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Graph Name Retrieved From View
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: d1bef74924efcb8bfaa00987b3f148d5a192b7a9
workflow graph Merge, annotate, and generate a TSV for SVs

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

Path: definitions/subworkflows/merge_svs.cwl

Branch/Commit ID: 2ae0a374fab650757cdae4391c8cbd32f02edf97
workflow graph BLAST against rRNA db

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

Path: bacterial_noncoding/wf_blastn.cwl

Branch/Commit ID: dcbbce152fbff2637f102471fb80318244492853
workflow graph functional-wf.cwl

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

Path: tests/checker_wf/functional-wf.cwl

Branch/Commit ID: 814bd0405a7701efc7d63e8f0179df394c7766f7
workflow graph exome alignment with qc

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

Path: definitions/pipelines/alignment_exome.cwl

Branch/Commit ID: eb0092603bf57acb7bda08a06e4f2f1e2a8c9b6d
workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: f225cd99b0e0a5043dd102f8b33a6139fefe9ea4
workflow graph count-lines13-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/count-lines13-wf.cwl

Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda
workflow graph tRNA_selection.cwl

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

Path: tools/tRNA_selection.cwl

Branch/Commit ID: 25129f55226dee595ef941edc24d3c44414e0523
workflow graph process VCF workflow

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

Path: definitions/subworkflows/strelka_process_vcf.cwl

Branch/Commit ID: 3bb0693076adc1543079a6b136c74906ad546ac6
workflow graph DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind.cwl

Branch/Commit ID: d1bef74924efcb8bfaa00987b3f148d5a192b7a9