Explore Workflows
View already parsed workflows here or click here to add your own
| Graph | Name | Retrieved From | View |
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Trim Galore RNA-Seq pipeline paired-end strand specific
Modified 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-dutp.cwl Branch/Commit ID: c9e7f3de7f6ba38ee663bd3f9649e8d7dbac0c86 |
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wf-variantcall.cwl
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https://github.com/FarahZKhan/bcbio_test_cwlprov.git
Path: somatic/somatic-workflow/wf-variantcall.cwl Branch/Commit ID: 7c46d5c6ef6501dc0e07a9b740e9de64ffec83f5 |
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RNA-Seq pipeline single-read stranded mitochondrial
Slightly changed 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. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-mitochondrial.cwl Branch/Commit ID: 2f0db4b3c515f91c5cfda19c78cf90d339390986 |
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ocrevaluation-performance-test-files-wf-pack.cwl#main
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https://github.com/kbnlresearch/ochre.git
Path: ochre/cwl/ocrevaluation-performance-test-files-wf-pack.cwl Branch/Commit ID: a62bf3b31df83784c017d30a83ed8e01d454bf1c Packed ID: main |
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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: a839eb6390974089e1a558c49fc07b4c66c50767 |
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QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data
### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data |
https://github.com/datirium/workflows.git
Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl Branch/Commit ID: e0a30aa1ad516dd2ec0e9ce006428964b840daf4 |
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bam_filtering
BAM filtering |
https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git
Path: structuralvariants/cwl/subworkflows/bam_filtering.cwl Branch/Commit ID: 32a040f94e9798bf91858da51598f0d68c35797d |
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RNA-Seq pipeline single-read stranded mitochondrial
Slightly changed 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. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-mitochondrial.cwl Branch/Commit ID: 17a4a68b20e0af656e09714c1f39fe761b518686 |
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RNA-Seq pipeline paired-end stranded mitochondrial
Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific pair-end** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with the pair-end strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-pe-dutp-mitochondrial.cwl Branch/Commit ID: a0b22644ca178b640fb74849d23b7c631022f0b5 |
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dynresreq-workflow-stepdefault.cwl
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https://github.com/common-workflow-language/cwl-v1.2.git
Path: tests/dynresreq-workflow-stepdefault.cwl Branch/Commit ID: 5f27e234b4ca88ed1280dedf9e3391a01de12912 |
