Explore Workflows

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Graph Name Retrieved From View
workflow graph kmer_cache_store

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 0d9e6bb52eac0c209af3977aa779e39aaa432458
workflow graph umi duplex alignment fastq workflow

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

Path: definitions/pipelines/umi_duplex_alignment.cwl

Branch/Commit ID: 735be84cdea041fcc8bd8cbe5728b29ca3586a21
workflow graph trim-rnaseq-se.cwl

Runs RNA-Seq BioWardrobe basic analysis with single-end data file.

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

Path: workflows/trim-rnaseq-se.cwl

Branch/Commit ID: cb5e5b8563be4977e9f2babc14fe084faa234847
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: b4d578c2ba4713a5a22163d9f8c7105acda1f22e
workflow graph strelka workflow

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

Path: definitions/subworkflows/strelka_and_post_processing.cwl

Branch/Commit ID: ec45fad68ca10fb64d5c58e704991b146dc31d28
workflow graph scatter-valuefrom-wf6.cwl

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf6.cwl

Branch/Commit ID: cd779a90a4336563dcf13795111f502372c6af83
workflow graph Single-Cell Multiome ATAC-Seq and RNA-Seq Filtering Analysis

Single-Cell Multiome ATAC-Seq and RNA-Seq Filtering Analysis Removes low-quality cells from the outputs of the “Cell Ranger Count (RNA+ATAC)” and “Cell Ranger Aggregate (RNA+ATAC)” pipelines. The results of this workflow are used in the “Single-Cell RNA-Seq Dimensionality Reduction Analysis” and “Single-Cell ATAC-Seq Dimensionality Reduction Analysis” pipelines.

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

Path: workflows/sc-multiome-filter.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e
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: 25aa4788dd4efb1cc8ed6f609cb7803896e4d28d
workflow graph RNA-Seq pipeline paired-end strand specific

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081
workflow graph wgs alignment with qc

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

Path: definitions/pipelines/alignment_wgs.cwl

Branch/Commit ID: 3bebaf9b70331de9f4845e2223c55082f5a812fb