Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.

Journal: Briefings in bioinformatics
PMID:

Abstract

Ribosome profiling (Ribo-seq) provides transcriptome-wide insights into protein synthesis dynamics, yet its analysis poses challenges, particularly for nonbioinformatics researchers. Large language model-based chatbots offer promising solutions by leveraging natural language processing. This review explores their convergence, highlighting opportunities for synergy. We discuss challenges in Ribo-seq analysis and how chatbots mitigate them, facilitating scientific discovery. Through case studies, we illustrate chatbots' potential contributions, including data analysis and result interpretation. Despite the absence of applied examples, existing software underscores the value of chatbots and the large language model. We anticipate their pivotal role in future Ribo-seq analysis, overcoming limitations. Challenges such as model bias and data privacy require attention, but emerging trends offer promise. The integration of large language models and Ribo-seq analysis holds immense potential for advancing translational regulation and gene expression understanding.

Authors

  • Zheyu Ding
    School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
  • Rong Wei
    From the Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA.
  • Jianing Xia
    School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
  • Yonghao Mu
    School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
  • Jiahuan Wang
    School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
  • Yingying Lin
    Department of Center of Integrated Traditional Chinese and Western Medicine, Peking University Ditan Teaching Hospital, Beijing, People's Republic of China.