Explore Workflows

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Graph Name Retrieved From View
workflow graph Filter Protein Seeds I; Find ProSplign Alignments I

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

Path: protein_alignment/wf_compart_filter_prosplign.cwl

Branch/Commit ID: 5463361069e263ad6455858e054c1337b1d9e752
workflow graph exome alignment and germline variant detection

https://github.com/genome/cancer-genomics-workflow.git

Path: germline_exome_workflow.cwl

Branch/Commit ID: eb565eac07209017b12ed79057b40cbf44fb6a0d
workflow graph fasta2taxa-plot

Input is a fasta file with n>1 samples, with sample id as sequence identifier prefix, and a sample id file. The workflow calls open otus and assigns taxa using greengenes. The output are taxa plots.

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

Path: CWL/Workflows/qiime/join-reads2reference2plot.cwl

Branch/Commit ID: f0a3250a372faea796fc4bd7b92aaf52247b6c47
workflow graph Whole genome alignment and somatic variant detection

https://github.com/tmooney/cancer-genomics-workflow.git

Path: definitions/pipelines/somatic_wgs.cwl

Branch/Commit ID: 233f026ffce240071edda526391be0c03186fce8
workflow graph dedup-3-pass-distr.cwl

run 3-pass dedup: algo LocusCollector + algo Dedup output_dup_read_name + algo Dedup dedup_by_read_name sequentially in distributed mode

 https://github.com/Sentieon/Sentieon-cwl.git

Path: stage/dedup-3-pass-distr.cwl

Branch/Commit ID: 845f4699c5fce96a4c708a553b3701c9cf296653
workflow graph Build Bismark indices

Copy fasta_file file to the folder and run run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the genome input.

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

Path: workflows/bismark-index.cwl

Branch/Commit ID: 9850a859de1f42d3d252c50e15701928856fe774
workflow graph Running cellranger count and lineage inference

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

Path: definitions/subworkflows/single_cell_rnaseq.cwl

Branch/Commit ID: 700e73aaed6db1ad538dd27b2e1709f436ad3edb
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: 104059e07a2964673e21d371763e33c0afeb2d03
workflow graph bulk scRNA-seq pipeline using Salmon

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

Path: bulk-pipeline.cwl

Branch/Commit ID: ce04e2cf5cd180448eb6107806b20d867d0411c6
workflow graph Scattered variant calling workflow

https://github.com/arvados/arvados-tutorial.git

Path: WGS-processing/cwl/helper/scatter-gatk-wf-with-interval.cwl

Branch/Commit ID: d147d1d1fafeeea06bd09d9479337b0f5aab43b0