MaizeStressDB is a robust data exploration tool tailored for the analysis of gene expression profiles in maize leaves under three distinct stresses: Cold, Dry, and Heat. Offering access to a comprehensive collection of 120 samples across 10-time points for each stress condition, MaizeStressDB empowers researchers with versatile tools for expression analysis, differential gene expression, and network exploration.

GraphSimViz provides a tool to estimate the agreement of different disease ontologies using network medicine. Users can compare diseasome, drugome and drug-disease network neighborhoods for selected drugs or diseases to assess how well or poorly these entities are represented in network medicine.

 Drugst.One  Drugst.One is an easy-to-use online tool that helps researchers find new uses for existing drugs. It turns complex scientific software into a simple web interface, making it easier to explore how drugs, diseases, and proteins interact. This tool is especially helpful for scientists in drug research, as it offers a straightforward way to analyze and visualize data.

DIGEST greatly facilitates in silico validation of gene and disease sets, clusterings or subnetworks via fully automated pipelines comprising disease and gene ID mapping, enrichment analysis, comparisons of shared genes and variants and background distribution estimation. Moreover, functionality is provided to automatically update the external databases used by the pipelines. DIGEST hence allows the user to assess the statistical significance of candidate mechanisms with regard to functional and genetic coherence and enables the computation of empirical P-values with just a few mouse clicks.

ProHarMeD (Proteomic Meta-Study Harmonization, Mechanotyping and Drug Repurposing Candidate Prediction) is a versatile tool designed to harmonize and compare proteomics data collected from multiple studies by its ability to perform ID and name conversionsbetween protein and gene levels, as well as across different organisms through ortholog mapping. It also aids in extracting disease mechanisms and identifying potential drug repurposing candidates.
 

FeatureCloud is an all-in-one platform to RUN, DEVELOP & PUBLISH federated & privacy-preserving machine learning algorithms.

​

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. 

​

Flimma is the federated implementation of the popular differential expression analysis workflow limma voom. Flimma provides several advantages over the existing approaches for gene expression analysis. Unlike limma voom, Flimma by design preserves the privacy of the data in the cohorts since the expression profiles never leave the local execution sites. In contrast to meta-analysis approaches, Flimma is particularly robust against heterogeneous distributions of data across the different cohorts, which makes it a powerful alternative for multi-center studies where patient privacy matters.

​

Partea (Privacy-Aware-Time-to-Event Analysis) is a federated-learning platform to perform time-to-event studies (survival analysis) on data that is distributed across different sites. The platform comes with a friendly user interface, does not require programming skills and makes it easy to collaborate with other institutions in the analysis of time-to-event data using Cox Proportional Hazards Model, Survival Curves or Log-rank tests. Through Federated Learning in combination with Privacy-enhancing technologies, such as differential privacy or secure multi-party computation, Partea makes sure that your sensitive data does never leave your local site while only insensitive model parameters are shared with the Partea server.

DiNiro can uncover novel and relevant mechanistic models that not only predict but also explain differential cellular gene expression programs. These mechanisms can be presented as small, easily interpretable transcriptional regulatory network modules. Start exploring the possibilities of scRNA-seq technology with DiNiro today and unlock a new level of understanding in gene function and disease mechanisms. 

NeDRex is an interactive network medicine platform for disease module identification and drug repurposing. It is build of three main components: a knowledgebase (NeDRexDB), a Cytoscape app (NeDRexApp), and an API (NeDRexAPI).

NeDRex integrates different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. It allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. The utility of NeDRex is shown with five specific use-cases in the paper (download from Nature).

​

NeDRex-Web is a user interface for the large heterogeneous network database and drug repurposing platform NeDRex. It allows access to all features and data of other NeDRex interfaces, just without the need for downloading any software.

CADDIE is a web application for drug repurposing in cancer integrating six human gene-gene and four drug-gene interaction databases, information regarding cancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically related diseases, and anticancer drugs. The website offers access to various network algorithms for identifying drug targets and drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutic targets or drug candidates, making network medicine algorithms accessible for clinical research.

Scellnetor is a novel clustering tool for scRNA-seq data that takes Scanpy generated AnnData objects in H5AD file-format as input. With Scellnetor you can compare two sets of cells that you manually select on one of your Scanpy-generated plots. The output will be connected components of genes where the genes are either differently or similarly expressed in the two sets. You can also do a clustering of a single set, where the genes in the connected components are similarly expressed. For every cluster, you get a plot showing mean gene expression and the genes' 95 % confidence intervals and a table with statistically significant GO-terms.

With the DeepCLIP online tool you can choose one or more trained state-of-the-art performing models and use them to generate predictions and binding profiles of RNA-sequences of interest. Both predictions and binding profiles can detect how mutations may change the affinity of RNA-binding proteins for analyzed sequences.

Biclustering constrained by networks (BiCoN) is a powerful new systems medicine tool to stratify patients while elucidating disease mechanisms. BiCoN is a network-constrained biclustering approach which restricts biclusters to functionally related genes connected in molecular interaction networks and maximizes the expression difference between two groups of patients.

DIGGER is an essential resource for studying the mechanistic consequences of alternative splicing such as isoform-specific interaction and consequence of exon skipping. The database integrates information of domain-domain and protein-protein interactions with residue-level interaction evidence from co-resolved structures. DIGGER allows users to seamlessly switch between isoform and exon-centric views of the interactome and to extract sub-networks of relevant isoforms (isoforms specific PPIs).

​

NEASE is a network-based approach for exon set enrichment. The python package NEASE  first detects protein features affected by AS such as domains, motifs and residues. Next, NEASE uses a protein-protein interactions integrated with domain-domain interactions, residue-level and domain-motif interactions to identify interaction partners and patways likely affected by AS.

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. It was first identified in Wuhan, China, and has since spread causing a global pandemic. Various studies have been performed to understand the molecular mechanisms of viral infection for predicting drug repurposing candidates. However, such information is spread across many publications and it is very time-consuming to access, integrate, explore, and exploit. We developed CoVex, the first interactive online platform for SARS-CoV-2 and SARS-CoV-1 host interactome exploration and drug (target) identification. CoVex integrates 1) experimentally validated virus-human protein interactions, 2) human protein-protein interactions and 3) drug-target interactions. The web interface allows user-friendly visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drugs. Thus, CoVex is an important resource, not only to understand the molecular mechanisms involved in SARS-CoV-2 and SARS-CoV-1 pathogenicity, but also in clinical research for the identification and prioritization of candidate therapeutics. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms integrating virus-host-drug interactions. We documented the CoVex development in our publication (download from Nature).

GrandForest is an online tool for de novo endophenotyping and mechanotyping of diseases using omics data. PathClass is an online tool for breast cancer subtyping from gene expression data. Instead of using gene panels, we employ pathway activity patterns for this purpose, which render statistically way more robust. Our web application allows to predict breast cancer subtypes for new samples, including custom uploaded samples as well as samples found on the Gene Expression Omnibus. Furthermore, selected pathways used for the individual predictors can be investigated.

ROBUST is a Python tool which uses enumeration of diverse prize-collecting Steiner trees to compute disease modules that are robust to random bias.

​

ROBUST-Web  is a user-friendly web implementation of the ROBUST disease module mining algorithm, with additional options to improve robustness by making use of study bias data. Taking advantage of the Drugst.One interface, ROBUST-Web supports further exploration of discovered targets via integrated gene set enrichment analysis, tissue expression annotations, and drug- protein, drug-disease, and disease-gene associations.

​

KeyPathwayMiner is a software for de novo network enrichment, aka network modules. It combines multiple OMICS data sets with biological networks to turn your expression, mutation, or association study into a systems biology story. It comes as Cytoscape app, as standalone software and as web service. It was downloaded >5000x from the Cytoscape app store alone and applied in various different biomedical settings.

TiCoNE is a software for the analysis of time course expression data (e.g. gene expression) together with biological networks. It will find time patterns emerging in the expression data and check for network modules enriched with genes of similar expression behavior over time. It comes as web server and as Cytoscape plugin.

Genes carry binding sites that allows specific microRNAs to repress their expression. According to the competing endogenous RNA hypothesis, genes also regulate each other in a competitive fashion via sponging microRNAs. We have contributed to the development of the (partial) correlation-based method SPONGE and the (conditional) mutual information-based method JAMI to infer such microRNA-mediated gene-gene interactions. In particular SPONGE is fast enough to infer ceRNA interactions genome-wide and thus facilitates the inference of a ceRNA regulatory network that can be used for hypothesis generation, biomarker detection and drug target discovery.

DiMmer is a Java tool for efficient processing of Illumina 450K and 850K EPIC chip data. It is fully parallelizable and can process even big cohort data sets. With DiMmer, one may now even compute empirical p-values using permutation tests and find differentially methylated regions including different strategies for correction for multiple testing. It can correct for cell composition effects and other confounders. Continuous outcome variables and confounders can be directly integrated using regression models. It comes with an intuitive user interface that guides the clinician or biologist through all steps of a typical Illumina EWAS chip data analysis.

CoNVaQ is a web service for CNV-based association study between two or more sample groups. The web interface allows one to quickly upload segmented CNV samples and search for variations that are overrepresented in a population. CoNVaQ differs from previous tools by allowing you to specify which CNVRs are considered significant using simple queries, e.g. “find the largest region duplicated in > 70% of cases and < 10% of controls”.
 

HitSeekR is the first web platform for analyzing high-throughput screening (HTS) data of various types, from miRNA (inhibitor) screens and RNAi assays to CRISPER/cas9 and drug response screens. It can accommodate, normalize, etc. small to ultra-large scale, and it turns your HTS data into a systems biology story.

BALSAM is a comprehensive web-platform to simplify and automate the analysis and discovery of metabolite patterns in Multi-Capillary-Column Ion-Mobility-Spectrometry data. It combines preprocessing, peak detection, feature extraction, visualization and pattern discovery.

​​

IMSDB is database system and machine learning user interface for biomarker mining and identification in breath data from GC/MS and MCC/IMS data sets.

Transitivity Clustering is a tool for solving the Weighted Graph Cluster Editing Problem using a force-based heuristic. It comes with several extensions. TransClustMV, for instance, works with missing values.

ClustEval is a platform for the evaluation of clustering tools in many different biomedical settings.

GEDEVO is a method for pairwise network (graph) alignment using optimizing a global criterion: minimal graph edit distance, i.e. the minimal number of node/edge additions/deletions to transform one graph into another one. GEDEVO-M is an extension of GEDEVO for the alignment of multiple (>2) input networks. And NABEECO solves network alignment using Bee Colony Optimization.

CytoGEDEVO is a Cytoscape implementation of the pairwise network alignment algorithm of GEDEVO, which allows the direct integration of external node similarities (e.g. BLAST or graphlets).

CoryneRegNet is the international reference database for corynebacterial transcriptional gene regulatory interactions.

​

EhecRegNet is a database of gene regulations conserved between E. coli K12 and human pathogenic EHEC strains.

​

PetriScape is a tool for discrete Petri net simulation of biological networks in Cytoscape.

PIPS is a tool specifically for the prediction of pathogenicity islands using sequence features.

​

GIPSy extends on the PIPS idea and implements a software for using genomic features to predict pathogenicity islands as well metabolic and resistance islands in bacterial genomes.

MIRACLE is an online platform for microarray R-based analysis of complex lysate experiments. It is bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation.

OpenLabFramework with its extension OpenLabNotes is an open-source laboratory information management system (LIMS) intended for advanced sample management in small to mid-sized laboratories. It has been developed with focus on the management of vector clone and genetically engineered cell lines.