EC-Earth3 workflow without wrappers running in MareNostrum 4 with Autosubmit v3.15.0b0, used to assess the effects of task aggregation on queueing times. Workflow configuration is based on the Auto-EC-Earth3's testing suite [1].

In order to reduce the size of the workflow, the /tmp directory has been deleted. Additionally, the experiment has been cleaned up with the Autosubmit clean command. ...

EC-Earth3 workflow with wrappers running in MareNostrum 4 with Autosubmit v3.15.0b0, used to assess the effects of task aggregation on queueing times. Workflow configuration is based on the Auto-EC-Earth3's testing suite [1].

In order to reduce the size of the workflow, the /tmp directory has been deleted. Additionally, the experiment has been cleaned up with the Autosubmit clean command. ...

RNA-SeqEZPZ-NF

Nextflow Pipeline for RNA-SeqEZPZ

A Point-and-Click Pipeline for Comprehensive Transcriptomics Analysis with Interactive Visualizations

RNA-SeqEZPZ-NF is another implementation of RNA-SeqEZPZ. RNA-SeqEZPZ-NF uses the same user interface as RNA-SeqEZPZ and runs the same pipeline, but runs the pipeline implemented by Nextflow. This pipeline is currently tested on HPC cluster with SLURM scheduler. Advanced ...

RNA-SeqEZPZ: A Point-and-Click Pipeline for Comprehensive Transcriptomics Analysis with Interactive Visualizations

RNA-SeqEZPZ is a pipeline to run analysis of RNA-Seq experiments from raw FASTQ files all the way to differential genes analysis. The pipeline is accessible through a graphical user interface implemented using a Shiny app and features interactive plots. Advanced users have the ability to customize the scripts provided with the pipeline. This pipeline is designed to run on an HPC ...

SciWIn Client Demo

A basic Workflow using SciWIn Client (s4n) can be created with the commands hereafter. This guide assumes the usage of unix based operating systems, however Windows should work, too. If not please open an issue.

Installation

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📄 Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen healthcare settings

This repository contains the code used for the experiments in the paper:

Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen healthcare settings by Diane Duroux, Paul P. Meyer, Giovanni Visonà, and Niko Beerenwinkel.

⚙️ Install the dependencies

Clone the repository, unzip OriginalData.zip, and install the necessary dependencies ...

MAGNETO

MAGNETO is an automated snakemake workflow dedicated to MAG (Metagenome-Assembled Genomes) reconstruction from metagenomic data.

It includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery.

Key Features

• Quality Control (QC): Automatically assesses the quality and the contamination of input reads, ensuring that low-quality data are filtered out to improve ...

Based on the Tail analysis workflow at https://git.embl.org/grp-cba/tail-analysis/-/blob/main/analysis_workflow.md

DUNE: Deep feature extraction by UNet-based Neuroimaging-oriented autoEncoder

A versatile neuroimaging encoder that captures brain complexity across multiple diseases: cancer, dementia and schizophrenia.

Overview

DUNE (Deep feature extraction by UNet-based Neuroimaging-oriented autoEncoder) is a neuroimaging-oriented deep learning model designed to extract deep features from multisequence brain MRIs, enabling their processing by basic machine learning algorithms. This project provides an ...

CryoDataBot

CryoDataBot is an automated pipeline designed to streamline dataset generation for cryogenic electron microscopy (cryoEM)-based atomic model building. It supports large-scale AI training and benchmarking by providing standardized tools for data retrieval, preprocessing, labeling, and quality control. CryoDataBot enables flexible configurations for diverse biomolecular structures, improves modeling reproducibility, and facilitates retraining of AI models such as U-Net and CryoREAD. ...