Explore Workflows
View already parsed workflows here or click here to add your own
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bam to trimmed fastqs and HISAT alignments
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Path: definitions/subworkflows/bam_to_trimmed_fastq_and_hisat_alignments.cwl Branch/Commit ID: 5cb188131f786ed33156e2f0e3dd63ab9c04245d |
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Seurat Cluster
Seurat Cluster ============== Runs filtering, integration, and clustering analyses for Cell Ranger Count Gene Expression or Cell Ranger Aggregate experiments. |
Path: workflows/seurat-cluster.cwl Branch/Commit ID: bf80c9339d81a78aefb8de661bff998ed86e836e |
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exome alignment and germline variant detection
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Path: definitions/subworkflows/germline_detect_variants.cwl Branch/Commit ID: 7b4b489474473c3d2d992a838b89632c2b97dc2c |
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wgs alignment with qc
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Path: definitions/pipelines/wgs_alignment.cwl Branch/Commit ID: d2c2f2eb846ae2e9cdcab46e3bb88e42126cb3f5 |
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createindex_singlevirus.cwl
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Path: workflow/createindex_singlevirus.cwl Branch/Commit ID: 43982758be93a31a0c079f448b377cae9fb9f3c7 |
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assm_assm_blastn_wnode
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Path: task_types/tt_assm_assm_blastn_wnode.cwl Branch/Commit ID: 252e7214ac64cb1128881e76743013e61bc7ec38 |
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FastQC - a quality control tool for high throughput sequence data
FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application |
Path: workflows/fastqc.cwl Branch/Commit ID: 10ce6e113f749c7bd725e426445220c3bdc5ddf1 |
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Cellranger Reanalyze
Cellranger Reanalyze ==================== |
Path: workflows/cellranger-reanalyze.cwl Branch/Commit ID: ebbf23764ede324cabc064bd50647c1f643726fa |
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exome alignment and somatic variant detection
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Path: definitions/pipelines/somatic_exome.cwl Branch/Commit ID: 051074fce4afd9732ef34db9dd43d3a1d8e979d6 |
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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: c6bfa0de917efb536dd385624fc7702e6748e61d |
