Explore Workflows

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

Graph Name Retrieved From View
workflow graph RNA-Seq pipeline paired-end

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-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 paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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/rnaseq-pe.cwl

Branch/Commit ID: 4dcc405133f22c63478b6091fb5f591b6be8950f
workflow graph exome alignment with qc, no bqsr, no verify_bam_id

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

Path: definitions/pipelines/alignment_exome_mouse.cwl

Branch/Commit ID: 9143dc4ebacb9e1df36a712b0be6fa5d982b0c4f
workflow graph bam to trimmed fastqs

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

Path: definitions/subworkflows/bam_to_trimmed_fastq.cwl

Branch/Commit ID: 3042812447d9e8889c6118986490e9c9b9b13223
workflow graph CreateSymlink-workflow.cwl

https://github.com/nal-i5k/organism_onboarding.git

Path: CreateSymlink-workflow.cwl

Branch/Commit ID: add45db6f08de518e224bdc3c04094fd69cad2d2
workflow graph Unaligned BAM to BQSR and VCF

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

Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl

Branch/Commit ID: f401b02285f30de1c12ac2859134099fe04be33f
workflow graph scatter-valuefrom-wf3.cwl#main

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf3.cwl

Branch/Commit ID: 26870e38cec81af880cd3e4789ae6cee8fc27020

Packed ID: main
workflow graph no-outputs-wf.cwl

Workflow without outputs.

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

Path: tests/no-outputs-wf.cwl

Branch/Commit ID: a0f2d38e37ff51721fdeaf993bb2ab474b17246b
workflow graph align_sort_sa

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: 16e3915d2a357e2a861b30911c832e5ddc0c1784
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: c602e3cdd72ff904dd54d46ba2b5146eb1c57022
workflow graph downsample unaligned BAM and align

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: bcc6adaf15035f5ce6fc851e27b1173b0fd20c1c