Evidence map›Paper›PMID 42202005›Full record

ArticleScience advances2026

PlasmidGPT: A generative framework for plasmid analysis and generation.

Bin Shao, Zequan Han, Zeyu Liang, Yi-Xin Huo

Abstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Bin ShaoSchool of Interdisciplinary Science, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0001-8697-2174
Zequan HanSchool of Interdisciplinary Science, Beijing Institute of Technology, Beijing 100081, China.ORCID 0009-0005-5003-1387
Zeyu LiangSchool of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Yi-Xin HuoSchool of Life Science, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0003-4644-999X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We introduce PlasmidGPT, a generative language model pretrained on 153,208 engineered plasmid sequences from Addgene. PlasmidGPT learns informative sequence embeddings that enable visualization of research topics across laboratories and analysis of plasmid diversity across vector types. Leveraging these embeddings, PlasmidGPT accurately predicts features of engineered plasmids and achieves state-of-the-art performance in lab-of-origin prediction. The learned representations also generalize to natural plasmids, enabling host taxonomy prediction at both the phylum and genus level. Moreover, PlasmidGPT enables controlled generation of functional plasmid sequences by using either a predefined input sequence or specified design constraints, producing outputs that recapitulate the part co-occurrence and synteny of real plasmids.

Indexed as

Computational BiologyPlasmidsGenerative Artificial IntelligenceLarge Language Models

Identifiers

PMID42202005
PMCPMC13215174

What OpenQuestion holds

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Registered trials

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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.