Nai-Yun Wu

PlantPAN 4.0 (http://PlantPAN.itps.ncku.edu.tw/) is an integrative resource for constructing transcriptional regulatory networks for diverse plant species. In this release, the gene annotation and promoter sequences were expanded to cover 115 species. PlantPAN 4.0 can help users characterize the evolutionary differences and similarities among cis-regulatory elements; furthermore, this system can now help in identification of conserved non-coding sequences among homologous genes. The updated transcription factor binding site repository contains 3428 nonredundant matrices for 18305 transcription factors; this expansion helps in exploration of combinational and nucleotide variants of cis-regulatory elements in conserved non-coding sequences. Additionally, the genomic landscapes of regulatory factors were manually updated, and ChIP-seq data sets derived from a single-cell green alga (Chlamydomonas reinhardtii) were added. Furthermore, the statistical review and graphical analysis components were improved to offer intelligible information through ChIP-seq data analysis. These improvements included easy-to-read experimental condition clusters, searchable gene-centered interfaces for the identification of promoter regions' binding preferences by considering experimental condition clusters and peak visualization for all regulatory factors, and the 20 most significantly enriched gene ontology functions for regulatory factors. Thus, PlantPAN 4.0 can effectively reconstruct gene regulatory networks and help compare genomic cis-regulatory elements across plant species and experiments.

Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user's gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

Long noncoding RNAs (lncRNAs) are regulatory RNAs involved in numerous biological processes. Many plant lncRNAs have been identified, but their regulatory mechanisms remain largely unknown. A resource that enables the investigation of lncRNA activity under various conditions is required because the co-expression between lncRNAs and protein-coding genes may reveal the effects of lncRNAs. This study developed JustRNA, an expression profiling resource for plant lncRNAs. The platform currently contains 1 088 565 lncRNA annotations for 80 plant species. In addition, it includes 3692 RNA-seq samples derived from 825 conditions in six model plants. Functional network reconstruction provides insight into the regulatory roles of lncRNAs. Genomic association analysis and microRNA target prediction can be employed to depict potential interactions with nearby genes and microRNAs, respectively. Subsequent co-expression analysis can be employed to strengthen confidence in the interactions among genes. Chromatin immunoprecipitation sequencing data of transcription factors and histone modifications were integrated into the JustRNA platform to identify the transcriptional regulation of lncRNAs in several plant species. The JustRNA platform provides researchers with valuable insight into the regulatory mechanisms of plant lncRNAs. JustRNA is a free platform that can be accessed at http://JustRNA.itps.ncku.edu.tw.

Cis-regulatory elements of promoters are essential for gene regulation by transcription factors (TFs). However, the regulatory roles of nonpromoter regions, TFs, and epigenetic marks remain poorly understood in plants. In this study, we characterized the cis-regulatory regions of 53 TFs and 19 histone marks in 328 chromatin immunoprecipitation (ChIP-seq) datasets from Arabidopsis. The genome-wide maps indicated that both promoters and regions around the transcription termination sites of protein-coding genes recruit the most TFs. The maps also revealed a diverse of histone combinations. The analysis suggested that exons play critical roles in the regulation of non-coding genes. Additionally, comparative analysis between heat-stress-responsive and nonresponsive genes indicated that the genes with distinct functions also exhibited substantial differences in cis-regulatory regions, histone regulation, and topologically associating domain (TAD) boundary organization. By integrating multiple high-throughput sequencing datasets, this study generated regulatory models for protein-coding genes, non-coding genes, and TAD boundaries to explain the complexity of transcriptional regulation.

The goal of higher education evaluation and accreditation is to ensure that education provided by institutions meets acceptable levels of quality. In 2011, the higher education evaluation and accreditation council of Taiwan (HEEACT) starts to evaluate the eighty-one higher education institutions through five predefined items, including (1) university goal, (2) university administration and management, (3) teaching and learning resource, (4) performance and society responsibility, and (5) improvement and quality assurance mechanism. As a result, in this paper, a novel fuzzy markup language (FML)-based university assessment system is developed to infer the passing possibility of the university assessment. Additionally, a fuzzy ontology model is utilized to represent the university evaluation and accreditation domain knowledge predefined by domain experts. Then, the FML is adopted to describe the knowledge base and rule base of the university assessment. Simulation results indicate that the proposed approach can effectively infer the passing level of the university assessment.