Explore Workflows

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Graph Name Retrieved From View
workflow graph protein similarities

run diamond on mutlple DBs and merge-sort results

 https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/protein-diamond.workflow.cwl

Branch/Commit ID: 81feefc84ec0faecf1ade718001d5f07610e616e
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: e0a30aa1ad516dd2ec0e9ce006428964b840daf4
workflow graph tt_hmmsearch_wnode.cwl

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

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: 22ffe27d9d4a899def7592d75d5871c1856adbdb
workflow graph protein_extract

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

Path: progs/protein_extract.cwl

Branch/Commit ID: 22ffe27d9d4a899def7592d75d5871c1856adbdb
workflow graph HS Metrics workflow

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

Path: definitions/subworkflows/hs_metrics.cwl

Branch/Commit ID: 0d2f354af9192a56af258a7d2426c7c160f4ec1a
workflow graph QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

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

Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl

Branch/Commit ID: 10ce6e113f749c7bd725e426445220c3bdc5ddf1
workflow graph readgroup_fastq_pe.cwl

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/readgroup_fastq_pe.cwl

Branch/Commit ID: 8edf6a5e4e7790434ad0742e50d0c97a5d0bb846
workflow graph extract_readgroup_fastq_pe_http.cwl

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/extract_readgroup_fastq_pe_http.cwl

Branch/Commit ID: dd7f86b3cc10eb1cda07dc2fc279ba2529c8ad61
workflow graph revsort.cwl

Reverse the lines in a document, then sort those lines.

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

Path: cwltool/schemas/v1.0/v1.0/revsort.cwl

Branch/Commit ID: 227f35a5ed50c423afba2353871950aa61d58872
workflow graph RNA-Seq pipeline paired-end stranded mitochondrial

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific pair-end** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with the pair-end strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-pe-dutp-mitochondrial.cwl

Branch/Commit ID: 8049a781ac4aae579fbd3036fa0bf654532f15be