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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/diffbind commit cc4c1c4131518b9cbf986a1f252767ff73ca938e
| author | iuc |
|---|---|
| date | Sat, 07 Apr 2018 15:45:41 -0400 |
| parents | d7725c5596ab |
| children | fa56d93f7980 |
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<tool id="diffbind" name="DiffBind" version="2.6.6.1"> <description> differential binding analysis of ChIP-Seq peak data</description> <requirements> <requirement type="package" version="2.6.6">bioconductor-diffbind</requirement> <requirement type="package" version="1.20.0">r-getopt</requirement> <requirement type="package" version="0.2.15">r-rjson</requirement> </requirements> <stdio> <regex match="Execution halted" source="both" level="fatal" description="Execution halted." /> <regex match="Input-Error 01" source="both" level="fatal" description="Error in your input parameters: Make sure you only apply factors to selected samples." /> <regex match="Error in" source="both" level="fatal" description="An undefined error occured, please check your intput carefully and contact your administrator." /> </stdio> <version_command><![CDATA[ echo $(R --version | grep version | grep -v GNU)", DiffBind version" $(R --vanilla --slave -e "library(DiffBind); cat(sessionInfo()\$otherPkgs\$DiffBind\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", rjson version" $(R --vanilla --slave -e "library(rjson); cat(sessionInfo()\$otherPkgs\$rjson\$Version)" 2> /dev/null | grep -v -i "WARNING: ") ]]></version_command> <command><![CDATA[ #import re #import json ## Adapted from DESeq2 wrapper #set $temp_factor_names = list() #set $temp_factor = list() #for $g in $rep_group: #set $peak_files = list() #set $bam_files = list() #set $bam_controls = list() #for $file in $g.peaks: #set $file_name = re.sub('[^\w\-\s]', '_', str($file.element_identifier)) ln -s '${file}' ${g.groupName}-${file_name}-peaks.bed && $peak_files.append(str($g.groupName) + '-' + $file_name + '-peaks.bed') #end for #for $bam in $g.bamreads: #set $bam_name = re.sub('[^\w\-\s]', '_', str($bam.element_identifier)) ln -s '${bam}' ${bam_name}-bamreads.bam && ln -s ${bam.metadata.bam_index} ${bam_name}-bamreads.bai && $bam_files.append($bam_name + '-bamreads.bam') #end for $temp_factor.append( {str($g.groupName): $peak_files} ) $temp_factor.append( {str($g.groupName): $bam_files} ) #if str( $g.bamcontrol ) != 'None': #for $ctrl in $g.bamcontrol: #set $ctrl_name = re.sub('[^\w\-\s]', '_', str($ctrl.element_identifier)) ln -s '${ctrl}' ${g.groupName}-${ctrl_name}-bamcontrol.bam && ln -s ${ctrl.metadata.bam_index} ${g.groupName}-${ctrl_name}-bamcontrol.bai && $bam_controls.append(str($g.groupName) + '-' + $ctrl_name + '-bamcontrol.bam') #end for $temp_factor.append( {str($g.groupName): $bam_controls} ) #end if #end for $temp_factor.reverse() $temp_factor_names.append([str($factorName), $temp_factor]) Rscript '$__tool_directory__/diffbind.R' -i '#echo json.dumps(temp_factor_names)#' -o '$outfile' -t $th -f $out.format -p '$plots' #if $scorecol: -n "$scorecol" #end if #if $lowerbetter: -l "$lowerbetter" #end if #if $summits: -s "$summits" #end if #if $out.binding_matrix: -b #end if #if $out.rdata: -r #end if #if $out.analysis_info: -a #end if #if $out.rscript: && cp '$__tool_directory__/diffbind.R' '$rscript' #end if ]]> </command> <inputs> <param name="factorName" type="text" label="Name" help="Name of experiment factor of interest (e.g. Condition). One factor must be entered and there must be two or more groups. NOTE: Please only use letters, numbers or underscores."> <sanitizer> <valid initial="string.letters,string.digits"><add value="_" /></valid> </sanitizer> </param> <repeat name="rep_group" title="Group" min="2" default="2"> <param name="groupName" type="text" label="Name" help="Name of group that the peak files belong to (e.g. Resistant or Responsive). NOTE: Please only use letters, numbers or underscores (case sensitive)."> <sanitizer> <valid initial="string.letters,string.digits"><add value="_" /></valid> </sanitizer> </param> <param name="peaks" type="data" format="bed" multiple="true" label="Peak files" help="Result of your Peak calling experiment"/> <param name="bamreads" type="data" format="bam" multiple="true" label="Read BAM file" help="Specify the Read BAM file used for Peak calling."/> <param name="bamcontrol" type="data" format="bam" multiple="true" optional="True" label="Control BAM file" help="If specifying a control BAM file, all samples are required to specify one."/> </repeat> <param name="scorecol" type="integer" min="0" value="8" label="Score Column" help="Column in peak files that contains peak scores. Default: 8 (narrowPeak)"/> <param name="lowerbetter" type="boolean" truevalue="True" falsevalue="" checked="False" label="Lower score is better?" help="DiffBind by default assumes that a higher score indicates a better peak, for example narrowPeaks -log10pvalue. If this is not the case, for example if the score is a p-value or FDR, set this option to Yes. Default: No" /> <param name="summits" type="integer" min="0" optional="True" label="Summits" help="Extend peaks Nbp up- and downstream of the summit. For punctate peaks it is advisable to extend (e.g. 250bp), see the DiffBind User Guide"/> <param name="th" type="float" value="0.05" min="0" max="1" label="FDR Threshold" help="Significance threshold; all sites with FDR less than or equal to this value will be included in the output. A value of 1 will output all binding sites. Default: 0.05"/> <!-- Output Options --> <section name="out" expanded="false" title="Output Options"> <param name="format" type="select" label="Output Format"> <option value="bed">BED</option> <option value="gff">GFF</option> <option value="wig">WIG</option> </param> <param name="pdf" type="boolean" truevalue="True" falsevalue="" checked="False" label="Visualising the analysis results" help="output an additional PDF file" /> <param name="binding_matrix" type="boolean" truevalue="True" falsevalue="" checked="False" label="Output binding affinity matrix?" help="Output a table of the binding scores" /> <param name="rdata" type="boolean" truevalue="True" falsevalue="" checked="False" label="Output RData file?" help="Output all the data used by R to construct the plots and tables, can be loaded into R. Default: No"/> <param name="rscript" type="boolean" truevalue="True" falsevalue="False" checked="False" label="Output Rscript?" help="If this option is set to Yes, the Rscript used will be provided as a text file in the output. Default: No"/> <param name="analysis_info" type="boolean" truevalue="True" falsevalue="False" checked="False" label="Output analysis info?" help="If this option is set to Yes, information from the dba.count and dba.analyze commmands will be output in a text file. Default: No"/> </section> </inputs> <outputs> <data name="outfile" format="bed" label="${tool.name} on ${on_string}: Differentially bound sites"> <change_format> <when input="format" value="wig" format="wig" /> <when input="format" value="gff" format="gff" /> </change_format> </data> <data name="plots" format="pdf" label="${tool.name} on ${on_string}: Plots"> <filter>out['pdf']</filter> </data> <data name="binding_matrix" format="tabular" from_work_dir="bmatrix.tab" label="${tool.name} on ${on_string}: Binding matrix"> <filter>out['binding_matrix']</filter> </data> <data name="rdata" format="rdata" from_work_dir="DiffBind_analysis.RData" label="${tool.name} on ${on_string}: RData file"> <filter>out['rdata']</filter> </data> <data name="rscript" format="txt" label="${tool.name} on ${on_string}: Rscript"> <filter>out['rscript']</filter> </data> <data name="analysis_info" format="txt" from_work_dir="DiffBind_analysis_info.txt" label="${tool.name} on ${on_string}: Analysis info"> <filter>out['analysis_info']</filter> </data> </outputs> <tests> <test expect_num_outputs="6"> <param name="factorName" value="Condition"/> <repeat name="rep_group"> <param name="groupName" value="Resistant"/> <param name="peaks" value="BT474_ER_1.bed.gz,BT474_ER_2.bed.gz"/> <param name="bamreads" ftype="bam" value="BT474_ER_1.bam,BT474_ER_2.bam" /> </repeat> <repeat name="rep_group"> <param name="groupName" value="Responsive"/> <param name="peaks" value="MCF7_ER_1.bed.gz,MCF7_ER_2.bed.gz"/> <param name="bamreads" ftype="bam" value="MCF7_ER_1.bam,MCF7_ER_2.bam" /> </repeat> <param name="scorecol" value="5" /> <param name="pdf" value="True" /> <param name="binding_matrix" value="True" /> <param name="rdata" value="True" /> <param name="rscript" value="True"/> <param name="analysis_info" value="True"/> <output name="outfile" value="out_diffbind.bed" /> <output name="plots" value="out_plots.pdf" compare="sim_size" /> <output name="binding_matrix" value="out_binding.matrix" /> <output name="rdata" value="DiffBind_analysis.RData" compare="sim_size"/> <output name="rscript" value="out_rscript.txt"/> <output name="analysis_info" value="out_analysis_info.txt" compare="sim_size" > <assert_contents> <has_text text="SessionInfo"/> </assert_contents> </output> </test> </tests> <help><![CDATA[ .. class:: infomark **What it does** DiffBind_ is a `Bioconductor package`_ that provides functions for processing ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. The primary emphasis of DiffBind is on identifying sites that are differentially bound between two sample groups. It includes functions to support the processing of peak sets, including overlapping and merging peak sets, counting sequencing reads overlapping intervals in peak sets, and identifying statistically significantly differentially bound sites based on evidence of binding affinity (measured by differences in read densities). To this end it uses statistical routines developed in an RNA-Seq context (primarily the Bioconductor packages edgeR and DESeq2). Additionally, the package builds on Rgraphics routines to provide a set of standardized plots to aid in binding analysis. The `DiffBind User Guide`_ includes a brief overview of the processing flow, followed by four sections of examples: the first focusing on the core task of obtaining differentially bound sites based on affinity data, the second working through the main plotting routines, the third discussing the use of a blocking factor, and the fourth revisiting occupancy data (peak calls) in more detail, as well as comparing the results of an occupancy-based analysis with an affinity-based one. Finally, certain technical aspects of the how these analyses are accomplished are detailed. Note this DiffBind tool requires a minimum of four samples (two groups with two replicates each). ----- **Inputs** DiffBind works primarily with peaksets, which are sets of genomic intervals representing candidate protein binding sites. Each interval consists of a chromosome, a start and end position, and usually a score of some type indicating confidence in, or strength of, the peak. Associated with each peakset are metadata relating to the experiment from which the peakset was derived. Additionally, files containing mapped sequencing reads (generally .bam files) can be associated with each peakset (one for the ChIP data, and optionally another representing a control sample) **Sample Information** You have to specify your sample information in the tool form above, where Factor is the groups you want to compare (e.g Resistant and Responsive). Example: ============= ============= **SampleID** **Group** ------------- ------------- BT4741 Resistant BT4742 Resistant MCF71 Responsive MCF72 Responsive ============= ============= **Peak files** Result of your Peak calling experiment in bed format, one file for each sample is required. The peak caller, format and score column can be specified in the tool form above. The default settings expect narrowPeak bed format, which has the score in the 8th column (-log10pvalue), and can be output from MACS2. Example (MACS.xls file in bed format): ======= ======= ======= =============== ============== 1 2 3 4 **5 (Score)** ======= ======= ======= =============== ============== chr18 215562 216063 MACS_peak_16037 56.11 chr18 311530 312105 MACS_peak_16038 222.49 chr18 356656 357315 MACS_peak_16039 92.06 chr18 371110 372092 MACS_peak_16040 123.86 chr18 395116 396464 MACS_peak_16041 1545.39 chr18 399014 400382 MACS_peak_16042 1835.19 chr18 499134 500200 MACS_peak_16043 748.32 chr18 503518 504552 MACS_peak_16044 818.30 chr18 531672 532274 MACS_peak_16045 159.30 chr18 568326 569282 MACS_peak_16046 601.11 ======= ======= ======= =============== ============== * BAM file which contains the mapped sequencing reads associated with each peakset, one file for each sample is required. * Optional: Control BAM file representing a control dataset. If used, has to be specified for all samples. Note that the DiffBind authors say control reads are best utilized prior to running DiffBind, at the peak calling stage (e.g. with MACS2) and in blacklists, see this `Bioconductor post`_. ----- **Outputs** This tool outputs * differentially bound sites in BED, WIG or GFF format Optionally, under **Output Options** you can choose to output * a PDF of plots (Heatmap, PCA, MA, Volcano, Boxplots) * a binding affinity matrix * the R script used by this tool * an RData file of the R objects generated * a text file with information on the analysis (number of Intervals, FriP scores, method used) **Differentially Bound Sites** As output format you can choose BED, GFF, WIG. Example - BED format: ======== ====== ====== ===== ====== ===== =============== ============== ======= ======== ======== seqnames start end width strand Conc Conc_Responsive Conc_Resistant Fold p.value **FDR** ======== ====== ====== ===== ====== ===== =============== ============== ======= ======== ======== chr18 394600 396513 1914 * 7.15 5.55 7.89 -2.35 7.06e-24 9.84e-21 chr18 111567 112005 439 * 5.71 6.53 3.63 2.89 1.27e-08 8.88e-06 chr18 346464 347342 879 * 5 5.77 3.24 2.52 6.51e-06 0.00303 chr18 399014 400382 1369 * 7.62 7 8.05 -1.04 1.04e-05 0.00364 chr18 371110 372102 993 * 4.63 3.07 5.36 -2.3 8.1e-05 0.0226 ======== ====== ====== ===== ====== ===== =============== ============== ======= ======== ======== Columns contain the following data: * **1st**: Chromosome name * **2nd**: Start position of site * **3rd**: End position of site * **4th**: Length of site * **5th**: Strand * **6th**: Mean read concentration over all the samples (the default calculation uses log2 normalized ChIP read counts with control read counts subtracted) * **7th**: Mean concentration over the first (e.g. Resistant) group * **8th**: Mean concentration over second (e.g. Responsive) group * **9th**: Fold shows the difference in mean concentrations between the two groups (e.g. Resistant - Responsive), with a positive value indicating increased binding affinity in the first group and a negative value indicating increased binding affinity in the second group. * **10th**: P-value confidence measure for identifying these sites as differentially bound * **11th**: a multiple testing corrected FDR p-value **Binding Affinity Matrix** The final result of counting is a binding affinity matrix containing a (normalized) read count for each sample at every potential binding site. With this matrix, the samples can be re-clustered using affinity, rather than occupancy, data. The binding affinity matrix can be used for QC plotting as well as for subsequent differential analysis. Example: ===== ====== ====== ================ ================ ================ ================ CHR START END MCF7_ER_1.bed MCF7_ER_2.bed BT474_ER_1.bed BT474_ER_2.bed ===== ====== ====== ================ ================ ================ ================ chr18 111567 112005 137.615208000375 59.878372946728 29.4139375878664 19.9594576489093 chr18 189223 189652 19.9594576489093 12.6059732519427 11.5554754809475 23.110950961895 chr18 215232 216063 11.5554754809475 15.7574665649284 31.5149331298568 72.4843461986707 chr18 311530 312172 17.8584621069189 11.5554754809475 54.6258840917518 43.0704086108043 chr18 346464 347342 75.6358395116564 40.9694130688139 21.0099554199046 16.8079643359236 chr18 356560 357362 11.5554754809475 14.7069687939332 57.7773774047375 53.5753863207566 chr18 371110 372102 8.40398216796182 9.45447993895705 81.9388261376278 82.989323908623 chr18 394600 396513 56.7268796337423 43.0704086108043 510.541916703681 438.05757050501 chr18 399014 400382 156.524167878289 117.655750351465 558.864814169461 496.885445680743 chr18 498906 500200 767.913870597511 278.381909313735 196.443083176108 181.736114382174 ===== ====== ====== ================ ================ ================ ================ ----- **More Information** Generally, processing data with DiffBind involves five phases: #. Reading in peaksets #. Occupancy analysis #. Counting reads #. Differential binding affinity analysis #. Plotting and reporting **Reading in peaksets**: The first step is to read in a set of peaksets and associated metadata. Peaksets are derived either from ChIP-Seq peak callers, such as **MACS2**, or using some other criterion (e.g. genomic windows, or all the promoter regions in a genome). A single experiment can have more than one associated peakset; e.g. if multiple peak callers are used for comparison purposes each sample would have more than one line in the sample sheet. Once the peaksets are read in, a merging function finds all overlapping peaks and derives a single set of unique genomic intervals covering all the supplied peaks (a consensus peakset for the experiment). **Occupancy analysis**: Peaksets, especially those generated by peak callers, provide an insight into the potential occupancy of the protein being ChIPed for at specific genomic loci. After the peaksets have been loaded, it can be useful to perform some exploratory plotting to determine how these occupancy maps agree with each other, e.g. between experimental replicates (re-doing the ChIP under the same conditions), between different peak callers on the same experiment, and within groups of samples representing a common experimental condition. DiffBind provides functions to enable overlaps to be examined, as well as functions to determine how well similar samples cluster together. Beyond quality control, the product of an occupancy analysis may be a consensus peakset, representing an overall set of candidate binding sites to be used in further analysis. **Counting reads**: Once a consensus peakset has been derived, DiffBind can use the supplied sequence read files to count how many reads overlap each interval for each unique sample. The peaks in the consensus peakset may be re-centered and trimmed based on calculating their summits (point of greatest read overlap) in order to provide more standardized peak intervals. The final result of counting is a binding affinity matrix containing a (normalized) read count for each sample at every potential binding site. With this matrix, the samples can be re-clustered using affinity, rather than occupancy, data. The binding affinity matrix is used for QC plotting as well as for subsequent differential analysis. **Differential binding affinity analysis**: The core functionality of DiffBind is the differential binding affinity analysis, which enables binding sites to be identified that are statistically significantly differentially bound between sample groups. To accomplish this, first a contrast (or contrasts) is established, dividing the samples into groups to be compared. Next the core analysis routines are executed, by default using DESeq2 . This will assign a p-value and FDR to each candidate binding site indicating confidence that they are differentially bound. **Plotting and reporting**: Once one or more contrasts have been run, DiffBind provides a number of functions for reporting and plotting the results. MA plots give an overview of the results of the analysis, while correlation heatmaps and PCA plots show how the groups cluster based on differentially bound sites. Boxplots show the distribution of reads within differentially bound sites corresponding to whether they gain or lose affinity between the two sample groups. A reporting mechanism enables differentially bound sites to be extracted for further processing, such as annotation, motif, and pathway analyses. ----- **References** DiffBind Authors: Rory Stark, Gordon Brown (2011) Wrapper authors: Bjoern Gruening, Pavankumar Videm .. _DiffBind: https://bioconductor.org/packages/release/bioc/html/DiffBind.html .. _`Bioconductor package`: https://bioconductor.org/packages/release/bioc/html/DiffBind.html .. _`DiffBind User Guide`: https://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf .. _`Bioconductor post`: https://support.bioconductor.org/p/69924/ ]]> </help> <citations> <citation type="doi">doi:10.1038/nature10730</citation> </citations> </tool>