The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies.
microRNAs (miRNAs) regulate expression by promoting degradation or repressing translation of target transcripts. miRNA target sites have been catalogued in databases based on experimental validation and computational prediction using various algorithms. Several online resources provide collections of multiple databases but need to be imported into other software, such as R, for processing, tabulation, graphing and computation. Currently available miRNA target site packages in R are limited in the number of databases, types of databases and flexibility.
Hundreds of genes and miRNAs have been identified as being involved in the determination of longevity, aging patterns and in the development of age-related diseases (ARDs). The interplay between these genes as well as the role of miRNAs in the context of protein-protein interaction networks has as yet been poorly addressed. This work was undertaken in order to integrate the data accumulated in the field, from a network-based perspective.
Over the past decades, studies have reported that the combinatorial regulation of transcription factors (TFs) and microRNAs (miRNAs) is essential for the appropriate execution of biological events and developmental processes. Dysregulations of these regulators often cause diseases. However, there are no available resources on the regulatory cascades of TFs and miRNAs in the context of human diseases. To fulfill this vacancy, we established the TMREC database in this study.
Identifying, amongst millions of publications available in MEDLINE, those that are relevant to specific microRNAs (miRNAs) of interest based on keyword search faces major obstacles. References to miRNA names in the literature often deviate from standard nomenclature for various reasons, since even the official nomenclature evolves. For instance, a single miRNA name may identify two completely different molecules or two different names may refer to the same molecule.
It is now widely accepted that microRNAs (miRNAs or miRs) along with transcription factors (TFs) weave a complex inter-regulatory network within the cell that is responsible for the combinatorial regulation of gene expression. Recently we have shown that miRNAs and TFs that form network clusters are also associated with a number of common diseases. However, the quest persists to find out topological structures that facilitate disease progression. In the current work we choose colorectal and breast cancers for our analysis.
MicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA-disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction methods do not explicitly facilitate forming hypotheses about the possible molecular causes of the association, thereby making the path to experimental follow-up longer.
The information gathered from the large number of omics experiments in renal biology is underexplored, as it is scattered over many publications or held in supplemental data. To address this, we have developed an open-source Kidney and Urinary Pathway Knowledge Base (KUPKB) that facilitates simple exploration of these omics data. The KUPKB currently comprises 220 data sets (miRNA, mRNA, proteins, and metabolites) extracted from existing publications or databases.
MicroRNAs (miRNAs) measured from blood samples are promising minimally invasive biomarker candidates that have been extensively studied in several case-control studies. However, the influence of age and sex as confounding variables remains largely unknown.
A large number of studies have suggested extracellular microRNAs (microRNAs in biofluids) as potential noninvasive biomarkers for pathophysiological conditions such as cancer. However, reported differentially expressed signatures of extracellular miRNAs in diseases are not uniformly consistent among studies. Here, we present "ExcellmiRDB", a curated online database that provides integrated information about miRNAs levels in biofluids in a user-friendly way.
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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