Explore Workflows

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Graph Name Retrieved From View
workflow graph exome alignment and germline variant detection

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/germline_exome.cwl

Branch/Commit ID: 0c4855bf23622828413ecb09dd30754691c28014
workflow graph umi duplex alignment workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/duplex_alignment.cwl

Branch/Commit ID: 3ee63d8757c341ca98b3b46ec4782862ad19b710
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: a68821bf3a9ceadc3b2ffbb535d601d9a645b377
workflow graph Replace legacy AML Trio Assay

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/aml_trio_cle.cwl

Branch/Commit ID: 3f3b186da9bf82a5e2ae74ba27aef35a46174ebe
workflow graph Detect DoCM variants

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: 3ee63d8757c341ca98b3b46ec4782862ad19b710
workflow graph Varscan Workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/varscan_germline.cwl

Branch/Commit ID: 3ee63d8757c341ca98b3b46ec4782862ad19b710
workflow graph cram_to_bam workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/cram_to_bam_and_index.cwl

Branch/Commit ID: 3ee63d8757c341ca98b3b46ec4782862ad19b710
workflow graph cluster_blastp_wnode and gpx_qdump combined

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d
workflow graph Detect Variants workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/detect_variants_nonhuman.cwl

Branch/Commit ID: 7638b3075863ae8172f4adaec82fb2eb8e80d3d5
workflow graph kmer_seq_entry_extract_wnode

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: e23abd0bc049a2983bd3b12dddca19d3b04506f2