Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics

TL;DR

This review discusses how NLP techniques, from classic to transformer-based models, are being adapted to analyze biological sequences in genomics, transcriptomics, and proteomics, revealing new insights into biological data.

Contribution

It provides a comprehensive overview of NLP methods applied to biological sequences, evaluating their strengths, limitations, and potential for future bioinformatics applications.

Findings

NLP models are increasingly used for biological sequence analysis.

Transformer models improve accuracy in gene and structure prediction.

NLP approaches enable large-scale genomic data interpretation.

Abstract

Natural Language Processing (NLP) has transformed various fields beyond linguistics by applying techniques originally developed for human language to the analysis of biological sequences. This review explores the application of NLP methods to biological sequence data, focusing on genomics, transcriptomics, and proteomics. We examine how various NLP methods, from classic approaches like word2vec to advanced models employing transformers and hyena operators, are being adapted to analyze DNA, RNA, protein sequences, and entire genomes. The review also examines tokenization strategies and model architectures, evaluating their strengths, limitations, and suitability for different biological tasks. We further cover recent advances in NLP applications for biological data, such as structure prediction, gene expression, and evolutionary analysis, highlighting the potential of these methods for extracting meaningful insights from large-scale genomic data. As language models continue to advance, their integration into bioinformatics holds immense promise for advancing our understanding of biological processes in all domains of life.