Explore Workflows

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

Path: cwltool/schemas/v1.0/v1.0/iwdr_with_nested_dirs.cwl

Branch/Commit ID: 2ae8117360a3cd4909d9d3f2b35c30bfffb25d0a

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

Path: tests/wf/scatter-wf4.cwl

Branch/Commit ID: 3ed10d0ea7ac57550433a89a92bdbe756bdb0e40

Packed ID: main

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

Path: testdata/stdout-wf_v1_2.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9

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

Path: cwltool/schemas/v1.0/v1.0/env-wf3.cwl

Branch/Commit ID: bfe56f3138e9e6fc0b9b8c06447553d4cea03d59

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

Path: definitions/subworkflows/qc_wgs.cwl

Branch/Commit ID: 1437aed13d240fd624f78df2c7efb096c5079d73

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

Path: definitions/subworkflows/bam_to_bqsr.cwl

Branch/Commit ID: 1437aed13d240fd624f78df2c7efb096c5079d73

Runs RNA-Seq BioWardrobe basic analysis with single-end data file.

https://github.com/Barski-lab/workflows.git

Path: workflows/trim-rnaseq-se.cwl

Branch/Commit ID: e706ffe742cfdf713c4315ab2fb56d07f7e688cb

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

Path: definitions/pipelines/detect_variants.cwl

Branch/Commit ID: ddb49a0951d9ad537269d7db3fe8f904495a8bf4

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

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: a2d6cd4c53bf3501f6bd79edebb7ca30bba8456f

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: f3e44d3b0f198cf5245c49011124dc3b6c2b06fd