DFAST - DDBJ Fast Annotation and Submission Tool

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.

Contents

• Overview
• Installation
• Installation via conda
• How to run
• Default workflow
• Options
• Software distribution
• Trouble shoot
• How to run within a Docker container
• Citation
• FAQ

Advanced contents

• Workflow details
• INSDC submission
• Cookbook

Overview

• 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.
  Bioconda package is also available.

• 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 through DDBJ. DFAST generates submission files for DDBJ Mass Submission System (MSS).

Installation

If you use Anaconda/Miniconda, see here to install using conda.

Prerequisites

• Python (3.7-)
  DFAST runs both on Python 3.7 or later. Python 2 is no longer supported.

• BioPython package
  You can install this with the Python package management tool pip:

  (sudo) pip install biopython
  

  If pip is not available, please follow the instruction of BioPython.

• 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 might be required:

  sudo yum install perl-Time-Piece
  

Source code

Available from the GitHub repository nigyta/dfast_core.

• 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, create links to DFAST executables in a directory specified by the PATH environment variable. For example,

ln -s $DFAST_APP_ROOT/dfast /usr/local/bin/
ln -s $DFAST_APP_ROOT/scripts/dfast_file_downloader.py /usr/local/bin/

Reference databases

After downloading the source code, prepare reference databases using the bundled utility script.
By default, database files will be generated into the directory under $DFAST_APP_ROOT/db/. You can also change the location of the directory by specifying either --dbroot option or DFAST_DB_ROOT environmental variable.

1. Default protein database

   dfast_file_downloader.py --protein dfast
   

   File downloading and database indexing for GHOSTX and BLASTP will be performed.
2. HMMer and RPS-BLAST databases (this may take time)

   dfast_file_downloader.py --cdd Cog --hmm TIGR
   

   DFAST default workflow requires COG database for RPS-BLAST and TIGRFAM database for hmmerscan.

• See help for more information.

  dfast_file_downloader.py -h
  

Installation via conda

DFAST is also available from Bioconda. Install with:

conda install -c bioconda -c conda-forge dfast

We recommend specifying the latest version. See available versions from here.

conda install -c bioconda -c conda-forge dfast=1.X.XX

If this does not work, please try to install DFAST into the fresh conda environment.

DFAST executables are added to the PATH environmental variable, and the software package is installed in the opt directory under the Anaconda/Miniconda root directory. (e.g. /home/USER/miniconda3/opt/dfast-X.X.X/)

After installing DFAST, download the reference databases:

dfast_file_downloader.py --protein dfast --cdd Cog --hmm TIGR

How to run

1. Help

   dfast -h
   

   or by specifying the Python interpreter,

   python $DFAST_APP_ROOT/dfast -h
   

2. 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.

3. Basic usage

   dfast --genome path/to/your_genome.fna(.gz)
   

   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.

4. Advanced usage
   By providing command line options, you can override the default settings described in the configuration file.

   dfast --genome your_genome.fna(.gz) --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.

5. 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(.gz) --config your_config.py
   

Default workflow

DFAST default annotation workflow accepts a genomic FASTA file (draft or complete) as an input and includes following processes. Read Workflow to learn more.

Structural annotation

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

Optionally, you can choose Prodigal/GeneMarkS2, RNAmmer, tRNAscan-SE to predict CDS, rRNA, tRNA, respectively. See FAQ. (You need to install them manually.)

Functional annotation

1. OrthoSearch (Optional. Set --references option to enable this.)
2. DBsearch using the Ghostx aligner against the DFAST default database
3. PseudoGeneDetection (internal stop codons and frameshifts)
4. HMMscan against the profile HMM database of TIGRFAM
5. CDDsearch against COG database from NCBI Conserved Domain Database

By default, GHOSTX is used to align protein sequences. Diamond/BLASTP can be used optionally. See FAQ. (Diamond needs to be installed manually.)

Output

• 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

Options

Basic usage:
  usage: dfast -g your_genome.fna [options]
  
Basic options:
  -g PATH, --genome PATH
                        Genomic FASTA file for input. Can be gzipped.
  -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:
  --threshold STR       Thresholds for default database search (format: "pident,q_cov,s_cov,e_value", default: "0,75,75,1e-6")
  --database PATH       Additional reference database to be searched against prior to the default database. (format: db_path[,db_name[,pident,q_cov,s_cov,e_value]])
  --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|diamond]
  --use_prodigal        Use Prodigal to predict CDS instead of MGA
  --use_genemarks2 STR  Use GeneMarkS2 to predict CDS instead of MGA. [auto|bact|arch]
  --use_trnascan STR    Use tRNAscan-SE to predict tRNA instead of Aragorn. [bact|arch]
  --use_rnammer STR     Use RNAmmer to predict rRNA instead of Barrnap. [bact|arch]
  --gcode INT           Genetic code [11(=default),4(=Mycoplasma)]
  --no_func_anno        Disable all functional annotation steps
  --no_hmm              Disable HMMscan
  --no_cdd              Disable CDDsearch
  --no_cds              Disable CDS prediction
  --no_rrna             Disable rRNA prediction
  --no_trna             Disable tRNA prediction
  --no_crispr           Disable CRISPR prediction
  --metagenome          Set options of MGA/Prodigal for metagenome contigs
  --gff GFF             [Preliminary implementation] Read GFF to import structural annotation. Ignores --use_original_name, --sort_sequence, --fix_origin.

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
  --use_locustag_as_gene_id
                        Use locustag as gene ID for FASTA and GFF. (Useful when providing DFAST results to other tools such as Roary)
  --dbroot PATH         DB root directory (default:APP_ROOT/db
  --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

Software distribution

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)

Trouble shoot

• 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 on 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
• libidn-11 on ArchLinux
  According to the report from users, DFAST fails on ArchLinux due to libidn-11 required for BLASTP. You may need to install libidn-133-compat from the AUR repository.

How to run DFAST within a Docker container.

The Docker container image is available from Dockerhub:nigyta/dtast_core and quay.io:biocontainers/dfast.
Use --dbroot to specity the location of the reference data. Download the reference data:

docker run --rm -v PATH/TO/DB:/dfast_db nigyta/dfast_core:latest dfast_file_downloader.py --protein dfast --cdd Cog --hmm TIGR --dbroot /dfast_db

Invoke DFAST:

docker run --rm -v PATH/TO/DB:/dfast_db -v PATH/TO/YOUR/DATA:/data nigyta/dfast_core:latest dfast --genome /data/your_genome.fa --out /data/your_result --dbroot /dfast_db

Citation

• 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; 2018; 34(6): 1037–1039.
  Yasuhiro TANIZAWA, Takatomo FUJISAWA, Yasukazu NAKAMURA
  https://academic.oup.com/bioinformatics/article/34/6/1037/4587587