BACKGROUND: In many cancers, microRNAs (miRs) contribute to metastatic progression by modulating phenotypic reprogramming processes such as epithelial-mesenchymal plasticity. This can be driven by miRs targeting multiple mRNA transcripts, inducing regulated changes across large sets of genes. The miR-target databases TargetScan and DIANA-microT predict putative relationships by examining sequence complementarity between miRs and mRNAs.
Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate.
A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is computationally intensive and significantly more expensive than its alternatives.
MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding genes (host genes).
GeneHub-GEPIS is a web application that performs digital expression analysis in human and mouse tissues based on an integrated gene database. Using aggregated expressed sequence tag (EST) library information and EST counts, the application calculates the normalized gene expression levels across a large panel of normal and tumor tissues, thus providing rapid expression profiling for a given gene.
Transcription factors (TFs) are important regulatory proteins that govern transcriptional regulation. Today, it is known that in higher organisms different TFs have to cooperate rather than acting individually in order to control complex genetic programs. The identification of these interactions is an important challenge for understanding the molecular mechanisms of regulating biological processes.
Obesity in humans has increased at an alarming rate over the past two decades and has become one of the leading public health problems worldwide. Studies have revealed a large number of genes/markers that are associated with obesity and/or obesity-related phenotypes, indicating an urgent need to develop a central database for helping the community understand the genetic complexity of obesity.
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.
Compared with complementary DNA (cDNA) or messenger RNA (mRNA) microarray data, microRNA (miRNA) microarray data are harder to normalize due to the facts that the total number of miRNAs is small, and that the majority of miRNAs usually have low expression levels. In bead-based microarrays, the hybridization is completed in several pools. As a result, the number of miRNAs tested in each pool is even smaller, which poses extra difficulty to intrasample normalization and ultimately affects the quality of the final profiles assembled from various pools.
MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of different algorithms with Bayesian Network.
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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