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Graph Name Retrieved From View
workflow graph rnaseq-pe.cwl

Runs RNA-Seq BioWardrobe basic analysis with pair-end data file.

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

Path: workflows/rnaseq-pe.cwl

Branch/Commit ID: e89b2c17aa5efccef6ca424dec5a0a021bd8d20c
workflow graph Build Bismark indices

Copy fasta_file file to the folder and run run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the genome input.

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

Path: workflows/bismark-index.cwl

Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4
workflow graph 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: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4
workflow graph RNASelector as a CWL workflow

https://doi.org/10.1007/s12275-011-1213-z

 https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: workflows/rna-selector.cwl

Branch/Commit ID: 5e8217435bcdd597b2ad236f3e847d13d4c21824
workflow graph extract_gencoll_ids

https://github.com/ncbi/pgap.git

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 8fb4ac7f5a66897206c7469101a471108b06eada
workflow graph kmer_top_n_extract

https://github.com/ncbi/pgap.git

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 72804b6506c9f54ec75627f82aafe6a28d7a49fa
workflow graph scatter-valuefrom-wf4.cwl#main

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: e8b3565a008d95859fc44227987a54e6a53a8c29

Packed ID: main
workflow graph 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.

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

Path: workflows/pca.cwl

Branch/Commit ID: 4dcc405133f22c63478b6091fb5f591b6be8950f
workflow graph SetParameterFromExternal

Receive parameter update (e.g., by querying an external source like a configuration or calibration database) or by expert input (e.g., by a member of a telescope team or a simulation pipeline expert).

 https://gitlab.desy.de/gernot.maier/cwlsandbox.git

Path: workflows/SetParameterFromExternal.cwl

Branch/Commit ID: 93ff5764b93a75088fa507912fa52f5feedb9690
workflow graph 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

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

Path: workflows/fastqc.cwl

Branch/Commit ID: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b