Explore Workflows

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

Graph Name Retrieved From View
workflow graph tt_fscr_calls_pass1

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f
workflow graph cache_asnb_entries

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: 803f6367d1b279a7b6dc1a4e8ae43f1bbec9f760
workflow graph revsort.cwl

Reverse the lines in a document, then sort those lines.

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

Path: tests/wf/revsort.cwl

Branch/Commit ID: fec7a10466a26e376b14181a88734983cfb1b8cb
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: 44214a9d02e6d85b03eb708552ed812ae3d4a733
workflow graph wf_clipseqcore_trim_partial_se_1barcode.cwl

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

Path: cwl/wf_clipseqcore_trim_partial_se_1barcode.cwl

Branch/Commit ID: c0fffc4979a92371dc0667a03e3d957bf7f77600
workflow graph count-lines3-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/count-lines3-wf.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b
workflow graph scatter-valuefrom-wf3.cwl#main

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf3.cwl

Branch/Commit ID: 3e9bca4e006eae7e9febd76eb9b8292702eba2cb

Packed ID: main
workflow graph js-expr-req-wf.cwl#wf

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

Path: cwltool/schemas/v1.0/v1.0/js-expr-req-wf.cwl

Branch/Commit ID: 596aab620489cd2611f4bc1d9a4fc914ddf34514

Packed ID: wf
workflow graph RNA-Seq pipeline single-read stranded mitochondrial

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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/rnaseq-se-dutp-mitochondrial.cwl

Branch/Commit ID: 44214a9d02e6d85b03eb708552ed812ae3d4a733
workflow graph kmer_cache_retrieve

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 146df33e2e44afa2a608ac72c036e6b6b871af93