Practical Utilization of OryzaExpress and Plant Omics Data Center Databases to Explore Gene Expression Networks in Oryza Sativa and Other Plant Species

Abstract The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

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Plant Omics Data Center: An Integrated Web Repository for Interspecies Gene Expression Networks with NLP-Based Curation

Plant and Cell Physiology ◽

10.1093/pcp/pcu188 ◽

2014 ◽

Vol 56 (1) ◽

pp. e9-e9 ◽

Cited By ~ 41

Author(s):

Hajime Ohyanagi ◽

Tomoyuki Takano ◽

Shin Terashima ◽

Masaaki Kobayashi ◽

Maasa Kanno ◽

...

Keyword(s):

Gene Expression ◽

Data Center ◽

Omics Data ◽

Web Repository

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Genetic variation and gene expression of anthocyanin synthesis and transport related enzymes in Oryza sativa against thiocyanate

Plant Physiology and Biochemistry ◽

10.1016/j.plaphy.2021.01.005 ◽

2021 ◽

Author(s):

Qing-Long Ling ◽

Yu-Xi Feng ◽

Chun-Jiao Lu ◽

Yu-Juan Lin ◽

Xiao-Zhang Yu

Keyword(s):

Gene Expression ◽

Genetic Variation ◽

Oryza Sativa ◽

Anthocyanin Synthesis

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Comparative Transcriptome Atlases Reveal Altered Gene Expression Modules between Two Cleomaceae C3 and C4 Plant Species

The Plant Cell ◽

10.1105/tpc.114.123752 ◽

2014 ◽

Vol 26 (8) ◽

pp. 3243-3260 ◽

Cited By ~ 59

Author(s):

Canan Külahoglu ◽

Alisandra K. Denton ◽

Manuel Sommer ◽

Janina Maß ◽

Simon Schliesky ◽

...

Keyword(s):

Gene Expression ◽

Plant Species ◽

Comparative Transcriptome ◽

C4 Plant ◽

Altered Gene Expression ◽

Altered Gene

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Nelumbo genome database, an integrative resource for gene expression and variants of Nelumbo nucifera

Scientific Data ◽

10.1038/s41597-021-00828-8 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hui Li ◽

Xingyu Yang ◽

Yue Zhang ◽

Zhiyan Gao ◽

Yuting Liang ◽

...

Keyword(s):

Gene Expression ◽

Plant Species ◽

Aquatic Plant ◽

Molecular Breeding ◽

Nelumbo Nucifera ◽

Sequence Analyses ◽

Genome Database ◽

Future Studies ◽

Sacred Lotus ◽

Coexpression Networks

AbstractSacred lotus (Nelumbo nucifera, or lotus) is one of the most widely grown aquatic plant species with important uses, such as in water gardening and in vegetable and herbal medicine. A public genomic database of lotus would facilitate studies of lotus and other aquatic plant species. Here, we constructed an integrative database: the Nelumbo Genome Database (NGD, http://nelumbo.biocloud.net). This database is a collection of the most updated lotus genome assembly and contains information on both gene expression in different tissues and coexpression networks. In the NGD, we also integrated genetic variants and key traits from our 62 newly sequenced lotus cultivars and 26 previously reported cultivars, which are valuable for lotus germplasm studies. As applications including BLAST, BLAT, Primer, Annotation Search, Variant and Trait Search are deployed, users can perform sequence analyses and gene searches via the NGD. Overall, the valuable genomic resources provided in the NGD will facilitate future studies on population genetics and molecular breeding of lotus.

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Genome-wide H3K9 Acetylation Level Increases with Age-Dependent Senescence of Flag Leaf in Rice (Oryza sativa)

10.1101/2021.11.14.468555 ◽

2021 ◽

Author(s):

Yu Zhang ◽

Yanyun Li ◽

Yuanyuan Zhang ◽

Zeyu Zhang ◽

Deyu Zhang ◽

...

Keyword(s):

Gene Expression ◽

Oryza Sativa ◽

Leaf Senescence ◽

Epigenetic Modification ◽

Flag Leaf ◽

Regulation Of Gene Expression ◽

Leaf Aging ◽

Genome Wide ◽

Age Dependent ◽

H3k9 Acetylation

Flag leaf senescence is an important biological process that drives the remobilization of nutrients to the growing organs of rice. Leaf senescence is controlled by genetic information via gene expression and epigenetic modification, but the precise mechanism is as of yet unclear. Here, we analyzed genome-wide acetylated lysine residue 9 of histone H3 (H3K9ac) enrichment by chromatin immunoprecipitation-sequencing (ChIP-seq) and examined its association with transcriptomes by RNA-seq during flag leaf aging in rice (Oryza sativa). We found that genome-wide H3K9 acetylation levels increased with age-dependent senescence in rice flag leaf, and there was a positive correlation between the density and breadth of H3K9ac and gene expression and transcript elongation. A set of 1,249 up-regulated, differentially expressed genes (DEGs) and 996 down-regulated DEGs showing a strong relationship between temporal changes in gene expression and gain/loss of H3K9ac was observed during rice flag leaf aging. We produced a landscape of H3K9 acetylation- modified gene expression targets that includes known senescence-associated genes, metabolism-related genes, as well as miRNA biosynthesis- related genes. Our findings reveal a complex regulatory network of metabolism- and senescence-related pathways mediated by H3K9ac and also elucidate patterns of H3K9ac-mediated regulation of gene expression during flag leaf aging in rice.

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A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

BMC Bioinformatics ◽

10.1186/s12859-021-04447-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ramin Hasibi ◽

Tom Michoel

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Gene Expression Data ◽

Biological Networks ◽

Molecular Interaction ◽

Interaction Networks ◽

Omics Data ◽

Expression Data ◽

Molecular Interaction Networks ◽

Graph Neural Networks

Abstract Background Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

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