RT Journal Article
SR Electronic
T1 CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.12.02.518786
DO 10.1101/2022.12.02.518786
A1 Ke Ding
A1 Gunjan Dixit
A1 Brian J. Parker
A1 Jiayu Wen
YR 2022
UL http://biorxiv.org/content/early/2022/12/02/2022.12.02.518786.abstract
AB Recent large datasets measuring the gene expression of millions of possible gene promoter sequences provide a resource to design and train optimised deep neural network architectures to predict expression from sequences. High predictive performance due to the modelling of dependencies within and between regulatory sequences is an enabler for biological discoveries in gene regulation through model interpretation techniques.To understand the regulatory code that delineates gene expression, we have designed a novel deep-learning model (CRMnet) to predict gene expression in Saccharomyces cerevisiae. Our model outperforms the current benchmark models and achieves a Pearson correlation coefficient of 0.971. Interpretation of informative genomic regions determined from model saliency maps, and overlapping the saliency maps with known yeast motifs, support that our model can successfully locate the binding sites of transcription factors that actively modulate gene expression. We compare our model’s training times on a large compute cluster with GPUs and Google TPUs to indicate practical training times on similar datasets.Competing Interest StatementThe authors have declared no competing interest.