Yoseph Barash Research Group

The lab develops machine learning algorithms that integrate high‑throughput data such as RNASeq and CLIPSeq to infer RNA biogenesis and function. They build probabilistic graphical models and other computational methods to predict effects of genetic variation on RNA processing, including splicing QTLs. Predictions from computational models are followed by experimental verifications using high‑throughput sequencing assays to test inferred regulatory mechanisms. Ongoing projects include integrating long‑read 3′ and 5′ assays and polyA site annotation to resolve isoform diversity and variant effects on transcript ends.