Explore Workflows

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

Path: definitions/subworkflows/filter_vcf_nonhuman.cwl

Branch/Commit ID: b9e7392e72506cadd898a6ac4db330baf6535ab6

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

Path: definitions/pipelines/somatic_wgs.cwl

Branch/Commit ID: 74647cc0f1abac4ee22950cfa89c44cf2ca3cffd

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/variant_calling/freebayes.cwl

Branch/Commit ID: c661469505c606e1353f23c21a6654724a9d8d63

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: f2bd4687f06f85ea848b6f1ce04ec97f48525334

This workflow normalizes clip aligned reads against a size-matched input sample. Then, an entropy score is calculated for each peak found.

https://github.com/YeoLab/merge_peaks.git

Path: cwl/wf_input_norm_and_entropy.cwl

Branch/Commit ID: aedc0a14d4ba109ee65678a3201a52c5bb6ad473

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: af78bfbc7625a817a2875e87c8ee267cf46b8c57

RNA-seq 04 quantification

https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/RNA-seq_pipeline/04-quantification-se-unstranded.cwl

Branch/Commit ID: 487af88ef0b971f76ecd1a215639bb47e3ee94e1

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

Path: definitions/pipelines/detect_variants.cwl

Branch/Commit ID: 7638b3075863ae8172f4adaec82fb2eb8e80d3d5

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

Path: genomic_source/wf_genomic_source.cwl

Branch/Commit ID: 7cee09fb3e33c851e4e1dfc965c558b82290a785

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: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5