Explore Workflows

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: 799575ce58746813f066a665adeacdda252d8cab

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

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: e2a6cbcc36212433d8fbc804919442787a5e2a49

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

Path: tests/fail-unconnected.cwl

Branch/Commit ID: 6397014050177074c9ccd0d771577f7fa9f728a3

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

Path: _includes/cwl/22-nested-workflows/1st-workflow.cwl

Branch/Commit ID: fb086088825d19c1136b97dd5997a060da8d44d6

https://github.com/kids-first/kf-alignment-workflow.git

Path: workflows/kfdrc_cram_to_gvcf.cwl

Branch/Commit ID: a25fbec152d701623a7a8fa494b1179cc3c39c38

https://github.com/ebi-wp/webservice-cwl.git

Path: workflows/workflow-blast-clustalo-phylogeny.cwl

Branch/Commit ID: f867131c9646cde56828c4d8999114f5ca958be9

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 6ac47e5703d8c8cdac698de91143829b3911e9b2

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: pipeline.cwl

Branch/Commit ID: 1af880947f14a474a3d48cb178397b1feca81145

ATAC-seq pipeline - reads: PE

https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/ATAC-seq_pipeline/pipeline-pe.cwl

Branch/Commit ID: 8aabde14169421a7115c5cd48c4740b3a7bd818f

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/ChIP-Seq/chip-seq-alignment.cwl

Branch/Commit ID: 0207b0171ab142dfb85db9c39050c5b4be51dd9e