Treffer: Accelerating primer design for amplicon sequencing using large language model-powered agents.
Title:
Accelerating primer design for amplicon sequencing using large language model-powered agents.
Authors:
Wang Y; MGI Tech, Shenzhen, China., Hou Y; MGI Tech, Shenzhen, China., Yang L; MGI Tech, Shenzhen, China.; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China., Li S; αLab AI department, MGI Tech R&D, Hongkong, China., Tang W; MGI Tech, Shenzhen, China., Tang H; MGI Tech, Shenzhen, China., He Q; MGI Tech, Shenzhen, China., Lin S; MGI Tech, Shenzhen, China.; αLab AI department, MGI Tech R&D, Hongkong, China., Zhang Y; MGI Tech, Shenzhen, China., Li X; MGI Tech, Shenzhen, China., Chen S; MGI Tech, Shenzhen, China., Huang Y; MGI Tech, Shenzhen, China., Kong L; MGI Tech, Shenzhen, China., Zhang H; MGI Tech, Shenzhen, China., Yu D; MGI Tech, Shenzhen, China.; αLab AI department, MGI Tech R&D, Hongkong, China., Mu F; MGI Tech, Shenzhen, China., Yang H; BGI, Shenzhen, China.; Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China.; James D. Watson Institute of Genome Sciences, Hangzhou, China., Wang J; MGI Tech, Shenzhen, China.; BGI, Shenzhen, China., Hirankarn N; Graduate Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. nattiyap@gmail.com., Yang M; MGI Tech, Shenzhen, China. yangmeng1@mgi-tech.com.; αLab AI department, MGI Tech R&D, Hongkong, China. yangmeng1@mgi-tech.com.; Graduate Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. yangmeng1@mgi-tech.com.
Source:
Nature biomedical engineering [Nat Biomed Eng] 2026 Feb; Vol. 10 (2), pp. 338-353. Date of Electronic Publication: 2025 Jul 30.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Nature Country of Publication: England NLM ID: 101696896 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2157-846X (Electronic) Linking ISSN: 2157846X NLM ISO Abbreviation: Nat Biomed Eng Subsets: MEDLINE
Imprint Name(s):
Publication: London : Springer Nature
Original Publication: [London] : Macmillan Publishers Limited, [2016]-
Original Publication: [London] : Macmillan Publishers Limited, [2016]-
MeSH Terms:
References:
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Substance Nomenclature:
0 (DNA Primers)
Entry Date(s):
Date Created: 20250730 Date Completed: 20260220 Latest Revision: 20260220
Update Code:
20260220
DOI:
10.1038/s41551-025-01455-z
PMID:
40738975
Database:
MEDLINE
Weitere Informationen
The pre-trained knowledge compressed in large language models is addressing diverse scientific challenges and catalysing the progression of autonomous laboratory systems, synergized with liquid handling robots. Here we introduce PrimeGen, an orchestrated multi-agent system powered by large language models, designed to streamline labour-intensive primer design tasks for targeted next-generation sequencing. PrimeGen uses GPT-4o as a central controller to engage with experimentalists for task planning and decomposition, coordinating various specialized agents to execute distinct subtasks. These include an interactive search agent for retrieving gene targets from databases, a primer agent for designing primer sequences across multiple scenarios, a protocol agent for generating executable robot scripts through retrieval-augmented generation and prompt engineering, and an experiment agent equipped with a vision language model for detecting and reporting anomalies. We experimentally demonstrate the effectiveness of PrimeGen across a variety of applications. PrimeGen can accommodate up to 955 amplicons, ensuring high amplification uniformity and minimizing dimer formation. Our development underscores the potential of collaborative agents, coordinated by generalist foundation models, as intelligent tools for advancing biomedical research.
(© 2025. The Author(s), under exclusive licence to Springer Nature Limited.)
Competing interests: J.W., D.Y. and F.M. declare stock holdings in MGI. The remaining authors declare no competing interests.