Explore Workflows

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

Graph Name Retrieved From View
workflow graph SoupX (workflow) - an R package for the estimation and removal of cell free mRNA contamination

Wrapped in a workflow SoupX tool for easy access to Cell Ranger pipeline compressed outputs.


Path: tools/soupx-subworkflow.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc

workflow graph download_gtf.cwl


Path: workflow/download_gtf.cwl

Branch/Commit ID: 49faf55f97c8f3084b426d2db6640519d6f2ce71

workflow graph xenbase-sra-to-fastq-se.cwl


Path: subworkflows/xenbase-sra-to-fastq-se.cwl

Branch/Commit ID: e627079d8431e4f1f1c7531af1ca2e7dcc684b90

workflow graph MAnorm SE - quantitative comparison of ChIP-Seq single-read 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 SE sample 1** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 **ChIP-Seq SE sample 2** * previously analyzed ChIP-Seq single-read 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


Path: workflows/manorm-se.cwl

Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd

workflow graph Cellranger reanalyze - reruns secondary analysis performed on the feature-barcode matrix

Devel version of Single-Cell Cell Ranger Reanalyze ================================================== Workflow calls \"cellranger aggr\" command to rerun secondary analysis performed on the feature-barcode matrix (dimensionality reduction, clustering and visualization) using different parameter settings. As an input we use filtered feature-barcode matrices in HDF5 format from cellranger count or aggr experiments. Note, we don't pass aggregation_metadata from the upstream cellranger aggr step. Need to address this issue when needed.


Path: workflows/cellranger-reanalyze.cwl

Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd

workflow graph rnaseq-se-dutp.cwl

Runs RNA-Seq dUTP BioWardrobe basic analysis with strand specific single-end data file.


Path: workflows/rnaseq-se-dutp.cwl

Branch/Commit ID: 12edfc2207507e53c6b5bb21e50decb5535a12f7

workflow graph revsort.cwl

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


Path: tests/wf/revsort.cwl

Branch/Commit ID: 478c2ffc09fb189c4f36ccb82aad945b3db5f9b3

workflow graph env-wf3.cwl


Path: cwltool/schemas/v1.0/v1.0/env-wf3.cwl

Branch/Commit ID: e835bc0487fe42fb330b6222c9be65d18dd81ec9

workflow graph count-lines13-wf.cwl


Path: cwltool/schemas/v1.0/v1.0/count-lines13-wf.cwl

Branch/Commit ID: e835bc0487fe42fb330b6222c9be65d18dd81ec9

workflow graph 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 **strand specific single-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. 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


Path: workflows/rnaseq-se-dutp.cwl

Branch/Commit ID: 4360fb2e778ecee42e5f78f83b78c65ab3a2b1df