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Graph Name Retrieved From View
workflow graph FastQC - a quality control tool for high throughput sequence data

FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application

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

Path: workflows/fastqc.cwl

Branch/Commit ID: a839eb6390974089e1a558c49fc07b4c66c50767
workflow graph sum-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/sum-wf.cwl

Branch/Commit ID: 596aab620489cd2611f4bc1d9a4fc914ddf34514
workflow graph kmer_seq_entry_extract_wnode

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: f5c11df465aaadf712c38ba4933679fe1cbe03ca
workflow graph MAnorm PE - quantitative comparison of ChIP-Seq paired-end 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 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: ee66d03be8a7fd61367db40c37a973ff55ece4da
workflow graph genomel_cohort_gatk4.cwl

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

Path: genomel/genomel_cohort_gatk4.cwl

Branch/Commit ID: 13c106834d6c9031de08496faeff521740a0c95f
workflow graph gathered exome alignment and somatic variant detection

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

Path: definitions/pipelines/gathered_somatic_exome.cwl

Branch/Commit ID: 1249b5d4e23d57ca5e3b8ad6d8e5f10ddb019f68
workflow graph xenbase-rnaseq-se.cwl

XenBase workflow for analysing RNA-Seq single-end data

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

Path: workflows/xenbase-rnaseq-se.cwl

Branch/Commit ID: 94471ee6c01b7bc17102e45e56e7366c2a52acdf
workflow graph wgs alignment and tumor-only variant detection

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

Path: definitions/pipelines/tumor_only_wgs.cwl

Branch/Commit ID: 889a077a20c0fdb01f4ed97aa4bc40f920c37a1a
workflow graph Exome QC workflow

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

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: 295e7b7f51727c0f2d6cc86ce817449b2e8dba3c
workflow graph Salmon quantification, FASTQ -> H5AD count matrix

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: steps/salmon-quantification.cwl

Branch/Commit ID: 14d004a163e02a5f8f8590e568a2f4f153508931