Embedding DNA-based natural language in microbes for the benefit of future researchers†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-10-14 DOI:10.1039/D4DD00251B
Heqian Zhang, Jiaquan Huang, Xiaoyu Wang, Zhizeng Gao, Song Meng, Hang Li, Shanshan Zhou, Shang Wang, Shan Wang, Xunyou Yan, Xinwei Yang, Xiaoluo Huang and Zhiwei Qin
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Abstract

Microorganisms are valuable resources as antibiotic producers, biocontrol agents, and symbiotic agents in various ecosystems and organisms. Over the past decades, there has been a notable increase in the identification and generation of both wild-type and genetically modified microbial strains from research laboratories worldwide. However, a substantial portion of the information represented in these strains remains scattered across the scientific literature. To facilitate the work of future researchers, in this perspective article, we advocate the adoption of the DNA-based natural language (DBNL) algorithm standard and then demonstrate it using a Streptomyces species as a proof of concept. This standard enables the sophisticated genome sequencing and subsequent extraction of valuable information encoded within a particular microbial species. In addition, it allows the access of such information for the continued research and applications even if a currently cultivated microbe cannot be cultured in the future. Embracing the DBNL algorithm standard promises to enhance the efficiency and effectiveness of microbial research, paving the way for innovative solutions and discoveries in diverse fields.

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在微生物中植入基于 DNA 的自然语言,造福于未来的研究人员†。
微生物是各种生态系统和生物体中的抗生素生产者、生物控制剂和共生剂,是宝贵的资源。过去几十年来,世界各地研究实验室鉴定和产生的野生型和转基因微生物菌株显著增加。然而,这些菌株所代表的大量信息仍然散见于科学文献中。为了促进未来研究人员的工作,我们在这篇视角文章中提倡采用基于 DNA 的自然语言(DBNL)算法标准,然后用一个链霉菌物种作为概念验证进行了演示。该标准可以进行复杂的基因组测序,随后提取特定微生物物种中编码的宝贵信息。此外,即使目前培养的微生物将来无法再培养,也能获取这些信息,用于继续研究和应用。采用 DBNL 算法标准有望提高微生物研究的效率和效果,为不同领域的创新解决方案和发现铺平道路。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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