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: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c

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

Path: tests/any-type-compat.cwl

Branch/Commit ID: 86c46cb397de029e4c91f02cca40fa2b54d22f37

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 146df33e2e44afa2a608ac72c036e6b6b871af93

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f

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

Path: tests/wf/mut3.cwl

Branch/Commit ID: 216fbe57afcf67d81c99b49c1aa3aee0844f0a6a

https://github.com/cwlviewer-test/Long-covid---aedea650-7a21-11ed-b9d2-e51f21933d80-1.git

Path: Long-covid.cwl

Branch/Commit ID: read-potential-cases-disc

https://github.com/cwlviewer-test/Long-covid---aedea650-7a21-11ed-b9d2-e51f21933d80-1.git

Path: Long-covid---a9e48a70-7a21-11ed-b9d2-e51f21933d80/Long-covid.cwl

Branch/Commit ID: read-potential-cases-disc

create sorted / filtered similarity file with feature sequences, and index by md5

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/index_sim_seq.workflow.cwl

Branch/Commit ID: f5839797da8209a9d3e441023f88130219751020

https://github.com/phenoflow/Long-covid---aede7f40-7a21-11ed-b9d2-e51f21933d80.git

Path: Long-covid.cwl

Branch/Commit ID: 7df854469636576fc354111c5e96c072e2035b49

Workflow converts input BAM file into bigWig and bedGraph files. Input BAM file should be sorted by coordinates (required by `bam_to_bedgraph` step). If `split` input is not provided use true by default. Default logic is implemented in `valueFrom` field of `split` input inside `bam_to_bedgraph` step to avoid possible bug in cwltool with setting default values for workflow inputs. `scale` has higher priority over the `mapped_reads_number`. The last one is used to calculate `-scale` parameter for `bedtools genomecov` (step `bam_to_bedgraph`) only in a case when input `scale` is not provided. All logic is implemented inside `bedtools-genomecov.cwl`. `bigwig_filename` defines the output name only for generated bigWig file. `bedgraph_filename` defines the output name for generated bedGraph file and can influence on generated bigWig filename in case when `bigwig_filename` is not provided. All workflow inputs and outputs don't have `format` field to avoid format incompatibility errors when workflow is used as subworkflow.

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

Path: tools/bam-bedgraph-bigwig.cwl

Branch/Commit ID: 12c29f88855329192bfff977f046990031f04931