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: 87f213456b3f966b773d396cce1fe5a272dad858

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: 7b5130d2408bce82ee15c666b37d931ef6f452e3

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

Path: definitions/subworkflows/molecular_alignment.cwl

Branch/Commit ID: f401b02285f30de1c12ac2859134099fe04be33f

Accept and archive simulation model parameter(s). Acceptances includes review of validation process.

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

Path: workflows/AcceptParameter.cwl

Branch/Commit ID: 13a1a949db93afa18ffe8180ff9549e395184e4b

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/mut.cwl

Branch/Commit ID: 9f3b9e7b74d5a904b12674dfd1300b56a48c3d33

Workflow to run the TMB analysis on a batch of samples and merge the results back into a single data clinical file

https://github.com/mskcc/pluto-cwl.git

Path: cwl/tmb_workflow.cwl

Branch/Commit ID: 462f6015c9268a4205b6e81de018a470b8a4a153

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/count-lines5-wf.cwl

Branch/Commit ID: 8010fd2bf1e7090ba6df6ca8c84bbb96e2272d32

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom2-wf.cwl

Branch/Commit ID: 2ae8117360a3cd4909d9d3f2b35c30bfffb25d0a

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: 44214a9d02e6d85b03eb708552ed812ae3d4a733

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

Path: definitions/subworkflows/filter_vcf_nonhuman.cwl

Branch/Commit ID: 4aba7c6591c2f1ebd827a36d325a58738c429bea