Explore Workflows

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Graph Name Retrieved From View
workflow graph Filter single sample sv vcf from paired read callers(Manta/Smoove)

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

Path: definitions/subworkflows/sv_paired_read_caller_filter.cwl

Branch/Commit ID: 457e101e3fb87e7fd792357afce00ed8ccbfbcdb
workflow graph alignment for nonhuman with qc

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

Path: definitions/pipelines/alignment_wgs_nonhuman.cwl

Branch/Commit ID: 04d21c33a5f2950e86db285fa0a32a6659198d8a
workflow graph Chipseq alignment with qc and creating homer tag directory

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

Path: definitions/pipelines/chipseq.cwl

Branch/Commit ID: 844c10a4466ab39c02e5bfa7a210c195b8efa77a
workflow graph conflict-wf.cwl#collision

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

Path: cwltool/schemas/v1.0/v1.0/conflict-wf.cwl

Branch/Commit ID: 4df56e95e6fceab69e677b539f3532cbf5946197

Packed ID: collision
workflow graph GAT - Genomic Association Tester

GAT: Genomic Association Tester ============================================== A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, in the interpretation of ChIP-Seq or RNA-Seq data. The Genomic Association Tester (GAT) is a tool for computing the significance of overlap between multiple sets of genomic intervals. GAT estimates significance based on simulation. Gat implemements a sampling algorithm. Given a chromosome (workspace) and segments of interest, for example from a ChIP-Seq experiment, gat creates randomized version of the segments of interest falling into the workspace. These sampled segments are then compared to existing genomic annotations. The sampling method is conceptually simple. Randomized samples of the segments of interest are created in a two-step procedure. Firstly, a segment size is selected from to same size distribution as the original segments of interest. Secondly, a random position is assigned to the segment. The sampling stops when exactly the same number of nucleotides have been sampled. To improve the speed of sampling, segment overlap is not resolved until the very end of the sampling procedure. Conflicts are then resolved by randomly removing and re-sampling segments until a covering set has been achieved. Because the size of randomized segments is derived from the observed segment size distribution of the segments of interest, the actual segment sizes in the sampled segments are usually not exactly identical to the ones in the segments of interest. This is in contrast to a sampling method that permutes segment positions within the workspace.

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

Path: workflows/gat-run.cwl

Branch/Commit ID: e0a30aa1ad516dd2ec0e9ce006428964b840daf4
workflow graph iwdr_with_nested_dirs.cwl

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

Path: v1.0/v1.0/iwdr_with_nested_dirs.cwl

Branch/Commit ID: f02557902989c749c9c2187c7045e340e2d76bfc
workflow graph make_search_pair_workflow.cwl

https://github.com/pvanheus/lukasa.git

Path: make_search_pair_workflow.cwl

Branch/Commit ID: 0ed49ec431ed0fdb481231b47c19d939d29c58c6
workflow graph Cellranger reanalyze - reruns secondary analysis performed on the feature-barcode matrix

Devel version of Single-Cell Cell Ranger Reanalyze ================================================== Workflow calls \"cellranger aggr\" command to rerun secondary analysis performed on the feature-barcode matrix (dimensionality reduction, clustering and visualization) using different parameter settings. As an input we use filtered feature-barcode matrices in HDF5 format from cellranger count or aggr experiments. Note, we don't pass aggregation_metadata from the upstream cellranger aggr step. Need to address this issue when needed.

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

Path: workflows/cellranger-reanalyze.cwl

Branch/Commit ID: e0a30aa1ad516dd2ec0e9ce006428964b840daf4
workflow graph EMG QC workflow, (paired end version). Benchmarking with MG-RAST expt.

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-qc-paired.cwl

Branch/Commit ID: d4e5e533ee6dc93bfaf1c4bbb2ab40812a8f4792
workflow graph wf_full_IDR_pipeline_2inputs_scatter.cwl

The main workflow that: produces two reproducible peaks via IDR given two eCLIP samples (1 input, 1 IP each). runs the 'rescue ratio' statistic runs the 'consistency ratio' statistic

https://github.com/YeoLab/merge_peaks.git

Path: cwl/wf_full_IDR_pipeline_2inputs_scatter.cwl

Branch/Commit ID: 18933d4d4b00e97a8a0d155abbebad1fdbc254aa