Projects
The Girke lab develops computational methods and software at the interface of genome biology, chemical genomics, and bioinformatics. Below is a brief overview of selected research projects, with each project highlighting a representative theme of the lab’s work.
systemPipeR: Reproducible Workflow Management for Data-Intensive Life Science Research
Modern life science research increasingly relies on complex, multi-step computational analyses of large and heterogeneous datasets, creating major challenges for workflow organization, reproducibility, and transparency. systemPipeR is a workflow management environment that enables researchers to design, execute, monitor, and document sophisticated data analysis pipelines within a unified framework. It integrates statistical analysis in R with widely used bioinformatics software and supports execution on personal computers as well as high-performance computing environments. By combining workflow visualization, scalable execution, and automated analysis reporting, systemPipeR helps make modern data-driven research more efficient, reproducible, and standardized.
Visual overview of the systemPipeR workflow framework.
Interventions Promoting Healthy Aging and Longevity
The Girke lab develops AI-based strategies to identify drugs, natural compounds, and combination therapies that promote healthy aging and delay age-related disease. By integrating large-scale omics signatures, genetic and pharmacological perturbation data, and new methods for drug–target discovery, this work seeks to uncover more effective and sustainable interventions for extending health span.
Experimental validation is performed in collaboration with consortium scientists using mouse models and human iPSC systems, including groups at the Salk Institute and the University of Michigan. These combined efforts aim to translate computational discoveries into more precise and personalized approaches for healthier aging in humans. This research is supported by a cooperative U19 grant from the National Institute on Aging at the National Institutes of Health.
Overview of Longevity Consortium.
spatialHeatmap: Visualizing Spatial Bulk and Single-Cell Assays in Anatomical Images
The spatialHeatmap package enables intuitive visualization of single-cell, spatial, and other tissue-resolved omics data by overlaying quantitative assay values onto anatomical images. It combines spatial heatmaps with complementary clustering and network-based views, and is available both as an R package and as an interactive Shiny application for exploratory analysis by computational and experimental users alike.
Spatial data visualization in anatomical images.
Large-scale bioactivity analysis
This project used large-scale bioactivity data to compare how approved drugs and other small molecules interact with many different protein targets. The study uncovered numerous promising new drug and target candidates while also helping distinguish compounds with selective activities from those with broad, less specific effects, providing a valuable resource for future drug discovery efforts.
Bioactivity data mining strategy.