Explore Workflows
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Build STAR indices
Workflow runs [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) to build indices for reference genome provided in a single FASTA file as fasta_file input and GTF annotation file from annotation_gtf_file input. Generated indices are saved in a folder with the name that corresponds to the input genome. |
![]() Path: workflows/star-index.cwl Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc |
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dynresreq-workflow-inputdefault.cwl
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![]() Path: tests/dynresreq-workflow-inputdefault.cwl Branch/Commit ID: 5f27e234b4ca88ed1280dedf9e3391a01de12912 |
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kallisto_single_end_workflow.cwl
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![]() Path: Kallisto/workflow/kallisto_single_end_workflow.cwl Branch/Commit ID: 3acab4d22ff0f9657dc8c5685799898a2fc2fd25 |
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DESeq - differential gene expression analysis
Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. |
![]() Path: workflows/deseq.cwl Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc |
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EMG core analysis
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![]() Path: workflows/emg-core-analysis-v4.cwl Branch/Commit ID: ecf044f3a5a7589cb2238487a19f22863c2bcdb1 |
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preprocessor_for_oxog.cwl
This workflow will perform preprocessing steps on VCFs for the OxoG/Variantbam/Annotation workflow. |
![]() Path: preprocessor_for_oxog.cwl Branch/Commit ID: 123a3151d35f98e442e703d903dc3e1d72f3c4b0 |
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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 |
![]() Path: workflows/genome-indices.cwl Branch/Commit ID: a839eb6390974089e1a558c49fc07b4c66c50767 |
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Trim Galore RNA-Seq pipeline single-read
The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-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 file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 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 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 |
![]() Path: workflows/trim-rnaseq-se.cwl Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc |
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step-valuefrom2-wf.cwl
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![]() Path: tests/step-valuefrom2-wf.cwl Branch/Commit ID: 5f27e234b4ca88ed1280dedf9e3391a01de12912 |
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Functional analyis of sequences that match the 16S SSU
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![]() Path: workflows/16S_taxonomic_analysis.cwl Branch/Commit ID: 0cd2d70d63a8ceb2de28f0faac19c919a7bd35ff |