Predictive models for regulatory-element function are only as good as the data behind them, and every major crop developer building one hits the same wall: for crops that data is scarce, noisy, or borrowed from other species. Public datasets rarely cover the tissue, condition or germplasm a real breeding program runs in, so a model trained on them does not transfer to the decisions that matter.
Annogen is the data engine for that gap. We generate large-scale, functionally measured sequence-to-expression datasets directly in your crop’s cells, calibrated and low in label noise, so your models learn from plant data that reflects the tissues, conditions and germplasm you actually breed in. The data, and the models you train on it, stay yours.
The regulatory genomics of crops is far less charted than the human genome, and the public data that does exist rarely covers the species, tissue or condition a given trait depends on. A model trained on generic or cross-species data struggles with the question a breeder actually asks: which non-coding change will move expression of this gene, in this tissue, in this crop?
Annogen generates the missing data through empirical, sensitive experiments. Using plant-optimized SuRE™, we quantify the activity of large libraries of regulatory sequences directly in protoplasts from the relevant tissue, and, for focused libraries, in planta. Each element is read by many barcodes, so every label is averaged over independent measurements, with references and controls built in for calibration. Saturation-mutagenesis designs add dense, single-base variant-effect maps, well suited to training variant-effect models. Where high-quality functional data is the bottleneck, this is the fastest route to a training set that reflects real plant biology.
Lab in the loop keeps the wet lab inside your model’s training cycle. Your model proposes variants or synthetic elements, we measure them in a plant-relevant SuRE™ screen, and the measured results feed the next round of design and training. Because a plant-based screen turns around in months, not the seasons an in-planta cycle takes, the loop closes fast enough to compound: each round sharpens both the model and the candidates, on data you own.
Data generated in protoplasts from the relevant tissue, or in planta, not predicted or borrowed from another species.
Barcode redundancy gives high signal-to-noise and calibrated labels, which matters more for model quality than raw row count.
Assuming the system is set up, plant-based SuRE™ screen turns around in months, so the design-measure-train loop can actually run within a program.
The dataset, and any model you train on it, are your intellectual property. Annogen retains the SuRE™ platform, not your data. This is a deliberate part of how we work, and a common reason clients choose us for model training.
Define the prediction problem and the biology: what your model needs to predict, in which cell type, vector and conditions.
Design the library to cover the sequence space your model needs to learn, with references and controls spiked in for calibration.
Measure at scale with plant-optimized SuRE™, in relevant-tissue protoplasts or, for focused libraries, in planta.
Deliver the dataset with the underlying design and quality-control metadata, ready for training.
Close the loop where wanted, measuring model-proposed sequences in successive rounds.
We imagine there could be some questions you want to ask us. Discover the most frequently asked questions about this subject right here.
