Explore Workflows

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

Graph Name Retrieved From View
workflow graph etl.cwl

https://github.com/NCI-GDC/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/etl.cwl

Branch/Commit ID: 1326fb7fedca91a274fb7596c9052a4d279eacf9
workflow graph Build Bismark indices

Copy fasta_file file to the folder and run run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the genome input.

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

Path: workflows/bismark-index.cwl

Branch/Commit ID: 282762f8bbaea57dd488115745ef798e128bade1
workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 609aead9804a8f31fa9b3dbc7e52105aec487f31
workflow graph scatterfail.cwl

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

Path: tests/wf/scatterfail.cwl

Branch/Commit ID: f207d168f4e7eb4dd2279840d4062ba75d9c79c3
workflow graph Trim Galore 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 **pair-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 files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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 files 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-pe.cwl

Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f
workflow graph CLIP-Seq pipeline for single-read experiment NNNNG

CLIP-Seq workflow for single-read experiment.

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

Path: workflows/clipseq-se.cwl

Branch/Commit ID: e284e3f6dff25037b209895c52f2abd37a1ce1bf
workflow graph allele-vcf-rnaseq-se.cwl

Allele specific RNA-Seq (using vcf) single-read workflow

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

Path: workflows/allele-vcf-rnaseq-se.cwl

Branch/Commit ID: e284e3f6dff25037b209895c52f2abd37a1ce1bf
workflow graph search.cwl#main

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

Path: cwltool/schemas/v1.0/v1.0/search.cwl

Branch/Commit ID: e835bc0487fe42fb330b6222c9be65d18dd81ec9

Packed ID: main
workflow graph pcr-bottleneck-coef.cwl

ChIP-seq - map - PCR Bottleneck Coefficients

 https://github.com/alexbarrera/GGR-cwl.git

Path: v1.0/map/pcr-bottleneck-coef.cwl

Branch/Commit ID: 33385c6a820a9d4d18cff6fc3a533ec8e3c11c6e
workflow graph GSEApy - Gene Set Enrichment Analysis in Python

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.

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

Path: workflows/gseapy.cwl

Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f