comparison tools/protein_analysis/promoter2.xml @ 20:a19b3ded8f33 draft

v0.2.11 Job splitting fast-fail; RXLR tools supports HMMER2 from BioConda; Capture more version information; misc internal changes

19:f3ecd80850e2

<tool id="promoter2" name="Promoter 2.0" version="0.0.10">
    <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="binary">promoter</requirement>
        <requirement type="package">promoter</requirement>
    </requirements>
    <stdio>
        <!-- Anything other than zero is an error -->
        <exit_code range="1:" />
        <exit_code range=":-1" />
    </stdio>
    <version_command interpreter="python">promoter2.py --version</version_command>
    <command interpreter="python">
        promoter2.py "\$GALAXY_SLOTS" "$fasta_file" "$tabular_file"
        ##If the environment variable isn't set, get "", and the python wrapper
        ##defaults to four threads.
    </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>

            <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):

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**

20:a19b3ded8f33

<tool id="promoter2" name="Promoter 2.0" version="0.0.12">
    <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>

            <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):

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**