The contribution of different mechanisms to the regulation of gene expression varies for different tissues and tumors. Complementation of predicted mRNA-miRNA and gene-transcription factor (TF) relationships with the results of expression correlation analyses derived for specific tumor types outlines the interactions with functional impact in the current biomaterial.
The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov) is a valuable data resource focused on an increasing number of well-characterized cancer genomes. In part, TCGA provides detailed information about cancer-dependent gene expression changes, including changes in the expression of transcription-regulating microRNAs.
Long noncoding RNA (lncRNA) influences post-transcriptional regulation by interfering with the microRNA (miRNA) pathways, acting as competing endogenous RNA (ceRNA). These lncRNAs have miRNA responsive elements (MRE) in them, and control endogenous miRNAs available for binding with their target mRNAs, thus reducing the repression of these mRNAs. lnCeDB provides a database of human lncRNAs (from GENCODE 19 version) that can potentially act as ceRNAs.
Transcription factors bind to the genome by forming specific contacts with the primary DNA sequence; however, RNA-binding proteins (RBPs) have greater scope to achieve binding specificity through the RNA secondary structure.
Massive parallel sequencing of transcriptomes, revealed the presence of many miRNAs and miRNAs variants named isomiRs with a potential role in several cellular processes through their interaction with a target mRNA. Many methods and tools have been recently devised to detect and quantify miRNAs from sequencing data. However, all of them are implemented on top of general purpose alignment methods, thus providing poorly accurate results and no information concerning isomiRs and conserved miRNA-mRNA interaction sites.
miRNA is known to regulate up to several hundreds coding genes, thus the integrated analysis of miRNA and mRNA expression data is an important problem. Unfortunately, the integrated analysis is challenging since it needs to consider expression data of two different types, miRNA and mRNA, and target relationship between miRNA and mRNA is not clear, especially when microarray data is used.
Endogenous small non-coding RNAs (sRNAs), including microRNAs, PIWI-interacting RNAs and small interfering RNAs, play important gene regulatory roles in animals and plants by pairing to the protein-coding and non-coding transcripts. However, computationally assigning these various sRNAs to their regulatory target genes remains technically challenging. Recently, a high-throughput degradome sequencing method was applied to identify biologically relevant sRNA cleavage sites.
Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented.
In the last few decades, technological and experimental advancements have enabled a more precise understanding of the mode of action of drugs with respect to human cell signaling pathways and have positively influenced the design of new drug compounds. However, as the design of compounds has become increasingly target-specific, the overall effects of a drug on adjacent cellular signaling pathways remain difficult to predict because of the complexity of the interactions involved. Off-target effects of drugs are known to influence their efficacy and safety.
Molecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, also known as network motifs. These motifs show interesting dynamical behaviors, in which co-operativity effects between the motif components play a critical role in human diseases. We have developed a motif-searching algorithm, which is able to identify common motif types from the cancer networks and signal transduction networks (STNs).
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).
Updates History:
