Explore Workflows

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: tools/LSU-from-tablehits.cwl

Branch/Commit ID: 25129f55226dee595ef941edc24d3c44414e0523

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

Path: bacterial_trna/wf_scan_and_dump.cwl

Branch/Commit ID: dcbbce152fbff2637f102471fb80318244492853

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

Path: tests/wf/secret_wf.cwl

Branch/Commit ID: e6c2d955a448225f026a04130443d13661844440

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

Path: definitions/subworkflows/align.cwl

Branch/Commit ID: 2ae0a374fab650757cdae4391c8cbd32f02edf97

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/emg-qc-single.cwl

Branch/Commit ID: cac44f2cf14110fde9951161c663c4525772f616

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

Path: v1.0/v1.0/io-int-optional-wf.cwl

Branch/Commit ID: 40fcfc01812046f012acf5153cc955ee848e69e3

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 0514ffe248dd11068a3f2268bc67b6ce5ab051d2

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

Path: tests/wf/1st-workflow.cwl

Branch/Commit ID: 814bd0405a7701efc7d63e8f0179df394c7766f7

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

Path: v1.0/v1.0/scatter-valuefrom-wf5.cwl

Branch/Commit ID: 148f11b11d31c098196e649f680797f0b4680114

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: d1bef74924efcb8bfaa00987b3f148d5a192b7a9