Explore Workflows
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RNA-Seq pipeline paired-end strand specific
The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-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 paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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/rnaseq-pe-dutp.cwl Branch/Commit ID: e99e80a2c19682d59947bde04a892d7b6d90091c |
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Quality assessment, amplicon classification and functional prediction
Workflow for quality assessment of paired reads and classification using NGTax 2.0 and functional annotation using picrust2. In addition files are exported to their respective subfolders for easier data management in a later stage. Steps: - FastQC (read quality control) - NGTax 2.0 - Picrust 2 - Export module for ngtax |
Path: cwl/workflows/workflow_ngtax_picrust2.cwl Branch/Commit ID: d6893a25b58b9b25fb76c5e060974b54d9eabc41 |
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SAMSA2 pipeline
SAMSA2 complete workflow for meta-omics read annotation Steps: - Diamond read blastx - Refseq - SEED - SAMSA2 processing |
Path: cwl/workflows/workflow_samsa2.cwl Branch/Commit ID: d6893a25b58b9b25fb76c5e060974b54d9eabc41 |
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Detect Variants workflow
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Path: definitions/pipelines/detect_variants_mouse.cwl Branch/Commit ID: 0805e8e0d358136468e0a9f49e06005e41965adc |
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taxonomy_check_16S
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Path: task_types/tt_taxonomy_check_16S.cwl Branch/Commit ID: 23f0ee7a36649ab37cabdd9277b7c82d098be79c |
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PCA - Principal Component Analysis
Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. |
Path: workflows/pca.cwl Branch/Commit ID: e99e80a2c19682d59947bde04a892d7b6d90091c |
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heatmap-prepare.cwl
Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order. |
Path: subworkflows/heatmap-prepare.cwl Branch/Commit ID: 7290eb220c90a087dffde04efae3678286aa92d0 |
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env-wf1.cwl
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Path: tests/env-wf1.cwl Branch/Commit ID: a0f2d38e37ff51721fdeaf993bb2ab474b17246b |
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mutect panel-of-normals workflow
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Path: definitions/pipelines/panel_of_normals.cwl Branch/Commit ID: ec5355f335852e51c6938809c16ea1d230a3f983 |
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pass-unconnected.cwl
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Path: tests/pass-unconnected.cwl Branch/Commit ID: b1d4a69df86350059bd49aa127c02be0c349f7de |
