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Graph	Name	Retrieved From	View
	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: 4ab9399a4777610a579ea2c259b9356f27641dcc
	extract_gencoll_ids	https://github.com/ncbi/pgap.git Path: task_types/tt_extract_gencoll_ids.cwl Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f
	align_sort_sa	https://github.com/ncbi/pgap.git Path: task_types/tt_align_sort_sa.cwl Branch/Commit ID: 61eaea2f746c8a1fc2a2b731056b068e28ca4e20
	PCA - Principal Component Analysis Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.	https://github.com/datirium/workflows.git Path: workflows/pca.cwl Branch/Commit ID: 4ab9399a4777610a579ea2c259b9356f27641dcc
	search.cwl#main	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/search.cwl Branch/Commit ID: 3e9bca4e006eae7e9febd76eb9b8292702eba2cb Packed ID: main
	kmer_build_tree	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_build_tree.cwl Branch/Commit ID: 803f6367d1b279a7b6dc1a4e8ae43f1bbec9f760
	workflow.cwl	https://gitlab.ebrains.eu/weidler/sc5-cwl-workflow.git Path: workflow.cwl Branch/Commit ID: 872880d684193c5e936c76dff3ebf77d2e3e5f59
	taxonomy_check_16S	https://github.com/ncbi/pgap.git Path: task_types/tt_taxonomy_check_16S.cwl Branch/Commit ID: 17bae57a1f00f5c6db8f3a82d86262f12b8153cf
	revsort.cwl Reverse the lines in a document, then sort those lines.	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/revsort.cwl Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b
	Bismark Methylation - pipeline for BS-Seq data analysis 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: 44214a9d02e6d85b03eb708552ed812ae3d4a733