Explore Workflows

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

Path: _includes/cwl/22-nested-workflows/1st-workflow.cwl

Branch/Commit ID: 4af7d2c63a1604b4558bd616ccd7dbb664fd8d1b

The main workflow that produces two reproducible peaks via IDR given two eCLIP samples (1 input, 1 IP each).

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

Path: cwl/wf_get_reproducible_eclip_peaks.cwl

Branch/Commit ID: aedc0a14d4ba109ee65678a3201a52c5bb6ad473

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: b1a5dabeeeb9079b30b2871edd9c9034a1e00c1c

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 42df0c0f9a4e5697abadd9cb52440691fafc8f5d

Filters ChIP/ATAC peaks with the neatest genes assigned for Tag Density Profile or Motif Enrichment analyses ============================================================================================================ Tool filters output from any ChIP/ATAC pipeline to create a file with regions of interest for Tag Density Profile or Motif Enrichment analyses. Peaks with duplicated coordinates are discarded.

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

Path: workflows/filter-peaks-for-heatmap.cwl

Branch/Commit ID: 1131f82a53315cca217a6c84b3bd272aa62e4bca

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

Path: definitions/subworkflows/hs_metrics.cwl

Branch/Commit ID: ecac0fda44df3a8f25ddfbb3e7a023fcbe4cbd0f

AltAnalyze CellHarmony ======================

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

Path: workflows/altanalyze-cellharmony.cwl

Branch/Commit ID: 1131f82a53315cca217a6c84b3bd272aa62e4bca

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

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: 39ac49f5d080bbb6bfa97246f46a5b621254f622

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

Path: definitions/subworkflows/sequence_to_bqsr.cwl

Branch/Commit ID: 39ac49f5d080bbb6bfa97246f46a5b621254f622

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

Path: definitions/subworkflows/cram_to_bam_and_index.cwl

Branch/Commit ID: 2e0562a5c3cd7aac24af4c622a5ae68a7fb23a71