Explore Workflows

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

Graph Name Retrieved From View
workflow graph fp_filter workflow

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

Path: definitions/subworkflows/fp_filter.cwl

Branch/Commit ID: 44ada20f3eeb59005d5bd999d2435102e9bae991
workflow graph process VCF workflow

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

Path: definitions/subworkflows/strelka_process_vcf.cwl

Branch/Commit ID: 44ada20f3eeb59005d5bd999d2435102e9bae991
workflow graph Bacterial Annotation, pass 2, blastp-based functional annotation (first pass)

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

Path: bacterial_annot/wf_bacterial_annot_pass2.cwl

Branch/Commit ID: be9d12a3f8e1924183a1dc6a0bda6ada4195ca71
workflow graph Subworkflow that runs cnvkit in single sample mode and returns a vcf file

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

Path: definitions/subworkflows/cnvkit_single_sample.cwl

Branch/Commit ID: 049f4aeff4c4a1b8421cac9b1c1c1f0da5848315
workflow graph Filter single sample sv vcf from paired read callers(Manta/Smoove)

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

Path: definitions/subworkflows/sv_paired_read_caller_filter.cwl

Branch/Commit ID: 049f4aeff4c4a1b8421cac9b1c1c1f0da5848315
workflow graph Super-enhancer post ChIP-Seq analysis

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: 4106b7dc96e968db291b7a61ecd1641aa3b3dd6d
workflow graph MAnorm PE - quantitative comparison of ChIP-Seq paired-end data

What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General **Experiment short name/Alias** * short name for you experiment to identify among the others **ChIP-Seq PE sample 1** * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 1 **ChIP-Seq PE sample 2** * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 2 **Genome** * Reference genome to be used for gene assigning ### Advanced **Reads shift size for sample 1** * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **Reads shift size for sample 2** * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **M-value (log2-ratio) cutoff** * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 **P-value cutoff** * P-value cutoff to define biased peaks. Default: 0.01 **Window size** * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000

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

Path: workflows/manorm-pe.cwl

Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf
workflow graph Running cellranger count and lineage inference

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

Path: definitions/subworkflows/single_cell_rnaseq.cwl

Branch/Commit ID: 40097e1ed094c5b42b68f3db2ff2cbe78c182479
workflow graph scatter-wf2.cwl

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf2.cwl

Branch/Commit ID: f997d13af87216e9b5048c732a511053c7ba714c
workflow graph strelka workflow

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

Path: definitions/subworkflows/strelka_and_post_processing.cwl

Branch/Commit ID: 44ada20f3eeb59005d5bd999d2435102e9bae991