EXCON (v2.3.1)

A Nextflow pipeline for gene family EXpansion and CONtraction analysis across multiple species using CAFE5.

Given a set of genome assemblies and annotations, EXCON builds orthogroups with OrthoFinder, fits and compares multiple CAFE models to identify gene families evolving at significantly different rates, and automatically selects the best-fitting model for downstream analysis. Optionally, GO enrichment analysis can be run on expanded and contracted gene families, and ...

Using:

• vadr annotation (model to select)
• vardict variant caller
• coverage depth

Provides summarizing files:

• png image of variant calling with annotations and coverage depths
• tsv file with all information of significant variants only
• vcf file with all information of significant variants only (to allow downstream NextStrain analyses)

Using:

• vadr annotation (model to select)
• vardict variant caller
• coverage depth

Provides summarizing files:

• png image of variant calling with annotations and coverage depths
• tsv file with all information of significant variants only
• vcf file with all information of significant variants only (to allow downstream NextStrain analyses)

Using:

• vadr annotation (model to select)
• vardict variant caller
• coverage depth

Provides summarizing files:

• png image of variant calling with annotations and coverage depths
• tsv file with all information of significant variants only
• vcf file with all information of significant variants only (to allow downstream NextStrain analyses)

Using:

• vadr annotation (model to select)
• vardict variant caller
• coverage depth

Provides summarizing files:

• png image of variant calling with annotations and coverage depths
• tsv file with all information of significant variants only
• vcf file with all information of significant variants only (to allow downstream NextStrain analyses)

iPSC Data Analysis

Description

This project is part of the Dutch X-omics initiative and focuses on the analysis of multi-omics data from several iPSC lines. The data included genomics, transcriptomics, proteomics, and several different types of metabolomics.

Goal

The goal is to identify disease-specific traits of the patient-derived iPSC compared to various control lines.

Pipeline overview

File outline

|- README.md     # This readme\ |- Data analysis plan.md ...

EATRIS-Plus Multi-omics Analysis Workflow

Analysis workflow used to analyze the cohort of healthy blood donors

Prerequisites

Multi-omics data set from Zenodo

• The data is stored in a Multi_Assay_Experiment object, which is used as input for the workflow
• The object can be downloaded here: https://doi.org/10.5281/zenodo.10782799

Install Nextflow using conda

Create a Conda Environment:

conda create -n nextflow-env 
conda activate nextflow-env 
conda install -c bioconda
...

CPSM: Cancer patient survival model - Workflow

Introduction

CPSM is an R package that provides a comprehensive computational pipeline for predicting survival probabilities and risk groups in cancer patients. It includes dedicated modules to perform key steps such as data preprocessing, training/test splitting, and normalization. CPSM enables feature selection through univariate cox-regression survival analysis, feature selection though LASSO method, and calculates a LASSO-based Prognostic ...

Integrative Prediction Strategy (IPS) predicts brain gene expression from blood-derived features using pre-trained machine learning models. The workflow integrates multiple feature selection strategies (unsupervised and supervised) and evaluates model performance across cross-validation folds.

Workflow Steps:

1. Load matched blood and brain gene expression data.
2. Apply feature selection:

• Unsupervised feature selection (features selected without using the target brain gene)
• Supervised ...