Explore Workflows
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Subworkflow to allow calling cnvkit with cram instead of bam files
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![]() Path: definitions/subworkflows/cram_to_cnvkit.cwl Branch/Commit ID: 72e0bdc1ec449d86df4534132e9a30ad7e9b8afd |
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Add snv and indel bam-readcount files to a vcf
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![]() Path: definitions/subworkflows/vcf_readcount_annotator.cwl Branch/Commit ID: 25aa4788dd4efb1cc8ed6f609cb7803896e4d28d |
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iwdr_with_nested_dirs.cwl
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![]() Path: cwltool/schemas/v1.0/v1.0/iwdr_with_nested_dirs.cwl Branch/Commit ID: 8010fd2bf1e7090ba6df6ca8c84bbb96e2272d32 |
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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: b4d578c2ba4713a5a22163d9f8c7105acda1f22e |
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fasta2taxa-plot
Input is a fasta file with n>1 samples, with sample id as sequence identifier prefix, and a sample id file. The workflow calls open otus and assigns taxa using greengenes. The output are taxa plots. |
![]() Path: CWL/Workflows/qiime/join-reads2plot.cwl Branch/Commit ID: 89758e17aaef533f68f01055eaaff88f400206db |
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phaseup-concat.cwl
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![]() Path: workflows/phaseup-concat.cwl Branch/Commit ID: 2a2fddfa07861980453a3e6a934f10f5814a588e |
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umi duplex alignment workflow
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![]() Path: definitions/subworkflows/duplex_alignment.cwl Branch/Commit ID: ffab5424bb8b5905aecf6f8e2e6387da7f3df562 |
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scatter GATK HaplotypeCaller over intervals
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![]() Path: definitions/subworkflows/gatk_haplotypecaller_iterator.cwl Branch/Commit ID: ec45fad68ca10fb64d5c58e704991b146dc31d28 |
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Feature expression merge - combines feature expression from several experiments
Feature expression merge - combines feature expression from several experiments ========================================================================= Workflows merges RPKM (by default) gene expression from several experiments based on the values from GeneId, Chrom, TxStart, TxEnd and Strand columns (by default). Reported unique columns are renamed based on the experiments names. |
![]() Path: workflows/feature-merge.cwl Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081 |
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bowtie-index.cwl
Generates indices for bowtie v1.2.0 (12/30/2016). |
![]() Path: workflows/bowtie-index.cwl Branch/Commit ID: cb5e5b8563be4977e9f2babc14fe084faa234847 |