SeqMaestro: interpretable machine learning links nucleotide sequences to biological hypotheses

A new arXiv paper introduces SeqMaestro, a method that analyzes nucleotide sequences using interpretable machine learning to connect raw sequence data with testable biological hypotheses. The approach aims to combine the interpretability of classical bioinformatics features, such as motifs and k-mer composition, with the predictive power of modern ML models. It targets applications across regulatory genomics, evolutionary biology, and phenotype prediction.

Google AI model scores every single-base edit to the human genome

Google researchers built an AI system that assesses the effects of every possible single-letter change across the human genome. Most such substitutions have no measurable impact, while a small subset produces meaningful biological effects. The work could help prioritize which genetic variants matter for disease research.

DeepMind releases AlphaGenome Atlas covering all possible human DNA variants

Google DeepMind has published a dataset that predicts the effects of roughly nine billion single-letter variations across the human genome. The resource is about one petabyte in size, roughly thirty times larger than the AlphaFold database. It was used to help identify a previously unexplained genetic cause in an epilepsy case.

DeepMind releases AlphaGenome Atlas covering all 9 billion human DNA letter changes

Google DeepMind has published a dataset called the AlphaGenome Atlas that predicts the possible consequences of roughly nine billion single-letter variations in the human genome. The collection is about one petabyte in size, which the outlet notes is more than 30 times larger than the AlphaFold database. A case involving epilepsy is cited as an example of how the resource was used.

Study argues exact enumeration outperforms RL for genomic tool selection

A new arXiv paper questions the widespread practice of training a policy with reinforcement learning on top of a frozen reasoning model to decide which external tools an AI system should call. The authors contend that in specialized scientific domains such as genomics, where the set of possible tool combinations is small enough to list exhaustively, sampling-based methods are unnecessary. They instead present an exact optimization approach that evaluates the full space of tool subsets to recover optimal selection policies.

Google DeepMind releases AlphaGenome Atlas with 9 billion variant effect predictions

Google DeepMind has published AlphaGenome Atlas, a precomputed resource that scores the predicted effects of roughly nine billion genetic substitutions. The aim is to help researchers decide which variants to prioritize for experimental testing by tying predictions to biological mechanisms, with the DNM1 gene used as an illustrative case.

Google DeepMind releases AlphaGenome Atlas mapping effects of 9 billion DNA variants

Google DeepMind has introduced the AlphaGenome Atlas, a resource that predicts the molecular impact of roughly 9 billion single-letter DNA changes across the human genome. Built on the AlphaGenome model, the atlas is intended to help researchers interpret how variants influence gene regulation and cellular function. It could assist in connecting genetic variants to disease and prioritizing candidates for further study.

WHY IT MATTERS ↘By precomputing predictions for ~9 billion variants, DeepMind converts an ML model into reusable research infrastructure that can undercut the cost of wet-lab variant triage and pressure startups selling variant-interpretation tools. It also sets a de facto benchmark for genomics models, extending DeepMind's model-led moat from protein structure into regulatory biology.