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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/diffbind commit 9de99de5fb4c62f889814ea43b8800ce8d28eb83
| author | iuc |
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| date | Sun, 28 Jan 2018 05:10:25 -0500 |
| parents | |
| children | d7725c5596ab |
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<tool id="diffbind" name="DiffBind" version="2.6.5.0"> <description> differential binding analysis of ChIP-Seq peak data</description> <requirements> <requirement type="package" version="2.6.5">bioconductor-diffbind</requirement> <requirement type="package" version="1.20.0">r-getopt</requirement> <!--added rmysql requirement to remove: "Warning: namespace ‘RMySQL’ is not available"--> <requirement type="package" version="0.10.11">r-rmysql</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: ")," getopt version" $(R --vanilla --slave -e "library(getopt); cat(sessionInfo()\$otherPkgs\$getopt\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", rmysql version" $(R --vanilla --slave -e "library(rmysql); cat(sessionInfo()\$otherPkgs\$rmysql\$Version)" 2> /dev/null | grep -v -i "WARNING: ") ]]></version_command> <command><![CDATA[ ## seems that diffbind also needs file extensions to work properly #set $counter = 1 #for $sample in $samples: ln -s $sample.bamreads #echo str($counter) + "_bamreads.bam"# && ln -s ${sample.bamreads.metadata.bam_index} #echo str($counter) + "_bamreads.bai"# && #if str( $sample.bamcontrol ) != 'None': ln -s $sample.bamcontrol #echo str($counter) + "_bamcontrol.bam"# && ln -s ${sample.bamcontrol.metadata.bam_index} #echo str($counter) + "_bamcontrol.bai"# && #end if #set $counter = $counter + 1 #end for Rscript '$__tool_directory__/diffbind.R' -i $infile -o '$outfile' -p '$plots' -f $format -t $th #if $binding_affinity_matrix: -b #end if ]]> </command> <configfiles> <configfile name="infile"><![CDATA[ #set $counter = 1 #for $sample in $samples: #if str( $sample.bamcontrol ) != 'None' and $counter == 1: SampleID,Tissue,Factor,Condition,Replicate,bamReads,bamControl,Peaks #elif $counter == 1: SampleID,Tissue,Factor,Condition,Replicate,bamReads,Peaks #end if #if str( $sample.bamcontrol ) != 'None': $sample.sample_id,$sample.tissue,$sample.factor,$sample.condition,$sample.replicate,#echo str($counter) + '_bamreads.bam'#,#echo str($counter) + '_bamcontrol.bam'#,$sample.peaks #else: $sample.sample_id,$sample.tissue,$sample.factor,$sample.condition,$sample.replicate,#echo str($counter) + '_bamreads.bam'#,$sample.peaks #end if #set $counter = $counter + 1 #end for]]></configfile> </configfiles> <inputs> <repeat name="samples" title="Samples" min="2"> <param name="sample_id" type="text" value="Sample ID" label="Specify a sample id" help="e.g. BT474.1-" /> <param name="tissue" type="text" value="Tissue" label="Specify the tissue" help="e.g. BT474" /> <param name="factor" type="text" value="Factor Name" label="Specify a factor name" help="e.g. ER" /> <param name="condition" type="text" value="Condition" label="Specify the condition" help="e.g. Resistent" /> <param name="replicate" type="integer" value="1" label="Specify the replicate number" help="e.g. 1" /> <param name="bamreads" type="data" format="bam" label="Read BAM file" help="Specify the Read BAM file, used for Peak calling."/> <param name="bamcontrol" type="data" format="bam" optional="True" label="Control BAM file" help="If specifying a control BAM file for this sample, then all samples are required to specify one."/> <param name="peaks" type="data" format="bed" label="Peak file" help="Result of your Peak calling experiment."/> </repeat> <param name="th" type="float" value="1" 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 report. A value of 1 will include all binding sites in the report. Default: 1"/> <param name="pdf" type="boolean" truevalue="" falsevalue="" checked="true" label="Visualising the analysis results" help="output an additional PDF file" /> <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="binding_affinity_matrix" type="boolean" truevalue="True" falsevalue="" checked="False" label="Output binding affinity matrix?" help="Output a table of the binding scores" /> </inputs> <outputs> <data name="outfile" format="bed" label="Differential binding sites on ${on_string}"> <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="Differential binding sites on ${on_string}"> <filter>pdf == True</filter> </data> <data name="binding_matrix" format="tabular" from_work_dir="bmatrix.tab" label="Differential binding sites on ${on_string}"> <filter>binding_affinity_matrix == True</filter> </data> </outputs> <tests> <test> <repeat name="samples"> <param name="sample_id" value="BT4741" /> <param name="tissue" value="BT474" /> <param name="factor" value="ER" /> <param name="condition" value="Resistant" /> <param name="replicate" value="1" /> <param name="bamreads" ftype="bam" value="BT474_ER_1.bam" /> <param name="peaks" ftype="bed" value="BT474_ER_1.bed.gz" /> </repeat> <repeat name="samples"> <param name="sample_id" value="BT4742" /> <param name="tissue" value="BT474" /> <param name="factor" value="ER" /> <param name="condition" value="Resistant" /> <param name="replicate" value="2" /> <param name="bamreads" ftype="bam" value="BT474_ER_2.bam" /> <param name="peaks" ftype="bed" value="BT474_ER_2.bed.gz" /> </repeat> <repeat name="samples"> <param name="sample_id" value="MCF71" /> <param name="tissue" value="MCF7" /> <param name="factor" value="ER" /> <param name="condition" value="Responsive" /> <param name="replicate" value="1" /> <param name="bamreads" ftype="bam" value="MCF7_ER_1.bam" /> <param name="peaks" ftype="bed" value="MCF7_ER_1.bed.gz" /> </repeat> <repeat name="samples"> <param name="sample_id" value="MCF72" /> <param name="tissue" value="MCF7" /> <param name="factor" value="ER" /> <param name="condition" value="Responsive" /> <param name="replicate" value="2" /> <param name="bamreads" ftype="bam" value="MCF7_ER_2.bam" /> <param name="peaks" ftype="bed" value="MCF7_ER_2.bed.gz" /> </repeat> <param name="pdf" value="True" /> <param name="binding_affinity_matrix" value="True" /> <output name="outfile" value="out_diffbind.bed" /> <output name="binding_matrix" value="out_binding.matrix" /> </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 DiffBind requires a minimum of four samples (two groups with two replicates each). .. _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 **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. Example: ============= ========== ========== ============= ============= **SampleID** **Tissue** **Factor** **Condition** **Replicate** ------------- ---------- ---------- ------------- ------------- BT4741 BT474 ER Resistant 1 BT4742 BT474 ER Resistant 2 MCF71 MCF7 ER Responsive 1 MCF72 MCF7 ER Responsive 2 MCF73 MCF7 ER Responsive 3 T47D1 T47D ER Responsive 1 T47D2 T47D ER Responsive 2 MCF7r1 MCF7 ER Resistant 1 MCF7r2 MCF7 ER Resistant 2 ZR751 ZR75 ER Responsive 1 ZR752 ZR75 ER Responsive 2 ============= ========== ========== ============= ============= Or provide a sample sheet tabular file such as below. Example: ======== ====== ====== ========== ========== ========= ==================== ========= ===================== ================= ========== SampleID Tissue Factor Condition Treatment Replicate bamReads ControlID bamControl Peaks PeakCaller ======== ====== ====== ========== ========== ========= ==================== ========= ===================== ================= ========== BT4741 BT474 ER Resistant Full-Media 1 Chr18_BT474_ER_1.bam BT474c Chr18_BT474_input.bam BT474_ER_1.bed.gz bed BT4742 BT474 ER Resistant Full-Media 2 Chr18_BT474_ER_2.bam BT474c Chr18_BT474_input.bam BT474_ER_2.bed.gz bed MCF71 MCF7 ER Responsive Full-Media 1 Chr18_MCF7_ER_1.bam MCF7c Chr18_MCF7_input.bam MCF7_ER_1.bed.gz bed MCF72 MCF7 ER Responsive Full-Media 2 Chr18_MCF7_ER_2.bam MCF7c Chr18_MCF7_input.bam MCF7_ER_2.bed.gz bed MCF73 MCF7 ER Responsive Full-Media 3 Chr18_MCF7_ER_3.bam MCF7c Chr18_MCF7_input.bam MCF7_ER_3.bed.gz bed T47D1 T47D ER Responsive Full-Media 1 Chr18_T47D_ER_1.bam T47Dc Chr18_T47D_input.bam T47D_ER_1.bed.gz bed T47D2 T47D ER Responsive Full-Media 2 Chr18_T47D_ER_2.bam T47Dc Chr18_T47D_input.bam T47D_ER_2.bed.gz bed MCF7r1 MCF7 ER Resistant Full-Media 1 Chr18_TAMR_ER_1.bam TAMRc Chr18_TAMR_input.bam TAMR_ER_1.bed.gz bed MCF7r2 MCF7 ER Resistant Full-Media 2 Chr18_TAMR_ER_2.bam TAMRc Chr18_TAMR_input.bam TAMR_ER_2.bed.gz bed ZR751 ZR75 ER Responsive Full-Media 1 Chr18_ZR75_ER_1.bam ZR75c Chr18_ZR75_input.bam ZR75_ER_1.bed.gz bed ZR752 ZR75 ER Responsive Full-Media 2 Chr18_ZR75_ER_2.bam ZR75c Chr18_ZR75_input.bam ZR75_ER_2.bed.gz bed ======== ====== ====== ========== ========== ========= ==================== ========= ===================== ================= ========== **Peak files** Result of your Peak calling experiment in bed format, one file for each sample is required. Example: ======= ======= ======= =============== ======= 1 2 3 4 **5** ======= ======= ======= =============== ======= 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 can be associated with each peakset * Control BAM file represents a control dataset and are optional, but have to specified for all when used. **Outputs** As output format you can choose BED, GFF, WIG. Example: ======== ====== =======+ seqnames ranges strand Conc Conc_Resistant 2452 chr18 [64490686, 64491186] * | 6.36 1.39 1291 chr18 [34597713, 34598213] * | 5.33 0.22 976 chr18 [26860997, 26861497] * | 7.3 3.13 2338 chr18 [60892900, 60893400] * | 7.13 1.84 2077 chr18 [55569087, 55569587] * | 5.52 1.89 Conc_Responsive Fold p-value FDR <numeric> <numeric> <numeric> <numeric> 2452 7 -5.61 3.57e-10 1.02e-06 1291 5.97 -5.75 1.1e-09 1.57e-06 976 7.92 -4.79 1.1e-08 1.05e-05 2338 7.77 -5.93 1.68e-08 1.17e-05 2077 6.13 -4.23 2.36e-08 1.17e-05 The value columns show the Conc mean read concentration over all the samples (the default calculation uses log2 normalized ChIP read counts with control read counts subtracted) Conc_Resistant mean concentration over the first (Resistant) group Conc_Responsive mean concentration over second (Responsive) group Fold column shows the difference in mean concentrations between the two groups (Conc_Resistant - Conc_Responsive), with a positive value indicating increased binding affinity in the Resistant group and a negative value indicating increased binding affinity in the Responsive group. p-value confidence measure for identifying these sites as differentially bound FDR 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: ====== ====== ====== ========== ========== ========= ====== ========= ==== ID Tissue Factor Condition Treatment Replicate Caller Intervals FRiP ====== ====== ====== ========== ========== ========= ====== ========= ==== BT4741 BT474 ER Resistant Full-Media 1 counts 2845 0.16 BT4742 BT474 ER Resistant Full-Media 2 counts 2845 0.15 MCF71 MCF7 ER Responsive Full-Media 1 counts 2845 0.27 MCF72 MCF7 ER Responsive Full-Media 2 counts 2845 0.17 MCF73 MCF7 ER Responsive Full-Media 3 counts 2845 0.23 T47D1 T47D ER Responsive Full-Media 1 counts 2845 0.10 T47D2 T47D ER Responsive Full-Media 2 counts 2845 0.06 MCF7r1 MCF7 ER Resistant Full-Media 1 counts 2845 0.20 MCF7r2 MCF7 ER Resistant Full-Media 2 counts 2845 0.13 ZR751 ZR75 ER Responsive Full-Media 1 counts 2845 0.32 ZR752 ZR75 ER Responsive Full-Media 2 counts 2845 0.22 ====== ====== ====== ========== ========== ========= ====== ========= ==== **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 MACS ([1]), or using some other criterion (e.g. genomic windows, or all the promoter regions in a genome). The easiest way to read in peaksets is using a comma-separated value (csv) sample sheet with one line for each peakset. (Spreadsheets in Excel® format, with a .xls or .xlsx suffix, are also accepted.) 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 ]]> </help> <citations> <citation type="doi">doi:10.1038/nature10730</citation> </citations> </tool>