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Graph Name Retrieved From View
workflow graph Create Genomic Collection for Bacterial Pipeline, ASN.1 input

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

Path: genomic_source/wf_genomic_source_asn.cwl

Branch/Commit ID: 551493f5c24b757a46cd22821a05e6ac6dcceb7f
workflow graph spurious_annot pass2

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

Path: spurious_annot/wf_spurious_annot_pass2.cwl

Branch/Commit ID: 551493f5c24b757a46cd22821a05e6ac6dcceb7f
workflow graph spurious_annot

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

Path: spurious_annot/wf_spurious_annot_pass1.cwl

Branch/Commit ID: 551493f5c24b757a46cd22821a05e6ac6dcceb7f
workflow graph Non-Coding Bacterial Genes

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

Path: bacterial_noncoding/wf_bacterial_noncoding.cwl

Branch/Commit ID: 551493f5c24b757a46cd22821a05e6ac6dcceb7f
workflow graph Bacterial Annotation, pass 4, blastp-based functional annotation (second pass)

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

Path: bacterial_annot/wf_bacterial_annot_pass4.cwl

Branch/Commit ID: 551493f5c24b757a46cd22821a05e6ac6dcceb7f
workflow graph Bacterial Annotation, pass 1, genemark training, by HMMs (first pass)

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

Path: bacterial_annot/wf_bacterial_annot_pass1.cwl

Branch/Commit ID: 551493f5c24b757a46cd22821a05e6ac6dcceb7f
workflow graph kmer_build_tree

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: a2d6cd4c53bf3501f6bd79edebb7ca30bba8456f
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: b5e16e359007150647b14dc6e038f4eb8dccda79
workflow graph taxonomy_check_16S

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: a7fced3ed8c839272c8f3a8db9da7bc8cd50271f
workflow graph count-lines6-wf.cwl

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

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

Branch/Commit ID: 1eb6bfe3c77aebaf69453a669d21ae7a5a78056f