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Graph Name Retrieved From View
workflow graph 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: a0b22644ca178b640fb74849d23b7c631022f0b5
workflow graph Build Bowtie indices

Workflow runs [Bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) to build indices for reference genome provided in a single FASTA file as fasta_file input. Generated indices are saved in a folder with the name that corresponds to the input genome

 https://github.com/datirium/workflows.git

Path: workflows/bowtie-index.cwl

Branch/Commit ID: a0b22644ca178b640fb74849d23b7c631022f0b5
workflow graph Apply filters to VCF file

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

Path: definitions/subworkflows/filter_vcf_mouse.cwl

Branch/Commit ID: 195b4ab487c939eb32a55d9f78bc1befd100caae
workflow graph secret_wf.cwl

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/secret_wf.cwl

Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda
workflow graph advanced-header.cwl

https://github.com/datirium/workflows.git

Path: metadata/advanced-header.cwl

Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620
workflow graph bulk scRNA-seq pipeline using Salmon

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: bulk-pipeline.cwl

Branch/Commit ID: c9aedcb8d3d153a1ce475939fd0c752269570a69
workflow graph Running cellranger count and lineage inference

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

Path: definitions/subworkflows/single_cell_rnaseq.cwl

Branch/Commit ID: 77ec4f26eb14ed82481828bd9f6ef659cfd8b40f
workflow graph HBA_target.cwl

https://git.astron.nl/eosc/prefactor3-cwl.git

Path: workflows/HBA_target.cwl

Branch/Commit ID: 0c4dd2bb6c1259ecc9eabbe287bd5f795195b755
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: a0b22644ca178b640fb74849d23b7c631022f0b5
workflow graph align_sort_sa

https://github.com/ncbi/pgap.git

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: 1b9094d70f620bb2e51072dd2150150aa4927439