Explore Workflows

https://github.com/kids-first/kf-alignment-workflow.git

Path: workflows/kfdrc_sentieon_gvcf_wf.cwl

Branch/Commit ID: 4b8dc1e00d3dd082ee31629afdeda6103f75dc3a

https://github.com/kids-first/kf-rnaseq-workflow.git

Path: workflow/kfdrc_annoFuse_wf.cwl

Branch/Commit ID: 65161d6565c436a7b1e0b3be56efb433a994ed9d

Cell Ranger Aggregate =====================

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

Path: workflows/cellranger-aggr.cwl

Branch/Commit ID: c6bfa0de917efb536dd385624fc7702e6748e61d

Single-cell Manual Cell Type Assignment Assigns cell types for clusters based on the provided metadata file.

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

Path: workflows/sc-ctype-assign.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e

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

Path: definitions/subworkflows/strelka_process_vcf.cwl

Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe

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

Path: tests/wf/wffail.cwl

Branch/Commit ID: 55ccde7c2fe3e7899136ce8606a341e292d7050a

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

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

Path: workflows/super-enhancer.cwl

Branch/Commit ID: bf80c9339d81a78aefb8de661bff998ed86e836e

# Set Operations for filtered gene lists This workflow takes as input multiple filtered genelists samples and performs the user-selected set operation on them. There is one input for list A from which \"scores\" will be taken (these are fold change values from deseq or diffbind) and used in the output set list. The second genelist input is for 1+ genelists, that will be aggregated and used for intersection and union directly, and be applied against list A for the relative complement operation. The output is a single filtered gene list in the same format as the input files (headerless BED file with [chrom start end name score strand]). The returned score value (column 5) is always derived from file A.

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

Path: workflows/genelists-sets.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

Single-Cell Differential Abundance Analysis Compares the composition of cell types between two tested conditions

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

Path: workflows/sc-rna-da-cells.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).

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

Path: workflows/bismark-methylation-se.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e