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Updated: May 1, 2026
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Published on: October 23, 2011
MiPRIME: an integrated and intelligent platform for mining primer and probe sequences of microbial species
Zhiming Zhang1, Jing Ren1, Lili Ren2
1Research and Development Department, Coyote Bioscience (Beijing) Co., Ltd., Building 22, Zone 3, Gaolizhang Road, Haidian District, Beijing, 10095, China.
Motivation:
Accurately detecting pathogenic microorganisms requires effective primers and probe designs. Literature-derived primers are a valuable resource as they have been tested and proven effective in previous research. However, manually mining primers from published texts is time-consuming and limited in species scop.
Results:
To address these challenges, we have developed MiPRIME, a real-time Microbial Primer Mining platform for primer/probe sequences extraction of pathogenic microorganisms with three highlights: (i) comprehensive integration. Covering >40 million articles and 548 942 organisms, the platform enables high-frequency microbial gene discovery from a global perspective, facilitating user-defined primer design and advancing microbial research. (ii) Using a BioBERT-based text mining model with 98.02% accuracy, greatly reducing information processing time. (iii) Using a primer ranking score, PRscore, for intelligent recommendation of species-specific primers. Overall, MiPRIME is a practical tool for primer mining in the pan-microbial field, saving time and cost of trial-and-error experiments.
Availability And Implementation:
The web is available at {{https://www.ai-bt.com}}.
Insights
MiPRIME is a novel platform for extracting microbial primer and probe sequences from scientific literature. It uses a BioBERT model for high accuracy and a ranking score for efficient, species-specific primer recommendations.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- Accurate detection of pathogenic microorganisms relies on effective primer and probe design.
- Literature-derived primers are valuable but manual mining is time-consuming and limited in scope.
Purpose of the Study:
- To develop an automated platform for mining microbial primer and probe sequences from scientific literature.
- To facilitate high-frequency microbial gene discovery and user-defined primer design.
Main Methods:
- Developed MiPRIME, a real-time Microbial Primer Mining platform.
- Integrated over 40 million articles and 548,942 organisms.
- Employed a BioBERT-based text mining model with 98.02% accuracy.
- Implemented a primer ranking score (PRscore) for intelligent primer recommendation.
Main Results:
- MiPRIME enables comprehensive microbial gene discovery from a global perspective.
- The BioBERT model significantly reduces information processing time.
- PRscore facilitates intelligent recommendation of species-specific primers, saving time and experimental costs.
Conclusions:
- MiPRIME is a practical tool for primer mining in the pan-microbial field.
- The platform advances microbial research by streamlining primer discovery.
- MiPRIME offers a cost-effective solution for identifying effective primers and probes.
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