GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction
Gonzalo Benegas1, Carlos Albors2, Alan J Aw3
1Graduate Group in Computational Biology, University of California, Berkeley.
Biorxiv : the Preprint Server for Biology
|October 24, 2023
Summary
We developed GPN-MSA, a novel DNA language model framework. It accurately predicts variant effects across the human genome, outperforming previous methods for both coding and non-coding regions.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Protein language models excel at predicting missense variant effects.
- DNA language models lag behind in genome-wide variant effect prediction, especially for complex genomes like humans.
Conclusions:
- GPN-MSA offers a competitive edge for genome-wide variant effect predictions.
- The framework is efficient, requiring only a few hours for training.
- Successfully predicts effects of both coding and non-coding variants.
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