Wolfberry genome database: integrated genomic datasets for studying molecular biology.

Wolfberry, also known as goji berry or Lycium barbarum, is a highly valued fruit with significant health benefits and nutritional value. For more efficient and comprehensive usage of published L. barbarum genomic data, we established the Wolfberry database. The utility of the Wolfberry Genome Database (WGDB) is highlighted through the Genome browser, which enables the user to explore the L. barbarum genome, browse specific chromosomes, and access gene sequences. Gene annotation features provide comprehensive information about gene functions, locations, expression profiles, pathway involvement, protein domains, and regulatory transcription factors. The transcriptome feature allows the user to explore gene expression patterns using transcripts per kilobase million (TPM) and fragments per kilobase per million mapped reads (FPKM) metrics. The Metabolism pathway page provides insights into metabolic pathways and the involvement of the selected genes. In addition to the database content, we also introduce six analysis tools developed for the WGDB. These tools offer functionalities for gene function prediction, nucleotide and amino acid BLAST analysis, protein domain analysis, GO annotation, and gene expression pattern analysis. The WGDB is freely accessible at https://cosbi7.ee.ncku.edu.tw/Wolfberry/. Overall, WGDB serves as a valuable resource for researchers interested in the genomics and transcriptomics of L. barbarum. Its user-friendly web interface and comprehensive data facilitate the exploration of gene functions, regulatory mechanisms, and metabolic pathways, ultimately contributing to a deeper understanding of wolfberry and its potential applications in agronomy and nutrition.

PlantPAN 4.0: updated database for identifying conserved non-coding sequences and exploring dynamic transcriptional regulation in plant promoters.

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.

JustRNA: a database of plant long noncoding RNA expression profiles and functional network.

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.

Databases and prospects of dynamic gene regulation in eukaryotes: A mini review.

In eukaryotes, dynamic regulation enables DNA polymerases to catalyze a variety of RNA products in spatial and temporal patterns. Dynamic gene expression is regulated by transcription factors (TFs) and epigenetics (DNA methylation and histone modification). The applications of biochemical technology and high-throughput sequencing enhance the understanding of mechanisms of these regulations and affected genomic regions. To provide a searchable platform for retrieving such metadata, numerous databases have been developed based on the integration of genome-wide maps (e.g., ChIP-seq, whole-genome bisulfite sequencing, RNA-seq, ATAC-seq, DNase-seq, and MNase-seq data) and functionally genomic annotation. In this mini review, we summarize the main functions of TF-related databases and outline the prevalent approaches used in inferring epigenetic regulations, their associated genes, and functions. We review the literature on crosstalk between TF and epigenetic regulation and the properties of non-coding RNA regulation, which are challenging topics that promise to pave the way for advances in database development.

Database for Plant Transcription Factor Binding Sites.

Reconstruction of gene regulatory networks is a very important but difficult issue in plant sciences. Recently, numerous high-throughput techniques, such as chromatin immunoprecipitation sequencing (ChIP-seq) and DNA affinity purification sequencing (DAP-seq), have been developed to identify the genomic binding landscapes of regulatory factors. To understand the relationships among transcription factors (TFs) and their corresponding binding sites on target genes is usually the first step for elucidating gene regulatory mechanisms. Therefore, a good database for plant TFs and transcription factor binding sites (TFBSs) will be useful for starting a series of complex experiments. In this chapter, PlantPAN (version 3.0) is utilized as an example to explain how bioinformatics systems advance research on gene regulation.

Mysteries of gene regulation: Promoters are not the sole triggers of gene expression.

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.

EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways.

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.

sRIS: A Small RNA Illustration System for Plant Next-Generation Sequencing Data Analysis.

Small RNA (sRNA), such as microRNA (miRNA) and short interfering RNA, are well-known to control gene expression based on degradation of target mRNA in plants. A considerable amount of research has applied next-generation sequencing (NGS) to reveal the regulatory pathways of plant sRNAs. Consequently, numerous bioinformatics tools have been developed for the purpose of analyzing sRNA NGS data. However, most methods focus on the study of sRNA expression profiles or novel miRNAs predictions. The analysis of sRNA target genes is usually not integrated into their pipelines. As a result, there is still no means available for identifying the interaction mechanisms between host and virus or the synergistic effects between two viruses. For the present study, a comprehensive system, called the Small RNA Illustration System (sRIS), has been developed. This system contains two main components. The first is for sRNA overview analysis and can be used not only to identify miRNA but also to investigate virus-derived small interfering RNA. The second component is for sRNA target prediction, and it employs both bioinformatics calculations and degradome sequencing data to enhance the accuracy of target prediction. In addition, this system has been designed so that figures and tables for the outputs of each analysis can be easily retrieved and accessed, making it easier for users to quickly identify and quantify their results. sRIS is available at http://sris.itps.ncku.edu.tw/.

The genome of cultivated peanut provides insight into legume karyotypes, polyploid evolution and crop domestication.

High oil and protein content make tetraploid peanut a leading oil and food legume. Here we report a high-quality peanut genome sequence, comprising 2.54 Gb with 20 pseudomolecules and 83,709 protein-coding gene models. We characterize gene functional groups implicated in seed size evolution, seed oil content, disease resistance and symbiotic nitrogen fixation. The peanut B subgenome has more genes and general expression dominance, temporally associated with long-terminal-repeat expansion in the A subgenome that also raises questions about the A-genome progenitor. The polyploid genome provided insights into the evolution of Arachis hypogaea and other legume chromosomes. Resequencing of 52 accessions suggests that independent domestications formed peanut ecotypes. Whereas 0.42-0.47 million years ago (Ma) polyploidy constrained genetic variation, the peanut genome sequence aids mapping and candidate-gene discovery for traits such as seed size and color, foliar disease resistance and others, also providing a cornerstone for functional genomics and peanut improvement.

PlantPAN3.0: a new and updated resource for reconstructing transcriptional regulatory networks from ChIP-seq experiments in plants.

The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN.itps.ncku.edu.tw/) is an effective resource for predicting regulatory elements and reconstructing transcriptional regulatory networks for plant genes. In this release (PlantPAN 3.0), 17 230 TFs were collected from 78 plant species. To explore regulatory landscapes, genomic locations of TFBSs have been captured from 662 public ChIP-seq samples using standard data processing. A total of 1 233 999 regulatory linkages were identified from 99 regulatory factors (TFs, histones and other DNA-binding proteins) and their target genes across seven species. Additionally, this new version added 2449 matrices extracted from ChIP-seq peaks for cis-regulatory element prediction. In addition to integrated ChIP-seq data, four major improvements were provided for more comprehensive information of TF binding events, including (i) 1107 experimentally verified TF matrices from the literature, (ii) gene regulation network comparison between two species, (iii) 3D structures of TFs and TF-DNA complexes and (iv) condition-specific co-expression networks of TFs and their target genes extended to four species. The PlantPAN 3.0 can not only be efficiently used to investigate critical cis- and trans-regulatory elements in plant promoters, but also to reconstruct high-confidence relationships among TF-targets under specific conditions.

Delineation of condition specific Cis- and Trans-acting elements in plant promoters under various Endo- and exogenous stimuli.

BACKGROUND: Transcription factors (TFs) play essential roles during plant development and response to environmental stresses. However, the relationships among transcription factors, cis-acting elements and target gene expression under endo- and exogenous stimuli have not been systematically characterized. RESULTS: Here, we developed a series of bioinformatics analysis methods to infer transcriptional regulation by using numerous gene expression data from abiotic stresses and hormones treatments. After filtering the expression profiles of TF-encoding genes, 291 condition specific transcription factors (CsTFs) were obtained. Differentially expressed genes were then classified into various co-expressed gene groups based on each CsTFs. In the case studies of heat stress and ABA treatment, several known and novel cis-acting elements were identified following our bioinformatics approach. Significantly, a palindromic sequence of heat-responsive elements is recognized, and also obtained from a 3D protein structure of heat-shock protein-DNA complex. Notably, overrepresented 3- and 4-mer motifs in an enriched 8-mer motif could be a core cis-element for a CsTF. In addition, the results suggest DNA binding preferences of the same CsTFs are different according to various conditions. CONCLUSIONS: The overall results illustrate this study may be useful in identifying condition specific cis- and trans- regulatory elements and facilitate our understanding of the relationships among TFs, cis-acting elements and target gene expression.

Paddy-upland rotation for sustainable agriculture with regards to diverse soil microbial community.

Diverse soil microbial community is determinant for sustainable agriculture. Rich microbial diversity has presumably improved soil health for economic crops to grow. In this work, the benefits of paddy-upland rotation on soil microbial diversity and specific microbes are thus intensively explored. The microbiome from multiple factor experiment (three fertilizations coupled with two rotation systems) were investigated by novel enrichment and co-occurrence analysis in a field well maintained for 25 years. Using next-generation sequencing technique, we firstly present explicit evidence that different rotation systems rather than fertilizations mightily governed the soil microbiome. Paddy-upland rotation (R1) obviously increase more microbial diversity than upland rotation (R2) whether organic (OF), chemical (CF) or integrated fertilizers (IF) were concomitantly applied. Besides, the specific bacterial composition dominated in OF soil is more similar to that of R1 than to CF, suggesting that paddy-upland rotation might be the best option for sustainable agriculture if chemical fertilizer is still required. Interestingly, the pot bioassay verified clearly the novel analysis prediction, illustrating that greater microbial diversity and specific microbial composition correlated significantly with disease resistance. This finding highlights the eminence of paddy-upland rotation in promoting microbial diversity and specific microbial compositions, preserving soil health for sustainable agriculture.

microRPM: a microRNA prediction model based only on plant small RNA sequencing data.

Motivation: MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants. A systematic approach to determine novel miRNAs without reference sequences is thus necessary. Results: In this study, an effective method was developed to identify miRNAs from non-model plants based only on NGS datasets. The miRNA prediction model was trained with several duplex structure-related features of mature miRNAs and their passenger strands using a support vector machine algorithm. The accuracy of the independent test reached 96.61% and 93.04% for dicots (Arabidopsis) and monocots (rice), respectively. Furthermore, true small RNA sequencing data from orchids was tested in this study. Twenty-one predicted orchid miRNAs were selected and experimentally validated. Significantly, 18 of them were confirmed in the qRT-PCR experiment. This novel approach was also compiled as a user-friendly program called microRPM (miRNA Prediction Model). Availability and implementation: This resource is freely available at http://microRPM.itps.ncku.edu.tw. Contact: nslin@sinica.edu.tw or sarah321@mail.ncku.edu.tw. Supplementary information: Supplementary data are available at Bioinformatics online.

EXPath tool-a system for comprehensively analyzing regulatory pathways and coexpression networks from high-throughput transcriptome data.

Next generation sequencing (NGS) has become the mainstream approach for monitoring gene expression levels in parallel with various experimental treatments. Unfortunately, there is no systematical webserver to comprehensively perform further analysis based on the huge amount of preliminary data that is obtained after finishing the process of gene annotation. Therefore, a user-friendly and effective system is required to mine important genes and regulatory pathways under specific conditions from high-throughput transcriptome data. EXPath Tool (available at: http://expathtool.itps.ncku.edu.tw/) was developed for the pathway annotation and comparative analysis of user-customized gene expression profiles derived from microarray or NGS platforms under various conditions to infer metabolic pathways for all organisms in the KEGG database. EXPath Tool contains several functions: access the gene expression patterns and the candidates of co-expression genes; dissect differentially expressed genes (DEGs) between two conditions (DEGs search), functional grouping with pathway and GO (Pathway/GO enrichment analysis), and correlation networks (co-expression analysis), and view the expression patterns of genes involved in specific pathways to infer the effects of the treatment. Additionally, the effectively of EXPath Tool has been performed by a case study on IAA-responsive genes. The results demonstrated that critical hub genes under IAA treatment could be efficiently identified.

PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants.

Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks.

AtmiRNET: a web-based resource for reconstructing regulatory networks of Arabidopsis microRNAs.

Compared with animal microRNAs (miRNAs), our limited knowledge of how miRNAs involve in significant biological processes in plants is still unclear. AtmiRNET is a novel resource geared toward plant scientists for reconstructing regulatory networks of Arabidopsis miRNAs. By means of highlighted miRNA studies in target recognition, functional enrichment of target genes, promoter identification and detection of cis- and trans-elements, AtmiRNET allows users to explore mechanisms of transcriptional regulation and miRNA functions in Arabidopsis thaliana, which are rarely investigated so far. High-throughput next-generation sequencing datasets from transcriptional start sites (TSSs)-relevant experiments as well as five core promoter elements were collected to establish the support vector machine-based prediction model for Arabidopsis miRNA TSSs. Then, high-confidence transcription factors participate in transcriptional regulation of Arabidopsis miRNAs are provided based on statistical approach. Furthermore, both experimentally verified and putative miRNA-target interactions, whose validity was supported by the correlations between the expression levels of miRNAs and their targets, are elucidated for functional enrichment analysis. The inferred regulatory networks give users an intuitive insight into the pivotal roles of Arabidopsis miRNAs through the crosstalk between miRNA transcriptional regulation (upstream) and miRNA-mediate (downstream) gene circuits. The valuable information that is visually oriented in AtmiRNET recruits the scant understanding of plant miRNAs and will be useful (e.g. ABA-miR167c-auxin signaling pathway) for further research. Database URL: http://AtmiRNET.itps.ncku.edu.tw/

EXPath: a database of comparative expression analysis inferring metabolic pathways for plants.

BACKGROUND: In general, the expression of gene alters conditionally to catalyze a specific metabolic pathway. Microarray-based datasets have been massively produced to monitor gene expression levels in parallel with numerous experimental treatments. Although several studies facilitated the linkage of gene expression data and metabolic pathways, none of them are amassed for plants. Moreover, advanced analysis such as pathways enrichment or how genes express under different conditions is not rendered. DESCRIPTION: Therefore, EXPath was developed to not only comprehensively congregate the public microarray expression data from over 1000 samples in biotic stress, abiotic stress, and hormone secretion but also allow the usage of this abundant resource for coexpression analysis and differentially expression genes (DEGs) identification, finally inferring the enriched KEGG pathways and gene ontology (GO) terms of three model plants: Arabidopsis thaliana, Oryza sativa, and Zea mays. Users can access the gene expression patterns of interest under various conditions via five main functions (Gene Search, Pathway Search, DEGs Search, Pathways/GO Enrichment, and Coexpression analysis) in EXPath, which are presented by a user-friendly interface and valuable for further research. CONCLUSIONS: In conclusion, EXPath, freely available at http://expath.itps.ncku.edu.tw, is a database resource that collects and utilizes gene expression profiles derived from microarray platforms under various conditions to infer metabolic pathways for plants.