Explore Workflows
View already parsed workflows here or click here to add your own
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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: 675a3ff982091faf304931e9261aacdbabf51702 |
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dynresreq-workflow-tooldefault.cwl
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Path: tests/dynresreq-workflow-tooldefault.cwl Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2 |
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steplevel-resreq.cwl
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Path: cwltool/schemas/v1.0/v1.0/steplevel-resreq.cwl Branch/Commit ID: b82ce7ae901a54c7a062fd5eefd8d5ceb5a4d684 |
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main.cwl
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Path: offline/streamflow/cwl/main.cwl Branch/Commit ID: e2c8ee3c187cb951066909296ead46b784cd2dee |
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Single-Cell Preprocessing Cell Ranger Pipeline
Devel version of Single-Cell Preprocessing Cell Ranger Pipeline =============================================================== |
Path: workflows/single-cell-preprocess-cellranger.cwl Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4 |
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ani_top_n
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Path: task_types/tt_ani_top_n.cwl Branch/Commit ID: 708e141d99f6e5f30d9402d9f890562606a0d97e |
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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-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 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) 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/rnaseq-se.cwl Branch/Commit ID: a68821bf3a9ceadc3b2ffbb535d601d9a645b377 |
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count-lines10-wf.cwl
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Path: cwltool/schemas/v1.0/v1.0/count-lines10-wf.cwl Branch/Commit ID: b82ce7ae901a54c7a062fd5eefd8d5ceb5a4d684 |
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schemadef-wf.cwl
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Path: tests/schemadef-wf.cwl Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b |
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js-expr-req-wf.cwl#wf
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Path: tests/js-expr-req-wf.cwl Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b Packed ID: wf |
