Explore Workflows

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: be5ae41801b19ebc69a2889d8fdb39e8e2359611

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

Path: cwltool/schemas/v1.0/v1.0/count-lines13-wf.cwl

Branch/Commit ID: 8010fd2bf1e7090ba6df6ca8c84bbb96e2272d32

https://github.com/tmooney/cancer-genomics-workflow.git

Path: definitions/pipelines/aml_trio_cle_gathered.cwl

Branch/Commit ID: 0db1a5f1ceedd4416ac550787c27b99c87dbe985

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 9e43bc5cff985574e1f8135d4c50b5a347517c9e

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

Path: testdata/md5sum_v11.cwl

Branch/Commit ID: 124a08ce3389eb49066c34a4163cbbed210a0355

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

Path: tests/wf/conflict.cwl

Branch/Commit ID: baa668bc96ade54607465d21bc6cfa15c9bff13c

Packed ID: main

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

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

Branch/Commit ID: 46b7f9766d1bc8a4871474eee25ec730b4e173da

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: 9b4dc225c537685b9c9a32d931d3892d20953dd7

# Set Operations for filtered gene lists This workflow takes as input multiple filtered genelists samples and performs the user-selected set operation on them. There is one input for list A from which \"scores\" will be taken (these are fold change values from deseq or diffbind) and used in the output set list. The second genelist input is for 1+ genelists, that will be aggregated and used for intersection and union directly, and be applied against list A for the relative complement operation. The output is a single filtered gene list in the same format as the input files (headerless BED file with [chrom start end name score strand]). The returned score value (column 5) is always derived from file A.

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

Path: workflows/genelists-sets.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e

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

Path: definitions/subworkflows/strelka_and_post_processing.cwl

Branch/Commit ID: e7e888df9e7d44f036c4c7985e474016ee9e6525