Genomic Language Models: Opportunities and Challenges
This is an incremental review discussing opportunities and challenges for applying existing LLM methods to genomic data, aimed at researchers in computational biology and genomics.
The paper explores the potential of Genomic Language Models (gLMs) to advance understanding of DNA sequences by applying large language models to genomics, highlighting applications like functional constraint prediction and sequence design, but notes challenges in development and evaluation for complex genomes.
Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of Natural Language Processing is to understand sequences of words, a major objective in biology is to understand biological sequences. Genomic Language Models (gLMs), which are LLMs trained on DNA sequences, have the potential to significantly advance our understanding of genomes and how DNA elements at various scales interact to give rise to complex functions. To showcase this potential, we highlight key applications of gLMs, including functional constraint prediction, sequence design, and transfer learning. Despite notable recent progress, however, developing effective and efficient gLMs presents numerous challenges, especially for species with large, complex genomes. Here, we discuss major considerations for developing and evaluating gLMs.