Explore Workflows
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Graph | Name | Retrieved From | View |
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Cellranger reanalyze - reruns secondary analysis performed on the feature-barcode matrix
Devel version of Single-Cell Cell Ranger Reanalyze ================================================== Workflow calls \"cellranger aggr\" command to rerun secondary analysis performed on the feature-barcode matrix (dimensionality reduction, clustering and visualization) using different parameter settings. As an input we use filtered feature-barcode matrices in HDF5 format from cellranger count or aggr experiments. Note, we don't pass aggregation_metadata from the upstream cellranger aggr step. Need to address this issue when needed. |
![]() Path: workflows/cellranger-reanalyze.cwl Branch/Commit ID: 09267e79fd867aa68a219c69e6db7d8e2e877be2 |
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running cellranger mkfastq and count
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![]() Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl Branch/Commit ID: 77ec4f26eb14ed82481828bd9f6ef659cfd8b40f |
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exome alignment and somatic variant detection
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![]() Path: definitions/pipelines/somatic_exome_nonhuman.cwl Branch/Commit ID: 8c4e7372247a7f4ed9ed478ef8ea1d239bc88af0 |
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exome alignment and tumor-only variant detection
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![]() Path: definitions/pipelines/tumor_only_exome.cwl Branch/Commit ID: 389f6edccab082d947bee9c032f59dbdf9f7c325 |
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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/) |
![]() Path: workflows/homer-motif-analysis-bg.cwl Branch/Commit ID: 09267e79fd867aa68a219c69e6db7d8e2e877be2 |
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Apply filters to VCF file
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![]() Path: definitions/subworkflows/filter_vcf.cwl Branch/Commit ID: 2e0562a5c3cd7aac24af4c622a5ae68a7fb23a71 |
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bacterial_screening.cwl
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![]() Path: vecscreen/bacterial_screening.cwl Branch/Commit ID: 75ea689c0a8c9902b4598b453455857cb08e885a |
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readme-genePrediction-workflow.cwl
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![]() Path: flow_create_readme/readme-genePrediction-workflow.cwl Branch/Commit ID: 0b58c250e8ab7c5efae29443f08ea74316127041 |
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Trim and reformat reads (single and paired end version)
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![]() Path: workflows/trim_and_reformat_reads.cwl Branch/Commit ID: 7bb76f33bf40b5cd2604001cac46f967a209c47f |
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Subworkflow that runs cnvkit in single sample mode and returns a vcf file
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![]() Path: definitions/subworkflows/cnvkit_single_sample.cwl Branch/Commit ID: 43c790e2ee6a0f3f42e40fb4d9a9005eb8de747a |