RiceLncPedia: a comprehensive database of rice long non-coding RNAs

ABSTRACTLong non-coding RNAs (lncRNAs) play significant functions in various biological processes including differentiation, development and adaptation to different environments. Although multi research focused on lncRNAs in rice, the systematic identification and annotation of lncRNAs expressed in different tissues, developmental stages under diverse conditions are still scarce. This impacts the elucidation of their functional significance and the further research on them. Here, RiceLncPedia (http://218.199.68.191:10092/) is constructed including rice lncRNAs explored from 2313 publically available rice RNA-seq libraries and characterize them with multi-omics data sets. In the current version, RiceLncPedia shows 6978 lncRNAs with abundant features: (i) expression profile across 2313 rice RNA-seq libraries; (ii) an online genome browser for rice lncRNAs; (iii) genome SNPs in lncRNA transcripts; (iv) lncRNA associations with phenotype; (v) overlap of lncRNAs with transposons; and (vi) LncRNA-miRNA interactions and lncRNAs as the precursors of miRNAs. In total, RiceLncPedia imported numerous of rice lncRNAs during development under various environments as well as their features extracted from multi-omics data and thus serve as a fruitful resource for rice-related research communities. RiceLncPedia will be further updated with experimental validation, functions association and epigenetic characteristics to greatly facilitate future investigation on rice lncRNAs.

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Genome-wide identification and characterization of long non-coding RNAs involved in flag leaf senescence of rice

Plant Molecular Biology ◽

10.1007/s11103-021-01121-3 ◽

2021 ◽

Author(s):

Xiaoping Huang ◽

Hongyu Zhang ◽

Qiang Wang ◽

Rong Guo ◽

Lingxia Wei ◽

...

Keyword(s):

Transcription Factors ◽

Leaf Senescence ◽

Flag Leaf ◽

Reduction Process ◽

Sequencing Analysis ◽

Biological Processes ◽

Genome Wide ◽

A Genome ◽

Non Coding Rnas ◽

Systematic Identification

Abstract Key message This study showed the systematic identification of long non-coding RNAs (lncRNAs) involving in flag leaf senescence of rice, providing the possible lncRNA-mRNA regulatory relationships and lncRNA-miRNA-mRNA ceRNA networks during leaf senescence. Abstract LncRNAs have been reported to play crucial roles in diverse biological processes. However, no systematic identification of lncRNAs associated with leaf senescence in plants has been studied. In this study, a genome-wide high throughput sequencing analysis was performed using rice flag leaves developing from normal to senescence. A total of 3953 lncRNAs and 38757 mRNAs were identified, of which 343 lncRNAs and 9412 mRNAs were differentially expressed. Through weighted gene co-expression network analysis (WGCNA), 22 continuously down-expressed lncRNAs targeting 812 co-expressed mRNAs and 48 continuously up-expressed lncRNAs targeting 1209 co-expressed mRNAs were considered to be significantly associated with flag leaf senescence. Gene Ontology results suggested that the senescence-associated lncRNAs targeted mRNAs involving in many biological processes, including transcription, hormone response, oxidation–reduction process and substance metabolism. Additionally, 43 senescence-associated lncRNAs were predicted to target 111 co-expressed transcription factors. Interestingly, 8 down-expressed lncRNAs and 29 up-expressed lncRNAs were found to separately target 12 and 20 well-studied senescence-associated genes (SAGs). Furthermore, analysis on the competing endogenous RNA (CeRNA) network revealed that 6 down-expressed lncRNAs possibly regulated 51 co-expressed mRNAs through 15 miRNAs, and 14 up-expressed lncRNAs possibly regulated 117 co-expressed mRNAs through 21 miRNAs. Importantly, by expression validation, a conserved miR164-NAC regulatory pathway was found to be possibly involved in leaf senescence, where lncRNA MSTRG.62092.1 may serve as a ceRNA binding with miR164a and miR164e to regulate three transcription factors. And two key lncRNAs MSTRG.31014.21 and MSTRG.31014.36 also could regulate the abscisic-acid biosynthetic gene BGIOSGA025169 (OsNCED4) and BGIOSGA016313 (NAC family) through osa-miR5809. The possible regulation networks of lncRNAs involving in leaf senescence were discussed, and several candidate lncRNAs were recommended for prior transgenic analysis. These findings will extend the understanding on the regulatory roles of lncRNAs in leaf senescence, and lay a foundation for functional research on candidate lncRNAs.

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Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting soybean genotypes subjected to phosphate starvation

BMC Genomics ◽

10.1186/s12864-021-07750-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jinyu Zhang ◽

Huanqing Xu ◽

Yuming Yang ◽

Xiangqian Zhang ◽

Zhongwen Huang ◽

...

Keyword(s):

Growth And Yield ◽

Biological Processes ◽

Rna Seq ◽

Plant Growth And Development ◽

P Deficiency ◽

Genome Wide ◽

Low Phosphorus ◽

Soybean Genotypes ◽

Non Coding Rnas ◽

Sensitive Genotype

Abstract Background Phosphorus (P) is essential for plant growth and development, and low-phosphorus (LP) stress is a major factor limiting the growth and yield of soybean. Long noncoding RNAs (lncRNAs) have recently been reported to be key regulators in the responses of plants to stress conditions, but the mechanism through which LP stress mediates the biogenesis of lncRNAs in soybean remains unclear. Results In this study, to explore the response mechanisms of lncRNAs to LP stress, we used the roots of two representative soybean genotypes that present opposite responses to P deficiency, namely, a P-sensitive genotype (Bogao) and a P-tolerant genotype (NN94156), for the construction of RNA sequencing (RNA-seq) libraries. In total, 4,166 novel lncRNAs, including 525 differentially expressed (DE) lncRNAs, were identified from the two genotypes at different P levels. GO and KEGG analyses indicated that numerous DE lncRNAs might be involved in diverse biological processes related to phosphate, such as lipid metabolic processes, catalytic activity, cell membrane formation, signal transduction, and nitrogen fixation. Moreover, lncRNA-mRNA-miRNA and lncRNA-mRNA networks were constructed, and the results identified several promising lncRNAs that might be highly valuable for further analysis of the mechanism underlying the response of soybean to LP stress. Conclusions These results revealed that LP stress can significantly alter the genome-wide profiles of lncRNAs, particularly those of the P-sensitive genotype Bogao. Our findings increase the understanding of and provide new insights into the function of lncRNAs in the responses of soybean to P stress.

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Single-Molecule Long-Read Sequencing Reveals the Diversity of Full-Length Transcripts in Leaves of Gnetum (Gnetales)

International Journal of Molecular Sciences ◽

10.3390/ijms20246350 ◽

2019 ◽

Vol 20 (24) ◽

pp. 6350 ◽

Cited By ~ 2

Author(s):

Nan Deng ◽

Chen Hou ◽

Fengfeng Ma ◽

Caixia Liu ◽

Yuxin Tian

Keyword(s):

Single Molecule ◽

Developmental Stages ◽

Alternative Polyadenylation ◽

Full Length ◽

Stomatal Development ◽

Rna Seq ◽

Leaf Transcriptome ◽

Long Read ◽

Non Coding Rnas ◽

A Site

The limitations of RNA sequencing make it difficult to accurately predict alternative splicing (AS) and alternative polyadenylation (APA) events and long non-coding RNAs (lncRNAs), all of which reveal transcriptomic diversity and the complexity of gene regulation. Gnetum, a genus with ambiguous phylogenetic placement in seed plants, has a distinct stomatal structure and photosynthetic characteristics. In this study, a full-length transcriptome of Gnetum luofuense leaves at different developmental stages was sequenced with the latest PacBio Sequel platform. After correction by short reads generated by Illumina RNA-Seq, 80,496 full-length transcripts were obtained, of which 5269 reads were identified as isoforms of novel genes. Additionally, 1660 lncRNAs and 12,998 AS events were detected. In total, 5647 genes in the G. luofuense leaves had APA featured by at least one poly(A) site. Moreover, 67 and 30 genes from the bHLH gene family, which play an important role in stomatal development and photosynthesis, were identified from the G. luofuense genome and leaf transcripts, respectively. This leaf transcriptome supplements the reference genome of G. luofuense, and the AS events and lncRNAs detected provide valuable resources for future studies of investigating low photosynthetic capacity of Gnetum.

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Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

10.7287/peerj.preprints.1839 ◽

2016 ◽

Author(s):

Xun Zhu ◽

Travers Ching ◽

Xinghua Pan ◽

Sherman Weissman ◽

Lana Garmire

Keyword(s):

Single Cell ◽

Hierarchical Clustering ◽

Matrix Factorization ◽

New Technology ◽

Data Sets ◽

Biological Processes ◽

Rna Seq ◽

Clustering Methods ◽

Hematopoietic Stem ◽

Non Negative Matrix Factorization

Single-cell RNA-Sequencing (scRNA-Seq) is a cutting edge technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we have investigated the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq data sets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy even when the clustering results of K-means and hierarchical clustering are enhanced by t-SNE. Moreover, NMF successfully detect the subpopulations, such as those in a single glioblastoma patient. Furthermore, in conjugation with the modularity detection method FEM, it reveals unique modules that are indicative of clinical subtypes. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data, and the NMFEM pipeline is suitable for modularity detection among single-cell RNA-Seq data.

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GhGPAT12/25 Are Essential for the Formation of Anther Cuticle and Pollen Exine in Cotton (Gossypium hirsutum L.)

Frontiers in Plant Science ◽

10.3389/fpls.2021.667739 ◽

2021 ◽

Vol 12 ◽

Author(s):

Meng Zhang ◽

Hengling Wei ◽

Pengbo Hao ◽

Aimin Wu ◽

Qiang Ma ◽

...

Keyword(s):

Developmental Stages ◽

Regulatory Mechanism ◽

Anther Development ◽

Biological Processes ◽

Rna Seq ◽

Loss Of Function ◽

Virus Induced Gene Silencing ◽

Cuticular Waxes ◽

Pollen Exine ◽

Anther Cuticle

Glycerol-3-phosphate acyltransferases (GPATs), critical for multiple biological processes like male fertility, have been extensively characterized. However, their precise functions and underlying regulatory mechanism in cotton anther development are unclear. This research demonstrated the importance of GhGPAT12/25 (a paralogs pair on A12/D12 sub-chromosome of cotton) to regulate the degradation of tapetum, anther cuticle formation, and pollen exine development. GhGPAT12 and GhGPAT25 exhibited specifically detected transcripts in tapetum and pollen exine during the early anther developmental stages. GhGPAT12/25 are sn-2 glycerol-3-phosphate acyltransferases and can transfer the acyl group of palmitoyl-CoA to glycerol-3-phosphate (G3P). CRISPR/Cas9-mediated knockout identified the functional redundancy of GhGPAT12 and GhGPAT25. Knockout of both genes caused completely male sterility associated with abnormal anther cuticle, swollen tapetum, and inviable microspores with defective exine and irregular unrestricted shape. RNA-seq analysis showed that the loss of function of GhGPAT12/25 affects the processes of wax metabolic, glycerol monomer biosynthesis, and transport. Consistently, cuticular waxes were dramatically reduced in mutant anthers. Yeast one-hybrid system (Y1H), virus-induced gene silencing (VIGS), and dual-luciferase (LUC) assays illustrated that GhMYB80s are likely to directly activate the expression of GhGPAT12/25. This study provides important insights for revealing the regulatory mechanism underlying anther development in cotton.

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Pan-cancer systematic identification of lncRNAs associated with cancer prognosis

PeerJ ◽

10.7717/peerj.8797 ◽

2020 ◽

Vol 8 ◽

pp. e8797 ◽

Cited By ~ 1

Author(s):

Matthew Ung ◽

Evelien Schaafsma ◽

Daniel Mattox ◽

George L. Wang ◽

Chao Cheng

Keyword(s):

Drug Targets ◽

Patient Survival ◽

Cancer Prognosis ◽

Rna Seq ◽

Non Coding Rna ◽

Cancer Types ◽

Non Coding Rnas ◽

Microarray Studies ◽

Systematic Identification ◽

Pan Cancer

Background The “dark matter” of the genome harbors several non-coding RNA species including Long non-coding RNAs (lncRNAs), which have been implicated in neoplasia but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. Methods In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. Results In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

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Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting soybean genotypes subjected to phosphate starvation

10.21203/rs.3.rs-67625/v2 ◽

2021 ◽

Author(s):

jinyu zhang ◽

Huanqing Xu ◽

Yuming Yang ◽

Xiangqian Zhang ◽

Zhongwen Huang ◽

...

Keyword(s):

Growth And Yield ◽

Biological Processes ◽

Rna Seq ◽

Plant Growth And Development ◽

P Deficiency ◽

Genome Wide ◽

Low Phosphorus ◽

Soybean Genotypes ◽

Non Coding Rnas ◽

Sensitive Genotype

Abstract Background: Phosphorus (P) is essential for plant growth and development, and low-phosphorus (LP) stress is a major factor limiting the growth and yield of soybean. Long noncoding RNAs (lncRNAs) have recently been reported to be key regulators in the responses of plants to stress conditions, but the mechanism through which LP stress mediates the biogenesis of lncRNAs in soybean remains unclear.Results: In this study, to explore the response mechanisms of lncRNAs to LP stress, we used the roots of two representative soybean genotypes that present opposite responses to P deficiency, namely, a P-sensitive genotype (Bogao) and a P-tolerant genotype (NN94156), for the construction of RNA sequencing (RNA-seq) libraries. In total, 4,166 novel lncRNAs, including 525 differentially expressed (DE) lncRNAs, were identified from the two genotypes at different P levels. GO and KEGG analyses indicated that numerous DE lncRNAs might be involved in diverse biological processes related to phosphate, such as lipid metabolic processes, catalytic activity, cell membrane formation, signal transduction, and nitrogen fixation. Moreover, lncRNA-mRNA-miRNA and lncRNA-mRNA networks were constructed, and the results identified several promising lncRNAs that might be highly valuable for further analysis of the mechanism underlying the response of soybean to LP stress.Conclusions: These results revealed that LP stress can significantly alter the genome-wide profiles of lncRNAs, particularly those of the P-sensitive genotype Bogao. Our findings increase the understanding of and provide new insights into the function of lncRNAs in the responses of soybean to P stress.

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Missing data in open-data era – a barrier to multiomics integration

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2017-0026 ◽

2018 ◽

Vol 14 (1) ◽

Author(s):

Monika Piwowar ◽

Wiktor Jurkowski

Keyword(s):

Quantitative Data ◽

Open Data ◽

Multidimensional Data ◽

Data Sets ◽

Biological Processes ◽

Rna Seq ◽

Data Set ◽

Complex Interactions ◽

Integrate Data ◽

Generation Sequencing

AbstractThe exploration of complex interactions in biological systems is one of the main aims in nature science nowadays. Progress in this area is possible because of high-throughput omics technologies and the computational surge. The development of analytical methods “is trying to keep pace” with the development of molecular biology methods that provide increasingly large amounts of data – omics data. Specialized databases consist of ever-larger collections of experiments that are usually conducted by one next-generation sequencing technique (e.g. RNA-seq). Other databases integrate data by defining qualitative relationships between individual objects in the form of ontologies, interactions, and pathways (e.g. GO, KEGG, and String). However, there are no open-source complementary quantitative data sets for the biological processes studied, including information from many levels of the organism organization, which would allow the development of multidimensional data analysis methods (multiscale and insightful overviews of biological processes). In the paper, the lack of omics complementary quantitative data set, which would help integrate the defined qualitative biological relationships of individual biomolecules with statistical, computational methods, is discussed.

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Multi-omics annotation of human long non-coding RNAs

Biochemical Society Transactions ◽

10.1042/bst20191063 ◽

2020 ◽

Vol 48 (4) ◽

pp. 1545-1556 ◽

Cited By ~ 2

Author(s):

Qianpeng Li ◽

Zhao Li ◽

Changrui Feng ◽

Shuai Jiang ◽

Zhang Zhang ◽

...

Keyword(s):

Human Genome ◽

Functional Annotation ◽

Molecular Mechanisms ◽

High Throughput Sequencing ◽

Rna World ◽

Omics Data ◽

Biological Processes ◽

Sequencing Technologies ◽

Non Coding Rnas ◽

Powerful Strategy

LncRNAs (long non-coding RNAs) are pervasively transcribed in the human genome and also extensively involved in a variety of essential biological processes and human diseases. The comprehensive annotation of human lncRNAs is of great significance in navigating the functional landscape of the human genome and deepening the understanding of the multi-featured RNA world. However, the unique characteristics of lncRNAs as well as their enormous quantity have complicated and challenged the annotation of lncRNAs. Advances in high-throughput sequencing technologies give rise to a large volume of omics data that are generated at an unprecedented rate and scale, providing possibilities in the identification, characterization and functional annotation of lncRNAs. Here, we review the recent important discoveries of human lncRNAs through analysis of various omics data and summarize specialized lncRNA database resources. Moreover, we highlight the multi-omics integrative analysis as a powerful strategy to efficiently discover and characterize the functional lncRNAs and elucidate their potential molecular mechanisms.

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PncStress: a manually curated database of experimentally validated stress-responsive non-coding RNAs in plants

Database ◽

10.1093/database/baaa001 ◽

2020 ◽

Vol 2020 ◽

Cited By ~ 1

Author(s):

Wenyi Wu ◽

Yan Wu ◽

Dahui Hu ◽

Yincong Zhou ◽

Yanshi Hu ◽

...

Keyword(s):

Stress Responses ◽

Abiotic Stresses ◽

Plant Stress ◽

Biological Information ◽

Network Visualization ◽

Circular Rnas ◽

Biological Processes ◽

Current Version ◽

Plant Stress Responses ◽

Non Coding Rnas

Abstract Non-coding RNAs (ncRNAs) are recognized as key regulatory molecules in many biological processes. Accumulating evidence indicates that ncRNA-related mechanisms play important roles in plant stress responses. Although abundant plant stress-responsive ncRNAs have been identified, these experimentally validated results have not been gathered into a single public domain archive. Therefore, we established PncStress by curating experimentally validated stress-responsive ncRNAs in plants, including microRNAs, long non-coding RNAs and circular RNAs. The current version of PncStress contains 4227 entries from 114 plants covering 48 biotic and 91 abiotic stresses. For each entry, PncStress has biological information and network visualization. Serving as a manually curated database, PncStress will become a valuable resource in support of plant stress response research.

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