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.
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.
MicroRNAs (miRNAs) are a class of small noncoding RNAs that act as efficient post-transcriptional regulators of gene expression. In 2012, the first cross-kingdom miRNA-based interaction had been evidenced, demonstrating that exogenous miRNAs act in a manner of mammalian functional miRNAs. Starting from this evidence, we defined the concept of cross-kingdom functional homology between plant and mammalian miRNAs as a needful requirement for vegetal miRNA to explicit a regulation mechanism into the host mammalian cell, comparable to the endogenous one.
Recent discovery of thousands of small and large noncoding RNAs, in parallel to technical improvements enabling scientists to study the transcriptome in much higher depth, has resulted in massive data generation. This burst of information prompts the development of easily accessible resources for storage, retrieval and analysis of raw and processed data, and hundreds of Web-based tools dedicated to these tasks have been made available.
RNAComposer is a fully automated, web-interfaced system for RNA 3D structure prediction, freely available at http://rnacomposer.cs.put.poznan.pl/ and http://rnacomposer.ibch.poznan.pl/. Its main components are: manually curated database of RNA 3D structure elements, highly efficient computational engine and user-friendly web application.
As a promising field of individualized therapy, non-coding RNA pharmacogenomics promotes the understanding of different individual responses to certain drugs and acts as a reasonable reference for clinical treatment. However, relevant information is scattered across the published literature, which is inconvenient for researchers to explore non-coding RNAs that are involved in drug resistance. To address this, we systemically identified validated and predicted drug resistance-associated microRNAs and long non-coding RNAs through manual curation and computational analysis.
MicroRNAs (miRNAs) play essential roles in plant growth, development and stress responses through post-transcriptionally regulating the expression levels of their target mRNAs. Although some tools and databases were developed for predicting the relationships between miRNAs and their targets (miR-Tar), most of them were dependent on computational methods without experimental validations. With development of degradome sequencing techniques, researchers can identify potential interactions based on degradome sequencing data.
We present MethHC (http://MethHC.mbc.nctu.edu.tw), a database comprising a systematic integration of a large collection of DNA methylation data and mRNA/microRNA expression profiles in human cancer. DNA methylation is an important epigenetic regulator of gene transcription, and genes with high levels of DNA methylation in their promoter regions are transcriptionally silent. Increasing numbers of DNA methylation and mRNA/microRNA expression profiles are being published in different public repositories.
Current outcome predictors based on "molecular profiling" rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients - a paradigm shift towards accurate "mechanism-anchored profiling". We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms.
Approximately 80% of Stage II colon cancer patients are cured by appropriate surgery. However, 20% relapse, and virtually all of these people will die due to metastatic disease. Adjuvant chemotherapy has little or no impact on relapse or survival in Stage II colon cancer, and can only add toxicity without benefit for 80% of the target population that has been cured by surgery.
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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