Genetic regulation by non-coding RNAs

Liwang, QI; Li, Xinmin; Zhang, Shougong; An, Daochang

doi:10.1007/s11427-006-0201-8

Cited by 8 publications

(6 citation statements)

References 159 publications

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“…Southern Australian rainfall is influenced by a large number of weather systems including extratropical and subtropical cyclones, low pressure troughs, thunderstorms, cold and warm fronts, and transient and blocking high pressure systems (Sturman and Tapper 1996). Several previous studies have investigated the contribution of these weather systems individually to Australian rainfall as well as how they may have changed over recent decades, including for low pressure systems (Pepler et al 2014a;Lavender and Abbs 2013;Ng et al 2015;Qi et al 2006), high pressure systems (Pepler et al 2019b), and northwest cloud bands (Reid et al 2019). In addition, there have been several studies that attempt to attribute all rainfall in a given region to a larger number of synoptic types using either manual classification or selforganising maps, including Victoria in southeast Australia (Wright 1989;Risbey et al 2013;Verdon-Kidd and Kiem 2009), southwestern Western Australia (Pook et al 2011), and tropical Australia (Moron et al 2019).…”

Section: Introductionmentioning

confidence: 99%

The contributions of fronts, lows and thunderstorms to southern Australian rainfall

et al. 2020

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A systematic analysis of the main weather types influencing southern Australian rainfall is presented for the period 1979–2015. This incorporates two multi-method datasets of cold fronts and low pressure systems, which indicate the more robust fronts and lows as distinguished from the weaker and less impactful events that are often indicated only by a single method. The front and low pressure system datasets are then combined with a dataset of environmental conditions associated with thunderstorms, as well as datasets of warm fronts and high pressure systems. The results demonstrate that these weather types collectively account for about 86% of days and more than 98% of rainfall in Australia south of 25° S. We also show how the key rain-bearing weather systems vary throughout the year and for different regions, with the co-occurrence of simultaneous lows, fronts and thunderstorm conditions particularly important during the spring and summer months in southeast Australia.

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Section: Introductionmentioning

confidence: 99%

The contributions of fronts, lows and thunderstorms to southern Australian rainfall

et al. 2020

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“…In total, 2327 lncRNAs were obtained after assessing the protein-coding potential of coexpressed mRNAs based on the two software programs, CNCI and CPC (Figure 1A), with 607 genes aligned against the known lncRNAs from the TAIR database of model plant Arabidopsis (Figure 1B), and 345 lncRNAs with coexpressed mRNAs of A. sinensis identified (Figure 1C) based on the SwissProt database. Based on the biological functions, the 272 characterized lncRNAs (Figure 1D) were divided into six categories: chromatin, DNA/RNA and protein modification (29); flowering (36); stress response (24); metabolism (117); biosignaling (23), and energy and transport (43) (Figure 1E). The base sequences of the 272 lncRNAs are shown in Table S2.…”

Section: Lncrnas Analysismentioning

confidence: 99%

Transcriptomic Analysis Reveals LncRNAs Associated with Flowering of Angelica sinensis during Vernalization

Liu

Luo

et al. 2022

CIMB

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Angelica sinensis is a “low-temperature and long-day” perennial plant that produces bioactive compounds such as phthalides, organic acids, and polysaccharides for various types of clinical agents, including those with cardio-cerebrovascular, hepatoprotective, and immunomodulatory effects. To date, the regulatory mechanism of flowering under the photoperiod has been revealed, while the regulatory network of flowering genes during vernalization, especially in the role of lncRNAs, has yet to be identified. Here, lncRNAs associated with flowering were identified based on the full-length transcriptomic analysis of A. sinensis at vernalization and freezing temperatures, and the coexpressed mRNAs of lncRNAs were validated by qRT-PCR. We obtained a total of 2327 lncRNAs after assessing the protein-coding potential of coexpressed mRNAs, with 607 lncRNAs aligned against the TAIR database of model plant Arabidopsis, 345 lncRNAs identified, and 272 lncRNAs characterized on the SwissProt database. Based on the biological functions of coexpressed mRNAs, the 272 lncRNAs were divided into six categories: (1) chromatin, DNA/RNA and protein modification; (2) flowering; (3) stress response; (4) metabolism; (5) bio-signaling; and (6) energy and transport. The differential expression levels of representatively coexpressed mRNAs were almost consistent with the flowering of A. sinensis. It can be concluded that the flowering of A. sinensis is positively or negatively regulated by lncRNAs, which provides new insights into the regulation mechanism of the flowering of A. sinensis.

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“…It is now evident [246][247][248][249][250] that nonprotein coding RNA (ncRNA) plays a critical role in regulating the timing and rate of protein translation.…”

Section: Regulatory Rnamentioning

confidence: 99%

“…Large scale cDNA sequencing and genome tiling array studies have shown [248] that around 50% of genomic DNA in humans is transcribed, of which 2% is translated into proteins and the remaining 98% is non-coding RNAs (ncRNAs). Today, we know very little about the regulatory mechanisms and functions of these ncRNAs.…”

Section: Regulatory Rnamentioning

confidence: 99%

Recent Computational Approaches to Understand Gene Regulation: Mining Gene Regulation In Silico

Abnizova

Subhankulova²,

Gilks³

2007

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This paper reviews recent computational approaches to the understanding of gene regulation in eukaryotes. Cis-regulation of gene expression by the binding of transcription factors is a critical component of cellular physiology. In eukaryotes, a number of transcription factors often work together in a combinatorial fashion to enable cells to respond to a wide spectrum of environmental and developmental signals. Integration of genome sequences and/or Chromatin Immunoprecipitation on chip data with gene-expression data has facilitated in silico discovery of how the combinatorics and positioning of transcription factors binding sites underlie gene activation in a variety of cellular processes.The process of gene regulation is extremely complex and intriguing, therefore all possible points of view and related links should be carefully considered. Here we attempt to collect an inventory, not claiming it to be comprehensive and complete, of related computational biological topics covering gene regulation, which may en-lighten the process, and briefly review what is currently occurring in these areas.We will consider the following computational areas:o gene regulatory network construction;o evolution of regulatory DNA;o studies of its structural and statistical informational properties;o and finally, regulatory RNA.

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Genetic regulation by non-coding RNAs

Cited by 8 publications

References 159 publications

The contributions of fronts, lows and thunderstorms to southern Australian rainfall

The contributions of fronts, lows and thunderstorms to southern Australian rainfall

Transcriptomic Analysis Reveals LncRNAs Associated with Flowering of Angelica sinensis during Vernalization

Recent Computational Approaches to Understand Gene Regulation: Mining Gene Regulation In Silico

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