Functional annotation of regulatory variants for accelerated breeding.
Your team knows the crop, the trait and the target gene. The hard part is knowing which change to make: which promoter, enhancer or variant will actually shift expression of that gene in the right direction, and which will not. Regulatory DNA is hard to read from sequence, and testing every option in planta is slow and expensive.
SuRE™ answers that question through empirical testing, not prediction. Our plant-optimized platform tests promoters, enhancers and their variants at scale, directly in cells from your crop, with each element read by dozens to hundreds of barcodes for the sensitivity to catch even small effects. The result is a ranked shortlist of the candidates most worth taking into plant validation, before you commit to the slow and costly transformation step. SuRE™ is species-agnostic and already relied on by leading crop developers, proven in crops from corn to sugar beet to tomato, and beyond plants in mammalian and livestock systems.
We begin with the gene or pathway your team wants to influence, and the expression change your crop trait requires: more, less, or in a specific tissue or condition.
SuRE™ tests thousands of promoters, enhancers and their variants, or your candidate edits, each traced by hundreds of barcodes, using our reporter system, measuring the activity of each rather than predicting it. We can screen in protoplasts or in planta, from our lines or your elite line.
We rank which promoters, enhancers or edits shift expression as intended, so your team can focus plant validation on a smaller, stronger set, before the slow and costly transformation step.
You own the results, and receive a detailed analysis report and a feedback meeting. A project typically takes 3 months.
Screen thousands of promoters, enhancers and their variants, or candidate edits, in one experiment, before committing to slow, resource-intensive plant validation.
Measure each element directly, tracked by dozens to hundreds of barcodes, with the sensitivity to separate real regulatory effects from screening noise. Measured, not predicted.
Test how regulatory DNA behaves in protoplasts from your crop, closer to real plant biology than a generic model organism or a computational model.
Identify the promoter edits most likely to shift endogenous gene expression toward the desired trait effect.
Genome-wide mapping of promoters enhancers active in specific tissues and conditions
Generate large-scale regulatory activity data to support AI and computational models for crop development.
Narrow the design space before plant validation.
Testing hundreds or thousands of promoters, enhancers, variants or edits directly in plants is rarely practical. SuRE™ gives crop developers an experimental funnel: screen them at scale first, measured directly in cells from your crop, then take only the strongest candidates into plant testing.
This lets your team spend slow, resource-intensive plant validation where the measured evidence is strongest, rather than on candidates a model only predicted.














Explore how tailored gene expression systems support crop trait development and regulatory edit prioritization.
Develop promoters and enhancers when therapeutic expression requires cell-specific control.
Discuss crop-specific screening questions around promoters, pathways, variants or predictive datasets.
Annogen designs and optimises the gene regulatory elements, mainly promoters, that determine where, when and how strongly your gene of interest is expressed. Our SuRE platform measures the activity of millions of regulatory sequences directly in relevant cells or organisms, so the elements we deliver are validated by experiment rather than predicted. You decide the expression profile you need; we find and build the element that achieves it.
It is real, large-scale wet-lab measurement, not prediction. SuRE screens millions of regulatory sequences in living cells and reads out their actual activity, which is why we can show what works rather than estimate it. We increasingly use this data to train models that design new sequences, but the empirical measurement stays at the core.
Hundreds of millions in a single experiment. That scale is the point: rather than testing a handful of candidate promoters, we survey the full design space and let the data show which elements perform best for your system.
The standard readout is transcriptional, measured by sequencing expressed barcodes, which lets us quantify very large libraries at once. Where a project needs it, we can discuss protein-level validation as a follow-up step.
Individual elements are usually around 500 base pairs, which covers most promoters and UTRs. We understand that plant promoters may entail several kilobases. Longer sequences are therefore possible with additional design steps, and the elements in a library do not all need to be the same length.
We imagine there could be some questions you want to ask us. Discover the most frequently asked questions about this subject right here.
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