Explore Workflows
View already parsed workflows here or click here to add your own
Graph | Name | Retrieved From | View |
---|---|---|---|
running cellranger mkfastq and count
|
https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl Branch/Commit ID: 869b331cfeb9dbd5907498e3eccdebc7c28283e5 |
||
Motif Finding with HOMER with custom background regions
Motif Finding with HOMER with custom 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. 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-bg.cwl Branch/Commit ID: 581156366f91861bd4dbb5bcb59f67d468b32af3 |
||
RNA-Seq alignment and transcript/gene abundance workflow
|
https://github.com/genome/analysis-workflows.git
Path: definitions/pipelines/rnaseq.cwl Branch/Commit ID: 174f3b239018328cec1d821947438b457552724c |
||
tt_hmmsearch_wnode.cwl
|
https://github.com/ncbi/pgap.git
Path: task_types/tt_hmmsearch_wnode.cwl Branch/Commit ID: 1e7aa9f0c34987ddafa35f9b1d2c77d99fafbdab |
||
Apply filters to VCF file
|
https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/germline_filter_vcf.cwl Branch/Commit ID: 889a077a20c0fdb01f4ed97aa4bc40f920c37a1a |
||
Cell Ranger ARC Aggregate
Cell Ranger ARC Aggregate ========================= |
https://github.com/datirium/workflows.git
Path: workflows/cellranger-arc-aggr.cwl Branch/Commit ID: 581156366f91861bd4dbb5bcb59f67d468b32af3 |
||
workflow.cwl
|
https://github.com/NAL-i5K/Organism_Onboarding.git
Path: flow_dispatch/2working_files/workflow.cwl Branch/Commit ID: 8b8c6dd16e06b43fbb50f1c0821856a31f1bbbc5 |
||
bact_get_kmer_reference
|
https://github.com/ncbi/pgap.git
Path: task_types/tt_bact_get_kmer_reference.cwl Branch/Commit ID: 92118627c800e4addb7e29b9dabcca073a5bae71 |
||
PCA - Principal Component Analysis
Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. |
https://github.com/datirium/workflows.git
Path: workflows/pca.cwl Branch/Commit ID: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c |
||
kmer_cache_retrieve
|
https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_cache_retrieve.cwl Branch/Commit ID: 8fb4ac7f5a66897206c7469101a471108b06eada |