Explore Workflows
View already parsed workflows here or click here to add your own
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joint genotyping for trios or small cohorts
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Path: definitions/subworkflows/joint_genotype.cwl Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe |
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exomeseq-gatk4-03-organizedirectories.cwl
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Path: subworkflows/exomeseq-gatk4-03-organizedirectories.cwl Branch/Commit ID: 5438ab97697f9a9b246719302bedc79f305a98f5 |
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Single-Cell RNA-Seq Differential Expression Analysis
Single-Cell RNA-Seq Differential Expression Analysis Identifies differentially expressed genes between any two groups of cells, optionally aggregating gene expression data from single-cell to pseudobulk form. |
Path: workflows/sc-rna-de-pseudobulk.cwl Branch/Commit ID: 69643d8c15f5357a320aa7e2f6adb2e71302fd20 |
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Trim Galore 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 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-dutp.cwl Branch/Commit ID: 69643d8c15f5357a320aa7e2f6adb2e71302fd20 |
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count-lines7-wf_v1_2.cwl
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Path: testdata/count-lines7-wf_v1_2.cwl Branch/Commit ID: 5759b4275906e6cfe13912c8426de2a2237cb4b0 |
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tt_univec_wnode.cwl
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Path: task_types/tt_univec_wnode.cwl Branch/Commit ID: c28cfb9882dedd3c522160f933cff1050ae24100 |
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scatter-valuefrom-wf4.cwl#main
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Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl Branch/Commit ID: c6cced7a2e6389d2eb43342e702677ccb7c7497c Packed ID: main |
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count-lines7-single-source-wf_v1_0.cwl
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Path: testdata/count-lines7-single-source-wf_v1_0.cwl Branch/Commit ID: 5759b4275906e6cfe13912c8426de2a2237cb4b0 |
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Cell Ranger Count (RNA)
Cell Ranger Count (RNA) Quantifies single-cell gene expression of the sequencing data from a single 10x Genomics library. The results of this workflow are primarily used in either “Single-Cell RNA-Seq Filtering Analysis” or “Cell Ranger Aggregate (RNA, RNA+VDJ)” pipelines. |
Path: workflows/single-cell-preprocess-cellranger.cwl Branch/Commit ID: 69643d8c15f5357a320aa7e2f6adb2e71302fd20 |
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ValidateCameraGainsAndEfficiency
Validate pixel gain and efficiency in absolute and relative units. |
Path: workflows/ValidateCameraGainsAndEfficiency.cwl Branch/Commit ID: bf4d4a44a543bcc04f4508ce020751c71550acf5 |
