Explore Workflows

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

Path: definitions/pipelines/aml_trio_cle_gathered.cwl

Branch/Commit ID: f77a920bcc73f6cfdb091eed75a149d02cd8a263

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

Path: tests/wf/mut3.cwl

Branch/Commit ID: ecdfe1ee769d05790f70ac87a711131f441f3753

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

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

Branch/Commit ID: a858bb4db58ef2df17b4856294ad7904643c5c6e

This workflow uses MEME suite for motif finding

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/ChIP-Seq/meme-motif.cwl

Branch/Commit ID: 0207b0171ab142dfb85db9c39050c5b4be51dd9e

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

Path: definitions/subworkflows/filter_vcf_mouse.cwl

Branch/Commit ID: 60edaf6f57eaaf02cda1a3d8cb9a825aa64a43e2

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: b4d578c2ba4713a5a22163d9f8c7105acda1f22e

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

Path: definitions/subworkflows/pindel.cwl

Branch/Commit ID: c235dc6d623879a6c4f5fb307f545c9806eb2d23

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/step-valuefrom5-wf.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9

PGAP pipeline for external usage, powered via containers

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

Path: wf_common.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e

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

Path: cwltool/schemas/v1.0/v1.0/count-lines12-wf.cwl

Branch/Commit ID: 203797516329f7fb8aa5e763e6f9b331c63c3060