Explore Workflows

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 7c8eb4d23c3c9859f57421643710c0b6d57b606c

https://github.com/vuillaut/cookbooks.git

Path: CWL-pipeline/hipepipe.cwl

Branch/Commit ID: 9f04d1255cfad97be87874930692fc984edc0030

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 ============================================== 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

https://github.com/nal-i5k/organism_onboarding.git

Path: flow_genomicsWorkspace/genomics-workspace-genome.cwl

Branch/Commit ID: 39b1d1a39a2ccdadd52db15b41422ecccc66e605

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

Path: bacterial_noncoding/wf_bacterial_noncoding.cwl

Branch/Commit ID: e2a6cbcc36212433d8fbc804919442787a5e2a49

https://github.com/ebi-wp/webservice-cwl.git

Path: workflows/workflow-fetch-hmmscan.cwl

Branch/Commit ID: f867131c9646cde56828c4d8999114f5ca958be9

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: c08fd46e8f715b9b5aa487466705863e4b1829df

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: b9e7392e72506cadd898a6ac4db330baf6535ab6

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: 2f0db4b3c515f91c5cfda19c78cf90d339390986