The Genomic Pre-trained Network (GPN), a multispecies model developed to learn genome-wide variant effects.
By Pooja Toshniwal PahariaOct 31 2023Reviewed by Lily Ramsey, LLM In a recent study published in PNAS, researchers introduced the Genomic Pre-trained Network , a multispecies model developed to learn genome-wide variant effects by self-supervised pretraining on genomic deoxyribonucleic acid sequences.
Unsupervised-type pretraining using large protein sequence databases has shown effectiveness in extracting complicated information about proteins and learning variation effects in coding areas. A logistic regression classifier was constructed using the averaged embeddings as features to measure GPN's capacity to discriminate genomic regions. Given the context, each genomic place was individually masked, as was the model output distribution over nucleotides.
Results The GPN model, which was trained without supervision, effectively learned gene structure and DNA patterns in Arabidopsis thaliana, a plant biology model organism closely linked to several agriculturally relevant species that could be used to provide insights into human disorders. Using the log-likelihood ratio between the alternative and reference alleles, GPN might determine a pathogenicity or functionality score for each SNP in the genome. The classification of variant types based on the lowest percentile of GPN scores was typically congruent with previously accepted ideas of deleteriousness.
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