Intro Class AND Workshop on “AlphaGenome”

Žiga Avsec et al., AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model.

Available at https://storage.googleapis.com/deepmind-media/papers/alphagenome.pdf.

Something special this week: a synthetic introductory class (at 00:00:00) and a synthetic academic workshop (at 00:24:51), both generated using enTalkenator (using Google Gemini 2.5 Pro). The intro class is generally accessible, but the workshop is a bit more challenging for a lay person.

Abstract: “Deep learning models that predict functional genomic measurements from DNA sequence are powerful tools for deciphering the genetic regulatory code. Existing methods trade off between input sequence length and prediction resolution, thereby limiting their modality scope and performance. We present AlphaGenome, which takes as input 1 megabase of DNA sequence and predicts thousands of functional genomic tracks up to single base pair resolution across diverse modalities – including gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chromatin contact maps, splice site usage, and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest respective available external models on 24 out of 26 evaluations on variant effect prediction. AlphaGenome’s ability to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically-relevant variants near the TAL1 oncogene. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.”