Explore Workflows
View already parsed workflows here or click here to add your own
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blastp_wnode_naming
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https://github.com/ncbi/pgap.git
Path: task_types/tt_blastp_wnode_naming.cwl Branch/Commit ID: 664e99a23a3ed4ba36c08323ac597c4fbcd88df1 |
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Cell Ranger Build Reference Indices
Cell Ranger Build Reference Indices =================================== |
https://github.com/datirium/workflows.git
Path: workflows/cellranger-mkref.cwl Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620 |
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SoupX Estimate
SoupX Estimate ============== |
https://github.com/datirium/workflows.git
Path: workflows/soupx.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b |
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bwa_mem
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https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git
Path: structuralvariants/cwl/subworkflows/bwa_mem.cwl Branch/Commit ID: 572d9e6b9264d98967b33d18110b0e1979b21d6c |
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kmer_ref_compare_wnode
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: 664e99a23a3ed4ba36c08323ac597c4fbcd88df1 |
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genomel_cohort_freebayes.cwl
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https://github.com/uc-cdis/genomel_pipelines.git
Path: genomel/genomel_cohort_freebayes.cwl Branch/Commit ID: 7504ead048c3acd64b9b92e44d044d558cb696f2 |
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group-isoforms-batch.cwl
Workflow runs group-isoforms.cwl tool using scatter for isoforms_file input. genes_filename and common_tss_filename inputs are ignored. |
https://github.com/Barski-lab/workflows.git
Path: tools/group-isoforms-batch.cwl Branch/Commit ID: 568da91bb1c6182ba4f146e2a729cac1c3d8783c |
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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-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. 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/rnaseq-se.cwl Branch/Commit ID: 104059e07a2964673e21d371763e33c0afeb2d03 |
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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: ad948b2691ef7f0f34de38f0102c3cd6f5182b29 |
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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 **strand specific single-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. 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/rnaseq-se-dutp.cwl Branch/Commit ID: 6bf56698c6fe6e781723dea32bc922b91ef49cf3 |
