Explore Workflows

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

Graph Name Retrieved From View
workflow graph status_postgres.cwl

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

Path: workflows/bamfastq_align/status_postgres.cwl

Branch/Commit ID: b110a23e2efaaadfd4feca4f9e130946d1c5418d
workflow graph trim-rnaseq-se.cwl

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

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

Path: workflows/trim-rnaseq-se.cwl

Branch/Commit ID: 9a2c389364674221fab3f0f6afdda799e6aa3247
workflow graph bam-bedgraph-bigwig.cwl

Workflow converts input BAM file into bigWig and bedGraph files. Input BAM file should be sorted by coordinates (required by `bam_to_bedgraph` step). If `split` input is not provided use true by default. Default logic is implemented in `valueFrom` field of `split` input inside `bam_to_bedgraph` step to avoid possible bug in cwltool with setting default values for workflow inputs. `scale` has higher priority over the `mapped_reads_number`. The last one is used to calculate `-scale` parameter for `bedtools genomecov` (step `bam_to_bedgraph`) only in a case when input `scale` is not provided. All logic is implemented inside `bedtools-genomecov.cwl`. `bigwig_filename` defines the output name only for generated bigWig file. `bedgraph_filename` defines the output name for generated bedGraph file and can influence on generated bigWig filename in case when `bigwig_filename` is not provided. All workflow inputs and outputs don't have `format` field to avoid format incompatibility errors when workflow is used as subworkflow.

 https://github.com/Barski-lab/workflows.git

Path: tools/bam-bedgraph-bigwig.cwl

Branch/Commit ID: ea2a2ab57710fcf067f67305f3dd6ad29094da1a
workflow graph Build STAR indices

Workflow runs [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) to build indices for reference genome provided in a single FASTA file as fasta_file input and GTF annotation file from annotation_gtf_file input. Generated indices are saved in a folder with the name that corresponds to the input genome.

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

Path: workflows/star-index.cwl

Branch/Commit ID: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5
workflow graph PCA - Principal Component Analysis

Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: 1131f82a53315cca217a6c84b3bd272aa62e4bca
workflow graph allele-rnaseq-se.cwl

Allele specific RNA-Seq single-read workflow

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

Path: workflows/allele-rnaseq-se.cwl

Branch/Commit ID: 94471ee6c01b7bc17102e45e56e7366c2a52acdf
workflow graph 02-trim-se.cwl

ChIP-seq 02 trimming - reads: SE

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

Path: v1.0/ChIP-seq_pipeline/02-trim-se.cwl

Branch/Commit ID: 33385c6a820a9d4d18cff6fc3a533ec8e3c11c6e
workflow graph Build Bowtie indices

Workflow runs [Bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) to build indices for reference genome provided in a single FASTA file as fasta_file input. Generated indices are saved in a folder with the name that corresponds to the input genome

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

Path: workflows/bowtie-index.cwl

Branch/Commit ID: 5f4f9c63a4183eabd10e11d9e86cf054ef7ced05
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: a839eb6390974089e1a558c49fc07b4c66c50767
workflow graph tt_kmer_compare_wnode

Pairwise comparison

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 5e92165ac2c11608ab2db42fe2d66eabe72dbb40