Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in...

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Veröffentlicht in:Frontiers in plant science Jg. 13; S. 831204
Hauptverfasser: Su, Lingtao, Xu, Chunhui, Zeng, Shuai, Su, Li, Joshi, Trupti, Stacey, Gary, Xu, Dong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media SA 03.03.2022
Frontiers Media S.A
Schlagworte:
autoencoder
Biological effects
Data compression
Datasets
deep learning
Gene expression
gene regulatory network
Genes
Genomes
Heterogeneity
Modules
Plant Science
Plant tissues
Source code
soybean
Soybeans
Statistical power
tissue-specific gene
Transcription factors
transcriptome analysis
Transcriptomes
functional module
soybean
deep learning
autoencoder
transcriptome analysis
gene regulatory network
tissue-specific gene
ISSN:1664-462X, 1664-462X
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Zusammenfassung:Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta .
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Reviewed by: Song Li, Virginia Tech, United States; John Louis Van Hemert, Corteva Agriscience™, United States
This article was submitted to Plant Bioinformatics, a section of the journal Frontiers in Plant Science
Edited by: Xiyin Wang, Agricultural University of Hebei, China
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2022.831204