Explore Workflows

Single-Cell RNA-Seq Cluster Analysis Clusters cells by similarity of gene expression data from the outputs of the “Single-Cell RNA-Seq Dimensionality Reduction Analysis” pipeline. The results of this workflow are used in the “Single-Cell Manual Cell Type Assignment”, “Single-Cell RNA-Seq Differential Expression Analysis”, “Single-Cell RNA-Seq Trajectory Analysis”, and “Single-Cell Differential Abundance Analysis” pipelines.

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

Path: workflows/sc-rna-cluster.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

Simulation steps pipeline

https://github.com/ILIAD-ocean-twin/application_package.git

Path: workflow_in_workflow/workflow_same_level.cwl

Branch/Commit ID: c7009260d3d659b77148dff5cd79b71d0e01ff41

Packed ID: main_pipeline

Animation of an oil spill with openoil

https://github.com/ILIAD-ocean-twin/application_package.git

Path: openoil/pipeline_v2.cwl

Branch/Commit ID: c7009260d3d659b77148dff5cd79b71d0e01ff41

Packed ID: openoil_pipeline

https://git.astron.nl/RD/LINC.git

Path: workflows/LBA_calibrator.cwl

Branch/Commit ID: 0c5bd78e3f2d08564f5c9a563bcc8bb7704e6202

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

Path: protein_alignment/wf_seed.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e

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

Path: workflows/chipseq-gen-bigwig.cwl

Branch/Commit ID: 4b8bb1a1ec39056253ca8eee976078e51f4a954e

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

Path: bacterial_trna/wf_trnascan.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e

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: b4d578c2ba4713a5a22163d9f8c7105acda1f22e

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: 8010fd2bf1e7090ba6df6ca8c84bbb96e2272d32

Packed ID: main

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

Path: tests/wf/1st-workflow.cwl

Branch/Commit ID: ae401a813472ca453a99ad067a5e6fc3bd71112b