In the rapidly evolving landscape of genomics, deep learning has emerged as a
useful tool for tackling complex computational challenges. This review focuses
on the transformative role of Large Language Models (LLMs), which are mostly
based on the transformer architecture, in genomics. Building on the foundation
of traditional convolutional neural networks and recurrent neural networks, we
explore both the strengths and limitations of transformers and other LLMs for
genomics. Additionally, we contemplate the future of genomic modeling beyond
the transformer architecture based on current trends in research. The paper
aims to serve as a guide for computational biologists and computer scientists
interested in LLMs for genomic data. We hope the paper can also serve as an
educational introduction and discussion for biologists to a fundamental shift
in how we will be analyzing genomic data in the future.

基于转换器架构的大型语言模型（LLMs）在基因组学中扮演了转变性的角色。本文旨在成为对基因组数据感兴趣的计算生物学家和计算机科学家的指南，同时也希望为生物学家们介绍和讨论我们将来如何分析基因组数据的基本转变。