Explore Workflows

https://github.com/umccr/cwl-ica.git

Path: workflows/dragen-germline-pipeline/4.2.4/dragen-germline-pipeline__4.2.4.cwl

Branch/Commit ID: 655acd3c7b6df8c7a54457f70e726c37e69f90bf

https://github.com/umccr/cwl-ica.git

Path: workflows/umccrise-pipeline/2.3.0--0/umccrise-pipeline__2.3.0--0.cwl

Branch/Commit ID: 655acd3c7b6df8c7a54457f70e726c37e69f90bf

Single-Cell WNN Cluster Analysis Clusters cells by similarity on the basis of both gene expression and chromatin accessibility data from the outputs of the “Single-Cell RNA-Seq Dimensionality Reduction Analysis” and “Single-Cell ATAC-Seq Dimensionality Reduction Analysis” pipelines run sequentially. 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”, “Single-Cell Differential Abundance Analysis”, “Single-Cell ATAC-Seq Differential Accessibility Analysis”, and “Single-Cell ATAC-Seq Genome Coverage” pipelines.

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

Path: workflows/sc-wnn-cluster.cwl

Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2

https://github.com/cr-ste-justine/chujs-alignment-workflow.git

Path: workflows/kfdrc_alignment_wf.cwl

Branch/Commit ID: f2a1a903cfdd8d339e022ef65a55e2d71e8d93b1

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: 57863b6131d8262c5ce864adaf8e4038401e71a2

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-pe.cwl

Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2

Runs RNA-Seq dUTP BioWardrobe basic analysis with strand specific single-end data file.

https://github.com/Barski-lab/workflows.git

Path: workflows/rnaseq-se-dutp.cwl

Branch/Commit ID: 749460273e6c8d9e8b7d2395f0ac157701d3495e

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

Path: testdata/workflow_input_sf_expr_v1_2.cwl

Branch/Commit ID: b926e330eba795f3acc1f71fd0645e75f925a2da

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

Path: definitions/pipelines/wgs_alignment.cwl

Branch/Commit ID: 4bc0a4577d626b65a4b44683e5a1ab2f7d7faf4c

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

Path: definitions/subworkflows/qc_exome_no_verify_bam.cwl

Branch/Commit ID: adcae308fdccaa1190083616118dfadb4df65dca