Explore Workflows

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

Path: tests/wf/1st-workflow.cwl

Branch/Commit ID: 0209b0b7ce66f03c8498b5a686f8d31690a2acb3

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: 799575ce58746813f066a665adeacdda252d8cab

https://github.com/Sentieon/Sentieon-cwl.git

Path: stage/tnscope-distr.cwl

Branch/Commit ID: 845f4699c5fce96a4c708a553b3701c9cf296653

ATAC-seq 03 mapping - reads: PE

https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/ATAC-seq_pipeline/03-map-pe.cwl

Branch/Commit ID: 487af88ef0b971f76ecd1a215639bb47e3ee94e1

Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).

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

Path: workflows/bismark-methylation-se.cwl

Branch/Commit ID: 799575ce58746813f066a665adeacdda252d8cab

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

Path: definitions/pipelines/gathered_downsample_and_recall.cwl

Branch/Commit ID: ecac0fda44df3a8f25ddfbb3e7a023fcbe4cbd0f

This workflow creates FASTA file from BED coordinates

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/File-formats/fasta-from-bed.cwl

Branch/Commit ID: b8f18a03ffbd7d5b78a7f220686b81d539686e98

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

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: 1b9094d70f620bb2e51072dd2150150aa4927439

Workflow for Metagenomics from raw reads to annotated bins. Steps: - workflow_quality.cwl: - FastQC (control) - fastp (quality trimming) - bbmap contamination filter - SPAdes (Assembly) - QUAST (Assembly quality report) - BBmap (Read mapping to assembly) - MetaBat2 (binning) - CheckM (bin completeness and contamination) - GTDB-Tk (bin taxonomic classification)

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

Path: cwl/workflows/workflow_metagenomics.cwl

Branch/Commit ID: d6893a25b58b9b25fb76c5e060974b54d9eabc41

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

Path: tests/wf/default-wf5.cwl

Branch/Commit ID: 07ebbea2bdf97955060c1dd563580b386388519b