<tool id="promoter2" name="Promoter 2.0" version="0.0.13">
    <description>Find eukaryotic PolII promoters in DNA sequences</description>
    <!-- If job splitting is enabled, break up the query file into parts -->
    <!-- Using 2000 per chunk so 4 threads each doing 500 is ideal -->
    <parallelism method="basic" split_inputs="fasta_file" split_mode="to_size" split_size="2000" merge_outputs="tabular_file"></parallelism>
    <requirements>
        <requirement type="package">promoter</requirement>
    </requirements>
    <version_command>
python $__tool_directory__/promoter2.py --version
    </version_command>
    <command detect_errors="aggressive">
python $__tool_directory__/promoter2.py "\$GALAXY_SLOTS" '$fasta_file' '$tabular_file'
    </command>
    <inputs>
        <param name="fasta_file" type="data" format="fasta" label="FASTA file of DNA sequences"/>
    </inputs>
    <outputs>
        <data name="tabular_file" format="tabular" label="Promoter2 on ${fasta_file.name}" />
    </outputs>
    <tests>
        <test>
            <param name="fasta_file" value="Adenovirus.fasta" ftype="fasta"/>
            <output name="tabular_file" file="Adenovirus.promoter2.tabular" ftype="tabular"/>
        </test>
        <test>
            <param name="fasta_file" value="empty.fasta" ftype="fasta"/>
            <output name="tabular_file" file="empty_promoter2.tabular" ftype="tabular"/>
        </test>
    </tests>
    <help>

**What it does**

This calls the Promoter 2.0 tool for prediction of eukaryotic PolII promoter sequences using a Neural Network (NN) model.

The input is a FASTA file of nucleotide sequences (e.g. upstream regions of your genes), and the output is tabular with five columns (one row per promoter):

====== ==================================================
Column Description
------ --------------------------------------------------
     1 Sequence identifier (first word of FASTA header)
     2 Promoter position, e.g. 600
     3 Promoter score, e.g. 1.063
     4 Promoter likelihood, e.g. Highly likely prediction
====== ==================================================

The scores are classified very simply as follows:

========= ========================
Score     Description
--------- ------------------------
below 0.5 ignored
0.5 - 0.8 Marginal prediction
0.8 - 1.0 Medium likely prediction
above 1.0 Highly likely prediction
========= ========================

Internally the input FASTA file is divided into parts (to allow multiple processors to be used), and the raw output is reformatted into this tabular layout suitable for downstream analysis within Galaxy.

**References**

If you use this Galaxy tool in work leading to a scientific publication please
cite the following papers:

Peter J.A. Cock, Björn A. Grüning, Konrad Paszkiewicz and Leighton Pritchard (2013).
Galaxy tools and workflows for sequence analysis with applications
in molecular plant pathology. PeerJ 1:e167
https://doi.org/10.7717/peerj.167

Steen Knudsen (1999).
Promoter2.0: for the recognition of PolII promoter sequences.
Bioinformatics, 15:356-61.
https://doi.org/10.1093/bioinformatics/15.5.356

See also http://www.cbs.dtu.dk/services/Promoter/output.php

This wrapper is available to install into other Galaxy Instances via the Galaxy
Tool Shed at http://toolshed.g2.bx.psu.edu/view/peterjc/tmhmm_and_signalp
    </help>
    <citations>
        <citation type="doi">10.7717/peerj.167</citation>
        <citation type="doi">10.1093/bioinformatics/15.5.356</citation>
    </citations>
</tool>