Explore Workflows

https://github.com/d3b-center/canine-dev.git

Path: subworkflows/canine_strelka2_module.cwl

Branch/Commit ID: 7da5645975f5712362cce7908d2ab138e05876fb

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: fa4f172486288a1a9d23864f1d6962d85a453e16

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/sum-wf-noET.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903

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

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: 8cee1920920ed73384fb3ab74272da9c92a20cf2

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

Path: definitions/pipelines/aml_trio_cle_gathered.cwl

Branch/Commit ID: 889a077a20c0fdb01f4ed97aa4bc40f920c37a1a

Cell Ranger Count (RNA+VDJ) Quantifies single-cell gene expression, performs V(D)J contigs assembly and clonotype calling of the sequencing data from a single 10x Genomics library in a combined manner. The results of this workflow are primarily used in either “Single-Cell RNA-Seq Filtering Analysis”, “Single-Cell Immune Profiling Analysis”, or “Cell Ranger Aggregate (RNA, RNA+VDJ)” pipelines.

https://github.com/datirium/workflows.git

Path: workflows/cellranger-multi.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

https://github.com/cancerit/cgpRna.git

Path: cwls/cgpRna_workflow.cwl

Branch/Commit ID: 6d77a181dc077de726bc78a19d30c22399797312

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines17-wf.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903

FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application

https://github.com/datirium/workflows.git

Path: workflows/fastqc.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

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

Path: definitions/subworkflows/single_sample_sv_callers.cwl

Branch/Commit ID: a28a8077a8c4dbf117d16799807483a2532af3f3