Explore Workflows

Reverse the lines in a document, then sort those lines.

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

Path: cwltool/schemas/v1.0/v1.0/revsort.cwl

Branch/Commit ID: a858bb4db58ef2df17b4856294ad7904643c5c6e

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/count-lines1-wf-noET.cwl

Branch/Commit ID: 86c46cb397de029e4c91f02cca40fa2b54d22f37

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

Path: definitions/subworkflows/cram_to_cnvkit.cwl

Branch/Commit ID: 2e298960837739717ec2928a99c5d811183012e6

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

Path: bacterial_annot/wf_bacterial_annot_pass4.cwl

Branch/Commit ID: e668f9c4047f1971ae53040a5af3eccc4bfc3c53

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

Path: tests/conditionals/cond-with-defaults.cwl

Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: 2d851682ba1bf2aaaacb3677253b55ceb59c8568

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

Path: task_types/tt_kmer_top_n.cwl

Branch/Commit ID: ef266744578e2dcbce57c110c6fa3b9eee91e316

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

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

Branch/Commit ID: a3d1f3b870eb834fed14ebcc928ea6a2270441a8

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: 7eef0294395d83ff0765fce61726a59d71126422

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

Path: tests/wf/count-lines1-wf.cwl

Branch/Commit ID: d3c7bd5d6c409e857b98f9034a55952ca95afdb3