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Graph Name Retrieved From View
workflow graph genomics-workspace-genome.cwl

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_genomicsWorkspace/genomics-workspace-genome.cwl

Branch/Commit ID: 0b58c250e8ab7c5efae29443f08ea74316127041
workflow graph snapanalysis_setup_and_analyze.cwl

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: steps/snapanalysis_setup_and_analyze.cwl

Branch/Commit ID: 4b2af54638bc471ee3e14e5f49546a2b6860804e
workflow graph genomics-workspace-cds.cwl

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_genomicsWorkspace/genomics-workspace-cds.cwl

Branch/Commit ID: 39b1d1a39a2ccdadd52db15b41422ecccc66e605
workflow graph umi duplex alignment fastq workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/alignment_umi_duplex.cwl

Branch/Commit ID: 74647cc0f1abac4ee22950cfa89c44cf2ca3cffd
workflow graph MAnorm PE - quantitative comparison of ChIP-Seq paired-end 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 PE sample 1** * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 1 **ChIP-Seq PE sample 2** * previously analyzed ChIP-Seq paired-end 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-pe.cwl

Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4
workflow graph umi duplex alignment workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/duplex_alignment.cwl

Branch/Commit ID: ecac0fda44df3a8f25ddfbb3e7a023fcbe4cbd0f
workflow graph Bisulfite alignment and QC

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: 2e0562a5c3cd7aac24af4c622a5ae68a7fb23a71
workflow graph 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: 2f0db4b3c515f91c5cfda19c78cf90d339390986
workflow graph umi molecular alignment workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: 2e0562a5c3cd7aac24af4c622a5ae68a7fb23a71
workflow graph bam to trimmed fastqs

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/bam_to_trimmed_fastq.cwl

Branch/Commit ID: 77ec4f26eb14ed82481828bd9f6ef659cfd8b40f