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.

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.

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.

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.

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.

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.

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.

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.