Modelling genomic language using NLP and LLMs

Author: Navya Tyagi 


Genomic data consists of DNA, RNA, and protein sequences that can be represented as strings of unstructured text. These sequences can be very large in size. For instance, human DNA is made of 3 billion A,G,C, and T letters. There are hidden patterns that can be considered equivalent to "words" in a natural language. But all of these words are not known and more importantly the grammar that genomic lanaguege follow is not well understood. These biological words with critical functions are of interest to study disease and development processes. Sometime a mutation in these "words" may result in a disease condition. Determining these "words" with biological function is a computational challenge.
Figure 1: a) Code of DNA can be written using letters A,C,G,T. A pairs wiht T and G pairs with C making a double stnraded (helical) structure out of two DNA strands. b) message from DNA is transcribed to RNA (also known as messenger RNA). The triplets from RNA are translated as amino acid sequence that form a protein. 

Natural Language Processing (NLP), as the name suggests, is traditionally used to analyze and generate natural languages. Remarkably, genomic data can be processed similarly to natural text sequences. Therefore genome language modelling or GLM involves using NLP to interpret and predict genetic sequences, treating them as a “language” with its own syntax and semantics, providing a powerful approach for analyzing vast genomic datasets. Through GLM, researchers are developing models that can predict and identify genetic features, annotate gene functions, and even make strides toward personalized medicine. 

Key Applications of GLM 


Classification: 

Classification is one of GLM's most potent applications in genomics. By recognizing patterns in genomic sequences, GLM-based models can categorize sequences by their biological function, disease relevance, or gene type. This capability allows researchers to identify gene expressions, categorize cell types, classify proteins and identify genes associated with specific diseases or mutations, advancing our understanding of genetic disorders. 

Prediction: 

Prediction tasks in GLM focus on foreseeing gene expression patterns, disease susceptibility and even potential outcomes of specific gene variations. Deep learning models are proving instrumental in predicting genetic markers that signal disease, aiding in preventive health measures. 

Language generation: 

GLM models can be used be used for sequence generation that conform to the grammar and structure of the genomic code. By capturing contextual relationships within genomic sequences, GLMs predict the next sequence element accurately, supporting tasks such as sequence completion, mutation prediction, and regulatory region identification. 

Functional Annotation: 

Functional annotation is crucial in genomics, where understanding the roles and regulatory functions of genes can drive breakthroughs in molecular biology and medicine. GLM models streamline this by accurately tagging genes and regulatory regions, revealing their potential interactions and contributions to gene expression and regulation. 

Current Limitation and Future Scope  

The application GLMs presents exciting new avenues for personaalized medicine. By integrating genomic and healthcare data, GLM models can identify genetic markers linked to individual lifestyle and physiological factors, leading to more accurate predictions of disease susceptibility. This can enable early diagnosis and the creation of personalized treatment plans. With BERT-based models showing success in identifying disease-related mutations, the potential for clinical applications, such as guiding therapeutic decisions, is vast. 
 However, challenges persist. Large-scale genomic datasets require significant computational resources and time, and issues like class imbalance can lead to biased predictions, particularly for rare genetic sequences. Additionally, model hallucinations, where predictions deviate from the actual biological data, remain a concern. 

Conclusion 

Despite the hurdles, the future of GLM in genomics is bright. By overcoming computational challenges and enhancing model interpretability, GLM has the potential to revolutionize our understanding of genetics and its application in medicine. As we address these limitations, the integration of GLM in personalized medicine and genomic research will likely yield transformative advancements in healthcare and disease treatment 

 Reference

Our latest review on GLM is available here https://www.preprints.org/manuscript/202411.0285/v1
(It is currently under peer review in a scientific journal)

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