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Graph Name Retrieved From View
workflow graph chipseq-gen-bigwig.cwl

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

Path: subworkflows/chipseq-gen-bigwig.cwl

Branch/Commit ID: 9a2c389364674221fab3f0f6afdda799e6aa3247

workflow graph tt_hmmsearch_wnode.cwl

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

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: a2d6cd4c53bf3501f6bd79edebb7ca30bba8456f

workflow graph count-lines4-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/count-lines4-wf.cwl

Branch/Commit ID: c6cced7a2e6389d2eb43342e702677ccb7c7497c

workflow graph RNA-Seq pipeline paired-end stranded mitochondrial

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific pair-end** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with the pair-end strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `samtools sort` to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using `GEEP` reads-counting utility; export results to file

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

Path: workflows/rnaseq-pe-dutp-mitochondrial.cwl

Branch/Commit ID: 3fc68366adb179927af5528c27b153abaf94494d

workflow graph scatter-wf3.cwl#main

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf3.cwl

Branch/Commit ID: 4700fbee9a5a3271eef8bc9ee595619d0720431b

Packed ID: main

workflow graph count-lines8-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/count-lines8-wf.cwl

Branch/Commit ID: 2ae8117360a3cd4909d9d3f2b35c30bfffb25d0a

workflow graph SSU-from-tablehits.cwl

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: tools/SSU-from-tablehits.cwl

Branch/Commit ID: 5e8217435bcdd597b2ad236f3e847d13d4c21824

workflow graph MAnorm SE - quantitative comparison of ChIP-Seq single-read data

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: 2b8146f76595f0c4d8bf692de78b21280162b1d0

workflow graph steplevel-resreq.cwl

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

Path: cwltool/schemas/v1.0/v1.0/steplevel-resreq.cwl

Branch/Commit ID: 7ec307b01442936fad9b1149f4500496557505ff

workflow graph exome alignment and somatic variant detection

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

Path: definitions/pipelines/somatic_exome.cwl

Branch/Commit ID: 22fce2dbdada0c4135b6f0677f78535cf980cb07