Explore Workflows

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Graph Name Retrieved From View
workflow graph cond-wf-012_nojs.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/conditionals/cond-wf-012_nojs.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903
workflow graph kmer_seq_entry_extract_wnode

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: 5b498b4c4f17bb8f17e6886aa4c5661d7aba34fc
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: dda6e8b5ada3f106a2b3bfcc1b151eccf9977726
workflow graph cnv_exomedepth

CNV ExomeDepth calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/cnv_exome_depth.cwl

Branch/Commit ID: cebfad2f456d672052ef8c83f5ff1b4f3b4368e4
workflow graph step-valuefrom3-wf.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/step-valuefrom3-wf.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903
workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 4ffbad9ffeab15ec8af5f6f91bd352ef96d1ef77
workflow graph workflow-htcondorcern.cwl

https://github.com/reanahub/reana-demo-root6-roofit.git

Path: workflow/cwl/workflow-htcondorcern.cwl

Branch/Commit ID: 4291d8d1fdb8f3c202b8711b23120b4481581517
workflow graph optional_src_mandatory_sink.cwl

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

Path: tests/wf/optional_src_mandatory_sink.cwl

Branch/Commit ID: 6d8c2a41e2c524e8d746020cc91711ecc3418a23
workflow graph bam to trimmed fastqs

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

Path: definitions/subworkflows/bam_to_trimmed_fastq.cwl

Branch/Commit ID: dc2c019c1aa24cc01b451a0f048cf94a35f163c4
workflow graph rest_parallel.cwl

https://github.com/anandanlk/community_based_fl.git

Path: decentralised_fl/CWL_Workflow/rest_parallel.cwl

Branch/Commit ID: 98d1e91413a4307cd4ee888d7ef9f6ad51f4f006