Explore Workflows

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: ac387721a55fd91df3dcdf16e199354618b136d1

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: 3fc68366adb179927af5528c27b153abaf94494d

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

Path: task_types/tt_align_merge_sas.cwl

Branch/Commit ID: 733ab7198a66a0153d0f03c3022ab53c17325ff8

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

Path: cwltool/schemas/v1.0/v1.0/echo-wf-default.cwl

Branch/Commit ID: e8b3565a008d95859fc44227987a54e6a53a8c29

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

Path: task_types/tt_kmer_top_n.cwl

Branch/Commit ID: 733ab7198a66a0153d0f03c3022ab53c17325ff8

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

Path: bacterial_ncrna/wf_gcmsearch.cwl

Branch/Commit ID: 17bae57a1f00f5c6db8f3a82d86262f12b8153cf

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

Path: cwltool/schemas/v1.0/v1.0/count-lines6-wf.cwl

Branch/Commit ID: c6cced7a2e6389d2eb43342e702677ccb7c7497c

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

Path: cwltool/schemas/v1.0/v1.0/sum-wf.cwl

Branch/Commit ID: e8b3565a008d95859fc44227987a54e6a53a8c29

Cell Ranger Reference (RNA, ATAC, RNA+ATAC) Builds a reference genome of a selected species for quantifying gene expression and chromatin accessibility. The results of this workflow are used in all “Cell Ranger Count” and “Cell Ranger Aggregate” pipelines.

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

Path: workflows/cellranger-mkref.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

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

Path: bacterial_noncoding/wf_bacterial_noncoding.cwl

Branch/Commit ID: 17bae57a1f00f5c6db8f3a82d86262f12b8153cf