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Graph Name Retrieved From View
workflow graph assm_assm_blastn_wnode

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: ac387721a55fd91df3dcdf16e199354618b136d1
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: 57863b6131d8262c5ce864adaf8e4038401e71a2
workflow graph gathered exome alignment and somatic variant detection

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

Path: definitions/pipelines/somatic_exome_gathered.cwl

Branch/Commit ID: cc3e7f1ccfdc7101c22bf88792608504eea7d53a
workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: cd97086739ae5988bab09b05e9259675c4b6bce6
workflow graph mut2.cwl

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

Path: tests/wf/mut2.cwl

Branch/Commit ID: 0e98de8f692bb7b9626ed44af835051750ac20cd
workflow graph Cell Ranger ARC Aggregate

Cell Ranger ARC Aggregate =========================

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

Path: workflows/cellranger-arc-aggr.cwl

Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf
workflow graph Kallisto index pipeline

This workflow indexes the input reference FASTA with kallisto, and generates a kallisto index file (.kdx). This index sample can then be used as input into the kallisto transcript-level quantification workflow (kallisto-quant-pe.cwl), or others that may include this workflow as an upstream source. ### __Inputs__ - FASTA file of the reference genome that will be indexed - number of threads to use for multithreading processes ### __Outputs__ - kallisto index file (.kdx). - stdout log file (output in Overview tab as well) - stderr log file ### __Data Analysis Steps__ 1. cwl calls dockercontainer robertplayer/scidap-kallisto to index reference FASTA with `kallisto index`, generating a kallisto index file. ### __References__ - Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525-527(2016), doi:10.1038/nbt.3519

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

Path: workflows/kallisto-index.cwl

Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2
workflow graph SoupX Estimate

SoupX Estimate ==============

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

Path: workflows/soupx.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908
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: cbefc215d8286447620664fb47076ba5d81aa47f
workflow graph FASTQ to BQSR

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

Path: definitions/subworkflows/fastq_to_bqsr.cwl

Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5