comparison deseq2.xml @ 8:57e2f9454d9e draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/deseq2 commit 50db5cdaef2ff3e54a63670108eb6e4ade2a630f

7:cb23006f34ff

<tool id="deseq2" name="DESeq2" version="2.11.38">
    <description>Determines differentially expressed features from count tables</description>
    <requirements>
        <requirement type="package" version="1.12.4">bioconductor-deseq2</requirement> 
    </requirements>
    <stdio>
        <regex match="Execution halted"
           source="both"
           level="fatal"

        echo $(R --version | grep version | grep -v GNU)", DESeq2 version" $(R --vanilla --slave -e "library(DESeq2); cat(sessionInfo()\$otherPkgs\$DESeq2\$Version)" 2> /dev/null | grep -v -i "WARNING: ")
    ]]>
    </version_command>
    <command>
    <![CDATA[

        #if $tximport.tximport_selector == 'tximport':
            #if $tximport.mapping_format.mapping_format_selector == 'gtf':
                ln -s '$tximport.mapping_format.gtf_file' mapping.gtf &&
            #else:
                ln -s '$tximport.mapping_format.tabular_file' mapping.txt &&

**What it does**

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution


**Inputs**

DESeq2_ takes count tables that generated from the htseq-count as input. Count tables must be generated for each sample individually. DESeq2 is capable of handling multiple factors that effect your experiment. The first factor you input is considered as the primary factor that affects gene expressions. You also input several secondary factors that might influence your experiment. But the final output will be changes in genes due to primary factor in presence of secondary factors. Each factor has two levels/states. You need to select appropriate count table from your history for each factor level.

The following table gives some examples of factors and their levels:

     6 p value for the statistical significance of this change
     7 p value adjusted for multiple testing with the Benjamini-Hochberg procedure
       which controls false discovery rate (FDR)
====== ==========================================================


.. _DESeq2: http://master.bioconductor.org/packages/release/bioc/html/DESeq2.html
]]>
    </help>
    <citations>
        <citation type="doi">10.1186/s13059-014-0550-8</citation>

8:57e2f9454d9e

<tool id="deseq2" name="DESeq2" version="2.11.39">
    <description>Determines differentially expressed features from count tables</description>
    <requirements>
        <requirement type="package" version="1.14.1">bioconductor-deseq2</requirement>
    </requirements>
    <stdio>
        <regex match="Execution halted"
           source="both"
           level="fatal"

        echo $(R --version | grep version | grep -v GNU)", DESeq2 version" $(R --vanilla --slave -e "library(DESeq2); cat(sessionInfo()\$otherPkgs\$DESeq2\$Version)" 2> /dev/null | grep -v -i "WARNING: ")
    ]]>
    </version_command>
    <command>
    <![CDATA[
        #if $tximport.tximport_selector == 'tximport':
            #if $tximport.mapping_format.mapping_format_selector == 'gtf':
                ln -s '$tximport.mapping_format.gtf_file' mapping.gtf &&
            #else:
                ln -s '$tximport.mapping_format.tabular_file' mapping.txt &&

**What it does**

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution

**Inputs**

DESeq2_ takes count tables that generated from the htseq-count as input. Count tables must be generated for each sample individually. DESeq2 is capable of handling multiple factors that effect your experiment. The first factor you input is considered as the primary factor that affects gene expressions. You also input several secondary factors that might influence your experiment. But the final output will be changes in genes due to primary factor in presence of secondary factors. Each factor has two levels/states. You need to select appropriate count table from your history for each factor level.

The following table gives some examples of factors and their levels:

     6 p value for the statistical significance of this change
     7 p value adjusted for multiple testing with the Benjamini-Hochberg procedure
       which controls false discovery rate (FDR)
====== ==========================================================

.. _DESeq2: http://master.bioconductor.org/packages/release/bioc/html/DESeq2.html
]]>
    </help>
    <citations>
        <citation type="doi">10.1186/s13059-014-0550-8</citation>