Explore Workflows

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Graph Name Retrieved From View
workflow graph qc_workflow_wo_waltz.cwl

This workflow is intended to be used to test the QC module, without having to run the long waltz step

https://github.com/mskcc/Innovation-Pipeline.git

Path: workflows/QC/qc_workflow_wo_waltz.cwl

Branch/Commit ID: 9998da2da694af2edad7c2135f6995e2282794a3
workflow graph scatter-valuefrom-wf4.cwl#main

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: a3d565bf8e630101d25d31804cfbceb0a0ba28de

Packed ID: main
workflow graph sum-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/sum-wf.cwl

Branch/Commit ID: 280a852e74aec08cf79687e8004e17b1ab464534
workflow graph scatter-valuefrom-wf4.cwl#main

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: 9e7c68c0834645ba53a7e2b5f70d53df9d051c92

Packed ID: main
workflow graph qc_workflow.cwl

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/QC/qc_workflow.cwl

Branch/Commit ID: 9e6eae9eb8448e68d509397a46303551a93a164d
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: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5
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: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5
workflow graph BlastP_RBH_workflow

https://github.com/ncbi/cwl-demos.git

Path: blast-pipelines/blast_workflow.cwl

Branch/Commit ID: 342d1669431d15c8a27589e4c22a91a05033b56c
workflow graph Compute average of average for core domain instances

Compute average structure for all averaged structures corresponding to core UniProt domain instances. First computes average per UniProt domain instance and then average all averaged structures.

https://gitlab.inria.fr/capsid.public_codes/CroMaSt.git

Path: Tools/core_avg_subwf.cwl

Branch/Commit ID: 9f3832867eab6c7a6363f8ca594a4bcf2ff7e96f
workflow graph Bismark Methylation - pipeline for BS-Seq data analysis

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: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb