Publications by authors named "Patrick Xuechun Zhao"

Red alder (Alnus rubra Bong.) is an ecologically significant and important fast-growing commercial tree species native to western coastal and riparian regions of North America, having highly desirable wood, pigment, and medicinal properties. We have sequenced the genome of a rapidly growing clone.

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The HapMap (haplotype map) projects have produced valuable genetic resources in life science research communities, allowing researchers to investigate sequence variations and conduct genome-wide association study (GWAS) analyses. A typical HapMap project may require sequencing hundreds, even thousands, of individual lines or accessions within a species. Due to limitations in current sequencing technology, the genotype values for some accessions cannot be clearly called.

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We report an advanced web server, the plant-specific small noncoding RNA interference tool , which can be used to design a pool of small interfering RNAs (siRNAs) for highly effective, specific, and nontoxic gene silencing in plants. In developing this tool, we integrated the transcript dataset of plants, several rules governing gene silencing, and a series of computational models of the biological mechanism of the RNA interference (RNAi) pathway. The designed pool of siRNAs can be used to construct a long double-strand RNA and expressed through virus-induced gene silencing (VIGS) or synthetic transacting siRNA vectors for gene silencing.

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A growing number of small secreted peptides (SSPs) in plants are recognized as important regulatory molecules with roles in processes such as growth, development, reproduction, stress tolerance, and pathogen defense. Recent discoveries further implicate SSPs in regulating root nodule development, which is of particular significance for legumes. SSP-coding genes are frequently overlooked, because genome annotation pipelines generally ignore small open reading frames, which are those most likely to encode SSPs.

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Plant regulatory small RNAs (sRNAs), which include most microRNAs (miRNAs) and a subset of small interfering RNAs (siRNAs), such as the phased siRNAs (phasiRNAs), play important roles in regulating gene expression. Although generated from genetically distinct biogenesis pathways, these regulatory sRNAs share the same mechanisms for post-translational gene silencing and translational inhibition. psRNATarget was developed to identify plant sRNA targets by (i) analyzing complementary matching between the sRNA sequence and target mRNA sequence using a predefined scoring schema and (ii) by evaluating target site accessibility.

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The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only tens of thousands of genes, compounds, proteins and RNAs but also the complicated interactions and co-ordination among them. These networks play critical roles in many fundamental mechanisms, such as plant growth, development and environmental response. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout the published literature, publicly available high-throughput data sets and third-party databases.

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RNA interference (RNAi) is one of the most popular and effective molecular technologies for knocking down the expression of an individual gene of interest in living organisms. Yet the technology still faces the major issue of nonspecific gene silencing, which can compromise gene functional characterization and the interpretation of phenotypes associated with individual gene knockdown. Designing an effective and target-specific small interfering RNA (siRNA) for induction of RNAi is therefore the major challenge in RNAi-based gene silencing.

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Along with the canonical miRNA, distinct miRNA-like sequences called sibling miRNAs (sib-miRs) are generated from the same pre-miRNA. Among them, isomeric sequences featuring slight variations at the terminals, relative to the canonical miRNA, constitute a pool of isomeric sibling miRNAs (isomiRs). Despite the high prevalence of isomiRs in eukaryotes, their features and relevance remain elusive.

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Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred.

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Background: Plants regulate intrinsic gene expression through transcription factors (TFs), transcriptional regulators (TRs), chromatin regulators (CRs), and the basal transcription machinery. An understanding of plant gene regulatory mechanisms at a systems level requires the identification of these regulatory elements on a genomic scale.

Results: Here, we present PlantTFcat, a high-performance web-based analysis tool that is designed to identify and categorize plant TF/TR/CR genes from genome-scale protein and nucleic acid sequences by systematically analyzing InterProScan domain patterns in protein sequences.

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The accurate construction and interpretation of gene association networks (GANs) is challenging, but crucial, to the understanding of gene function, interaction and cellular behavior at the genome level. Most current state-of-the-art computational methods for genome-wide GAN reconstruction require high-performance computational resources. However, even high-performance computing cannot fully address the complexity involved with constructing GANs from very large-scale expression profile datasets, especially for the organisms with medium to large size of genomes, such as those of most plant species.

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Legumes play a vital role in maintaining the nitrogen cycle of the biosphere. They conduct symbiotic nitrogen fixation through endosymbiotic relationships with bacteria in root nodules. However, this and other characteristics of legumes, including mycorrhization, compound leaf development and profuse secondary metabolism, are absent in the typical model plant Arabidopsis thaliana.

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Plant endogenous non-coding short small RNAs (20-24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs). For example, many transcription factors and development-related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process.

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Eukaryotic messenger RNA (mRNA) contains not only protein-coding regions but also a plethora of functional cis-elements that influence or coordinate a number of regulatory aspects of gene expression, such as mRNA stability, splicing forms, and translation rates. Understanding the rules that apply to each of these element types (e.g.

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Plant microRNAs (miRNA) target recognition mechanism was once thought to be simple and straightforward, i.e. through perfect reverse complementary matching; therefore, very few target prediction tools and algorithms were developed for plants as compared to those for animals.

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Recently, simplified graphical modeling approaches based on low-order conditional (in-)dependence calculations have received attention because of their potential to model gene regulatory networks. Such methods are able to reconstruct large-scale gene networks with a small number of experimental measurements, at minimal computational cost. However, unlike Bayesian networks, current low-order graphical models provide no means to distinguish between cause and effect in gene regulatory relationships.

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Background: Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms.

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Plant secretory trichomes have a unique capacity for chemical synthesis and secretion and have been described as biofactories for the production of natural products. However, until recently, most trichome-specific metabolic pathways and genes involved in various trichome developmental stages have remained unknown. Furthermore, only a very limited amount of plant trichome genomics information is available in scattered databases.

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In plants, short RNAs including approximately 21-nt microRNA (miRNA) and 21-nt trans-acting siRNA (ta-siRNA) compose a 'miRNA --> ta-siRNA --> target gene' cascade pathway that regulates gene expression at the posttranscriptional level. In this cascade, biogenesis of ta-siRNA clusters requires 21-nt intervals (i.e.

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