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Graph Name Retrieved From View
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: b5e16e359007150647b14dc6e038f4eb8dccda79
workflow graph allele-process-reference.cwl

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

Path: subworkflows/allele-process-reference.cwl

Branch/Commit ID: 9b4dc225c537685b9c9a32d931d3892d20953dd7
workflow graph Pairwise genomic regions intersection

Pairwise genomic regions intersection ============================================= Overlaps peaks from two ChIP/ATAC experiments

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

Path: workflows/peak-intersect.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e
workflow graph scatter-wf1_v1_2.cwl

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/scatter-wf1_v1_2.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee
workflow graph timelimit-wf.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/timelimit-wf.cwl

Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2
workflow graph scatter-wf3_v1_1.cwl#main

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/scatter-wf3_v1_1.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

Packed ID: main
workflow graph calibrator_sun.cwl

https://github.com/peijin94/LOFAR-Sun-tools.git

Path: utils/IM/LINC/lincSun/workflow/calibrator_sun.cwl

Branch/Commit ID: f44502475496d46a6cde17b9881aa19d852debfb
workflow graph exome alignment and germline variant detection

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

Path: definitions/subworkflows/germline_detect_variants.cwl

Branch/Commit ID: 641083e9ed933d388f36fa04c00c20a810599e94
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

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

Path: workflows/manorm-se.cwl

Branch/Commit ID: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b
workflow graph workflow_input_sf_expr.cwl

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/workflow_input_sf_expr.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee