Workflows

What is a Workflow?
763 Workflows visible to you, out of a total of 825
Use Case 10: Guilt by Association

The workflow starts with a gene set created from Example gene set. CTD is applied which diffuses through all nodes in STRING[1] to identify nodes that are "guilty by association" and highly connected to the initial gene set of interest[2][3]. A list of Highly Connected Genes was obtained from the CTD output. A list of Guilty By Association Genes was obtained from the CTD output.

  1. Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic ...

Type: Playbook Workflow Builder Workflow

Creator: Playbook Partnership NIH CFDE

Submitter: Daniel Clarke

DOI: 10.48546/workflowhub.workflow.1244.1

Use Case 7: Variant Expression in Tumor/Healthy

The workflow starts with selecting chr2:g.39417578C>G as the search term. The closest gene to the variant was found using MyVariant.info[1]. Gene expression in tumors for CDKL4 were queried from the Open Pediatric Cancer Atlas API[3]. Median expression of CDKL4 was obtained from the GTEx Portal[4] using the portal's API. To visualize the level of expression across tumor gene expression, a bar plot was created Fig..

  1. Lelong, S. et al. BioThings SDK: a toolkit for building high-performance ...

Type: Playbook Workflow Builder Workflow

Creator: Playbook Partnership NIH CFDE

Submitter: Daniel Clarke

DOI: 10.48546/workflowhub.workflow.1243.1

Use Case 6: CFDE Knowledge about a Gene

The workflow starts with selecting KLF6 as the search term. RNA-seq-like LINCS L1000 Signatures[1] which mimick or reverse the the expression of KLF6 were visualized. Median expression of KLF6 was obtained from the GTEx Portal[6] using the portal's API. To visualize the scored tissues, a vertical bar plot was created Fig..

  1. Evangelista, J. E. et al. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Research vol. 50 W697–W709 (2022). ...

Type: Playbook Workflow Builder Workflow

Creator: Playbook Partnership NIH CFDE

Submitter: Daniel Clarke

DOI: 10.48546/workflowhub.workflow.1242.1

Use Case 6: CFDE Knowledge about a Variant

The workflow starts with selecting chr10:g.3823823G>A as the search term. The closest gene to the variant was found using MyVariant.info[1]. RNA-seq-like LINCS L1000 Signatures[3] which mimick or reverse the the expression of KLF6 were visualized. Median expression of KLF6 was obtained from the GTEx Portal[8] using the portal's API. To visualize the scored tissues, a vertical bar plot was created Fig..

  1. Lelong, S. et al. BioThings SDK: a toolkit for building high-performance data APIs in ...

Type: Playbook Workflow Builder Workflow

Creator: Playbook Partnership NIH CFDE

Submitter: Daniel Clarke

DOI: 10.48546/workflowhub.workflow.1241.1

Use Case 5: Small Molecules to Induce a Biological Process

The workflow starts with selecting Autophagy as the search term. Gene sets with set labels containing Autophagy were queried from Enrichr[1]. Identified matching terms from the MGI Mammalian Phenotype Level 4 2019[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for MGI_Mammalian_Phenotype_Level_4_2019. All the identified gene sets were combined using the union set operation. Reversers and mimickers from over 1 million signatures were identified ...

Type: Playbook Workflow Builder Workflow

Creator: Playbook Partnership NIH CFDE

Submitter: Daniel Clarke

DOI: 10.48546/workflowhub.workflow.1240.1

Use Case 4: Identify the Tissue Activity for a TF based on its Targets

The workflow starts with selecting KLF4 as the search term. Gene sets with set labels containing KLF4 were queried from Enrichr[1]. Identified matching terms from the ENCODE TF ChIP-seq 2015[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ENCODE_TF_ChIP-seq_2015. Identified matching terms from the ChEA 2022[4] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ChEA_2022. Identified ...

Type: Playbook Workflow Builder Workflow

Creator: Playbook Partnership NIH CFDE

Submitter: Daniel Clarke

DOI: 10.48546/workflowhub.workflow.1239.1

Use Case 2: Explain MOAs of Side Effects for Approved Drugs

The workflow starts with selecting atrial fibrillation as the search term. The workflow starts with selecting Ibrutinib as the search term. Gene sets with set labels containing atrial fibrillation were queried from Enrichr[1]. Identified matching terms from the MGI Mammalian Phenotype Level 4 2021[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for MGI_Mammalian_Phenotype_Level_4_2021. A consensus gene set was created by only retaining genes ...

Type: Playbook Workflow Builder Workflow

Creator: Playbook Partnership NIH CFDE

Submitter: Daniel Clarke

DOI: 10.48546/workflowhub.workflow.1238.1

Use Case 1: Explain Drug-Drug Interactions

The workflow starts with selecting Inflammation as the search term. The workflow starts with selecting Penicillin as the search term. The workflow starts with selecting Cortisol as the search term. Gene sets with set labels containing Inflammation were queried from Enrichr[1]. Identified matching terms from the GWAS Catalog 2019[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for GWAS_Catalog_2019. All the identified gene sets were combined ...

Type: Playbook Workflow Builder Workflow

Creator: Playbook Partnership NIH CFDE

Submitter: Daniel Clarke

DOI: 10.48546/workflowhub.workflow.1237.2

nf-core/references

help community build references

Type: Nextflow

Creator: @maxulysse

Submitter: WorkflowHub Bot

Connectome-based predictive modeling (CPM) with Python using Ray for parallel processing
Work-in-progress

Installation

Other than cloning this repository, you need to have bash installed (which is most likely the case if you use Linux, *BSD or even MacOS). For the Python code, the arguably easiest and cleanest way is to set up a Python virtual environment and install the dependencies there:

$ python3 -m venv ./hcp-suite-venv # Setup the virtual environment 
$ source ./hcp-suite-venv/bin/activate # Activate the virtual environment 
$ pip install pandas pingouin networkx nilearn nibabel ray
...

Type: Python

Creator: Tobias Bachmann

Submitter: Tobias Bachmann

DOI: 10.48546/workflowhub.workflow.1234.1

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