Explore Workflows

Generates tag density heatmap and histogram for the list of features in a headerless regions file. Inputs used are the bigWig file(s) of one or more ChIP/ATAC/C&R samples, and one or more filtered feature file(s) from the filtering and/or set operation workflows. The latter format contains `chrom start end name score strand`, only the first 3 columns are used in deeptools computeMatrix tool. The matrix is then used as input to plotHeatmap to generate the tag density plot and tag enrichment heatmap. computeMatrix paramters: --regionsFileName, -R File name, in BED format, containing the regions to plot. If multiple bed files are given, each one is considered a group that can be plotted separately. Also, adding a “#” symbol in the bed file causes all the regions until the previous “#” to be considered one group. --scoreFileName, -S bigWig file(s) containing the scores to be plotted. BigWig files can be obtained by using the bamCoverage or bamCompare tools. More information about the bigWig file format can be found at http://genome.ucsc.edu/goldenPath/help/bigWig.html --outFileName, -o File name to save the gzipped matrix file needed by the “plotHeatmap” and “plotProfile” tools. --beforeRegionStartLength=0, -b=0, --upstream=0 Distance upstream of the start site of the regions defined in the region file. If the regions are genes, this would be the distance upstream of the transcription start site. --regionBodyLength=1000, -m=1000 Distance in bases to which all regions will be fit. --afterRegionStartLength=0, -a=0, --downstream=0 Distance downstream of the end site of the given regions. If the regions are genes, this would be the distance downstream of the transcription end site. --numberOfProcessors=max/2, -p=max/2 Number of processors to use. Type “max/2” to use half the maximum number of processors or “max” to use all available processors. plotHeatmap parameters: --matrixFile, -m Matrix file from the computeMatrix tool. --outFileName, -out File name to save the image to. The file ending will be used to determine the image format. The available options are: “png”, “eps”, “pdf” and “svg”, e.g., MyHeatmap.png. --sortRegions=descend Whether the heatmap should present the regions sorted. The default is to sort in descending order based on the mean value per region. Possible choices: descend, ascend, no --sortUsing=mean Indicate which method should be used for sorting. For each row the method is computed. Possible choices: mean, median, max, min, sum, region_length --colorMap=RdYlBu Color map to use for the heatmap. Available values can be seen here: http://matplotlib.org/users/colormaps.html The available options are: ‘Spectral’, ‘summer’, ‘coolwarm’, ‘Set1’, ‘Set2’, ‘Set3’, ‘Dark2’, ‘hot’, ‘RdPu’, ‘YlGnBu’, ‘RdYlBu’, ‘gist_stern’, ‘cool’, ‘gray’, ‘GnBu’, ‘gist_ncar’, ‘gist_rainbow’, ‘CMRmap’, ‘bone’, ‘RdYlGn’, ‘spring’, ‘terrain’, ‘PuBu’, ‘spectral’, ‘gist_yarg’, ‘BuGn’, ‘bwr’, ‘cubehelix’, ‘YlOrRd’, ‘Greens’, ‘PRGn’, ‘gist_heat’, ‘Paired’, ‘hsv’, ‘Pastel2’, ‘Pastel1’, ‘BuPu’, ‘copper’, ‘OrRd’, ‘brg’, ‘gnuplot2’, ‘jet’, ‘gist_earth’, ‘Oranges’, ‘PiYG’, ‘YlGn’, ‘Accent’, ‘gist_gray’, ‘flag’, ‘BrBG’, ‘Reds’, ‘RdGy’, ‘PuRd’, ‘Blues’, ‘Greys’, ‘autumn’, ‘pink’, ‘binary’, ‘winter’, ‘gnuplot’, ‘RdBu’, ‘prism’, ‘YlOrBr’, ‘rainbow’, ‘seismic’, ‘Purples’, ‘ocean’, ‘PuOr’, ‘PuBuGn’, ‘nipy_spectral’, ‘afmhot’ --kmeans Number of clusters to compute. When this option is set, the matrix is split into clusters using the k-means algorithm. Only works for data that is not grouped, otherwise only the first group will be clustered. If more specific clustering methods are required, then save the underlying matrix and run the clustering using other software. The plotting of the clustering may fail with an error if a cluster has very few members compared to the total number or regions.

https://github.com/datirium/workflows.git

Path: workflows/deeptools.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/basename-fields-test.cwl

Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda

https://github.com/common-workflow-language/common-workflow-language.git

Path: v1.0/examples/nestedworkflows.cwl

Branch/Commit ID: 4fe434e969c93c94b690ba72db295d9d52a6f576

https://github.com/ncbi/pgap.git

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: e71779665f42fcf34601b0f65e030bb0dd47fa79

https://github.com/datirium/workflows.git

Path: metadata/advanced-header.cwl

Branch/Commit ID: 667280228c27c475969c6a331d26e0cf2337d677

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/alignment_umi_molecular.cwl

Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470

https://github.com/ncbi/pgap.git

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: e668f9c4047f1971ae53040a5af3eccc4bfc3c53

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/mut.cwl

Branch/Commit ID: 5bdb3d3dd47d8d1b3a1685220b4b6ce0f94c055e

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/bam_to_trimmed_fastq.cwl

Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5

Motif Finding with HOMER with random background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

https://github.com/datirium/workflows.git

Path: workflows/homer-motif-analysis.cwl

Branch/Commit ID: 675a3ff982091faf304931e9261aacdbabf51702