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Graph Name Retrieved From View
workflow graph Deprecated. Single-Cell Preprocessing Pipeline

Devel version of Single-Cell Preprocessing Pipeline ===================================================

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

Path: workflows/single-cell-preprocess.cwl

Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf
workflow graph Generate genome indices for STAR & bowtie

Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files

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

Path: workflows/genome-indices.cwl

Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf
workflow graph running cellranger mkfastq and count

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

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: 049f4aeff4c4a1b8421cac9b1c1c1f0da5848315
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: 26870e38cec81af880cd3e4789ae6cee8fc27020
workflow graph Single-cell Assign Cell Types

Single-cell Assign Cell Types ============================= Assigns cell types to Seurat clusters.

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

Path: workflows/sc-assign-cell-types.cwl

Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
workflow graph RNA-Seq pipeline single-read strand specific

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 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) 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-se-dutp.cwl

Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
workflow graph Single-cell RNA-Seq Cluster Analysis

Single-cell RNA-Seq Cluster Analysis Clusters single-cell RNA-Seq datasets, identifies gene markers.

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

Path: workflows/sc-rna-cluster.cwl

Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
workflow graph Motif Finding with HOMER from FASTA files

Motif Finding with HOMER from FASTA files --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis.cwl

Branch/Commit ID: ee66d03be8a7fd61367db40c37a973ff55ece4da
workflow graph trim-chipseq-pe.cwl

Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files.

https://github.com/Barski-lab/workflows.git

Path: workflows/trim-chipseq-pe.cwl

Branch/Commit ID: 801f7b363e0599b9a28ecda696dfdb1c0e40ce71
workflow graph Varscan Workflow

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

Path: definitions/subworkflows/varscan_pre_and_post_processing.cwl

Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5