Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph wf-loadContents2.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/wf-loadContents2.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9
workflow graph Pipeline for evaluating differential expression of genes across datasets

https://github.com/hubmapconsortium/rna-data-products.git

Path: pipeline.cwl

Branch/Commit ID: 3ac99bca8bfc5686823b65cb95b7ae487f567d3c
workflow graph cnv_exomedepth

CNV ExomeDepth calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/cnv_exome_depth.cwl

Branch/Commit ID: 3f6a871f81f343cf81a345f73ff2eeac70804b8c
workflow graph Cell Ranger Build Reference Indices

Devel version of Cell Ranger Build Reference Indices pipeline =============================================================

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

Path: workflows/cellranger-mkref.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081
workflow graph PCA - Principal Component Analysis

Principal Component Analysis -------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: d6f58c383d0676269afb519399061191a1144a6a
workflow graph tt_blastn_wnode

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: e0fb04a0d8bc648183c6b71d099ce7aea3c3b3ff
workflow graph bacterial_orthology_cond

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

Path: bacterial_orthology/wf_bacterial_orthology_conditional.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e
workflow graph Align reference proteins plane complete workflow, with miniprot

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

Path: protein_alignment/wf_protein_alignment_miniprot.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e
workflow graph timelimit4-wf.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/timelimit4-wf.cwl

Branch/Commit ID: c7c97715b400ff2194aa29fc211d3401cea3a9bf
workflow graph Trim Galore RNA-Seq pipeline single-read strand specific

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file

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

Path: workflows/trim-rnaseq-se-dutp.cwl

Branch/Commit ID: d6f58c383d0676269afb519399061191a1144a6a