Predicting gene expression levels from DNA sequences and post-transcriptional information with transformers. (October 2022) - Pipoli, Vittorio; Cappelli, Mattia; Palladini, Alessandro; Peluso, Carlo; Lovino, Marta; Ficarra, Elisa
Highlights: Predicting gene expression levels is crucial due to its clinical applications. Post-transcriptional processes are essential in understanding the gene expression regulatory mechanisms. Previous models do not include post-transcriptional information. We present Transformer DeepLncLoc to predict gene expression levels from DNA sequences and transcription factors. Transformer DeepLncLoc reached 0.76 of the R 2 evaluation metric, outperforming existing methods. Transcription factor post-transcriptional regulation resulted in a massive performance boost. Graphical abstract: Abstract: Background and objectives: In the latest years, the prediction of gene expression levels has been crucial due to its potential applications in the clinics. In this context, Xpresso and others methods based on Convolutional Neural Networks and Transformers were firstly proposed to this aim. However, all these methods embed data with a standard one-hot encoding algorithm, resulting in impressively sparse matrices. In addition, post-transcriptional regulation processes, which are of uttermost importance in the gene expression process, are not considered in the model. Methods: This paper presents Transformer DeepLncLoc, a novel method to predict the abundance of the mRNA (i.e., gene expression levels) by processing gene promoter sequences, managing the problem as a regression task. The model exploits a transformer-based architecture, introducing the DeepLncLoc method to perform the data embedding. Since DeepLncloc is based on word2vec algorithm, it avoids the sparse matrices problem. Results: Post-transcriptional information related to mRNA stability and transcription factors is included in the model, leading to significantly improved performances compared to the state-of-the-art works. Transformer DeepLncLoc reached 0.76 of R 2 evaluation metric compared to 0.74 of Xpresso. Conclusion: The Multi-Headed Attention mechanisms which characterizes the transformer methodology is suitable for modeling the interactions between DNA's locations, overcoming the recurrent models. Finally, the integration of the transcription factors data in the pipeline leads to impressive gains in predictive power.