Building Better AI With High-Resolution Functional Genomics Data

Scaling Functional Genomics for AI: Introducing FiCS Perturb-seq and the X-Atlas/Orion Dataset Building Better AI With High-Resolution Functional Genomics Data Biological foundation models require high-quality functional datasets that can scale with AI ambitions. In this webinar, Dr. Ci Chu, Vice President of Early Discovery at Xaira Therapeutics, introduces X-Atlas/Orion, the largest publicly available Perturb-seq dataset to date, covering every human protein-coding gene across eight million deeply sequenced cells. He also presents FiCS Perturb-seq, a new platform that addresses key bottlenecks in perturbation data generation by enabling scalable, reproducible single-cell transcriptomic profiling. Learn how, together, FiCS Perturb-seq and X-Atlas/Orion unlock the ability to model dose-dependent genetic effects, accelerating the development of more predictive and mechanistic AI models for biology. As AI models in biology scale, the need for high-quality, reproducible functional genomics data has become a critical bottleneck. This webinar explores new approaches to generating large-scale, single-cell perturbation datasets that overcome key challenges in throughput, variability and batch effects. Learn from our speaker how recent advances are capturing dose-dependent genetic effects – essential for building more predictive, mechanistic models of gene function – and discover insights into the evolving infrastructure powering the next generation of functional genomics and virtual cell modeling.