Call somatic, germline and LoH event variants from PE Illumina sequencing data obtained from matched pairs of tumor and normal tissue samples.

This workflow can be used with whole-genome and whole-exome sequencing data as input. For WES data, parts of the analysis can be restricted to the exome capture kits target regions by providing the optional "Regions of Interest" bed dataset.

The current version uses bwa-mem for read mapping and varscan somatic for variant calling and somatic status ...

MMV Im2Im Transformation

A generic python package for deep learning based image-to-image transformation in biomedical applications

The main branch will be further developed in order to be able to use the latest state of the art techniques and methods in the future. To reproduce the results of our manuscript, we refer to the branch ...

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":
...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...