Article-GADES

This repository represents generating and benchmarking the results of the GADES package for Distance Matrix Calculation

Installation

git lfs install 
git clone https://github.com/lab-medvedeva/Article-GADES.git 
cd Article-GADES 

Put the Real datasets in the MEX format to the folder Datasets/Real.

Running benchmark using Docker Deployment

docker run --gpus all \ 
-v $PWD/Datasets:/workspace/Article-GADES/Datasets
...

Process argo data with the Pangeo Ecosystem and visualise them with Ocean Data View (ODV)

GraphRBF is a state-of-the-art protein-protein/nucleic acid interaction site prediction model built by enhanced graph neural networks and prioritized radial basis function neural networks. This project serves users to use our software to directly predict protein binding sites or train our model on a new database. Identification of protein-protein and protein-nucleic acid binding sites provides insights into biological processes related to protein functions and technical guidance for disease ...

Swedish Earth Biogenome Project - Genome Assembly Workflow

The primary genome assembly workflow for the Earth Biogenome Project at NBIS.

Workflow overview

General aim:

flowchart LR 
hifi[/ HiFi reads /] --> data_inspection 
ont[/ ONT reads /] --> data_inspection 
hic[/ Hi-C reads /] --> data_inspection 
data_inspection[[ Data inspection ]] --> preprocessing 
preprocessing[[ Preprocessing ]] --> assemble 
assemble[[ Assemble ]] --> validation 
validation[[ Assembly
...

Secondary metabolite biosynthetic gene cluster (SMBGC) Annotation using Neural Networks Trained on Interpro Signatures

This workflow takes a cell-type-annotated AnnData object (processed with SnapATAC2) and performs peak calling with MACS3 on the cell types. Next, a cell-by-peak matrix is constructed and differential accessibility tests are performed for comparison of either two cell types or one cell type with a background of all other cells. Lastly, differentially accessible marker regions for each cell type are identified.

This Workflow takes a dataset collection of single-cell ATAC-seq fragments and performs:

• preprocessing
• filtering
• concatenation
• dimension reduction
• batch correction (with Harmony and optionally Scanorama and MNC-correct)
• leiden clustering

• new SnapATAC2 version: from 2.5.3 to 2.6.4

Workflow for Single-cell ATAC-seq standard processing with SnapATAC2. This workflow takes a fragment file as input and performs the standard steps of scATAC-seq analysis: filtering, dimension reduction, embedding and visualization of marker genes with SnapATAC2. Finally, the clusters are manually annotated with the help of marker genes. In an alternative step, the fragment file can also be generated from a BAM file.

• newer Version: Updated SnapATAC2 version from 2.5.3 to 2.6.4

CoCoMiCo analyses on collections of simulated communities. Community: scatterplots and KW tests of cooperation and competition potentials, for each collection. Similarity: cooperation and competition potentials vs model similarity, defined as the Jaccard distance on sets of reactions. Added value: boxplots comparing the added value of models in communities.