Explore Workflows

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Graph	Name	Retrieved From	View
	EMG assembly for paired end Illumina	https://github.com/proteinswebteam/ebi-metagenomics-cwl.git Path: workflows/emg-assembly.cwl Branch/Commit ID: 25129f55226dee595ef941edc24d3c44414e0523
	EMG assembly for paired end Illumina	https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git Path: workflows/emg-assembly.cwl Branch/Commit ID: 1b0851e6456ccb1fca237a805ff85c53bc9d58c9
	EMG QC workflow, (paired end version). Benchmarking with MG-RAST expt.	https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git Path: workflows/emg-qc-paired.cwl Branch/Commit ID: 583307878ab83c5845c897f03db920ae8e1929e2
	Running cellranger count and lineage inference	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/single_cell_rnaseq.cwl Branch/Commit ID: 6b365b79675b2aabfb8d5829bb8df0a6e986b037
	Trim Galore RNA-Seq pipeline single-read The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for a single-end experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. Use 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/trim-rnaseq-se.cwl Branch/Commit ID: 9e3c3e65c19873cd1ed3cf7cc3b94ebc75ae0cc5
	BAM to BEDPE Comvert BAM to BEDPE and compress the output	https://github.com/ncbi/cwl-ngs-workflows-cbb.git Path: workflows/File-formats/bamtobedpe-gzip.cwl Branch/Commit ID: 527251ebb77750d02dcc9a370d978a153fc9328f
	kmer_ref_compare_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: 4b73bfeb967ee9f57a0410276f7c39e784f0846f
	maf2vcf_gz_workflow.cwl Workflow to convert a maf file into a vcf.gz with .tbi index	https://github.com/mskcc/pluto-cwl.git Path: cwl/maf2vcf_gz_workflow.cwl Branch/Commit ID: ba3ff09328cc646d7254b2d2ee0fbe1abca3d4ad
	Trim Galore RNA-Seq pipeline single-read strand specific Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for a single-end experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. Use 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/trim-rnaseq-se-dutp.cwl Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd
	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: 581156366f91861bd4dbb5bcb59f67d468b32af3