Explore Workflows

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/record-in-secondaryFiles-missing-wf.cwl

Branch/Commit ID: master

This workflow returns the reproducible number of split peaks given a single bam file and its size-matched input pair. This workflow splits the bam file first, but does not do anything to the input.

https://github.com/YeoLab/merge_peaks.git

Path: cwl/wf_split_self_and_idr.cwl

Branch/Commit ID: master

Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files.

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

Path: workflows/chipseq-pe.cwl

Branch/Commit ID: 9eda09a700c765566e605aca580f7c6fedd2713d

https://github.com/lux563624348/WDL-HuBMAP-salmon-rnaseq.git

Path: pipeline_cavatica.cwl

Branch/Commit ID: main

Proteins - predict, cluster, identify, annotate

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/protein-annotation.workflow.cwl

Branch/Commit ID: master

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

Path: cwltool/schemas/v1.0/v1.0/js-expr-req-wf.cwl

Branch/Commit ID: 8010fd2bf1e7090ba6df6ca8c84bbb96e2272d32

Packed ID: wf

https://github.com/lanl/BEE.git

Path: examples/cat-grep-tar/workflow.cwl

Branch/Commit ID: develop

https://github.com/mr-c/4dn-dcic-pipelines-cwl.git

Path: cwl_awsem_v1/hi-c-processing-pairs.cwl

Branch/Commit ID: master

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: d2cd04c27cbba4b0f37fd98edf3d0dbaab48d0d8

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

Path: definitions/pipelines/panel_of_normals.cwl

Branch/Commit ID: 9a657bc8c462542dc7f57fba9e04dc1669f966ba