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Graph	Name	Retrieved From	View
	kmer_top_n_extract	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_top_n_extract.cwl Branch/Commit ID: d218e081d8f6a4fdab56a38ce0fc2fae6216cecc
	taxonomy_check_16S	https://github.com/ncbi/pgap.git Path: task_types/tt_taxonomy_check_16S.cwl Branch/Commit ID: 609aead9804a8f31fa9b3dbc7e52105aec487f31
	kmer_ref_compare_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: 09774c78a965dd8f6c315597a53eef5998a3c1b6
	GAT - Genomic Association Tester GAT: Genomic Association Tester ============================================== A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, in the interpretation of ChIP-Seq or RNA-Seq data. The Genomic Association Tester (GAT) is a tool for computing the significance of overlap between multiple sets of genomic intervals. GAT estimates significance based on simulation. Gat implemements a sampling algorithm. Given a chromosome (workspace) and segments of interest, for example from a ChIP-Seq experiment, gat creates randomized version of the segments of interest falling into the workspace. These sampled segments are then compared to existing genomic annotations. The sampling method is conceptually simple. Randomized samples of the segments of interest are created in a two-step procedure. Firstly, a segment size is selected from to same size distribution as the original segments of interest. Secondly, a random position is assigned to the segment. The sampling stops when exactly the same number of nucleotides have been sampled. To improve the speed of sampling, segment overlap is not resolved until the very end of the sampling procedure. Conflicts are then resolved by randomly removing and re-sampling segments until a covering set has been achieved. Because the size of randomized segments is derived from the observed segment size distribution of the segments of interest, the actual segment sizes in the sampled segments are usually not exactly identical to the ones in the segments of interest. This is in contrast to a sampling method that permutes segment positions within the workspace.	https://github.com/datirium/workflows.git Path: workflows/gat-run.cwl Branch/Commit ID: 8049a781ac4aae579fbd3036fa0bf654532f15be
	kmer_seq_entry_extract_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl Branch/Commit ID: 09774c78a965dd8f6c315597a53eef5998a3c1b6
	Pairwise genomic regions intersection Pairwise genomic regions intersection ============================================= Overlaps peaks from two ChIP/ATAC experiments	https://github.com/datirium/workflows.git Path: workflows/peak-intersect.cwl Branch/Commit ID: 8049a781ac4aae579fbd3036fa0bf654532f15be
	tt_fscr_calls_pass1	https://github.com/ncbi/pgap.git Path: task_types/tt_fscr_calls_pass1.cwl Branch/Commit ID: 09774c78a965dd8f6c315597a53eef5998a3c1b6
	workflow.cwl	https://github.com/NAL-i5K/Organism_Onboarding.git Path: flow_dispatch/2other_species/workflow.cwl Branch/Commit ID: 89cff9f0d36a23bf57b3f4bdbd3ed57e3347c945
	cnv_exomedepth CNV ExomeDepth calling	https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git Path: structuralvariants/subworkflows/cnv_exome_depth.cwl Branch/Commit ID: 0864e19de6a1732a1376c6a64a93794d2fc45d23
	MAnorm SE - quantitative comparison of ChIP-Seq single-read data What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General Experiment short name/Alias * short name for you experiment to identify among the others ChIP-Seq SE sample 1 * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 ChIP-Seq SE sample 2 * previously analyzed ChIP-Seq single-read experiment to be used as Sample 2 Genome * Reference genome to be used for gene assigning ### Advanced Reads shift size for sample 1 * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 Reads shift size for sample 2 * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 M-value (log2-ratio) cutoff * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 P-value cutoff * P-value cutoff to define biased peaks. Default: 0.01 Window size * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000	https://github.com/datirium/workflows.git Path: workflows/manorm-se.cwl Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620