Explore Workflows

https://github.com/nigyta/bact_genome.git

Path: cwl/workflow/platanusB-w-rRNA.cwl

Branch/Commit ID: d0d36f70eb62164d57c1ba27a6b63bee7d41e370

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/varscan_pre_and_post_processing.cwl

Branch/Commit ID: 8da2b1cd6fa379b2c22baf9dad762d39630e6f46

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/mutect.cwl

Branch/Commit ID: 8da2b1cd6fa379b2c22baf9dad762d39630e6f46

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/PerformanceSummaryGenome_v0_1_0.cwl

Branch/Commit ID: d1bcf010d5c39d5fc01b8862db4f258df7d4f65d

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/genomel_cohort_genotyping.cwl

Branch/Commit ID: 13c106834d6c9031de08496faeff521740a0c95f

Whole Exome Sequence analysis using GATK best practices - Germline SNP & Indel Discovery

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

Path: workflows/exomeseq.cwl

Branch/Commit ID: bbe24d8d7fde2e918583b96805909a2867b749d6

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/variant_calling/haplotypecaller.cwl

Branch/Commit ID: 13c106834d6c9031de08496faeff521740a0c95f

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: 2cad55523d1b4ee7fd9e64df0f6263c6545e4b0e

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/genomel_individual_workflow.cwl

Branch/Commit ID: 13c106834d6c9031de08496faeff521740a0c95f

https://github.com/EBI-Metagenomics/pipeline-v5.git

Path: workflows/subworkflows/rna_prediction-sub-wf.cwl

Branch/Commit ID: a83ee883bb3c7480010fa952939fac771491ddf4