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Graph	Name	Retrieved From	View
	format_rrnas_from_seq_entry	https://github.com/ncbi/pgap.git Path: task_types/tt_format_rrnas_from_seq_entry.cwl Branch/Commit ID: 7cee09fb3e33c851e4e1dfc965c558b82290a785
	kmer_ref_compare_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: 75ea689c0a8c9902b4598b453455857cb08e885a
	Per-region pindel	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/pindel_cat.cwl Branch/Commit ID: 2e0562a5c3cd7aac24af4c622a5ae68a7fb23a71
	extract_gencoll_ids	https://github.com/ncbi/pgap.git Path: task_types/tt_extract_gencoll_ids.cwl Branch/Commit ID: 1a6b9e5dea09caa0debbaff30ca39005dfa5e4d4
	kmer_ref_compare_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: e2a6cbcc36212433d8fbc804919442787a5e2a49
	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: a839eb6390974089e1a558c49fc07b4c66c50767
	assm_assm_blastn_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_assm_assm_blastn_wnode.cwl Branch/Commit ID: 7c8eb4d23c3c9859f57421643710c0b6d57b606c
	hipepipe.cwl	https://github.com/vuillaut/cookbooks.git Path: CWL-pipeline/hipepipe.cwl Branch/Commit ID: 9f04d1255cfad97be87874930692fc984edc0030
	genomics-workspace.cwl	https://github.com/NAL-i5K/Organism_Onboarding.git Path: flow_genomicsWorkspace/genomics-workspace.cwl Branch/Commit ID: 39b1d1a39a2ccdadd52db15b41422ecccc66e605
	GSEApy - Gene Set Enrichment Analysis in Python GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.	https://github.com/datirium/workflows.git Path: workflows/gseapy.cwl Branch/Commit ID: 9850a859de1f42d3d252c50e15701928856fe774