How far are we from personalized gene expression prediction using sequence-to-expression deep neural networks?

Sasse, A.; Ng, B.; Spiro, A.; Tasaki, S.; Bennett, D.; Gaiteri, C.; De Jager, P. L.; Chikina, M.; Mostafavi, S.

doi:10.1101/2023.03.16.532969

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bioRxiv · 10.1101/2023.03.16.532969

How far are we from personalized gene expression prediction using sequence-to-expression deep neural networks?

Sasse, A.·Ng, B.·Spiro, A.·Tasaki, S.·Bennett, D.·Gaiteri, C.·De Jager, P. L.·Chikina, M.·Mostafavi, S.

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Abstract

Introductory Paragraph Deep learning methods have recently become the state-of-the-art in a variety of regulatory genomic tasks1-6 including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions, however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluates their utility as personal DNA interpreters. We used paired Whole Genome Sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learnt sequence motif grammar, and suggest new model training strategies to improve performance.

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Sasse, A., Ng, B., Spiro, A., Tasaki, S., Bennett, D., Gaiteri, C., De Jager, P. L., Chikina, M., Mostafavi, S.. 2023-03-20. How far are we from personalized gene expression prediction using sequence-to-expression deep neural networks?. https://doi.org/10.1101/2023.03.16.532969

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