Evidence map›Paper›PMID 42420730›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Programming RNA Trans-Splicing for Versatile Gene Regulation and Complex Cellular Logic Computation.

Yuanli Gao, Baojun Wang

Abstract read
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Article in Methods in molecular biology (Clifton, N.J.), 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

2 authors.

Yuanli GaoCollege of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.
Baojun WangCollege of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China. baojun.wang@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synthetic biology aims to customize biological systems by programming genetic circuits. However, the scale and complexity of genetic circuits are limited by a shortage of composable, programmable, and orthogonal regulatory parts. Recently, split-intron-enabled trans-splicing riboregulators (SENTRs) have emerged as a promising tool for regulating gene expression at the post-transcriptional level, featuring low leakage expression, wide dynamic range (> 1000-fold), high predictability using machine learning, and orthogonality across multiple intronic components. SENTR's composability with other regulatory modalities, such as split inteins, enables the establishment of a novel split-biomolecule-enabled circuit design paradigm, unleashing the information-processing capacity of a single regulator gene for complex cellular logic computation with up to six inputs. Here, we describe the methods and strategies to implement SENTRs for efficient gene regulation, including computational design, experimental assessment, and statistical analysis of SENTR-based genetic circuits. Validated on ten genes encoding fluorescent proteins, transcription factors, and ncRNAs, this method can be readily utilized to develop SENTRs for diverse applications, such as stringent gene regulation, complex logic computation, and biosensing for intracellular RNAs or physiological biomarkers.

Indexed as

Gene Expression RegulationGene Regulatory NetworksSynthetic BiologyTrans-SplicingInteinsIntronsBiocomputingGroup I intronRiboregulatorRNA regulationRNA splicingSynthetic gene circuit

Identifiers

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.