Identifying disease-causing variants among a large number of single nucleotide variants (SNVs) is still a major challenge. Recently, N6-methyladenosine (m6A) has become a research hotspot because of its critical roles in many fundamental biological processes and a variety of diseases. Therefore, it is important to evaluate the effect of variants on m6A modification, in order to gain a better understanding of them.
The complex traits of an organism are associated with a complex interplay between genetic factors (GFs) and environmental factors (EFs). However, compared with protein-coding genes and microRNAs, there is a paucity of computational methods and bioinformatic resource platform for understanding the associations between lncRNA and EF.
The MiRNA SNP Disease Database (MSDD, http://www.bio-bigdata.com/msdd/) is a manually curated database that provides comprehensive experimentally supported associations among microRNAs (miRNAs), single nucleotide polymorphisms (SNPs) and human diseases. SNPs in miRNA-related functional regions such as mature miRNAs, promoter regions, pri-miRNAs, pre-miRNAs and target gene 3'-UTRs, collectively called 'miRSNPs', represent a novel category of functional molecules.
MicroRNA (miRNA) family is a group of miRNAs that derive from the common ancestor. Normally, members from the same miRNA family have similar physiological functions; however, they are not always conserved in primary sequence or secondary structure. Proper family prediction from primary sequence will be helpful for accurate identification and further functional annotation of novel miRNA.
Currently, there are a number of databases which store microRNA (miRNA) information, and tools available which provide miRNA target prediction. In this article, we describe a novel web-based tool that integrate the miRNA-targeted mRNA data, protein-protein interactions (PPI) records, tissues, biochemical pathways, human disease and gene function information to establish a disease-related miRNA target pathway database. This database is unique in the sense that it links miRNA target genes with their PPI partners according to being tissue- and diseases-specific or both.
MicroRNAs are a class of small non-coding regulatory RNA molecules that modulate the expression of several genes at post-transcriptional level and play a vital role in disease pathogenesis. Recent research shows that a range of miRNAs are involved in the regulation of immunity and its deregulation results in immune mediated diseases such as cancer, inflammation and autoimmune diseases. Computational discovery of these immune miRNAs using a set of specific features is highly desirable.
MicroRNAs are short non-coding RNA which function to fine-tune protein levels in all cells. This is achieved mainly by sequence-specific binding to 3' untranslated regions of target mRNA. The result is post-transcriptional interference in gene expression which reduces protein levels either by promoting destabilisation of mRNA or translational repression. Research published since 2010 shows that microRNAs are important regulators of gene expression in epilepsy.
The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms.
Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results.
MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information.
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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