We help identify which sequence changes are most likely to influence gene expression by evaluating candidate variants experimentally, so decisions rest on functional evidence rather than prediction alone.
Effects in non-coding regulatory elements are hard to predict from sequence. Whether the variants come from a genome-wide association study, where one causal variant hides among many non-functional variants, or from a set of candidate edits, the question is the same: which ones shift expression?
We assess large numbers of variants in parallel and measure each one’s effect on regulatory activity. Annogen doesn’t do the editing in the organism; we identify which variants are most likely to be functionally relevant, so our partners can move downstream with confidence.
Pinpoint which non-coding variants affect expression, for example, the causal variant among many candidates from a GWAS.
Identify which candidate base edits are most likely to create the desired change in regulatory activity.
Screen large numbers of variants in parallel to generate training or validation data for improved modelling.
What a project looks like depends on the organism, trait, and candidate sequence space. Typically, it involves five steps:
SuRE™ is Annogen’s platform for large-scale functional screening of regulatory sequences. Here, it’s used to compare candidate variants in parallel and measure their effect on regulatory activity, turning a long list of possible edits into a shorter, experimentally supported set.
Let’s start with your ambitions.
Share your challenge, question or how we could improve your therapy or crop. We will help you explore how tailored gene expression can unlock tomorrow’s innovations.
