Workflows

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736 Workflows visible to you, out of a total of 791
rquest-omop-worker-workflow
Work-in-progress

rquest-omop-worker-workflows

Source for workflow definitions for the open source RQuest OMOP Worker tool developed for Hutch/TRE-FX

Note: ARM workflows are currently broken. x86 ones work.

Inputs

### Body Sample input payload:

{ 
"task_id": "job-2023-01-13-14: 20: 38-", 
"project": "", 
"owner": "", 
"cohort": { 
"groups": [ 
{ 
"rules": [ 
{ 
"varname": "OMOP", 
"varcat": "Person", 
"type": "TEXT", 
"oper": "=", 
"value": "8507" 
} 
], 
"rules_oper": "AND" 
} 
], 
"groups_oper": "OR" 
}, 
"collection":
...

Type: Common Workflow Language

Creator: Vasiliki Panagi

Submitters: Stian Soiland-Reyes, Vasiliki Panagi, Jon Couldridge

DOI: 10.48546/workflowhub.workflow.471.3

ScreenDOP - Screening of strategies for disease outcome prediction
Stable

Summary

The data preparation pipeline contains tasks for two distinct scenarios: leukaemia that contains microarray data for 119 patients and ovarian cancer that contains next generation sequencing data for 380 patients.

The disease outcome prediction pipeline offers two strategies for this task:

Graph kernel method: It starts generating personalized networks for ...

Type: Python

Creator: Yasmmin Martins

Submitter: Yasmmin Martins

DReCaS - Pipeline for drug ranking based on computed pathway scores of disease and healthy samples
Stable

Summary

This pipeline contains the following functions: (1) Data processing to handle the tansformations needed to obtain the original pathway scores of the samples according to single sample analysis GSEA (2) Model training based on the disease and healthy sample pathway scores, to classify them (3) Scoring matrix weights optimization according to a gold standard list of drugs (those that went on clinical trials or are approved for the disease).It tests the weights in a range of 0 to 30 (you ...

Type: Python

Creator: Yasmmin Martins

Submitter: Yasmmin Martins

PipePatExp - Pipeline to aggregate gene expression correlation information for PPI
Stable

Summary

The PPI information aggregation pipeline starts getting all the datasets in GEO database whose material was generated using expression profiling by high throughput sequencing. From each database identifiers, it extracts the supplementary files that had the counts table. Once finishing the download step, it identifies those that were normalized or had the raw counts to normalize. It also identify and map the gene ids to uniprot (the ids found usually ...

Type: Python

Creator: Yasmmin Martins

Submitter: Yasmmin Martins

PPIntegrator - PPI Triplification Process
Stable

Summary

This pipeline has as major goal provide a tool for protein interactions (PPI) prediction data formalization and standardization using the OntoPPI ontology. This pipeline is splitted in two parts: (i) a part to prepare data from three main sources of PPI data (HINT, STRING and PredPrin) and create the standard files to be processed ...

Type: Python

Creator: Yasmmin Martins

Submitter: Yasmmin Martins

PPIVPro - PPI Validation Process
Stable

Summary

The validation process proposed has two pipelines for filtering PPIs predicted by some IN SILICO detection method, both pipelines can be executed separately. The first pipeline (i) filter according to association rules of cellular locations extracted from HINT database. The second pipeline (ii) filter according to scientific papers where both proteins in the PPIs appear in interaction context in the sentences.

The pipeline (i) starts extracting cellular component annotations from ...

Type: Python

Creator: Yasmmin Martins

Submitter: Yasmmin Martins

PredPrIn - Scientific workflow to predict protein-protein interactions based in a combined analysis of multiple protein characteristics.

Summary

PredPrIn is a scientific workflow to predict Protein-Protein Interactions (PPIs) using machine learning to combine multiple PPI detection methods of proteins according to three categories: structural, based on primary aminoacid sequence and functional annotations.

PredPrIn contains three main steps: (i) acquirement and treatment of protein information, (ii) feature generation, and (iii) classification and analysis.

(i) The first step builds a knowledge base with the available annotations ...

Type: Python

Creator: Yasmmin Martins

Submitter: Yasmmin Martins

baredsc/baredSC-1d-logNorm

Run baredSC in 1 dimension in logNorm for 1 to N gaussians and combine models.

Type: Galaxy

Creator: Lucille Delisle

Submitter: WorkflowHub Bot

openms-metaprosip/main

Automated inference of stable isotope incorporation rates in proteins for functional metaproteomics

Type: Galaxy

Creator: Matthias Bernt

Submitter: WorkflowHub Bot

HPPIDiscovery - Scientific workflow to augment, predict and evaluate host-pathogen protein-protein interactions
Stable

Summary

HPPIDiscovery is a scientific workflow to augment, predict and perform an insilico curation of host-pathogen Protein-Protein Interactions (PPIs) using graph theory to build new candidate ppis and machine learning to predict and evaluate them by combining multiple PPI detection methods of proteins according to three categories: structural, based on primary aminoacid sequence and functional annotations.

HPPIDiscovery contains three main steps: (i) acquirement of pathogen and host proteins ...

Type: Snakemake

Creator: Yasmmin Martins

Submitter: Yasmmin Martins

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