Genetic regulation by non-coding RNAs

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

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.

FPQRNA: HARDWARE-ACCELERATED QRNA PACKAGE FOR NONCODING RNA GENE DETECTING ON FPGA

Noncoding RNAs (ncRNAs) have important functional roles in biological processes and have become a central research interest in modern molecular biology. However, how to find ncRNA attracts much more attention since ncRNA gene sequences do not have strong statistical signals, unlike protein coding genes. QRNA is a powerful program and has been widely used as an efficient analysis tool to detect ncRNA gene at present. Unfortunately, the O(L3) computing requirements and complicated data dependency greatly limit the usefulness of QRNA package with the explosion in gene database. In this paper, we present a fine-grained parallel QRNA prototype system, FPQRNA, for accelerating ncRNA gene detection application on FPGA chip. We propose a systolic-like array architecture with multiple PEs (Processing Elements). We partition the tasks by columns and assign tasks to PEs for load balance. We exploit data reuse schemes to reduce the need to load matrices from external memory. The experimental results show a speedup factor of more than 18× over the QRNA - 2.0.3c software running on a PC platform with AMD Phenom 9650 Quad CPU for pairwise sequence alignment with 996 residues, however the power consumption of our FPGA accelerator is only about 30% of that of the general-purpose microprocessors.

RNA: state memory and mediator of cellular phenotype

It has become increasingly clear that the genome is dynamic and exquisitely sensitive, changing expression patterns in response to age, environmental stimuli and pharmacological and physiological manipulations. Similarly, cellular phenotype, traditionally viewed as a stable end-state, should be viewed as versatile and changeable. The phenotype of a cell is better defined as a 'homeostatic phenotype' implying plasticity resulting from a dynamically changing yet characteristic pattern of gene/protein expression. A stable change in phenotype is the result of the movement of a cell between different multidimensional identity spaces. Here, we describe a key driver of this transition and the stabilizer of phenotype: the relative abundances of the cellular RNAs. We argue that the quantitative state of RNA can be likened to a state memory, that when transferred between cells, alters the phenotype in a predictable manner.

Recent computational approaches to understand gene regulation: mining gene regulation in silico

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.