Explore Workflows
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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: a0b22644ca178b640fb74849d23b7c631022f0b5 |
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align_sort_sa
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https://github.com/ncbi/pgap.git
Path: task_types/tt_align_sort_sa.cwl Branch/Commit ID: 1b9094d70f620bb2e51072dd2150150aa4927439 |
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Apply filters to VCF file
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/filter_vcf_nonhuman.cwl Branch/Commit ID: 77ec4f26eb14ed82481828bd9f6ef659cfd8b40f |
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js_output_workflow.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: tests/wf/js_output_workflow.cwl Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda |
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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: a0b22644ca178b640fb74849d23b7c631022f0b5 |
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Motif Finding with HOMER with target and background regions from peaks
Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/) |
https://github.com/datirium/workflows.git
Path: workflows/homer-motif-analysis-peak.cwl Branch/Commit ID: a0b22644ca178b640fb74849d23b7c631022f0b5 |
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Aligning chipseq data of mouse
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/sequence_to_bqsr_mouse.cwl Branch/Commit ID: 3a822294da63b4e19446a285e2fef075e23cf3d0 |
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cache_test_workflow.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: tests/wf/cache_test_workflow.cwl Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda |
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FragPipe_TMT_Integrator_and_QC.wf.cwl
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https://github.com/davidroberson/fragpipe-proteomics-pipeline-tutorial.git
Path: FragPipe_TMT_Integrator_and_QC.wf.cwl Branch/Commit ID: ccbb5317e8a2ddf36aab215a12d65f89d152e63b |
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sec-wf-out.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: tests/wf/sec-wf-out.cwl Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda |
