Explore Workflows

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

Graph Name Retrieved From View
workflow graph wgs alignment and tumor-only variant detection

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

Path: definitions/pipelines/tumor_only_wgs.cwl

Branch/Commit ID: dc2c019c1aa24cc01b451a0f048cf94a35f163c4
workflow graph mut.cwl

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

Path: tests/wf/mut.cwl

Branch/Commit ID: 478c2ffc09fb189c4f36ccb82aad945b3db5f9b3
workflow graph kmer_top_n

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

Path: task_types/tt_kmer_top_n.cwl

Branch/Commit ID: 6a29751f2b16659c1592f1e94837c989e68f3b8b
workflow graph RNA-Seq pipeline single-read

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-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

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

Path: workflows/rnaseq-se.cwl

Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0
workflow graph RNA-seq (VCF) alelle specific pipeline for paired-end data

Allele specific RNA-Seq (using vcf) paired-end workflow

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

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

Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c
workflow graph Generate ATDP heatmap using Homer

Generate ATDP heatmap centered on TSS from an array of input BAM files and genelist TSV file. Returns array of heatmap JSON files with the names that have the same basenames as input BAM files, but with .json extension

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

Path: workflows/heatmap.cwl

Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0
workflow graph taxonomy_check_16S

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 546742b523ce12f6246a52c838a51920a08dad4b
workflow graph Cellranger aggr - aggregates data from multiple Cellranger runs

Devel version of Single-Cell Cell Ranger Aggregate ================================================== Workflow calls \"cellranger aggr\" command to combine output files from \"cellranger count\" (the molecule_info.h5 file from each run) into a single feature-barcode matrix containing all the data. When combining multiple GEM wells, the barcode sequences for each channel are distinguished by a GEM well suffix appended to the barcode sequence. Each GEM well is a physically distinct set of GEM partitions, but draws barcode sequences randomly from the pool of valid barcodes, known as the barcode whitelist. To keep the barcodes unique when aggregating multiple libraries, we append a small integer identifying the GEM well to the barcode nucleotide sequence, and use that nucleotide sequence plus ID as the unique identifier in the feature-barcode matrix. For example, AGACCATTGAGACTTA-1 and AGACCATTGAGACTTA-2 are distinct cell barcodes from different GEM wells, despite having the same barcode nucleotide sequence. This number, which tells us which GEM well this barcode sequence came from, is called the GEM well suffix. The numbering of the GEM wells will reflect the order that the GEM wells were provided in the \"molecule_info_h5\" and \"gem_well_labels\" inputs. When combining data from multiple GEM wells, the \"cellranger aggr\" pipeline automatically equalizes the average read depth per cell between groups before merging. This approach avoids artifacts that may be introduced due to differences in sequencing depth. It is possible to turn off normalization or change the way normalization is done through the \"normalization_mode\" input. The \"none\" value may be appropriate if you want to maximize sensitivity and plan to deal with depth normalization in a downstream step.

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

Path: workflows/cellranger-aggr.cwl

Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f
workflow graph SoupX - an R package for the estimation and removal of cell free mRNA contamination

Devel version of Single-Cell Advanced Cell Ranger Pipeline (SoupX) =================================================================

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

Path: workflows/soupx.cwl

Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f
workflow graph umi duplex alignment fastq workflow

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

Path: definitions/pipelines/alignment_umi_duplex.cwl

Branch/Commit ID: dc2c019c1aa24cc01b451a0f048cf94a35f163c4