Soybean is one of the most important crops in the world and its production needs to be significantly increased to meet the escalating global demand. Elucidating the genetic regulatory networks underlying soybean organ development is critical for breeding elite and resilient varieties to ensure an increase in soybean production under the changing climates. Integrated transcriptomic atlas that leverages multiple types of transcriptomic data can facilitate the characterization of temporal-spatial expression patterns of most organ development-related genes and thereby help understand organ developmental processes. Here, we constructed a comprehensive integrated transcriptomic atlas for soybean, integrating bulk RNA-seq dataset from 314 samples across the soybean life cycle, along with snRNA-seq and Stereo-seq datasets from five organs: root, nodule, shoot apical, leaf and stem. Taking the investigations of genes related to organ specificity, blade development and nodule formation as examples, we show that the atlas has robust power for exploring key genes involved in organ formation. In addition, we built a user-friendly panoramic database for the transcriptomic atlas, facilitating easy access and queries, which will serve as a valuable resource to significantly advance future soybean functional studies.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.molp.2025.02.003 | DOI Listing |
Brief Bioinform
March 2025
School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, Changchun 130012, Jilin Province, China.
Identifying genes causally linked to cancer from a multi-omics perspective is essential for understanding the mechanisms of cancer and improving therapeutic strategies. Traditional statistical and machine-learning methods that rely on generalized correlation approaches to identify cancer genes often produce redundant, biased predictions with limited interpretability, largely due to overlooking confounding factors, selection biases, and the nonlinear activation function in neural networks. In this study, we introduce a novel framework for identifying cancer genes across multiple omics domains, named ICGI (Integrative Causal Gene Identification), which leverages a large language model (LLM) prompted with causality contextual cues and prompts, in conjunction with data-driven causal feature selection.
View Article and Find Full Text PDFPurinergic Signal
March 2025
Université Côte dAzur, CNRS, INSERM, IRCAN, Nice, France.
Over the past few years, transcriptomics has emerged as a pillar for modern scientific research, enabling the comprehensive profiling of gene expression. The availability of large-scale public datasets, such as NCBI Gene Expression Omnibus, International Cancer Genome Consortium, and The Cancer Genome Atlas, has significantly boosted many scientific discoveries. However, to analyze and interpret these vast datasets, sophisticated bioinformatic tools are often necessary.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
March 2025
Department of Respiratory and Critical Care Medicine, Deyang People's Hospital, Affiliated Hospital of Chengdu College of Medicine, Deyang, Sichuan Province, China.
Cancer-associated fibroblasts (CAFs) are related to drug resistance and prognosis of tumor patients. This study aimed to investigate the relationship between prognosis and drug treatment response in patients with CAF and lung adenocarcinoma (LUAD). The data pertaining to LUAD patients were obtained from The Cancer Genome Atlas-LUAD and GSE68465 datasets.
View Article and Find Full Text PDFPhysiol Mol Biol Plants
February 2025
School of Life Science, Anhui Agricultural University, Hefei, 230036 Anhui China.
Single-cell transcriptomics overcomes the limitations of conventional transcriptome methods by isolating and sequencing RNA from individual cells, thus capturing unique expression values for each cell. This technology allows unprecedented precision in observing the stochasticity and heterogeneity of gene expression within cells. However, single-cell RNA sequencing (scRNA-seq) experiments often fail to capture all cells and genes comprehensively, and single-modality data is insufficient to explain cell states and systemic changes.
View Article and Find Full Text PDFHortic Res
April 2025
Key Laboratory of Biobreeding for Specialty Horticultural Crops of Jiangsu Province, College of Horticulture and Landscape Architecture, Yangzhou University, No. 88, Southern road of Daxue, 225009, Yangzhou, China.
The garlic bulb comprises several cloves, the swelling growth of which is significantly hindered by the accumulation of viruses. Herein, we describe a single-cell transcriptomic atlas of swelling cloves with virus accumulation, which comprised 19 681 high-quality cells representing 11 distinct cell clusters. Cells of two clusters, clusters 7 (C7) and 11 (C11), were inferred to be from the meristem.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!