Explore Workflows

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.

https://github.com/datirium/workflows.git

Path: workflows/pca.cwl

Branch/Commit ID: 664de58d95728edbf7d369d894f9037ebe2475fa

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/sum-wf.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

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

https://github.com/datirium/workflows.git

Path: workflows/rnaseq-pe-dutp.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/count-lines7-wf_v1_0.cwl

Branch/Commit ID: 7af75226f084349e401b1114f25bdcdee060e127

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.

https://github.com/Barski-lab/workflows.git

Path: subworkflows/heatmap-prepare.cwl

Branch/Commit ID: ca2dbb71d0537b1d93a8bd44719250cf8949b157

An example workflow created using Autosubmit's basic a000 workflow as reference. The `platform.yml` is ignored as it contains only information about platforms (e.g. it could be given to a tool like Troika as-is). `expdef.yml` and `autosubmit.yml` basically provide CWL inputs. `jobs.yml` contains the steps of the CWL workflow, with their dependencies. In CWL dependencies are specified via inputs and outputs. When task A outputs a value X, and task B has an input of type A/X, then the dependency A -> B is created in CWl. This is different than Autosubmit, and needs some care to guarantee the correct order in the workflow graph of start dates, members, chunks, etc.

https://github.com/kinow/kinoshita.eti.br.git

Path: notes/autosubmit/autosubmit.cwl

Branch/Commit ID: b785efcfde521ce05882006355019187ab67fac7

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

https://github.com/datirium/workflows.git

Path: workflows/rnaseq-se.cwl

Branch/Commit ID: ebbf23764ede324cabc064bd50647c1f643726fa

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: steps/sc_atac_seq_initial_analysis.cwl

Branch/Commit ID: 3da5dd0c6f974ec62f78d654f0ce7948975e741f

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/step_valuefrom5_wf_with_id_v1_2.cwl

Branch/Commit ID: 7af75226f084349e401b1114f25bdcdee060e127

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: steps/sc_atac_seq_prep_process_init.cwl

Branch/Commit ID: 3da5dd0c6f974ec62f78d654f0ce7948975e741f