Evidence map›Paper›PMID 39858589›Full record

ArticleGenes2025

Identifying Candidate Genes Related to Soybean (

Cheng Wang, Pingchun Fu, Tingting Sun, Yan Wang, Xueting Li, Shulin Lan, Hui Liu, Yongji Gou, Qiaoxia Shang, Weiyu Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

10 authors.

Cheng WangCollege of Plant Science and Technology, Beijing Key Laboratory of New Agricultural Technology in Agriculture Application, National Demonstration Center for Experimental Plant Production Education, Beijing University of Agriculture, Beijing 102206, China.
Pingchun FuKey Laboratory for Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs Beijing University of Agriculture, Beijing 102206, China.
Tingting SunCollege of Plant Science and Technology, Beijing Key Laboratory of New Agricultural Technology in Agriculture Application, National Demonstration Center for Experimental Plant Production Education, Beijing University of Agriculture, Beijing 102206, China.
Yan WangKey Laboratory for Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs Beijing University of Agriculture, Beijing 102206, China.
Xueting LiCollege of Plant Science and Technology, Beijing Key Laboratory of New Agricultural Technology in Agriculture Application, National Demonstration Center for Experimental Plant Production Education, Beijing University of Agriculture, Beijing 102206, China.
Shulin LanCollege of Plant Science and Technology, Beijing Key Laboratory of New Agricultural Technology in Agriculture Application, National Demonstration Center for Experimental Plant Production Education, Beijing University of Agriculture, Beijing 102206, China.
Hui LiuCollege of Plant Science and Technology, Beijing Key Laboratory of New Agricultural Technology in Agriculture Application, National Demonstration Center for Experimental Plant Production Education, Beijing University of Agriculture, Beijing 102206, China.
Yongji GouKey Laboratory for Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs Beijing University of Agriculture, Beijing 102206, China.
Qiaoxia ShangKey Laboratory for Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs Beijing University of Agriculture, Beijing 102206, China.
Weiyu LiCollege of Plant Science and Technology, Beijing Key Laboratory of New Agricultural Technology in Agriculture Application, National Demonstration Center for Experimental Plant Production Education, Beijing University of Agriculture, Beijing 102206, China.

Funding

Beijing Innovation Consortium of Agriculture Research System Project BAIC01Science and Technology innovation support program of Beijing University of Agriculture Project BUA-HHXD2024The high-level teaching innovation team of the construction support program for the teachers in Beijing universities BPHR20220211
6 · The paper itself

Abstract

backgroundThe quality of soybeans is reflected in the seed coat color, which indicates soybean quality and commercial value. Researchers have identified genes related to seed coat color in various plants. However, research on the regulation of genes related to seed coat color in soybeans is rare.

methodsIn this study, four lines of seed coats with different colors (medium yellow 14, black, green, and brown) were selected from the F

resultsDMACA revealed that black seed coat soybeans produce anthocyanins first and have the deepest staining. Clustering and principal component analysis (PCA) of the RNA-seq data divided the eight samples into two groups, resulting in 16,456 DEGs, including 5359 TFs. GO and KEGG enrichment analyses revealed that the flavonoid biosynthesis, starch and sucrose metabolism, carotenoid biosynthesis, and circadian rhythm pathways were significantly enriched. We also conducted statistical and expression pattern analyses on the differentially expressed transcription factors. Based on weighted gene coexpression network analysis (WGCNA), we identified seven specific modules that were significantly related to the four soybean lines with different seed coat colors. The connectivity and functional annotation of genes within the modules were calculated, and 21 candidate genes related to soybean seed coat color were identified, including six transcription factor (TF) genes and three flavonoid pathway genes.

conclusionsThese findings provide a theoretical basis for an in-depth understanding of the molecular mechanisms underlying differences in soybean seed coat color and provide new genetic resources.

Indexed as

Gene Regulatory NetworksGlycine maxPigmentationSeedsAnthocyaninsColorGene Expression Regulation, PlantPlant ProteinsRNA-SeqAnthocyaninsPlant Proteinscandidate genesGlycine maxRNA-seqseed coat color

Identifiers

PMID39858589
PMCPMC11764550

What Socratic holds

Textmetadata
LicenceCC BY
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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.