A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development.

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العنوان:	A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development.
المؤلفون:	Wang M; State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Department of Biochemistry, Institute of Plant Biology, School of Life Sciences, Fudan University, Shanghai 200438, China.; Biotech Research Institute, Shanghai Key Laboratory of Agricultural Genetics and Breeding, Shanghai Academy of Agricultural Sciences, Shanghai 201106, China., Li Z; National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 300 Fenglin Road, Shanghai 200032, China., Wang H; Henan University, School of Life Science, Kaifeng, Henan 457000, China., Lin K; National Key Laboratory for Crop Genetics and Germplasm Enhancement and Utilization, CIC-MCP, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China., Zheng S; State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, and The Innovation Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China., Feng Y; National Key Laboratory for Crop Genetics and Germplasm Enhancement and Utilization, CIC-MCP, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China., Teng W; State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, and The Innovation Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China., Tong Y; State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, and The Innovation Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China., Zhang W; National Key Laboratory for Crop Genetics and Germplasm Enhancement and Utilization, CIC-MCP, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China., Liu C; Biotech Research Institute, Shanghai Key Laboratory of Agricultural Genetics and Breeding, Shanghai Academy of Agricultural Sciences, Shanghai 201106, China., Ling HQ; State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, and The Innovation Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China.; Yazhouwan National Laboratory (YNL), Sanya, Hainan 572025, China., Hu YQ; State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Department of Biochemistry, Institute of Plant Biology, School of Life Sciences, Fudan University, Shanghai 200438, China., Zhang Y; State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Department of Biochemistry, Institute of Plant Biology, School of Life Sciences, Fudan University, Shanghai 200438, China.
المصدر:	Molecular biology and evolution [Mol Biol Evol] 2024 Aug 30. Date of Electronic Publication: 2024 Aug 30.
Publication Model:	Ahead of Print
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: Oxford University Press Country of Publication: United States NLM ID: 8501455 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1537-1719 (Electronic) Linking ISSN: 07374038 NLM ISO Abbreviation: Mol Biol Evol Subsets: MEDLINE
أسماء مطبوعة:	Publication: 2003- : New York, NY : Oxford University Press Original Publication: [Chicago, Ill.] : University of Chicago Press, [c1983-
مستخلص:	Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high-dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. Here, we developed a quantitative computational framework, pseudo-genome divergence quantification (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from scRNA-seq data allowed for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagonostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies demonstrated that applying pgDQ to single cell and bulk tissue transcriptome data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution. (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)
فهرسة مساهمة:	Keywords: allopolyploid; evo-devo; pgDQ; scRNA-seq; subgenome diversity
تواريخ الأحداث:	Date Created: 20240830 Latest Revision: 20240830
رمز التحديث:	20240902
DOI:	10.1093/molbev/msae178
PMID:	39213378
قاعدة البيانات:	MEDLINE

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تدمد:	1537-1719
DOI:	10.1093/molbev/msae178