A review on the applications of Transformer-based language models for nucleotide sequence analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics
• Natural Language Processing (NLP)

Background:

• Transformer-based language models have significantly advanced Natural Language Processing (NLP).
• Biological sequences (DNA, RNA) share structural similarities with natural languages, enabling NLP model adaptation.
• The application of these advanced models to bioinformatics is a rapidly growing area.

Purpose of the Study:

• To review and analyze recent developments of Transformer-based models in nucleotide sequence analysis.
• To provide an overview of Transformer architecture and functionality for bioinformatics applications.
• To guide researchers on customizing Transformer models for diverse bioinformatics challenges.

Main Methods:

• Comprehensive literature review and analysis of application-based research papers.
• Examination of Transformer model characteristics and adaptation strategies for biological sequences.
• Synthesis of current methodologies and their effectiveness in nucleotide sequence analysis.

Main Results:

• Identified key features and diverse approaches for customizing Transformer models for nucleotide sequences.
• Demonstrated the significant potential of Transformers in analyzing biological sequence data.
• Provided insights into the underlying mechanisms and reasons for Transformer model efficacy.

Conclusions:

• Transformer models offer powerful computational capabilities for advancing nucleotide sequence analysis in bioinformatics.
• This review equips the scientific community with knowledge to leverage Transformers for biological sequence research.
• Encourages further exploration and application of Transformer methodologies to solve complex bioinformatics problems.