Explore Workflows
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MAnorm SE - quantitative comparison of ChIP-Seq single-read 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 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: 5561f7ee11dd74848680351411a19aa87b13d27b |
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THOR - differential peak calling of ChIP-seq signals with replicates
What is THOR? -------------- THOR is an HMM-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates. THOR performs genomic signal processing, peak calling and p-value calculation in an integrated framework. For more information please refer to: ------------------------------------- Allhoff, M., Sere K., Freitas, J., Zenke, M., Costa, I.G. (2016), Differential Peak Calling of ChIP-seq Signals with Replicates with THOR, Nucleic Acids Research, epub gkw680. |
https://github.com/datirium/workflows.git
Path: workflows/rgt-thor.cwl Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2 |
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Run tRNAScan
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https://github.com/ncbi/pgap.git
Path: bacterial_trna/wf_trnascan.cwl Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5 |
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Execute CRISPR
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https://github.com/ncbi/pgap.git
Path: bacterial_mobile_elem/wf_bacterial_mobile_elem.cwl Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5 |
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spurious_annot
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https://github.com/ncbi/pgap.git
Path: spurious_annot/wf_spurious_annot_pass1.cwl Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5 |
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Bacterial Annotation, pass 4, blastp-based functional annotation (second pass)
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https://github.com/ncbi/pgap.git
Path: bacterial_annot/wf_bacterial_annot_pass4.cwl Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5 |
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Bacterial Annotation, pass 2, blastp-based functional annotation (first pass)
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https://github.com/ncbi/pgap.git
Path: bacterial_annot/wf_bacterial_annot_pass2.cwl Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5 |
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PGAP Pipeline
PGAP pipeline for external usage, powered via containers |
https://github.com/ncbi/pgap.git
Path: wf_common.cwl Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5 |
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sample-workflow.cwl
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https://github.com/mskcc/argos-cwl.git
Path: workflows/sample-workflow.cwl Branch/Commit ID: 9afe0b169cff4df57d5a5cbf18c6e34d5cdc6998 |
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chipseq-pe.cwl
Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files. |
https://github.com/Barski-lab/workflows.git
Path: workflows/chipseq-pe.cwl Branch/Commit ID: a8e4c1245950715d2e07682d3ac4865ce1d73777 |
