Explore Workflows

https://github.com/nal-i5k/organism_onboarding.git

Path: flow_genomicsWorkspace/genomics-workspace.cwl

Branch/Commit ID: 0b58c250e8ab7c5efae29443f08ea74316127041

FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application

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

Path: workflows/fastqc.cwl

Branch/Commit ID: 09267e79fd867aa68a219c69e6db7d8e2e877be2

Some workflow engines won't stage files in our nested structure, so parse it out here

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

Path: definitions/subworkflows/sequence_align_and_tag_adapter.cwl

Branch/Commit ID: 336f7d1af649f42543baa6be2594cd872919b5b5

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

Path: flow_create_genomics-workspace_yml/flow_create_yml/workflow.cwl

Branch/Commit ID: 0b58c250e8ab7c5efae29443f08ea74316127041

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 70e530b65b33301032b7510095d89e497bf5e34e

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: 09267e79fd867aa68a219c69e6db7d8e2e877be2

This workflow retrieve SRA fastqc data and execute QC, alignment and quantification from TPMCalculator

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

Path: workflows/RNA-Seq/rnaseq-alignment-quantification.cwl

Branch/Commit ID: e541470bc9d0b064bc4ed7dd2b45d8ec67760613

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: 42df0c0f9a4e5697abadd9cb52440691fafc8f5d

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

Path: definitions/pipelines/alignment_exome.cwl

Branch/Commit ID: 336f7d1af649f42543baa6be2594cd872919b5b5

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

Path: vecscreen/vecscreen.cwl

Branch/Commit ID: 75ea689c0a8c9902b4598b453455857cb08e885a