Explore Workflows

Reverse the lines in a document, then sort those lines.

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

Path: tests/wf/revsort.cwl

Branch/Commit ID: 6300a49ec29be956ab451311fe9781522f461aee

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

Path: tests/wf/default-wf5.cwl

Branch/Commit ID: 1397d96ad97fe8abfd1184675d728a8a04699d67

1. Convert input SRA file into pair of upsrtream and downstream FASTQ files (run fastq-dump) 2. Analyze quality of FASTQ files (run fastqc with each of the FASTQ files) 3. If any of the following fields in fastqc generated report is marked as failed for at least one of input FASTQ files: \"Per base sequence quality\", \"Per sequence quality scores\", \"Overrepresented sequences\", \"Adapter Content\", - trim adapters (run trimmomatic) 4. Align original or trimmed FASTQ files to reference genome, calculate genes and isoforms expression (run RSEM) 5. Count mapped reads number in sorted BAM file (run bamtools stats) 6. Generate genome coverage BED file (run bedtools genomecov) 7. Sort genearted BED file (run sort) 8. Generate genome coverage bigWig file from BED file (run bedGraphToBigWig)

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

Path: workflows/xenbase-rnaseq-pe.cwl

Branch/Commit ID: d6f58c383d0676269afb519399061191a1144a6a

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

Path: definitions/pipelines/germline_wgs_gvcf.cwl

Branch/Commit ID: 00df82a529a58d362158110581e1daa28b4d7ecb

https://github.com/process-project/PREFACTOR-XENON-CWL.git

Path: workflow1.cwl

Branch/Commit ID: 55fde09d37cb68efafca77ec8d59f2b4428e0d3e

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/conditionals/cond-wf-010.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

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

Path: genomic_source/wf_genomic_source.cwl

Branch/Commit ID: 5b498b4c4f17bb8f17e6886aa4c5661d7aba34fc

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/output-arrays-file-wf.cwl

Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b

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: 93b844a80f4008cc973ea9b5efedaff32a343895

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: ef266744578e2dcbce57c110c6fa3b9eee91e316