AlphaGenome.Advancing regulatory variant effect prediction with AlphaGenome

 AlphaGenome.

El resumen rápido que hacen es que el modelo está especializado en predecir los efectos de variantes en regiones reguladoras del genoma, que controlan cuándo y cómo se expresan los genes. AlphaGenome sirve entender el genoma “regulador”: no se centra solo en el 2% del ADN que codifica proteínas, sino en ese 98% que actúa como panel de control de los genes (cuándo se encienden, en qué tejido, con qué intensidad). El valor de esto estriba en que muchas variantes asociadas a enfermedad están en regiones no codificantes y son difíciles de interpretar. Que AlphaGenome ayude a predecir el impacto molecular de una variante reguladora permite priorizar qué mutaciones merecen experimento. No es que “diagnostice” por sí solo sino que acelera hipótesis, pero la validación sigue siendo de laboratorio/clinica. Google presume de más 3,000 usuarios científicos totales y de haber liberando el modelo y los pesos para todo el mundo. Si AlphaFold fue “estructura de proteínas”, AlphaGenome apunta a ser “gramática del ADN regulador”. Un avance realmente potente, un impulso enorme a la investigación científica y un puntazo de la gente de Deepmind de ofrecerlo al mundo.

Advancing regulatory variant effect prediction with AlphaGenome

Deep learning models that predict functional genomic measurements from DNA sequences are powerful tools for deciphering the genetic regulatory code. Existing methods involve a trade-off between input sequence length and prediction resolution, thereby limiting their modality scope and performance1,2,3,4,5. We present AlphaGenome, a unified DNA sequence model, which takes as input 1 Mb of DNA sequence and predicts thousands of functional genomic tracks up to single-base-pair resolution across diverse modalities. The modalities include 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 available external models in 25 of 26 evaluations of variant effect prediction. The ability of AlphaGenome to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically relevant variants near the TAL1 oncogene6. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.

Interpreting the impact of genome sequence variation remains a central biological challenge. Non-coding variants, which reside outside of protein-coding regions, are particularly challenging to interpret because of the diverse molecular consequences they can elicit. For example, non-coding variants can modulate genome properties such as chromatin accessibility, epigenetic modifications and three-dimensional chromatin conformation. Variants can further influence messenger RNA (mRNA) availability by altering expression levels or modifying sequence composition through splicing changes.

Computational methods can learn patterns from experimental data to predict and explain variant effects. One class of methods,sequence-to-function models1,2,3,4,5, takes a DNA sequence as input and predicts genome tracks, a data format associating each DNA base pair with a value (representing read coverage, count or signal) derived from experimental assays performed in cell lines or tissues

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