Deciphering genomic codes using advanced natural language processing techniques: a scoping review Article Swipe
Shuyan Cheng
,
Yishu Wei
,
Yiliang Zhou
,
Zihan Xu
,
Drew Wright
,
Jinze Liu
,
Yifan Peng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocaf029
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocaf029
Objectives The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of natural language processing (NLP) techniques, particularly large language models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. The goal of this review is to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data. Materials and Methods Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopus, Web of Science, Embase, and ACM Digital Library. Studies were included if they focused on NLP methodologies applied to genomic sequencing data analysis, without restrictions on publication date or article type. Results A total of 26 studies published between 2021 and April 2024 were selected for review. The review highlights that tokenization and transformer models enhance the processing and understanding of genomic data, with applications in predicting regulatory annotations like transcription-factor binding sites and chromatin accessibility. Discussion The application of NLP and LLMs to genomic sequencing data interpretation is a promising field that can help streamline the processing of large-scale genomic data while also providing a better understanding of its complex structures. It has the potential to drive advancements in personalized medicine by offering more efficient and scalable solutions for genomic analysis. Further research is also needed to discuss and overcome current limitations, enhancing model transparency and applicability. Conclusion This review highlights the growing role of NLP, particularly LLMs, in genomic sequencing data analysis. While these models improve data processing and regulatory annotation prediction, challenges remain in accessibility and interpretability. Further research is needed to refine their application in genomics.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocaf029
- https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocaf029/62167460/ocaf029.pdf
- OA Status
- bronze
- Cited By
- 3
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407941982
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4407941982Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jamia/ocaf029Digital Object Identifier
- Title
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Deciphering genomic codes using advanced natural language processing techniques: a scoping reviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-25Full publication date if available
- Authors
-
Shuyan Cheng, Yishu Wei, Yiliang Zhou, Zihan Xu, Drew Wright, Jinze Liu, Yifan PengList of authors in order
- Landing page
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https://doi.org/10.1093/jamia/ocaf029Publisher landing page
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https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocaf029/62167460/ocaf029.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocaf029/62167460/ocaf029.pdfDirect OA link when available
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Computer science, Data science, Lexical analysis, Scalability, Annotation, Artificial intelligence, Computational biology, Data mining, Bioinformatics, Biology, DatabaseTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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