AI-Based Functional Genomics Pipelines

Abstract

Functional genomics -- the systematic study of gene function through high-throughput perturbation, expression profiling, and phenotypic screening -- generates datasets of extraordinary scale and complexity. CRISPR screens interrogate 20,000+ genes in parallel, single-cell RNA-seq profiles millions of cells, and Perturb-seq combines both to measure transcriptomic responses to thousands of genetic perturbations simultaneously. Extracting biological insight from these datasets requires computational pipelines that integrate quality control, normalisation, dimensionality reduction, hit calling, gene set enrichment, network inference, and causal modelling. AI-based pipelines replace hand-crafted heuristics with learned models at each step, potentially improving sensitivity and reducing analyst bias. We present the Functional Genomics AI Pipeline Assessment Framework (FGPAF), evaluating five AI-augmented pipeline architectures-- deep learning-based CRISPR screen analysis, foundation model-powered single-cell workflows, Perturb-seq causal inference engines, multi-omics integration platforms, and automated hypothesis-driven analysis agents -- across four functional genomics modalities (genome-wide CRISPR knockout screens, CRISPRi/a transcriptional modulation, single-cell RNA-seq atlas construction, and Perturb-seq perturbation-response mapping). Our Functional Genomics Pipeline Score (FGPS) measures hit detection sensitivity, biological coherence, reproducibility, computational efficiency, and novel discovery rate. Foundation model-powered single-cell workflows achieve the highest FGPS (0.924) through pre-trained representations that capture cell-state structure across tissues and conditions, while automated hypothesis-driven agents achieve the highest novel discovery rate (0.960) by iteratively generating and testing analytical hypotheses.