Explore Workflows

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

Graph Name Retrieved From View
workflow graph running cellranger mkfastq and count

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

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141
workflow graph conflict.cwl#main

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

Path: tests/wf/conflict.cwl

Branch/Commit ID: 5ae5798f1c0c8d2178986b77cfd74edff510877a

Packed ID: main
workflow graph iwdr_with_nested_dirs.cwl

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

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

Branch/Commit ID: 596aab620489cd2611f4bc1d9a4fc914ddf34514
workflow graph QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

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

Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl

Branch/Commit ID: a1f6ca50fcb0881781b3ba0306dd61ebf555eaba
workflow graph taxonomy_check_16S

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: bba6c580ab88e077f6aa2c2ee7c73159f3f9156e
workflow graph phase VCF

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141
workflow graph umi molecular alignment workflow

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

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: a7838a5ca72b25db5c2af20a15f34303a839980e
workflow graph alignment_bwa_mem_no_trim.cwl

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/harmonization/alignment_bwa_mem_no_trim.cwl

Branch/Commit ID: 3c62f1ddf8a54601da3cfc16c37d0cb0d6a2ea28
workflow graph downsample unaligned BAM and align

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: f77a920bcc73f6cfdb091eed75a149d02cd8a263
workflow graph DESeq - differential gene expression analysis

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5