Explore Workflows

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

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: 369afa7090a7480e6a0b144eff967a4a52b6fde2

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/samtools_sort.cwl

Branch/Commit ID: b62c7bfcf5eb7ac3c1ed06879200fdf5db947e4b

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 656113dcac0de7cef6cff6c688f61441ee05872a

GAT: Genomic Association Tester ============================================== A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, in the interpretation of ChIP-Seq or RNA-Seq data. The Genomic Association Tester (GAT) is a tool for computing the significance of overlap between multiple sets of genomic intervals. GAT estimates significance based on simulation. Gat implemements a sampling algorithm. Given a chromosome (workspace) and segments of interest, for example from a ChIP-Seq experiment, gat creates randomized version of the segments of interest falling into the workspace. These sampled segments are then compared to existing genomic annotations. The sampling method is conceptually simple. Randomized samples of the segments of interest are created in a two-step procedure. Firstly, a segment size is selected from to same size distribution as the original segments of interest. Secondly, a random position is assigned to the segment. The sampling stops when exactly the same number of nucleotides have been sampled. To improve the speed of sampling, segment overlap is not resolved until the very end of the sampling procedure. Conflicts are then resolved by randomly removing and re-sampling segments until a covering set has been achieved. Because the size of randomized segments is derived from the observed segment size distribution of the segments of interest, the actual segment sizes in the sampled segments are usually not exactly identical to the ones in the segments of interest. This is in contrast to a sampling method that permutes segment positions within the workspace.

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

Path: workflows/gat-run.cwl

Branch/Commit ID: 10ce6e113f749c7bd725e426445220c3bdc5ddf1

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

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

Path: tests/wf/trick_revsort.cwl

Branch/Commit ID: 12993a6eb60f5ccb4edbe77cb6de661cfc496090

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 8cc9b995bca666c54c673a5eb8d9b8c6f8e84490

https://github.com/CRI-iAtlas/iatlas-workflows.git

Path: Kallisto/workflow/kallisto_scatter_synapse_paired_end_workflow.cwl

Branch/Commit ID: 3acab4d22ff0f9657dc8c5685799898a2fc2fd25

Single-cell Differential Expression =================================== Runs differential expression analysis for a subset of cells between two selected conditions.

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

Path: workflows/sc_diff_expr.cwl

Branch/Commit ID: 10ce6e113f749c7bd725e426445220c3bdc5ddf1

Seurat Cluster ============== Runs filtering, integration, and clustering analyses for Cell Ranger Count Gene Expression or Cell Ranger Aggregate experiments.

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

Path: workflows/seurat-cluster.cwl

Branch/Commit ID: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb

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: d1bef74924efcb8bfaa00987b3f148d5a192b7a9