ONLY FOR A FINISHED GENOME)
--offset INT Offset from the start codon of the dnaA gene. (for
--fix_origin option, default=0)
Locus_tag settings:
--locus_tag_prefix STR
Locus tag prefix (defaut:LOCUS)
--step INT Increment step of locus tag (default:10)
--use_separate_tags BOOL
Use separate tags according to feature types
[t(=default)|f]
Workflow options:
--database PATH Additional reference database to be searched prior to
the default database
--references PATH Reference file(s) for OrthoSearch. Use semicolons for
multiple files, e.g. 'genome1.faa;genome2.gbk'
--aligner STR Aligner to use [ghostx(=default)|blastp]
--no_hmm Disable HMMscan
--no_cdd Disable CDDsearch
Genome source modifiers and metadata [advanced]:
These values are only used to create INSDC submission files and do not
affect the annotation result. See documents for more detail.
--seq_names STR Sequence names for each sequence (for complete genome)
--seq_types STR Sequence types for each sequence (chromosome/plasmid,
for complete genome)
--seq_topologies STR Sequence topologies for each sequence
(linear/circular, for complete genome)
--additional_modifiers STR
Additional modifiers for source features
--metadata_file PATH Path to a metadata file (optional for DDBJ submission
file)
--center_name STR Genome center name (optional for GenBank submission
file)
Run options:
--cpu INT Number of CPUs to use
--force Force overwriting output
--debug Run in debug mode (Extra logging and retaining
temporary files)
--show_config Show pipeline configuration and exit
--version Show program version
-h, --help Show this help message
DFAST is freely available as open-source under the GPLv3 license (See LICENSE).
This distribution contains following external programs.
- MetaGeneAnnotator (© Hideki Noguchi)
Redistributed by courtesy of Hideki Noguchi at National Institute of Genetics. - Aragorn (GPLv3)
- Barrnap (GPLv3)
- CRT (Public domain)
- GHOSTX (BSD-2-Clause)
- GHOSTZ (CC BY 4.0)
- blastp, makeblastdb, blastdbcmd, rpsblast, rpsbproc from NCBI-BLAST+ package. (Public domain)
- hmmpress, hmmscan from HMMer package (GPLv3)
- LAST (GPLv3)
- DBsearch is slow
The default aligner GHOSTX is fast but requires a large amount of memory. In our environment, it uses 1.8Gbyte memory per process.
If your machine does not have enough memory, decrease the number of CPUs (--cpu 2
or--cpu 1
) or use BLASTP instead (--aligner blastp
). - GLIBCXX not found error in Linux system
If your system is old, DFAST will abort with the message "/usr/lib64/libstdc++.so.6: version 'GLIBCXX_3.4.15' not found".
In this case, you need to update "libstdc++.so.6". (You might need to install a newer version of GCC.)
Please check the file as following:strings /usr/lib64/libstdc++.so.6 | grep GLIBCXX
- on-line version of DFAST
DFAST and DAGA: web-based integrated genome annotation tools and resources
Biosci Microbiota Food Health. 2016; 35(4): 173–184.
Yasuhiro TANIZAWA, Takatomo FUJISAWA, Eli KAMINUMA, Yasukazu NAKAMURA, and Masanori ARITA - stand-alone version (DFAST-core)
DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication.
Bioinformatics. 2017 Nov 2. doi: 10.1093/bioinformatics/btx713 (advance article).
Yasuhiro TANIZAWA, Takatomo FUJISAWA, Yasukazu NAKAMURA
https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx713