Explore Workflows

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

Graph Name Retrieved From View
workflow graph original.cwl#main_pipeline

Simulation steps pipeline

 https://github.com/ILIAD-ocean-twin/application_package.git

Path: workflow_in_workflow/original.cwl

Branch/Commit ID: c7009260d3d659b77148dff5cd79b71d0e01ff41

Packed ID: main_pipeline
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: 30031ca5e69cec603c4733681de54dc7bffa20a3
workflow graph Bacterial Annotation, structural annotation, functional annotation: ab initio GeneMark, by WP, by HMM (second pass)

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

Path: bacterial_annot/wf_bacterial_annot_2nd_pass.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e
workflow graph ROSE: rank ordering of super-enhancers

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

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

Path: workflows/super-enhancer.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3
workflow graph Bacterial Annotation, pass 1, genemark training, by HMMs (first pass)

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

Path: bacterial_annot/wf_orf_hmms.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e
workflow graph spurious_annot pass2

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

Path: spurious_annot/wf_spurious_annot_pass2.cwl

Branch/Commit ID: 656113dcac0de7cef6cff6c688f61441ee05872a
workflow graph Workflow that executes the Sounder SIPS end-to-end L1a processing

Cognito credentials to access the U-DS services are retrieved from the AWS Parameter Store with the supplied keys.

 https://github.com/unity-sds/unity-sps-workflows.git

Path: sounder_sips/ssips_L1a_workflow.cwl

Branch/Commit ID: 40117db4f27a7dd24407b06f2a6f18388002f12c
workflow graph DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081
workflow graph Cell Ranger Count (RNA+VDJ)

Cell Ranger Count (RNA+VDJ) Quantifies single-cell gene expression, performs V(D)J contigs assembly and clonotype calling of the sequencing data from a single 10x Genomics library in a combined manner. The results of this workflow are primarily used in either “Single-Cell RNA-Seq Filtering Analysis”, “Single-Cell Immune Profiling Analysis”, or “Cell Ranger Aggregate (RNA, RNA+VDJ)” pipelines.

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

Path: workflows/cellranger-multi.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3
workflow graph bact_get_kmer_reference

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e