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Graph Name Retrieved From View
workflow graph 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

https://git.wageningenur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_ngtax_picrust2.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0
workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 803f6367d1b279a7b6dc1a4e8ae43f1bbec9f760
workflow graph tt_univec_wnode.cwl

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

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f
workflow graph scatter-wf1.cwl

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf1.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b
workflow graph allele-rnaseq-pe.cwl

Allele specific RNA-Seq paired-end workflow

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

Path: workflows/allele-rnaseq-pe.cwl

Branch/Commit ID: cf107bc24a37883ef01b959fd89c19456aaecc02
workflow graph dedup-3-pass-distr.cwl

run 3-pass dedup: algo LocusCollector + algo Dedup output_dup_read_name + algo Dedup dedup_by_read_name sequentially in distributed mode

 https://github.com/sentieon/sentieon-cwl.git

Path: stage/dedup-3-pass-distr.cwl

Branch/Commit ID: d20382adfe7285cb517a25d95d2bcb7586546e23
workflow graph DESeq - differential gene expression analysis

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: 4ab9399a4777610a579ea2c259b9356f27641dcc
workflow graph extract_gencoll_ids

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

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f
workflow graph align_sort_sa

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: 61eaea2f746c8a1fc2a2b731056b068e28ca4e20
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: 4ab9399a4777610a579ea2c259b9356f27641dcc