BackgroundOver 200 published studies of more than 30 plant species have reported a role for miRNAs in regulating responses to abiotic stresses. However, data from these individual reports has not been collected into a single database. The lack of a curated database of stress-related miRNAs limits research in this field, and thus a cohesive database system should necessarily be constructed for data deposit and further application.DescriptionPASmiR, a literature-curated and web-accessible database, was developed to provide detailed, searchable descriptions of miRNA molecular regulation in different plant abiotic stresses. PASmiR currently includes data from ~200 published studies, representing 1038 regulatory relationships between 682 miRNAs and 35 abiotic stresses in 33 plant species. PASmiR’s interface allows users to retrieve miRNA-stress regulatory entries by keyword search using plant species, abiotic stress, and miRNA identifier. Each entry upon keyword query contains detailed regulation information for a specific miRNA, including species name, miRNA identifier, stress name, miRNA expression pattern, detection method for miRNA expression, a reference literature, and target gene(s) of the miRNA extracted from the corresponding reference or miRBase. Users can also contribute novel regulatory entries by using a web-based submission page. The PASmiR database is freely accessible from the two URLs of http://hi.ustc.edu.cn:8080/PASmiR, and http://pcsb.ahau.edu.cn:8080/PASmiR.ConclusionThe PASmiR database provides a solid platform for collection, standardization, and searching of miRNA-abiotic stress regulation data in plants. As such this database will be a comprehensive repository for miRNA regulatory mechanisms involved in plant response to abiotic stresses for the plant stress physiology community.

This meta-analysis suggests that the TNF-α-308A genotype is associated with an increased risk of COPD in Asian but not Caucasian populations. Further studies are necessary to evaluate the relationship between TNF-α polymorphisms and the risk of COPD.

Currently, some efforts have been devoted to the text analysis of disease phenotype data, and their results indicated that similar disease phenotypes arise from functionally related genes. These related genes work together, as a functional module, to perform a desired cellular function. We constructed a text‐based human disease phenotype network and detected 82 disease‐specific gene functional modules, each corresponding to a different phenotype cluster, by means of graph‐based clustering and mapping from disease phenotype to gene. Since genes in such gene functional modules are functionally related and cause clinically similar diseases, they may share common genetic origin of their associated disease phenotypes. We believe the investigation may facilitate the ultimate understanding of the common pathophysiologic basis of associated diseases.

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.