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bioRxiv · 10.1101/2023.06.30.547100

Personal transcriptome variation is poorly explained by current genomic deep learning models

Huang, C.·Shuai, R.·Baokar, P.·Chung, R.·Rastogi, R.·Kathail, P.·Ioannidis, N. M.
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Abstract

Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the reference genome, their ability to explain expression variation between individuals due to cis-regulatory genetic variants remains largely unexplored. Here we evaluate four state-of-the-art models on paired personal genome and transcriptome data and find limited performance when explaining variation in expression across individuals.

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Huang, C., Shuai, R., Baokar, P., Chung, R., Rastogi, R., Kathail, P., Ioannidis, N. M.. 2023-06-30. Personal transcriptome variation is poorly explained by current genomic deep learning models. https://doi.org/10.1101/2023.06.30.547100

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