Evidence map›Paper›PMID 40474618›Full record

ArticleMolecular biology and evolution2025

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

John B Allard, Sudhir Kumar

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

John B AllardInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.ORCID 0009-0000-7766-0769
Sudhir KumarInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.ORCID 0000-0002-9918-8212

Funding

Methods For Evolutionary Genomics AnalysisR35GM139540 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI Sudhir Kumar · 2021 to 2026
$2.9M
NIGMS NIH HHS R35 GM139540NIH HHS R35GM139540-05
6 · The paper itself

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.

Indexed as

Artificial IntelligenceEvolution, MolecularSoftwareartificial intelligenceChatGPTMEGAphylogenyRAG

Identifiers

PMID40474618
PMCPMC12141744

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.