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MEGA-GPT:使用MEGA软件的人工智能指导与构建分析协议

MEGA-GPT: Artificial Intelligence Guidance and Building Analytical Protocols Using MEGA Software.

作者信息

Allard John B, Kumar Sudhir

机构信息

Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.

Department of Biology, Temple University, Philadelphia, PA 19122, USA.

出版信息

Mol Biol Evol. 2025 Jun 4;42(6). doi: 10.1093/molbev/msaf101.

DOI:10.1093/molbev/msaf101
PMID:40474618
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12141744/
Abstract

Over the past three decades, the Molecular Evolutionary Genetics Analysis (MEGA) software has evolved into a powerful tool with an ever-expanding suite of functionalities. Yet, despite its user-friendly design and widespread adoption by researchers and students, the software's extensive feature set can overwhelm both new and experienced users who are unfamiliar with its latest capabilities. To address this challenge, we developed MEGA-GPT, an AI-driven resource that leverages ChatGPT augmented with retrieval techniques to guide users through MEGA's analytical workflows via natural language queries. By integrating MEGA's help documentation, version-specific articles, and other key publications, MEGA-GPT enhances ChatGPT's standard responses to deliver step-by-step protocols, clarify analytical settings, and recommend optimal workflows. Our evaluations indicate that MEGA-GPT offers significantly improved guidance while minimizing the hallucinations and inaccuracies observed in standard ChatGPT outputs. We propose that such customized, retrieval-augmented query interfaces can substantially enhance the usability of complex scientific computing packages. MEGA-GPT is freely available to all users with a ChatGPT account by accessing the URL https://tinyurl.com/gpt-mega, which is also integrated into MEGA's graphical user interface.

摘要

在过去三十年里,分子进化遗传学分析(MEGA)软件已发展成为一个功能不断扩展的强大工具。然而,尽管其设计用户友好且被研究人员和学生广泛采用,但该软件丰富的功能集可能会让不熟悉其最新功能的新用户和有经验的用户都应接不暇。为应对这一挑战,我们开发了MEGA-GPT,这是一种由人工智能驱动的资源,它利用结合了检索技术的ChatGPT,通过自然语言查询引导用户完成MEGA的分析工作流程。通过整合MEGA的帮助文档、特定版本的文章和其他关键出版物,MEGA-GPT增强了ChatGPT的标准回复,以提供逐步的协议、阐明分析设置并推荐最佳工作流程。我们的评估表明,MEGA-GPT在提供显著改进的指导的同时,最大限度地减少了标准ChatGPT输出中出现的幻觉和不准确之处。我们提出,这种定制的、检索增强的查询界面可以大幅提高复杂科学计算软件包的可用性。所有拥有ChatGPT账户的用户都可以通过访问网址https://tinyurl.com/gpt-mega免费使用MEGA-GPT,该网址也已集成到MEGA的图形用户界面中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/12141744/48f8ee1eaa05/msaf101f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/12141744/32346c3f64b1/msaf101f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/12141744/48f8ee1eaa05/msaf101f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/12141744/32346c3f64b1/msaf101f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/12141744/48f8ee1eaa05/msaf101f2.jpg

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Discovering Fragile Clades and Causal Sequences in Phylogenomics by Evolutionary Sparse Learning.通过进化稀疏学习在系统基因组学中发现脆弱的进化枝和因果序列。
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Embracing Green Computing in Molecular Phylogenetics.拥抱分子系统发生学中的绿色计算。
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MEGA11: Molecular Evolutionary Genetics Analysis Version 11.MEGA11:分子进化遗传学分析版本 11。
Mol Biol Evol. 2021 Jun 25;38(7):3022-3027. doi: 10.1093/molbev/msab120.
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Molecular Evolutionary Genetics Analysis (MEGA) for macOS.用于 macOS 的分子进化遗传学分析(MEGA)。
Mol Biol Evol. 2020 Apr 1;37(4):1237-1239. doi: 10.1093/molbev/msz312.
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Estimating TimeTrees with MEGA and the TimeTree Resource.使用 MEGA 和 TimeTree 资源估算时间树。
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