Explore Workflows

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_dispatch/workflow.cwl

Branch/Commit ID: aa375dcaa5ccfbb4e2aa4433d10948c641b044eb

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_apollo2_data_processing/processing/workflow.cwl

Branch/Commit ID: aa375dcaa5ccfbb4e2aa4433d10948c641b044eb

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: 2801ce53744a085580a8de91cd007c45146b51e8

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

Path: definitions/subworkflows/joint_genotype.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8

Workflow converts input BAM file into bigWig and bedGraph files. Input BAM file should be sorted by coordinates (required by `bam_to_bedgraph` step). If `split` input is not provided use true by default. Default logic is implemented in `valueFrom` field of `split` input inside `bam_to_bedgraph` step to avoid possible bug in cwltool with setting default values for workflow inputs. `scale` has higher priority over the `mapped_reads_number`. The last one is used to calculate `-scale` parameter for `bedtools genomecov` (step `bam_to_bedgraph`) only in a case when input `scale` is not provided. All logic is implemented inside `bedtools-genomecov.cwl`. `bigwig_filename` defines the output name only for generated bigWig file. `bedgraph_filename` defines the output name for generated bedGraph file and can influence on generated bigWig filename in case when `bigwig_filename` is not provided. All workflow inputs and outputs don't have `format` field to avoid format incompatibility errors when workflow is used as subworkflow.

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

Path: tools/bam-bedgraph-bigwig.cwl

Branch/Commit ID: 3d280a2a4b4f1560f56991086f712fa22ddc3364

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: 3d280a2a4b4f1560f56991086f712fa22ddc3364

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: 64c3985e5ce7fa16ce7692faa879c5d58f31089f

CNV ExomeDepth calling

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

Path: structuralvariants/cwl/abstract_operations/subworkflows/cnv_exome_depth.cwl

Branch/Commit ID: 26ae4914651d5b3e188028d1e9d88a391b3f6730

Motif Finding with HOMER with random background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis.cwl

Branch/Commit ID: 3d280a2a4b4f1560f56991086f712fa22ddc3364

https://github.com/InsightSoftwareConsortium/GetYourBrainStraight.git

Path: HCK01_2022_Virtual/Tutorials/GetYourBrainPipelined/CWL-Demo/examplePipeline.cwl

Branch/Commit ID: e1d1de4a01a201201ddaf2b386aaa248fb043fdc