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 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: 5e7385b8cfa4ddae822fff37b6bd22eb0370b389

https://github.com/sevenbridges-openworkflows/Broad-Best-Practice-Germline-snps-and-indels-variant-calling-CWL1.0-workflow-GATK-4.1.0.0.git

Path: gatk-best-practice-generic-germline-short-variant-per-sample-cal_decomposed.cwl

Branch/Commit ID: 6ef8ee1c173d2bc78146b60c02731ab53016a1c9

This demonstrates how to wrap a \"real\" tool with a checker workflow that runs both the tool and a tool that performs verification of results

https://github.com/Sage-Bionetworks-Workflows/dockstore-tool-synapseclient.git

Path: checkers/checker-synapse-get-anntotations.cwl

Branch/Commit ID: 1137877e5e0e863f0d676e4bb83cf4ae4b33b425

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

Path: task_types/tt_blastp_wnode_naming.cwl

Branch/Commit ID: 7c8eb4d23c3c9859f57421643710c0b6d57b606c

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: 389f6edccab082d947bee9c032f59dbdf9f7c325

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.

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

Path: workflows/gseapy.cwl

Branch/Commit ID: 5e7385b8cfa4ddae822fff37b6bd22eb0370b389

https://github.com/lrodrin/vre-process_cwl-executor.git

Path: tests/basic/data/workflows/basic_example.cwl

Branch/Commit ID: f4bbddb93cb38e50b8c2eb89d43b926138fa3455

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/functional_analysis.cwl

Branch/Commit ID: 25129f55226dee595ef941edc24d3c44414e0523

https://github.com/cancerit/workflow-seq-import.git

Path: cwls/chksum_seqval_wf_paired_fq.cwl

Branch/Commit ID: 4765cf6955bdce1320fdead7fe51be0e1b63c33d

https://github.com/cr-ste-justine/chujs-alignment-workflow.git

Path: workflows/wgsp_alignment_fq_wf.cwl

Branch/Commit ID: c8255e2aab840f671d9f142d74f915c76415ff51