Tokenization and deep learning architectures in genomics: A comprehensive review.

Abstract

The development of modern DNA sequencing technologies has resulted in the rapid growth of genomic data. Alongside the collection of this data, there is an increasing need for the development of modern computational tools leveraging this data for tasks including but not limited to antimicrobial resistance and gene annotation. Current deep learning architectures and tokenization techniques have been explored for the extraction of meaningful underlying information contained within this sequencing data. We aim to survey current and foundational literature surrounding the area of deep learning architectures and tokenization techniques in the field of genomics. Our survey of the literature outlines that significant work remains in developing efficient tokenization techniques that can capture or model underlying motifs within DNA sequences. While deep learning models have become more efficient, many current tokenization methods either reduce scalability through naive sequence representation, incorrectly model motifs or are borrowed directly from NLP tasks for use with biological sequences. Current and future model architectures should seek to implement and support more advanced, and biologically relevant, tokenization techniques to more effectively model the underlying information in biological sequencing data.