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.
The monarch butterfly (Danaus plexippus) is emerging as a model organism to study the mechanisms of circadian clocks and animal navigation, and the genetic underpinnings of long-distance migration. The initial assembly of the monarch genome was released in 2011, and the biological interpretation of the genome focused on the butterfly's migration biology.
Over 200 published studies of more than 30 plant species have reported a role for miRNAs in regulating responses to abiotic stresses. However, data from these individual reports has not been collected into a single database. The lack of a curated database of stress-related miRNAs limits research in this field, and thus a cohesive database system should necessarily be constructed for data deposit and further application.
The miRBase is the official miRNA repository which keeps the annotation updated on newly discovered miRNAs: it is also used as a reference for the design of miRNA profiling platforms. Nomenclature ambiguities generated by loosely updated platforms and design errors lead to incompatibilities among platforms, even from the same vendor. Published miRNA lists are thus generated with different profiling platforms that refer to diverse and not updated annotations.
Dicer, an RNase III enzyme, plays a vital role in the processing of pre-miRNAs for generating the miRNAs. The structural and sequence features on pre-miRNA which can facilitate position and efficiency of cleavage are not well known. A precise cleavage by Dicer is crucial because an inaccurate processing can produce miRNA with different seed regions which can alter the repertoire of target genes.
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.
MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding.
The AthaMap database generates a map of predicted transcription factor binding sites (TFBS) for the whole Arabidopsis thaliana genome. AthaMap has now been extended to include data on post-transcriptional regulation. A total of 403,173 genomic positions of small RNAs have been mapped in the A. thaliana genome. These identify 5772 putative post-transcriptionally regulated target genes.
To date, a significant amount of research has been conducted for computational modeling of the microRNA (miRNA)-target gene interactions and inferring different kinds of biologically relevant association from their variable expressions, available from microarray experiments. However, topological organization of the miRNA-transcription factor (TF) induced regulatory network has not yet been analyzed at a genome scale. Evidently, by ignoring the regulatory relationship among the constituent molecules, we expose our model to a great deal of noise.
Plant microRNAs (miRNAs) have been revealed to play important roles in developmental control, hormone secretion, cell differentiation and proliferation, and response to environmental stresses. However, our knowledge about the regulatory mechanisms and functions of miRNAs remains very limited. The main difficulties lie in two aspects. On one hand, the number of experimentally validated miRNA targets is very limited and the predicted targets often include many false positives, which constrains us to reveal the functions of miRNAs.
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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