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Computes the self-consistency ratio (see Gabe's protocols paper, or CHIP SEQ). Given two replicates, split each and perform IDR on each fragment. Returns the ratio of max(N1, N2)/min(N1, N2) where N1, N2 are the numbers of reproducible peaks found between each rep split pair.

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

Path: cwl/wf_self_consistency_ratio.cwl

Branch/Commit ID: 55f4f4f9c10a09ce03c5c531dd176e6080118977

https://github.com/mskcc/roslin-variant.git

Path: setup/cwl/module-1.cwl

Branch/Commit ID: f1d57f1774b959979ed590c89e11f05b2c639d7c

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

Path: subworkflows/xenbase-fastq-bowtie-bigwig-se-pe.cwl

Branch/Commit ID: afbec98437a7796a509fffbad8c3370aa099f059

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: c235dc6d623879a6c4f5fb307f545c9806eb2d23

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

Path: subworkflows/chipseq-gen-bigwig.cwl

Branch/Commit ID: 8d7ba680b7904da84ad611d184caf247da4a5dc7

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

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

Path: workflows/super-enhancer.cwl

Branch/Commit ID: ce058d892d330125cd03d0a0d5fb3b321cda0be3

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 0d9e6bb52eac0c209af3977aa779e39aaa432458

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

Path: definitions/pipelines/umi_duplex_alignment.cwl

Branch/Commit ID: 735be84cdea041fcc8bd8cbe5728b29ca3586a21

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

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

Path: workflows/trim-rnaseq-se.cwl

Branch/Commit ID: cb5e5b8563be4977e9f2babc14fe084faa234847

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: b4d578c2ba4713a5a22163d9f8c7105acda1f22e