New tool solves a decade-old problem in measuring cell sugars
Researchers have released GlycoForge, a software tool that generates realistic test data for validating methods that measure complex carbohydrates in cells—a bottleneck in drug development and diagnostics. The tool fills a critical gap: scientists have lacked reliable ways to test whether their carbohydrate-analysis methods actually work, which has slowed adoption of glycomics technologies across pharma and biotech.
Originaltitel: GlycoForge generates realistic glycomics data under known ground truth for rigorous method benchmarking
Abstract Quantifying all complex carbohydrates in a sample produces glycomics data, which constitutes compositional data and is stymied by biosynthetic dependencies between glycans, requiring dedicated analytic workflows. Properly assessing such methods frequently requires simulated data with known ground truths and injectable effects. However, simulating glycomics data, especially with control over effects and biases, is still unsolved. Here, we present GlycoForge, a feature-complete solution for simulating comparative glycomics data. GlycoForge supports simulating fully synthetic glycomics data, with specified motif-level effects, drawn from Dirichlet distributions, and templated simulations based on real-world data. We further support the injection of batch effects, both mean and variance shifts, via center-log ratio transformations to maintain compositional closure, and realistic missing data simulation. We showcase the utility of GlycoForge by evaluating batch effect correction algorithms for glycomics data, with automated guidelines for when to use such methods on real-world data. GlycoForge is available as an open-access Python package at https://github.com/BojarLab/GlycoForge .