Explore Workflows

https://github.com/Barski-lab/workflows.git

Path: subworkflows/allele-process-strain.cwl

Branch/Commit ID: fb355eda4555a7e7182a91ce045212b0a087d73f

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **ChIP-Seq** basic analysis workflow for a **paired-end** experiment. A [FASTQ](http://maq.sourceforge.net/fastq.shtml) input file has to be provided. The pipeline produces a sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, coverage by estimated fragments as a BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot. Workflow starts with step *fastx\_quality\_stats* from FASTX-Toolkit to calculate quality statistics for input FASTQ file. At the same time `bowtie` is used to align reads from input FASTQ file to reference genome *bowtie\_aligner*. The output of this step is an unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` *samtools\_sort\_index*. Depending on workflow’s input parameters indexed and sorted BAM file can be processed by `samtools rmdup` *samtools\_rmdup* to get rid of duplicated reads. If removing duplicates is not required the original BAM and BAI files are returned. Otherwise step *samtools\_sort\_index\_after\_rmdup* repeat `samtools sort` and `samtools index` with BAM and BAI files without duplicates. Next `macs2 callpeak` performs peak calling *macs2\_callpeak* and the next step reports *macs2\_island\_count* the number of islands and estimated fragment size. If the latter is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (*macs2\_callpeak\_forced*). It is also possible to force MACS2 to use pre set fragment size in the first place. Next step (*macs2\_stat*) is used to define which of the islands and estimated fragment size should be used in workflow output: either from *macs2\_island\_count* step or from *macs2\_island\_count\_forced* step. If input trigger of this step is set to True it means that *macs2\_callpeak\_forced* step was run and it returned different from *macs2\_callpeak* step results, so *macs2\_stat* step should return [fragments\_new, fragments\_old, islands\_new], if trigger is False the step returns [fragments\_old, fragments\_old, islands\_old], where sufix \"old\" defines results obtained from *macs2\_island\_count* step and sufix \"new\" - from *macs2\_island\_count\_forced* step. The following two steps (*bamtools\_stats* and *bam\_to\_bigwig*) are used to calculate coverage from BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it as a BEDgraph file whichis then sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. Step *get\_stat* is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step *island\_intersect* assigns nearest genes and regions to the islands obtained from *macs2\_callpeak\_forced*. Step *average\_tag\_density* is used to calculate data for average tag density plot from the BAM file.

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

Path: workflows/chipseq-pe.cwl

Branch/Commit ID: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c

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

Path: cwltool/schemas/v1.0/v1.0/basename-fields-test.cwl

Branch/Commit ID: fc6ca8b1498926f705dcfde7ab0a365bd09a9675

Runs the genome retrieval application with genome identifiers from the European Nucleotide Archive

https://git.wageningenur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_ena_retrieval.cwl

Branch/Commit ID: 0dd868de067a386be8ec6b147df007e213c7275a

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

Path: tests/wf/nested.cwl

Branch/Commit ID: 03af16c9df2ee77485d4ab092cd64ae096d2e71c

Single-cell RNA-Seq Alignment Runs Cell Ranger Count to quantify gene expression from a single-cell RNA-Seq library.

https://github.com/Barski-lab/sc-seq-analysis.git

Path: workflows/sc-rna-align-wf.cwl

Branch/Commit ID: e70b7fab45e4bd2abfb7dab2b8b1f79ce904ac69

Workflow runs [Bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) to build indices for reference genome provided in a single FASTA file as fasta_file input. Generated indices are saved in a folder with the name that corresponds to the input genome

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

Path: workflows/bowtie-index.cwl

Branch/Commit ID: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf3.cwl

Branch/Commit ID: e59538cd9899a88d7e31e0f259bc56734f604383

Packed ID: main

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

Path: tools/tRNA_selection.cwl

Branch/Commit ID: 43d2fb8a5430dc56b55e84e3986d0079cad8d185

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: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c