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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: 00ea05e22788029370898fd4c17798b11edf0e57
workflow graph bacterial_kmer

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

Path: bacterial_kmer/wf_bacterial_kmer.cwl

Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5
workflow graph group-isoforms-batch.cwl

Workflow runs group-isoforms.cwl tool using scatter for isoforms_file input. genes_filename and common_tss_filename inputs are ignored.

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

Path: tools/group-isoforms-batch.cwl

Branch/Commit ID: b5e16e359007150647b14dc6e038f4eb8dccda79
workflow graph List ZIP content for zenodo community

For a given Zenodo community, list file content of its downloadable *.zip files

https://github.com/stain/ro-index-paper.git

Path: code/data-gathering/workflows/zenodo-zip-content.cwl

Branch/Commit ID: dedbb79f76f110eaafd065aee8401052c8d51a0e
workflow graph Cellranger ATAC Aggregate

Cellranger ATAC Aggregate Aggregates outputs from multiple runs of Cell Ranger Count Chromatin Accessibility experiments

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

Path: workflows/cellranger-atac-aggr.cwl

Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf
workflow graph Filter Protein Alignments

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

Path: protein_alignment/wf_align_filter.cwl

Branch/Commit ID: 4b73bfeb967ee9f57a0410276f7c39e784f0846f
workflow graph ani_top_n

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

Path: task_types/tt_ani_top_n.cwl

Branch/Commit ID: 861d9baa067af98d794ba0ed4e43aa42e37d8a24
workflow graph Salmon quantification, FASTQ -> H5AD count matrix

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

Path: steps/salmon-quantification.cwl

Branch/Commit ID: d020fd86704208b32b513b3edfc2e2d1e0b85022
workflow graph Cell Ranger Count (RNA)

Cell Ranger Count (RNA) Quantifies single-cell gene expression of the sequencing data from a single 10x Genomics library. The results of this workflow are primarily used in either “Single-Cell RNA-Seq Filtering Analysis” or “Cell Ranger Aggregate (RNA, RNA+VDJ)” pipelines.

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

Path: workflows/single-cell-preprocess-cellranger.cwl

Branch/Commit ID: cc6fa135d04737fdde3b4414d6e214cf8c812f6e
workflow graph SoupX Estimate

SoupX Estimate ==============

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

Path: workflows/soupx.cwl

Branch/Commit ID: 00ea05e22788029370898fd4c17798b11edf0e57