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1 <tool id="macs2_bdgcmp" name="MACS2 bdgcmp" version="2.0.10.0">
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2 <description>Deduct noise by comparing two signal tracks in bedGraph</description>
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3 <expand macro="requirements" />
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4 <expand macro="version_command" />
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5 <macros>
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6 <import>macs2_macros.xml</import>
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7 </macros>
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8 <command>
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9 macs2 bdgcmp
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10 -t "${ infile_treatment }"
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11 -c "${ infile_control }"
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12
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13 -m "${ bdgcmp_options.bdgcmp_options_selector }"
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14 #if str($bdgcmp_options.bdgcmp_options_selector) in ['FE', 'logFE', 'logLR']:
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15 -p "${ bdgcmp_options.pseudocount }"
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16 #end if
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17 -o "${ outfile }"
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18
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19 </command>
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20 <expand macro="stdio" />
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21 <inputs>
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22 <param name="infile_control" type="data" format="bedgraph" label="Control bedGraph file" />
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23 <param name="infile_treatment" type="data" format="bedgraph" label="Treatment bedGraph file" />
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24
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25 <conditional name="bdgcmp_options">
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26 <param name="bdgcmp_options_selector" type="select" label="Method to use while calculating a score in any bin by comparing treatment value and control value">
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27 <option value="ppois" selected="true">Poisson pvalue (-log10) using control as lambda and treatment as observation (ppois)</option>
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28 <option value="qpois">q-value through a BH process for poisson pvalues (qpois)</option>
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29 <option value="subtract">subtraction from treatment (subtract)</option>
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30 <option value="logFE">log10 fold enrichment (logFR)</option>
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31 <option value="FE">linear scale fold enrichment (FE)</option>
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32 <option value="logLR">log10 likelihood between ChIP-enriched model and open chromatin model (logLR)</option>
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33 <option value="slogLR">symmetric log10 likelihood between two ChIP-enrichment models and open chromatin model (slogLR)</option>
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34 </param>
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35 <when value="FE">
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36 <param name="pseudocount" type="float" label="Set pseudocount" value="0.0" help="The count will be applied after normalization of sequencing depth. default: 0.0, no pseudocount is applied."/>
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37 </when>
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38 <when value="logLR">
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39 <param name="pseudocount" type="float" label="Set pseudocount" value="0.0" help="The count will be applied after normalization of sequencing depth. default: 0.0, no pseudocount is applied."/>
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40 </when>
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41 <when value="logFE">
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42 <param name="pseudocount" type="float" label="Set pseudocount" value="0.0" help="The count will be applied after normalization of sequencing depth. default: 0.0, no pseudocount is applied."/>
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43 </when>
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44 <when value="ppois"/>
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45 <when value="qpois"/>
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46 <when value="subtract"/>
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47 <when value="slogLR"/>
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48 </conditional>
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49 </inputs>
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50 <outputs>
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51 <data name="outfile" format="bedgraph" label="${tool.name} on ${on_string}" />
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52 </outputs>
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53 <tests>
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54 <test>
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55 <param name="infile_control" value="callpeak_control_part.bdg" ftype="bedgraph"/>
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56 <param name="infile_treatment" value="callpeak_treatment_part.bdg" ftype="bedgraph"/>
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57 <param name="bdgcmp_options_selector" value="slogLR"/>
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58 <output name="outfile" file="bdgcmp_on_Control_and_ChIP_slogLR.bdg"/>
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59 </test>
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60 </tests>
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61 <help>
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62 **What it does**
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63
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64 With the improvement of sequencing techniques, chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq)
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65 is getting popular to study genome-wide protein-DNA interactions. To address the lack of powerful ChIP-Seq analysis method, we present a novel algorithm, named Model-based Analysis of ChIP-Seq (MACS), for
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66 identifying transcript factor binding sites. MACS captures the influence of genome complexity to evaluate the significance of enriched ChIP regions, and MACS improves the spatial resolution of
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67 binding sites through combining the information of both sequencing tag position and orientation. MACS can be easily used for ChIP-Seq data alone, or with control sample with the increase of specificity.
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68
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69 View the original MACS2 documentation: https://github.com/taoliu/MACS/blob/master/README
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70
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71 ------
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72
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73 **Usage**
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74
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75 **Compare .bdg files**: Deduct noise by comparing two signal tracks in bedGraph.
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76
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77
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78 @citation@
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79 </help>
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80 </tool>
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