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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: a409db2289b86779897ff19003bd351701a81c50

Prepare user input for NCBI-PGAP pipeline

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

Path: prepare_user_input2.cwl

Branch/Commit ID: 4b73bfeb967ee9f57a0410276f7c39e784f0846f

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

Path: tests/dynresreq-workflow-stepdefault.cwl

Branch/Commit ID: 86c46cb397de029e4c91f02cca40fa2b54d22f37

AltAnalyze ICGS ===============

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

Path: workflows/altanalyze-icgs.cwl

Branch/Commit ID: 10ce6e113f749c7bd725e426445220c3bdc5ddf1

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

Path: cwltool/schemas/v1.0/v1.0/count-lines1-wf.cwl

Branch/Commit ID: 4df56e95e6fceab69e677b539f3532cbf5946197

Demultiplexing sequences from https://docs.qiime2.org/2018.4/tutorials/moving-pictures/

https://github.com/bespin-workflows/16s-qiime2.git

Path: subworkflows/qiime2-02-demux-emp-single.cwl

Branch/Commit ID: b4c07a7e07ba9ce862a2be057a905d300f3c8882

ChIP-seq - Quantification - samples: treatment and control

https://github.com/alexbarrera/GGR-cwl.git

Path: v1.0/ChIP-seq_pipeline/05-quantification-with-control.cwl

Branch/Commit ID: 33385c6a820a9d4d18cff6fc3a533ec8e3c11c6e

Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files

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

Path: workflows/genome-indices.cwl

Branch/Commit ID: 581156366f91861bd4dbb5bcb59f67d468b32af3

Compute average structure for all averaged structures corresponding to core UniProt domain instances. First computes average per UniProt domain instance and then average all averaged structures.

https://github.com/HrishiDhondge/CroMaSt.git

Path: Tools/core_avg_subwf.cwl

Branch/Commit ID: 9f3832867eab6c7a6363f8ca594a4bcf2ff7e96f

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/BQSR/bqsr_workflow.cwl

Branch/Commit ID: 0bd60a8962cc9960b7e4f6528547e220bcd2b941