Explore Workflows

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

Path: definitions/subworkflows/bisulfite_qc.cwl

Branch/Commit ID: 2f65fc96207a71b1cda4e246f808bed056608cd0

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

Path: tests/count-lines12-wf.cwl

Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.

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

Path: workflows/gseapy.cwl

Branch/Commit ID: ee66d03be8a7fd61367db40c37a973ff55ece4da

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: 31602b94b34ff55876147c7299e1bec47e8d1a31

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

Path: tests/wf/default-dir5.cwl

Branch/Commit ID: bffea7fd5e864c5221c13a815d00d0a2fad178cc

https://github.com/Marco-Salvi/dtc51.git

Path: ST520104.cwl

Branch/Commit ID: 272db37d2b8108a146769f0fb0383bb824c9788f

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

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

Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: 66b5bc323dcd23e1b2c14bf4783babf0f15ca43b

Runs RNA-Seq BioWardrobe basic analysis with strand specific pair-end data file.

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

Path: workflows/trim-rnaseq-pe-dutp.cwl

Branch/Commit ID: a9551ece898f619167db58e4b74a6cae2d7f7d13

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: 4106b7dc96e968db291b7a61ecd1641aa3b3dd6d