Stress2TF: a manually curated database of TF regulation in plant response to stress.

Considerable studies demonstrate that plant transcription factors (TFs) play key regulatory roles in abiotic/biotic stress conditions, such as drought and pathogen attack. However, there is no effort dedicated to curate experimentally validated stress-TF regulatory relationships from these individual reports into a central database, which put an obstacle in the exploration of stress-TF regulations in plants. To address this issue, we presented a literature-curated database 'Stress2TF' that currently documented 1533 regulatory relationships between 71 abiotic/biotic stresses and 558 TFs in 47 plant species. Each entry in Stress2TF contains detailed information about a stress-TF relationship such as plant name, stress name, TF and brief description of stress-TF relationship. Stress2TF provided a user-friendly interface for entry browse, search and download. In addition, a submission page and several useful tools (e.g., BLAST, network visualization) were integrated. Stress2TF may be a valuable resource for the research of stress-TF regulatory mechanisms in plants. Stress2TF is available at http://csgenomics.ahau.edu.cn/Stress2TF.

TBC2health: a database of experimentally validated health-beneficial effects of tea bioactive compounds.

Tea is one of the most consumed beverages in the world. Considerable studies show the exceptional health benefits (e.g. antioxidation, cancer prevention) of tea owing to its various bioactive components. However, data from these extensively published papers had not been made available in a central database. To lay a foundation in improving the understanding of healthy tea functions, we established a TBC2health database that currently documents 1338 relationships between 497 tea bioactive compounds and 206 diseases (or phenotypes) manually culled from over 300 published articles. Each entry in TBC2health contains comprehensive information about a bioactive relationship that can be accessed in three aspects: (i) compound information, (ii) disease (or phenotype) information and (iii) evidence and reference. Using the curated bioactive relationships, a bipartite network was reconstructed and the corresponding network (or sub-network) visualization and topological analyses are provided for users. This database has a user-friendly interface for entry browse, search and download. In addition, TBC2health provides a submission page and several useful tools (e.g. BLAST, molecular docking) to facilitate use of the database. Consequently, TBC2health can serve as a valuable bioinformatics platform for the exploration of beneficial effects of tea on human health. TBC2health is freely available at http://camellia.ahau.edu.cn/TBC2health.

Topological characteristics of target genes regulated by abiotic-stress-responsible miRNAs in a rice interactome network.

A great number of microRNAs (miRNAs) have been identified in responding and acting in gene regulatory networks associated with plant tolerance to abiotic stress conditions, such as drought, salinity, and high temperature. The topological exploration of target genes regulated by abiotic-stress-responsible miRNAs (ASRmiRs) in a network facilitates to discover the molecular basis of plant abiotic stress response. This study was based on the staple food rice (Oryza sativa) in which ASRmiRs were manually curated. After having compared the topological properties of target genes (stress-miR-targets) with those (non-stress-miR-targets) not regulated by ASRmiRs in a rice interactome network, we found that stress-miR-targets exhibited distinguishable topological properties. The interaction probability analysis and k-core decomposition showed that stress-miR-targets preferentially interacted with non-stress-miR-targets and located at the peripheral positions in the network. Our results indicated an obvious topological distinction between the two types of genes, reflecting the specific mechanisms of action of stress-miR-targets in rice abiotic stress response. Also, the results may provide valuable clues to elucidate molecular mechanisms of crop response to abiotic stress.

Modular organization of the human disease genes: a text-based network inference.

The analysis of disease phenotype data with genetic information indicated that genes associated with clinically similar diseases tend to be functionally related and work together to perform a specific biological function. Therefore, it is of interest to relate disease phenotype data to mirror modular property implied in the association map of disease genes. Hence, we constructed a textbased human disease gene network (HDGN) by using the phenotypic similarity of their associated disease phenotype records in the OMIM database. Analysis shows that the network is highly modular and it is highly correlated with the physiological classification of genetic diseases. Using a graph clustering algorithm, we found 139 gene modules in the network of 1,865 genes and their gene products (proteins) in these gene modules tend to interact with each other via the computation of PPI intensity. Genes in such gene modules are functionally related and may represent the shared genetic basis of their corresponding diseases. These genes, alone or in combination, could be considered as potential therapeutic targets in future clinical therapy.

PLNlncRbase: A resource for experimentally identified lncRNAs in plants.

Accumulating published reports have confirmed the critical biological role (e.g., cell differentiation, gene regulation, stress response) for plant long non-coding RNAs (lncRNAs). However, a literature-derived database with the aim of lncRNA curation, data deposit and further distribution remains still absent for this particular lncRNA clade. PLNlncRbase has been designed as an easy-to-use resource to provide detailed information for experimentally identified plant lncRNAs. In the current version, PLNlncRbase has manually collected data from nearly 200 published literature, covering a total of 1187 plant lncRNAs in 43 plant species. The user can retrieve plant lncRNA entries from a well-organized interface through a keyword search by using the name of plant species or a lncRNA identifier. Each entry upon a query will be returned with detailed information for a specific plant lncRNA, including the species name, a lncRNA identifier, a brief description of the potential biological role, the lncRNA sequence, the lncRNA classification, an expression pattern of the lncRNA, the tissue/developmental stage/condition for lncRNA expression, the detection method for lncRNA expression, a reference literature, and the potential target gene(s) of the lncRNA extracted from the original reference. This database will be regularly updated to greatly facilitate future investigations of plant lncRNAs pertaining to their biological significance. The PLNlncRbase database is now freely available at http://bioinformatics.ahau.edu.cn/PLNlncRbase.