DICAST comprises several alternative splicing event detection tools for analyzing RNA-Seq data. It can be used as a benchmark to compare the results of the different tools. More information and instructions can be found [here](https://dicast.readthedocs.io/en/master/contents.html#).
NeDRex is an integrative and interactive platform for network-base drug repurposing and disease module identification. More information, tutorials and a download link for the app can be found [here](https://nedrex.net/index.html).
NEASE (Network-based Enrichment method for Alternative Splicing Events) is a Python package for the functional enrichment of alternative splicing events. The tool is available on [GitHub](https://github.com/louadi/NEASE).
sPLINK (safe PLINK) allows the federated, privacy-preserving analysis of GWAS data. It works on distributed datasets without exchanging raw data and is robust against imbalanced phenotype distributions across cohorts. Federated and user-friendly analysis with sPLINK, thus, has the potential to replace meta-analysis as the gold standard for collaborative GWAS. The tool is available online [here](https://www.exbio.wzw.tum.de/splink/).
To address the pandemic of the Coronavirus Disease-2019 (COVID-19), drug repurposing can be a helpful approach since it offers the possibility to find alternative fields of application for already approved drugs. **CoVex** is the first network and systems medicine online data analysis platform that integrates virus-human interaction data for SARS-CoV-2 and SARS-CoV. It is available as [interactive webtool](https://exbio.wzw.tum.de/covex/). More information and current updates can be found at the [CoVex blog](https://www.baumbachlab.net/exbio-vs-covid-part-1) at the *Chair of Experimental Bioinformatics* website.
Scellnetor is a novel scRNA-seq clustering tool. It allows the analysis of pseudo time-courses in single-cell sequencing data via a network-constrained clustering algorithm. Scellnetor is available as interactive online application at the [Scellnetor website](https://exbio.wzw.tum.de/scellnetor/).
EpiGEN is a Python pipeline for simulating epistasis data. It supports epistasis models of arbitrary size, which can be specified either extensionally or via parametrized risk models. Moreover, the user can specify the minor allele frequencies (MAFs) of both noise and disease SNPs, and provide a bias target distribution for the generated phenotypes to simulate observation bias. EpiGEN is freely available as python 3 package on [GitHub](https://github.com/baumbachlab/epigen).