Evidence map›Paper›PMID 35258588›Full record

ArticleThe Plant cell2022

Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.

Takashi Akagi, Kanae Masuda, Eriko Kuwada, Kouki Takeshita, Taiji Kawakatsu, Tohru Ariizumi, Yasutaka Kubo, Koichiro Ushijima, Seiichi Uchida

Abstract read
In one paragraph

Article in The Plant cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  5. Investigating cis-regulatory elements and gene expression in multiple tomato varieties using interpretable deep learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Takashi AkagiGraduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan.ORCID 0000-0001-9993-8880
Kanae MasudaGraduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan.ORCID 0000-0001-7843-0367
Eriko KuwadaGraduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan.ORCID 0000-0002-5814-3085
Kouki TakeshitaDepartment of Advanced Information Technology, Kyushu University, Fukuoka 819-0395, Japan.
Taiji KawakatsuInstitute of Agrobiological Sciences, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8602, Japan.ORCID 0000-0002-6871-9199
Tohru AriizumiFaculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba Plant Innovation Research Center, Tsukuba, Japan.ORCID 0000-0003-3221-2369
Yasutaka KuboGraduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan.ORCID 0000-0002-2442-6829
Koichiro UshijimaGraduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan.ORCID 0000-0003-3904-5663
Seiichi UchidaDepartment of Advanced Information Technology, Kyushu University, Fukuoka 819-0395, Japan.ORCID 0000-0001-8592-7566

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expression behaviors from CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato (Solanum lycopersicum) fruit from the DNA sequences in gene regulatory regions. By fixing the effects of trans-acting factors using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model for crucial expression patterns in the initiation of tomato fruit ripening. Feature visualization of the CNNs identified nucleotide residues critical to the objective expression pattern in each gene, and their effects were validated experimentally in ripening tomato fruit. This cis-decoding framework will not only contribute to the understanding of the regulatory networks derived from CREs and transcription factor interactions, but also provides a flexible means of designing alleles for optimized expression.

Indexed as

Deep LearningSolanum lycopersicumFruitGene Expression Regulation, PlantPlant ProteinsRegulatory Sequences, Nucleic AcidTranscription FactorsPlant ProteinsTranscription Factors

Identifiers

PMID35258588
PMCPMC9134063

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.