As two kinds of important gene expression regulators, both epigenetic modification and microRNA (miRNA) can play significant roles in a wide range of human diseases. Recently, many studies have demonstrated that epigenetics and miRNA can affect each other in various ways.
Protein-protein interactions (PPIs) and microRNA (miRNA)-target interactions are important for deciphering the mechanisms of tumorigenesis. However, current PPI databases do not support cancer-specific analysis. Also, no available databases can be used to retrieve cancer-associated miRNA-target interactions. As the pathogenesis of human cancers is affected by several miRNAs rather than a single miRNA, it is needed to uncover miRNA synergism in a systems level.
Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places.
Mammalian genomes encode for thousands of microRNAs, which can potentially regulate the majority of protein-coding genes. They have been implicated in development and disease, leading to great interest in understanding their function, with computational methods being widely used to predict their targets. Most computational methods rely on sequence features, thermodynamics, and conservation filters; essentially scanning the whole transcriptome to predict one set of targets for each microRNA.
Increasing evidence indicated that long non-coding RNAs (lncRNAs) were involved in various biological processes and complex diseases by communicating with mRNAs/miRNAs each other. Exploiting interactions between lncRNAs and mRNA/miRNAs to lncRNA functional similarity (LFS) is an effective method to explore function of lncRNAs and predict novel lncRNA-disease associations.
Parkinson's disease (PD) is one of the most common neurodegenerative disorders, clinically characterized by impaired motor function. Since the etiology of PD is diverse and complex, many researchers have created PD-related research resources. However, resources for brain and PD studies are still lacking. Therefore, we have constructed a database of PD-related gene and genetic variations using the substantia nigra (SN) in PD and normal tissues. In addition, we integrated PD-related information from several resources.
Genome-wide transcriptome profiling after gene perturbation is a powerful means of elucidating gene functional mechanisms in diverse contexts. The comprehensive collection and analysis of the resulting transcriptome profiles would help to systematically characterize context-dependent gene functional mechanisms and conduct experiments in biomedical research.
Schizophrenia is a major debilitating psychiatric disorder affecting approximately 1% of the population worldwide. A tremendous amount of effort has been expended in the last two decades to identify genes influencing susceptibility to this disorder. Although there is a strong trend toward integrating data obtained from various genetic studies and their related biological information into a comprehensive resource for many complex diseases, we were unable to find such an effort for schizophrenia or for any other psychiatric disorder yet.
Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information.
DNA methylation undergoes dynamic changes during mouse development and plays crucial roles in embryogenesis, cell-lineage determination and genomic imprinting. Bisulfite sequencing enables profiling of mouse developmental methylomes on an unprecedented scale; however, integrating and mining these data are challenges for experimental biologists. Therefore, we developed DevMouse, which focuses on the efficient storage of DNA methylomes in temporal order and quantitative analysis of methylation dynamics during mouse development.
Our database aim to make it easy for researchers to find the right tools or data source for their own specific study in miRNA field. Now we have collect >1000 tools. This database will update when every new 100 tools add in. Last update time 22nd April, 2018 (v1.2).
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