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Graph Name Retrieved From View
workflow graph Trim Galore RNA-Seq pipeline paired-end

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **pair-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 single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 4. Use 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 files 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/trim-rnaseq-pe.cwl

Branch/Commit ID: c602e3cdd72ff904dd54d46ba2b5146eb1c57022
workflow graph kmer_build_tree

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: 72804b6506c9f54ec75627f82aafe6a28d7a49fa
workflow graph Cellranger ATAC Aggregate

Cellranger ATAC Aggregate Aggregates outputs from multiple runs of Cell Ranger Count Chromatin Accessibility experiments

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

Path: workflows/cellranger-atac-aggr.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e
workflow graph umi duplex alignment workflow

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

Path: definitions/subworkflows/duplex_alignment.cwl

Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5
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: 261c0232a7a40880f2480b811ed2d7e89c463869
workflow graph wgs alignment and germline variant detection

https://github.com/apaul7/cancer-genomics-workflow.git

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: bfcb5ffbea3d00a38cc03595d41e53ea976d599d
workflow graph scatter-valuefrom-wf2.cwl

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

Path: v1.0/v1.0/scatter-valuefrom-wf2.cwl

Branch/Commit ID: 4fd45edb9531a03223c18a586e32d0baf0d5acb2
workflow graph mut2.cwl

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

Path: tests/wf/mut2.cwl

Branch/Commit ID: 9f3b9e7b74d5a904b12674dfd1300b56a48c3d33
workflow graph blastp_wnode_struct

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

Path: task_types/tt_blastp_wnode_struct.cwl

Branch/Commit ID: 122aba2dafbb63241413c82b725b877c04523aaf
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: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5