Integrating genomics with diverse data modalities has the potential to revolution- ize personalized medicine. However, this integration poses significant challenges due to the fundamental differences in data types and structures. The vast size of the genome necessitates transformation into a condensed representation contain- ing key biomarkers and relevant features to ensure interoperability with other modalities. This commentary explores both conventional and state-of-the-art approaches to genome language modeling (GLM), with a focus on representing and extracting meaningful features from genomic sequences. We focus on the latest trends of applying language modeling techniques on genomics sequence data, treating it as a text modality. Effective feature extraction is essential in enabling machine learning models to effectively analyze large genomic datasets, particularly within multimodal frameworks. We first provide a step-by-step guide to various genomic sequence pre-processing and tokenization techniques. Then we explore feature extraction methods for the transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss ML applications in genomics, focusing on classification, regression, language processing algorithms, and multimodal inte- gration. Additionally, we explore the role of GLM in functional annotation, emphasizing how advanced ML models, such as Bidirectional encoder represen- tations (BERT) from transformers, enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.

The genome, which serves as the inherent language directing the blueprint of life, offers significant analysis prospects by combining Natural Language Processing (NLP) and machine learning (ML). Integrating biological sequences with other digital healthcare information has potential to transform data-driven diagnostics. Large language models (LLMs) can be harnessed to decode the genomic language. This endeavor encounters three critical challenges: First, long biomolecular sequences require segmentation into smaller subunits, which is non-trivial since many biological “words” remain unknown. Second, the analysis of extended DNA sequences using LLMs demands a compute-intensive infrastructure. Third, ensuring reproducibility and reusability of modeling workflows remains an unresolved issue. To tackle these challenges, we introduce an empirical DNA tokenisation approach and a versatile, semantic-aware, genome language model — genomicBERT . The model is species-agnostic and operates seamlessly at the DNA or RNA levels. By introducing a reduced and specialized DNA vocabulary, our approach minimizes computational overhead and optimizes performance. Our benchmarking demonstrates that the genomicBERT matches or surpasses the performance of contemporary tools on the same datasets under different experimental conditions. To encourage collaboration and ease of access, we introduce genomicBERT as an integral component of the openly accessible conda package, genomeNLP . Validated across diverse case studies, genomicBERT lowers the barriers to decoding genomic language, relying solely on sequence data to extract meaningful insights. Highlights: This novel model offers a compelling solution for DNA sequence analysis by significantly reducing model size and computational costs without compromising performance, setting a new standard for efficient model development. We demonstrate that a powerful vocabulary and tokenization method helps to derive patterns from biological sequence data while accounting for hidden semantic rules. Our method is agnostic to species or biomolecule type as it is data-driven. Hence, it can be applied to DNA and RNA We validate the important genomicBERT tokens by mapping back to the biologically significant motifs. We present a publicly available genome language modeling toolkit called genomeNLP , specifically designed to combine computational linguistics and genomics, enabling researchers from biology backgrounds to analyze and interpret genomic sequences effectively.

Combining genomics with digital healthcare information is set to transform per- sonalized medicine. However, this integration is challenging due to the differing nature of the data modalities. The large size of the genome makes it impossible to store it as part of the standard electronic health record (EHR) system. Rep- resenting the genome as a condensed representation containing biomarkers and usable features is required to make the genome interoperable with EHR data. This systematic review examines both conventional and state-of-the-art methods for genome language modeling (GLM), which involves representing and extract- ing features from genomic sequences. Feature extraction is an essential step for applying machine learning (ML) models to large genomic datasets, especially within integrated workflows. We first provide a step-by-step guide to various genomic sequence pre-processing and representation techniques. Then we explore feature extraction methods including tokenization, and transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss ML applications in genomics, focusing on classification, prediction, and language processing algorithms. Additionally, we explore the role of GLM in func- tional annotation, emphasizing how advanced ML models, such as Bidirectional encoder representations from transformers (BERT), enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.