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Graph	Name	Retrieved From	View
	trim-rnaseq-pe-dutp.cwl Runs RNA-Seq BioWardrobe basic analysis with strand specific pair-end data file.	https://github.com/Barski-lab/workflows.git Path: workflows/trim-rnaseq-pe-dutp.cwl Branch/Commit ID: ca2dbb71d0537b1d93a8bd44719250cf8949b157
	spurious_annot	https://github.com/ncbi/pgap.git Path: spurious_annot/wf_spurious_annot_pass1.cwl Branch/Commit ID: c64599f5db2437f9323d41cc3d8d9efb20a2667e
	Execute CRISPR	https://github.com/ncbi/pgap.git Path: bacterial_mobile_elem/wf_bacterial_mobile_elem.cwl Branch/Commit ID: c64599f5db2437f9323d41cc3d8d9efb20a2667e
	bacterial_orthology_cond	https://github.com/ncbi/pgap.git Path: bacterial_orthology/wf_bacterial_orthology_conditional.cwl Branch/Commit ID: c64599f5db2437f9323d41cc3d8d9efb20a2667e
	Bacterial Annotation, pass 1, genemark training, by HMMs (first pass)	https://github.com/ncbi/pgap.git Path: bacterial_annot/wf_ab_initio_training.cwl Branch/Commit ID: c64599f5db2437f9323d41cc3d8d9efb20a2667e
	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: c64599f5db2437f9323d41cc3d8d9efb20a2667e
	Bacterial Annotation, pass 1, genemark training, by HMMs (first pass)	https://github.com/ncbi/pgap.git Path: bacterial_annot/wf_orf_hmms.cwl Branch/Commit ID: c64599f5db2437f9323d41cc3d8d9efb20a2667e
	Bacterial Annotation, pass 2, blastp-based functional annotation (first pass)	https://github.com/ncbi/pgap.git Path: bacterial_annot/wf_bacterial_annot_pass2.cwl Branch/Commit ID: c64599f5db2437f9323d41cc3d8d9efb20a2667e
	Bismark Methylation SE Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).	https://github.com/datirium/workflows.git Path: workflows/bismark-methylation-se.cwl Branch/Commit ID: bf80c9339d81a78aefb8de661bff998ed86e836e
	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: 282762f8bbaea57dd488115745ef798e128bade1