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Drug discovery, repurposing & response prediction
Network-based drug repurposing, rigorous benchmarking of drug response prediction models, and high-throughput screening data analysis
HitSeekR
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.
In collaboration with CoSyBio
CoVex
CoVex is a unique online network and systems medicine platform for data analysis that integrates virus-human interactions for SARS-CoV-2 and SARS-CoV-1. It implements different network-based approaches for the identification of new drug targets and new repurposable drugs.
NeDReX
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.
CADDIE
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.
In collaboration with CoSyBio
Drugst.One
Plugin to turn your tool that outputs a list of genes or proteins into a feature rich, drug repurposing web tool with interactive network visualization.
drevalpy
Standardized, reproducible Python standalon for benchmarking drug response prediction models with automated preprocessing workflows, fair hyperparameter tuning, and support for any model type from statistical methods to complex neural networks.
In collaboration with Data Integration in the Life Sciences (DILiS), CompMS and KüsterLab within the DROP2AI project
nf-core/drugresponseeval
Scalable solution for benchmarking drug response prediction models, allows using drevalpy efficiently in HPC environments.
In collaboration with Data Integration in the Life Sciences (DILiS), CompMS and KüsterLab within the DROP2AI project
Drugst.One DREAM
Drugst.One DREAM (Drug Repurposing through Expert Annotation and Modification) is an extension of the Drugst.One web tool that lets biomedical experts refine candidate disease modules interactively, helping identify meaningful disease mechanisms and candidate drug targets. Its main features include network editing (manual modification and systematic pruning of nodes, such as promiscuous proteins), pathway enrichment analysis, network clustering, and dedicated layouts that highlight subcellular localization and causal relationships drawn from OmniPath.
Disease module discovery and multi-omics network integration
De novo network enrichment, biclustering, and interaction-network tools that integrate multi-omics data with the interactome
Athena
Quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana
In collaboration with KüsterLab
BiCoN
BiCoN allows to stratify patients while elucidating disease mechanisms. BiCoN is a network-constrained biclustering approach, which restricts biclusters to functionally related genes connected in molecular networks and maximizes the expression difference between two groups of patients.
Keypathwayminer
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, R package, and web tool.
In collaboration with CoSyBio
ROBUST
Mines disease modules from protein-protein interaction networks via enumeration of diverse prize-collecting Steiner trees. Prioritizes robustness, ensuring minimal variation in output subnetworks across repeated runs on equivalent input.
nf-core/diseasemodulediscovery
nf-core/diseasemodulediscovery is a pipeline for identifying active/disease modules. Disease modules aim to characterize the molecular mechanisms of diseases by analyzing the local neighborhood of disease-associated genes or proteins (seeds) within the interactome. The pipeline includes a range of different methods for this purpose, as well as an extensive evaluation framework to assess the reliability of the results.
Developed within the RePo4EU project
miRNA interactions & ceRNA networks
The SPONGE universe and related tools for competing endogenous RNA, circular RNA, and microRNA analysis
JAMI
Tool for fast computation of conditional mutual information for ceRNA network analysis
In collaboration with SchulzLab
SPONGE
SPONGE infers genome-wide networks of competing endogenous RNAs. By leveraging sparse partial correlation models, SPONGE filters out indirect co-expression effects to reveal regulatory drivers in complex biological systems.
In collaboration with SchulzLab
SPONGEdb
SPONGEdb is an interactive resource (database + website) of competing endogenous RNA networks across 30+ cancer types. It enables network visualization, patient-specific disease module identification, and miRNA binding site investigation to streamline and accelerate oncological discoveries.
In collaboration with HoffmannLab
nf-core/circrna
Back-splice-junction detection, quantification, miRNA target prediction, and differential expression analysis of circRNAs from RNA-seq data.
In collaboration with HoffmannLab
spongEffects
SpongEffects is a network-based biomarker extraction method that infers patient-specific ceRNA module enrichment scores from individual gene expression profiles. By summarizing genome-wide ceRNA interaction patterns into interpretable, low-dimensional representations, it enables highly accurate identification of cancer types and clinical subtypes.
In collaboration with HoffmannLab
circRNA-sponging
circRNA-sponging is a nextflow pipeline that (i) identifies circRNAs via backsplicing junctions detected in RNA-seq data, (ii) quantifies their expression values in relation to their linear counterparts spliced from the same gene, (iii) performs differential expression analysis, (iv) identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, (v) predicts miRNA binding sites on circRNAs, (vi) systematically investigates potential circRNA–miRNA sponging events, (vii) creates a network of competing endogenous RNAs and (viii) identifies potential circRNA biomarkers.
In collaboration with HoffmannLab
Alternative splicing tools
Tools that allow you to download and visualize alternative splicing information, simulate it, and evaluate its longitudinal and functional effects through enrichment.
DIGGER
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.
In collaboration with CoSyBio and SciBiome within the Sys_CARE project
ASimulator
The R package ASimulatoR simulates gold standard RNA-Seq datasets with fine-grained control over the distribution of AS events, which allow for evaluating alternative splicing tools, e.g. to study the effect of sequencing depth on the performance of AS event detection.
Developed within the Sys_CARE project
NEASE
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.
In collaboration with CoSyBio and SciBiome within the Sys_CARE project
Spycone
Spycone is a python package that provides systematic analysis of time course transcriptomics data. Spycone uses gene or isoform expression as an input. Spycone features a novel method for IS detection and employs the sum of changes of all isoforms relative abundances (total isoform usage) across time points. Spycone provides downstream analysis such as clustering by total isoform usage, i.e. grouping genes that are most likely to be coregulated, and network enrichment, i.e. extracting subnetworks or pathways that are over-represented by a list of genes. These analyses can be coupled with gene set enrichment analysis and visualization.
In collaboration with CoSyBio and SciBiome within the Sys_CARE project
DIGGER 2.0
DIGGER 2.0 is a tool for functionally interpreting how alternative splicing rewires protein interaction networks in human and, now, mouse disorders. The updated database expands domain–domain interaction coverage with high-confidence computational predictions (via PPIDM) and integrates the NEASE enrichment tool directly into the web server, letting users move from a differential splicing event straight to the biological pathways and functions it disrupts.
In collaboration with TsoyLab
Epigenomics & gene regulation
Tools and resources for different layers of epigenetics and gene regulation, including DNAm heterogeneity analysis, transcription factor activity, and (condition- and patient-specific) regulatory network inference
WSH
R package for the calculation of the following Within-Sample Heterogeneity Scores(WSH) in bisulfite sequencing data: FDRP, qFDRP, PDR, Epipolymorphism, Methylation Entropy and MHL
In collaboration with SchererLab
TF-Prioritizer
TF-Prioritizer is an automated pipeline that prioritizes condition-specific transcription factors from multimodal data and generates an interactive web report. It accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity
In collaboration with HoffmannLab
DysRegNet
DysRegNet is a method for studying patient-specific dysregulation of transcription factor–target gene interactions. It takes as input a reference gene regulatory network, gene expression data for case and control samples, and optionally sample-level confounders. The method then compares the co-expression patterns of individual case samples against those observed across the control samples, in order to detect regulatory relationships that are non-functional in a given case sample. The accompanying web tool allows for interactive exploration of results across 11 cancer types.
DiReG
Interactive Directed Reprogramming that helps researcher identify optimal transcription-factor combinations for direct cellular reprogramming by integrating predictions from established tools with literature-mining validation
EpiATLAS
A reference of thousands of uniformly processed human epigenomic datasets (ChIP-seq, WGBS and RNA-seq) compiled by the International Human Epigenome Consortium (IHEC).
Developed within the International Human Epigenome Consortium project
Cell type deconvolution
Methods to apply and compare in silico estimation of cell-type composition from bulk data, based most regularly on gene expression or DNA methylation.
SimBu
Simulates bulk RNA-seq datasets with variable cell-type fractions based on various simulation scenarios using annotated single-cell RNA-seq data. Designed to rigorously validate performances of cell-type deconvolution methods or to be applied in other single-cell RNA-seq downstream tasks.
In collaboration with FinotelloLab
immunedeconv
Unified access to first-generation computational methods for estimating immune cell fractions from bulk RNA-sequencing data. Includes a benchmark of included methods and serves as the entrypoint to cell-type deconvolution for widely used blood samples.
In collaboration with FinotelloLab
deconvMe
Unified access to deconvolution methods that use DNA Methylation features (CpGs) to estimate cell-type proportions of a bulk sample. Includes a comparison with gene-expression-based deconvolution methods, where we could show superior performance of DNA methylation based approaches.
In collaboration with FinotelloLab
omnideconv
Improved accessibility to second-generation deconvolution methods, which use scRNA-seq data to build tissue- and disease-specific cell-type signatures 'on the fly', offering simplified access to a wide selection of current methods. Building on the earlier pseudobulk simulation method SimBu as well as a unique collection of validation datasets (deconvData), eight methods were extensively benchmarked to understand their strengths and failure modes; for full reproducibility, these benchmarking scenarios are automated within the deconvBench Nextflow pipeline.
In collaboration with FinotelloLab
Proteomics & cytometry
Preprocessing, differential analysis, and curated data resources for proteomics and cytometry research
ProteomicsDB
ProteomicsDB is a multi-omics and multi-organism resource for life science research. It covers e.g. proteomics, transcriptomics, and phenomics data for e.g. human, mouse, arabidopsis and rice. Different visualization are available allowing e.g. a protein- and drug-centric interrogation as well as combined analysis via our analytics section.
In collaboration with CompMS
CYANUS
Interactive analysis tool for flow and mass cytometry data with a focus on differential analysis.
In collaboration with BongiovanniLab
PRONE
Tool for preprocessing MS-based proteomics data with a special focus on benchmarking different normalization approaches. Also includes quality control visualization and functionalities and evaluation of normalization extends to downstream analyses tasks such as differential expression analysis.
platlas
Platlas is a comprehensive platelet atlas designed to provide access to platelet transcriptome data presented in our study. This platform focuses on visualizing and presenting data that differentiates between reticulated platelets (RPs) and mature platelets (MPs). It offers valuable insights into the transcriptomic differences observed in patients with coronary artery disease (CAD).
In collaboration with BongiovanniLab
Microbiome
Amplicon and 16S rRNA microbiome data analysis
Namco
Interactive web tool for end-to-end analysis of 16S-rRNA microbiome data. Includes key analysis steps from processing raw fastq files, over basic clustering, differential abundance and diversity scores, up to more advanced concepts such as differential network analysis and support for multi-omics data.
In collaboration with CoSyBio and SciBiome within the SFB1371 project
Genetic data and GWAS
Epistasis simulation, detection, and genome-wide association tools
EpiGEN
EpiGEN is an easy-to-use epistasis simulation pipeline written in Python. It supports epistasis models of arbitrary size, the specification of the minor allele frequencies for both noise and disease SNPs, and the simulation of observation bias.
NeEDL
Command line tool for network-based epistasis detection in complex diseases.
Epistasis Disease Atlas
Web tool for interactive exploration of putative epistatic interactions in eight complex diseases (late-onset Alzheimer’s disease, bipolar disorder, coronary artery disease, hypertension, type-1 diabetes, type-2 diabetes, rheumatoid arthritis, inflammatory bowel disease).
GNExT
Framework for deploying custom web platforms for GWAS summary statistics exploration and translating variant signals into network-medicine analyses and therapeutic hypotheses
In collaboration with Bionets within the DyHealthNet project
Single-cell and spatial data
Various tools for single-cell and spatial transcriptomics, including benchmarking, simulation, and multi-modal integration
Tangram Refinements
Tangram is a valuable tool for single-cell to spatial mapping. We extended it by gene/cell selection, regularization, and spatial-context incorporation strategies that improve the consistency and reliability of Tangram.
In collaboration with RöttgerLab within the MOPITAS project
BeastSim
BeastSim systematically evaluates spatial transcriptomics simulation methods against data-property distributions and biological-signal preservation, and similarity-based metrics. It ensures that simulations go beyond simple data replication, instead introducing biologically meaningful variation. Further, it was used to create a decision tree that helps users select the most suitable simulation model based on their data and goals.
In collaboration with RöttgerLab within the MOPITAS project
Federated learning
Privacy-preserving analysis platforms that let institutions jointly compute on distributed biomedical data without sharing raw data
flimma
Flimma is a privacy-preserving hybrid federated tool for differential gene expression analysis. Flimma by design preserves the privacy of the local data, since the expression profiles never leave the local execution sites and shared meta-parameters are protected via secure multi-party computation.
sPLINK
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.
FeatureCloud
FeatureCloud is an all-in-one platform to RUN, DEVELOP & PUBLISH federated & privacy-preserving machine learning algorithms.
Miscellaneous
General-purpose tools and data resources
BALSAM
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.
In collaboration with CoSyBio
AIMe
The AIMe registry for artificial intelligence in biomedical research is a community-driven platform for reporting biomedical AI systems. It allows authors of new biomedical AIs to report their models in an explicit and transparent fashion and thereby fosters comparability and reproducibility.
NApy
Python package with Numba and OpenMP-powered C++ backends for fast and memory-efficient statistical testing in the presence of missing data.
In collaboration with Bionets within the DyHealthNet project
Legacy tool
OpenLabFramework
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.
In collaboration with CoSyBio
MIRACLE
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.
In collaboration with CoSyBio
SAVANAH
SAVANAH supports the HTS community in managing and analyzing HTS experiments with an emphasis on serially diluted molecular libraries
In collaboration with CoSyBio
BioAtlas
Interactive web application closing the gap between sequence databases, taxonomy profiling and geo/body-location information. It enables users to browse taxonomically annotated sequences across (i) the world map, (ii) human body maps and (iii) user-defined maps. It further allows for (iv) uploading of own sample data, which can be placed on existing maps to (v) browse the distribution of the associated taxonomies.
In collaboration with CoSyBio