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 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: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b

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

Path: testdata/workflow_input_sf_expr.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: 16e3915d2a357e2a861b30911c832e5ddc0c1784

Simulation steps pipeline

https://github.com/ILIAD-ocean-twin/application_package.git

Path: workflow_in_workflow/workflow_same_level_v2.cwl

Branch/Commit ID: aab1b560c6a855c645f43635f155a15800a7ce0d

Packed ID: main_pipeline

Simulation steps pipeline

https://github.com/ILIAD-ocean-twin/application_package.git

Path: workflow_in_workflow/workflow_same_level.cwl

Branch/Commit ID: aab1b560c6a855c645f43635f155a15800a7ce0d

Packed ID: main_pipeline

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

Path: testdata/workflow_input_format_expr.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/count-lines7-wf_v1_2.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/workflow_input_sf_expr_array_v1_1.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/workflow_input_sf_expr_v1_1.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/step_valuefrom5_wf_v1_1.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee