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[![Build Status](https://travis-ci.org/tseemann/barrnap.svg?branch=master)](https://travis-ci.org/tseemann/barrnap) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [](#lang-au)

# Barrnap

BAsic Rapid Ribosomal RNA Predictor

## Description

Barrnap predicts the location of ribosomal RNA genes in genomes.
It supports bacteria (5S,23S,16S), archaea (5S,5.8S,23S,16S),
metazoan mitochondria (12S,16S) and eukaryotes (5S,5.8S,28S,18S).

It takes FASTA DNA sequence as input, and write GFF3 as output.
It uses the new `nhmmer` tool that comes with HMMER 3.1 for HMM searching in RNA:DNA style.
Multithreading is supported and one can expect roughly linear speed-ups with more CPUs.

## Installation

### Requirements
* [Perl 5.xx](https://dev.perl.org/perl5/) (core modules only)
* [nhmmer](https://hmmer.org/) (part of HMMER 3.x)
* [bedtools](http://bedtools.readthedocs.io/en/latest/)

### Conda
Install [Conda](https://conda.io/docs/) or [Miniconda](https://conda.io/miniconda.html):
```
conda -c bioconda -c conda-forge install barrnap
barrnap --version
```

### Homebrew
Install [HomeBrew](http://brew.sh/) (Mac OS X) or [LinuxBrew](http://brew.sh/linuxbrew/) (Linux).
```
brew install brewsci/bio/barrnap
barrnap --help
```

### Source
This will install the latest version direct from Github. 
You'll need to add the `bin` directory to your PATH.
```
cd $HOME
tar zxvf barrnap-0.X.tar.gz
barrnap-0.X/barrnap -h
```

## Usage

```
% barrnap --quiet examples/small.fna
##gff-version 3
P.marinus	barrnap:0.8	rRNA	353314	354793	0	+	.	Name=16S_rRNA;product=16S ribosomal RNA
P.marinus	barrnap:0.8	rRNA	355464	358334	0	+	.	Name=23S_rRNA;product=23S ribosomal RNA
P.marinus	barrnap:0.8	rRNA	358433	358536	7.5e-07	+	.	Name=5S_rRNA;product=5S ribosomal RNA

% barrnap -q -k mito examples/mitochondria.fna 
##gff-version 3
AF346967.1	barrnap:0.8	rRNA	643	1610	.	+	.	Name=12S_rRNA;product=12S ribosomal RNA
AF346967.1	barrnap:0.8	rRNA	1672	3228	.	+	.	Name=16S_rRNA;product=16S ribosomal RNA
  
% barrnap -o rrna.fa < contigs.fa > rrna.gff
% head -n 3 rrna.fa
>16S_rRNA::gi|329138943|tpg|BK006945.2|:455935-456864(-)
ACGGTCGGGGGCATCAGTATTCAATTGTCAGAGGTGAAATTCTTGGATT
TATTGAAGACTAACTACTGCGAAAGCATTTGCCAAGGACGTTTTCATTA
```

## Options

### General
* `--help` show help and exit
* `--version` print version in form `barrnap X.Y` and exit 
* `--citation` print a citation and exit
### Search
* `--kingdom` is the database to use: Bacteria:`bac`, Archaea:`arc`, Eukaryota:`euk`, Metazoan Mitochondria:`mito`
* `--threads` is how many CPUs to assign to `nhmmer` search
* `--evalue` is the cut-off for `nhmmer` reporting, before further scrutiny
* `--lencutoff` is the proportion of the full length that qualifies as `partial` match
* `--reject` will not include hits below this proportion of the expected length
### Output
* `--quiet` will not print any messages to `stderr`
* `--incseq` will include the full input sequences in the output GFF
* `--outseq` creates a FASTA file with the hit sequences

## Caveats

Barrnap does not do anything fancy. It has HMM models for each different rRNA gene. 
They are built from full length seed alignments. 

## Comparison with RNAmmer

Barrnap is designed to be a substitute for [RNAmmer](http://www.cbs.dtu.dk/services/RNAmmer/). 
It was motivated by my desire to remove [Prokka's](https://github.com/tseemann/prokka)
dependency on RNAmmer which is encumbered by a free-for-academic sign-up
license, and by RNAmmer's dependence on legacy HMMER 2.x which conflicts
with HMMER 3.x that most people are using now.

RNAmmer is more sophisticated than Barrnap, and more accurate because it
uses HMMER 2.x in glocal alignment mode whereas NHMMER 3.x currently only
supports local alignment (Sean Eddy expected glocal to be supported in 2014, 
but it still isn't available in 2018). 

In practice, Barrnap will find all the typical rRNA genes in a few seconds
(in bacteria), but may get the end points out by a few bases and will
probably miss wierd rRNAs.  The HMM models it uses are derived from Rfam,
Silva and RefSeq.

## Data sources for HMM models

```
Bacteria (70S)  
        LSU 50S
                5S      RF00001
                23S     SILVA-LSU-Bac
        SSU 30S
                16S     RF00177

Archaea (70S)   
        LSU 50S
                5S      RF00001
                5.8S    RF00002
                23S     SILVA-LSU-Arc
        SSU 30S
                16S     RF01959

Eukarya (80S)   
        LSU 60S
                5S      RF00001
                5.8S    RF00002
                28S     SILVA-LSU-Euk
        SSU 40S
                18S     RF01960

Metazoan Mito
                12S     RefSeq (MT-RNR1, s-rRNA, rns)
                16S     RefSeq (MT-RNR2, l-rRNA, rnl)       
```

## Models I would like to add

```
Fungi	[Sajeet Haridas]
        LSU 35S ?
                5S
                5.8S
                25S
        SSU ?
                18S
        Mito [http://www.ncbi.nlm.nih.gov/nuccore/NC_001224.1]
                15S 
                21S (multiple exons)
                
Apicoplast [http://www.ncbi.nlm.nih.gov/nuccore/U87145.2]
                LSU ~2500bp 28S ?
                SSU ~1500bp 16S ?

Plant [Shaun Jackman]
	Mito [https://www.ncbi.nlm.nih.gov/nucleotide?cmd=Retrieve&dopt=GenBank&list_uids=26556996]	
		5S	~118 bp  ?	rrn5 	(use RF00001 ?)
		18S	~1935 bp ?	rrn18	(use RF01960 ?)
		26S	~2568 bp ?	rrn26   
```

## Where does the name come from?

The name Barrnap was originally derived from _Bacterial/Archaeal Ribosomal RNA Predictor_.
However it has since been extended to support mitochondrial and eukaryotic rRNAs, and has been
given the new backronym _BAsic Rapid Ribosomal RNA Predictor_.
The project was originally spawned at CodeFest 2013 in Berlin, Germany 
by Torsten Seemann and Tim Booth.

## License

* Barrnap: [GPLv3](https://raw.githubusercontent.com/tseemann/barrnap/master/LICENSE)
* Rfam: [CC0](https://raw.githubusercontent.com/tseemann/barrnap/master/LICENSE.Rfam)
* SILVA: [Free for academic use](https://raw.githubusercontent.com/tseemann/barrnap/master/LICENSE.SILVA)

## Author

Torsten Seemann