Explore Workflows
View already parsed workflows here or click here to add your own
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kmer_top_n_extract
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Path: task_types/tt_kmer_top_n_extract.cwl Branch/Commit ID: cd97086739ae5988bab09b05e9259675c4b6bce6 |
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mut2.cwl
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Path: tests/wf/mut2.cwl Branch/Commit ID: 0e98de8f692bb7b9626ed44af835051750ac20cd |
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Cell Ranger ARC Aggregate
Cell Ranger ARC Aggregate ========================= |
Path: workflows/cellranger-arc-aggr.cwl Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf |
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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 |
Path: workflows/kallisto-index.cwl Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2 |
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SoupX Estimate
SoupX Estimate ============== |
Path: workflows/soupx.cwl Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908 |
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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. |
Path: workflows/deseq.cwl Branch/Commit ID: cbefc215d8286447620664fb47076ba5d81aa47f |
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FASTQ to BQSR
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Path: definitions/subworkflows/fastq_to_bqsr.cwl Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5 |
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dynresreq-workflow-tooldefault.cwl
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Path: tests/dynresreq-workflow-tooldefault.cwl Branch/Commit ID: e515226f8ac0f7985cd94dae4a301150adae3050 |
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Interval overlapping alignments counts
Interval overlapping alignments counts ====================================== Reports the count of alignments from multiple samples that overlap specific intervals. |
Path: workflows/bedtools-multicov.cwl Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf |
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Tag enrichment heatmap and density profile around regions of interest
Generates tag density heatmap and histogram for the centered list of features in a headerless regions file. - If provided regions file is a gene list with the following columns `chrom start end name score strand` set `Gene TSS` as a re-centering criteria. - If provided regions file is a peak list with the following columns `chrom start end name` set `Peak Center` as a re-centering criteria. `score` column is always ignored. |
Path: workflows/heatmap.cwl Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2 |
