Explore Workflows

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: 8a92669a566589d80fde9d151054ffc220ed4ddd

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/16S_taxonomic_analysis.cwl

Branch/Commit ID: 3f85843d4a6debdabe96bc800bf2a4efdcda1ef3

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/samtools_mpileup_subpipeline.cwl

Branch/Commit ID: ccca639fe0b3a8104ff9fcfa285f1134706032b8

### Pipeline for Lexogen's QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina [Lexogen original documentation](https://www.lexogen.com/quantseq-3mrna-sequencing/) * Cost-saving and streamlined globin mRNA depletion during QuantSeq library preparation * Genome-wide analysis of gene expression * Cost-efficient alternative to microarrays and standard RNA-Seq * Down to 100 pg total RNA input * Applicable for low quality and FFPE samples * Single-read sequencing of up to 9,216 samples/lane * Dual indexing and Unique Molecular Identifiers (UMIs) are available ### QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina The QuantSeq FWD Kit is a library preparation protocol designed to generate Illumina compatible libraries of sequences close to the 3’ end of polyadenylated RNA. QuantSeq FWD contains the Illumina Read 1 linker sequence in the second strand synthesis primer, hence NGS reads are generated towards the poly(A) tail, directly reflecting the mRNA sequence (see workflow). This version is the recommended standard for gene expression analysis. Lexogen furthermore provides a high-throughput version with optional dual indexing (i5 and i7 indices) allowing up to 9,216 samples to be multiplexed in one lane. #### Analysis of Low Input and Low Quality Samples The required input amount of total RNA is as low as 100 pg. QuantSeq is suitable to reproducibly generate libraries from low quality RNA, including FFPE samples. See Fig.1 and 2 for a comparison of two different RNA qualities (FFPE and fresh frozen cryo-block) of the same sample.  Figure 1 | Correlation of gene counts of FFPE and cryo samples.  Figure 2 | Venn diagrams of genes detected by QuantSeq at a uniform read depth of 2.5 M reads in FFPE and cryo samples with 1, 5, and 10 reads/gene thresholds. #### Mapping of Transcript End Sites By using longer reads QuantSeq FWD allows to exactly pinpoint the 3’ end of poly(A) RNA (see Fig. 3) and therefore obtain accurate information about the 3’ UTR.  Figure 3 | QuantSeq read coverage versus normalized transcript length of NGS libraries derived from FFPE-RNA (blue) and cryo-preserved RNA (red). ### Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Separates UMIes and trims adapters from input FASTQ file 2. Uses ```STAR``` to align reads from input FASTQ file according to the predefined reference indices; generates unsorted BAM file and alignment statistics file 3. Uses ```fastx_quality_stats``` to analyze input FASTQ file and generates quality statistics file 4. Uses ```samtools sort``` and generates coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 2 (after running STAR) 5. Uses ```umi_tools dedup``` and generates final filtered sorted BAM(+BAI) file pair 6. Generates BigWig file on the base of sorted BAM file 7. Maps input FASTQ file to predefined rRNA reference indices using ```bowtie``` to define the level of rRNA contamination; exports resulted statistics to file 8. Calculates isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; exports results to file

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

Path: workflows/trim-quantseq-mrnaseq-se.cwl

Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd

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

Path: tests/wf/sec-wf.cwl

Branch/Commit ID: e6c2d955a448225f026a04130443d13661844440

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/conditional_markduplicates.cwl

Branch/Commit ID: dd7f86b3cc10eb1cda07dc2fc279ba2529c8ad61

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

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

Path: tests/wf/revsort_datetime.cwl

Branch/Commit ID: aec33fcfa3459a90cbba8c88ebb991be94d21429

Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order.

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

Path: tools/heatmap-prepare.cwl

Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/emg-assembly.cwl

Branch/Commit ID: 3f85843d4a6debdabe96bc800bf2a4efdcda1ef3

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/integrity.cwl

Branch/Commit ID: dd7f86b3cc10eb1cda07dc2fc279ba2529c8ad61