Identifying DNA Sequence Motifs Using Deep Learning
Asmita Poddar Vladimir Uzun Elizabeth Tunbridge Wilfried Haerty Alejo Nevado-Holgado
Splice sites play a crucial role in gene expression, and accurate prediction
of these sites in DNA sequences is essential for diagnosing and treating
genetic disorders. We address the challenge of splice site prediction by
introducing DeepDeCode, an attention-based deep learning sequence model to
capture the long-term dependencies in the nucleotides in DNA sequences. We
further propose using visualization techniques for accurate identification of
sequence motifs, which enhance the interpretability and trustworthiness of
DeepDeCode. We compare DeepDeCode to other state-of-the-art methods for splice
site prediction and demonstrate its accuracy, explainability and efficiency.
Given the results of our methodology, we expect that it can used for healthcare
applications to reason about genomic processes and be extended to discover new
splice sites and genomic regulatory elements.