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Graph Name Retrieved From View
workflow graph workflow_input_format_expr_v1_2.cwl

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

Path: testdata/workflow_input_format_expr_v1_2.cwl

Branch/Commit ID: 124a08ce3389eb49066c34a4163cbbed210a0355
workflow graph bulk-atac-seq-pipeline.cwl

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

Path: bulk-atac-seq-pipeline.cwl

Branch/Commit ID: 5465f666aac446a6d66fed1819e48a79395f6b02
workflow graph Bismark Methylation - pipeline for BS-Seq data analysis

Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).

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

Path: workflows/bismark-methylation-se.cwl

Branch/Commit ID: 7eef0294395d83ff0765fce61726a59d71126422
workflow graph extract_gencoll_ids

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

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d
workflow graph umi duplex alignment workflow

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

Path: definitions/subworkflows/duplex_alignment.cwl

Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe
workflow graph count-lines14-wf.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines14-wf.cwl

Branch/Commit ID: 1f3ef888d9ef2306c828065c460c1800604f0de4
workflow graph kmer_compare_wnode

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: ca75d68eb74c93b35b404ec7908dc5b260e16466
workflow graph Seed Search Compartments

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

Path: protein_alignment/wf_seed.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d
workflow graph bact_get_kmer_reference

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d
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: ce058d892d330125cd03d0a0d5fb3b321cda0be3