Prokka User Manual

Author
Torsten Seemann <torsten.seemann@monash.edu>
Institute
Victorian Bioinformatics Consortium <http://www.bioinformatics.net.au/>
Last updated
Wed 7 Aug 2013

Contents

  1. Introduction
  2. Why use Prokka?
  3. Installation
  4. Invokation
  5. Output
  6. Options
  7. Dependencies
  8. Databases
  9. FAQ
  10. TODO
  11. Changes
  12. Bugs
  13. Citation

Introduction

Whole genome annotation is the process of identifying features of interest in a set of genomic DNA sequences, and labelling them with useful information. Prokka is a software tool to annotate bacterial, archaeal and viral genomes quickly to produce standards-compliant output files.

Why use Prokka?

It is fast!
A typical 4 Mbp bacterial genome will be annotated in 10 minutes on a typical quad-core desktop. The more CPU cores you have, the faster it will go.
Uses freely accessible tools
Prokka is written in Perl and only uses core modules. Although various external tools are required, Prokka checks for them and lets you know what is missing.
Simple to run
Using Prokka can be as simple as typing "prokka contigs.fasta". You are not forced to wade through command line options before getting your first result.
Bundled databases
Prokka comes with custom BLAST and HMMER databases, so there is no need to have any installed, but power users can add their own.
Smoother Genbank submission
Prokka produces a template ASN1 file (Seq-Entry:), which you can edit with Sequin to create a Seq-Submt: to submit to NCBI/ENA/DDBJ.
Clean interface
Prokka is neatly packaged, with no hard-coded directories. Everything can be controlled via command line options. You can add it to your private genomics pipeline, including Galaxy.

Installation

Download

Download the latest prokka-1.x.tar.gz archive from http://www.bioinformatics.net.au/

Extract

  1. Choose somewhere to put it, for example: /opt
  2. Untar it: sudo tar -C /opt prokka-1.x.tar.gz
  3. Check it is there: ls /opt/prokka-1.x/

Add to PATH

Add the following line to your $HOME/.bashrc file, or to /etc/profile.d/prokka.sh to make it available to all users:
export PATH=$PATH:/opt/prokka-1.x

Install dependencies

Consult the list of dependencies.

Choose a rRNA predictor

Option 1 - Don't use one

If Prokka can't find a predictor for rRNA featues (either Barrnap or RNAmmer below) then it simply won't annotate any. Most people don't care that much about them anyway,

Option 2 - Barrnap

This was written by the author of Prokka and is recommended if you prefer speed over absolute accuracy. It uses the new multi-core NHMMER for DNA:DNA profile searches. Download it here.

Option 3 - RNAmmer

RNAmmer was written when HMMER 2.x was the latest release. Since them, HMMER 3.x has been released, and uses the same executable binary names. Prokka needs HMMER3 and RNAmmer (and hence HMMER2) so you need to edit your RNAmmer script to explicitly point your HMMER2 binary instead of using the HMMER3 binary which is more likely to be in your PATH first.

Type which rnammer to find the script, and then edit it with your favourite editor. Find the following lines at the top:

if ( $uname eq "Linux" ) {
#       $HMMSEARCH_BINARY = "/usr/cbs/bio/bin/linux64/hmmsearch";    # OLD
        $HMMSEARCH_BINARY = "/path/to/my/hmmer-2.3.2/bin/hmmsearch"; # NEW (yours)
}

If you are using Mac OS X, you'll also have to change the "Linux" to "Darwin" too..

As you can see, I have commented out the original part, and replaced it with the location of my HMMER2 hmmsearch tool, so it doesn't run the HMMER3 one. You need to ensure HMMER3 is in your PATH before the old HMMER2 too.

Test

Invoking Prokka

Beginner

# Vanilla (but with free toppings)
% prokka contigs.fa

# Look for a folder called PROKKA_yyyymmdd (today's date) and look at stats
% cat PROKKA_yyyymmdd/*.txt

Moderate

# Choose the names of the output files
% prokka --outdir mydir --prefix mygenome contigs.fa

# Visualize it in Artemis
% art mydir/mygenome.gff

Expert

# It's not just for bacteria, people
% prokka --kingdom Archaea --outdir mydir --genus Pyrococcus --locustag PYCC

# Search for my favourite gene
% exonerate --bestn 1 zetatoxin.fasta mydir/PYCC_06072012.faa | less

Wizard

# Watch and learn
% prokka --outdir mydir --locustag EHEC --proteins NewToxins.faa --evalue 0.001 --gram neg --addgenes contigs.fa

# Check to see if anything went really wrong
% less mydir/EHEC_06072012.err

# Add final details using Sequin
% sequin mydir/EHEC_0607201.sqn

Genbank submitter

# Register your BioProject and your locus_tag prefix first!
% prokka --compliant --centre UoN --outdir PRJNA123456 --locustag EHEC --prefix EHEC-Chr1 contigs.fa

# Check to see if anything went really wrong
% less PRJNA123456/EHEC-Chr1.err

# Add final details using Sequin
% sequin PRJNA123456/EHEC-Chr1.sqn

Crazy Person

# No stinking Perl script is going to control me
% prokka \
    --outdir $HOME/genomes/Ec_POO247 --force \
    --prefix Ec_POO247 --addgenes --locustag ECPOOp \
    --increment 10 --gffver 2 --centre CDC  --compliant \
    --genus Escherichia --species coli --strain POO247 --plasmid pECPOO247 \
    --kingdom Bacteria --gcode 11 --usegenus \
    --proteins /opt/prokka/db/trusted/Ecocyc-17.6 \
    --evalue 1e-9 --rfam \
    plasmid-closed.fna

Output Files

.gff
This is the master annotation in GFF3 format, containing both sequences and annotations. It can be viewed directly in Artemis or IGV.
.gbk
This is a standard Genbank file derived from the master .gff. If the input to prokka was a multi-FASTA, then this will be a multi-Genbank, with one record for each sequence.
.fna
Nucleotide FASTA file of the input contig sequences.
.faa
Protein FASTA file of the translated CDS sequences.
.ffn
Nucleotide FASTA file of all the annotated sequences, not just CDS.
.sqn
An ASN1 format "Sequin" file for submission to Genbank. It needs to be edited to set the correct taxonomy, authors, related publication etc.
.fsa
Nucleotide FASTA file of the input contig sequences, used by "tbl2asn" to create the .sqn file. It is mostly the same as the .fna file, but with extra Sequin tags in the sequence description lines.
.tbl
Feature Table file, used by "tbl2asn" to create the .sqn file.
.err
Unacceptable annotations - the NCBI discrepancy report.
.log
Contains all the output that Prokka produced during its run. This is a record of what settings you used, even if the --quiet option was enabled.
.txt
Statistics relating to the annotated features found

Options

General:
  --help            This help
  --version         Print version and exit
  --docs            Show full manual/documentation
  --listdb          List all configured databases
  --citation        Print citation for referencing Prokka
  --quiet           No screen output (default OFF)
Outputs:
  --outdir [X]      Output folder [auto] (default '')
  --force           Force overwriting existing output folder (default OFF)
  --prefix [X]      Filename output prefix [auto] (default '')
  --addgenes        Add 'gene' features for each 'CDS' feature (default OFF)
  --locustag [X]    Locus tag prefix (default 'PROKKA')
  --increment [N]   Locus tag counter increment (default '1')
  --gffver [N]      GFF version (default '3')
  --compliant       Force Genbank/ENA/DDJB compliance: --genes --mincontiglen 200 --centre XXX (default OFF)
  --centre [X]      Sequencing centre ID. (default '')
Organism details:
  --genus [X]       Genus name (default 'Genus')
  --species [X]     Species name (default 'species')
  --strain [X]      Strain name (default 'strain')
  --plasmid [X]     Plasmid name or identifier (default '')
Annotations:
  --kingdom [X]     Annotation mode: Archaea|Bacteria|Viruses (default 'Bacteria')
  --gcode [N]       Genetic code / Translation table (set if --kingdom is set) (default '0')
  --gram [X]        Gram: -/neg +/pos (default '')
  --usegenus        Use genus-specific BLAST databases (needs --genus) (default OFF)
  --proteins [X]    Fasta file of trusted proteins to first annotate from (default '')
  --metagenome      Improve gene predictions for highly fragmented genomes (default OFF)
Computation:
  --fast            Fast mode - skip CDS /product searching (default OFF)
  --cpus [N]        Number of CPUs to use [0=all] (default '8')
  --mincontiglen [N] Minimum contig size [NCBI needs 200] (default '1')
  --evalue [n.n]    Similarity e-value cut-off (default '1e-06')
  --rfam            Enable searching for ncRNAs with Infernal+Rfam (SLOW!) (default '0')
  --norrna          Don't run rRNA search (default OFF)
  --notrna          Don't run tRNA search (default OFF)

Dependencies

GNU Parallel
A shell tool for executing jobs in parallel using one or more computers O. Tange, GNU Parallel - The Command-Line Power Tool, ;login: The USENIX Magazine, Feb 2011:42-47.
BioPerl
Used for input/output of various file formats Stajich et al, The Bioperl toolkit: Perl modules for the life sciences. Genome Res. 2002 Oct;12(10):1611-8.
Aragorn
Finds transfer RNA features (tRNA) Laslett D, Canback B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 2004 Jan 2;32(1):11-6.
Barrnap
Used to predict ribosomal RNA features (rRNA). My licence-free replacement for RNAmmmer. Manuscript under preparation.
RNAmmer
Finds ribosomal RNA features (rRNA) Lagesen K et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35(9):3100-8.
Prodigal
Finds protein-coding features (CDS) Hyatt D et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010 Mar 8;11:119.
SignalP >= 4.0
Finds signal peptide features in CDS (sig_peptide) Petersen TN et al. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011 Sep 29;8(10):785-6.
BLAST+
Used for similarity searching against protein sequence libraries Camacho C et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009 Dec 15;10:421.
HMMER3
Used for similarity searching against protein family profiles Finn RD et al. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011 Jul;39(Web Server issue):W29-37.
Infernal
Used for similarity searching against ncRNA family profiles D. L. Kolbe, S. R. Eddy. Fast Filtering for RNA Homology Search. Bioinformatics, 27:3102-3109, 2011.

Databases

The Core Databases

Prokka uses a variety of databases when trying to assign function to the predicted CDS features. It takes a hierarchial approach to make it fast. A small, core set of well characterized proteins are first searched using BLAST+. This combination of small database and fast search typically completes about 70% of the workload. Then a series of slower but more sensitive HMM databases are searched using HMMER3.

The initial core databases are derived from UniProtKB; there is one per "kingdom" supported. To qualify for inclusion, a protein must be (1) from Bacteria (or Archaea or Viruses); (2) not be "Fragment" entries; and (3) have an evidence level ("PE") of 2 or lower, which corresponds to experimental mRNA or proteomics evidence.

Making a Core Databases

If you want to modify these core databases, the included script prokka-uniprot_to_fasta_db, along with the official uniprot_sprot.dat, can be used to generate a new database to put in /path/to/prokka/db/kingdom/. If you add new ones, the command prokka --listdb will show you whether it has been detected properly.

The Genus Databases

If you enable --usegenus and also provide a Genus via --genus then it will first use a BLAST database which is Genus specific. Prokka comes with a set of databases for the most common Bacterial genera; type prokka --listdb to see what they are.

Adding a Genus Databases

If you have a set of Genbank files and want to create a new Genus database, Prokka comes with a tool called prokka-genbank_to_fasta_db to help. For example, if you had four annotated "Coccus" genomes, you could do the following:

% quokka-genbank_to_fasta_db Coccus1.gbk Coccus2.gbk Coccus3.gbk Coccus4.gbk > Coccus.faa
% cd-hit -i Coccus.faa -o Coccus -T 0 -M 0 -g 1 -s 0.8 -c 0.9
% rm -fv Coccus.faa Coccus.bak.clstr Coccus.clstr
% makeblastdb -dbtype prot -in Coccus
% mv Coccus.p* /path/to/prokka/db/genus/

The HMM Databases

Prokka comes with a bunch of HMM libraries for HMMER3. They are mostly Bacteria-specific. They are searched after the core and genus databases. You can add more simply by putting them in /path/to/prokka/db/hmm. Type prokka --listdb to confirm they are recognised.

Database format

Prokka understands two annotation tag formats, a plain one and a detailed one.

The plain one is a standard FASTA-like line with the ID after the > sign, and the protein "/product" after the ID (the "description" part of the line):

>SeqID product

The detailed one consists of a special encoded three-part description line. The parts are the /EC_number, the /gene code, then the /product - and they are separated by a special "~~~" sequence:

>SeqID EC_number~~~gene~~~product

Here are some examples. Note that not all parts need to be present, but the "~~~" should still be there:

>YP_492693.1 2.1.1.48~~~ermC~~~rRNA adenine N-6-methyltransferase
MNEKNIKHSQNFITSKHNIDKIMTNIRLNEHDNIFEIGSGKGHFTLELVQRCNFVTAIEI
DHKLCKTTENKLVDHDNFQVLNKDILQFKFPKNQSYKIFGNIPYNISTDIIRKIVF*
>YP_492697.1 ~~~traB~~~transfer complex protein TraB
MIKKFSLTTVYVAFLSIVLSNITLGAENPGPKIEQGLQQVQTFLTGLIVAVGICAGVWIV
LKKLPGIDDPMVKNEMFRGVGMVLAGVAVGAALVWLVPWVYNLFQ*
>YP_492694.1 ~~~~~~transposase
MNYFRYKQFNKDVITVAVGYYLRYALSYRDISEILRGRGVNVHHSTVYRWVQEYAPILYQ
QSINTAKNTLKGIECIYALYKKNRRSLQIYGFSPCHEISIMLAS*

The same description lines apply to HMM models, except the "NAME" and "DESC" fields are used:

NAME  PRK00001
ACC   PRK00001
DESC  2.1.1.48~~~ermC~~~rRNA adenine N-6-methyltransferase
LENG  284

FAQ

Where does the name "Prokka" come from?
Prokka is a contraction of "prokaryotic annotation". It's also relatively unique within Google, and also rhymes with a native Australian marsupial called the quokka.
Can I annotate by eukaryote genome with Prokka?
No. Prokka is specifically designed for Bacteria, Archaea and Viruses. It can't handle multi-exon gene models; I would recommend using MAKER 2 for that purpose.
Why does Prokka keeps on crashing when it gets to tge "tbl2asn" stage?
It seems that the tbl2asn program from NCBI "expires" after 12 months, and refuses to run. Unfortunately you need to install a newer version which you can download from here.
The hmmscan step seems to hang and do nothing?
The problem here is GNU Parallel. It seems the Debian package for hmmer has modified it to require the --gnu option to behave in the 'default' way. There is no clear reason for this. The only way to restore normal behaviour is to edit the prokka script and change "parallel" to "parallel --gnu".
Why does prokka fail when it gets to hmmscan?
Unfortunately HMMER keeps changing it's database format, and they aren't upward compatible. If you upgraded HMMER (from 3.0 to 3.1 say) then you need to "re-press" the files. This can be done as follows:
cd /path/to/prokka/db/hmm
mkdir new
for D in *.hmm ; do hmmconvert $D > new/$D ; done
cd new
for D in *.hmm ; do hmmpress $D ; done
mv * ..
rmdir new
Why does Prokka take so long to download?
Our server is in Australia, and the international pipes aren't always flowing as well as we'd like. I try to put it on GoogleDrive. Dropbox is no longer possible due to bandwidth quotas. If you are able to mirror Prokka (~2 GB) outside please let me know.
Why can't I load Prokka .GBK files into Mauve?
Mauve is very picky about Genbank files.

It does not like long contig names, like those from Velvet or Spades. The simple solution is to use "--centre XXX" in Prokka and it will rename all your contigs to be NCBI (and Mauve) compliant.

It does not like the ACCESSION and VERSION strings that Prokka produces via the "tbl2asn" tool. The following Unix command will fix them:
egrep -v '^(ACCESSION|VERSION)' prokka.gbk > mauve.gbk

Still To Do

Changes

See the ChangeLog.txt file in the doc/ subdirectory of Prokka.

Bugs

Citation

The manuscript is currently being prepared. If you use Prokka in your research before then, please cite: Prokka: Prokaryotic Genome Annotation System - http://vicbioinformatics.com/

Appendix A - The Canonical Bacterial Gene Model

       gene =================================================>
   promoter ===>
       mRNA       ===========================================>
        RBS         =>
        CDS            ================================>
sig_peptide            ====>