Algorithms predicting microRNA (miR)-mRNA interactions generate high numbers of possible interactions, many of which might be non-existent or irrelevant in a certain biological context. It is desirable to develop a transparent, user-friendly, unbiased tool to enrich miR-mRNA predictions.
MicroRNAs (miRNAs) are small non-coding RNAs that have been found in most of the eukaryotic organisms. They are involved in the regulation of gene expression at the post-transcriptional level in a sequence specific manner. MiRNAs are produced from their precursors by Dicer-dependent small RNA biogenesis pathway. Involvement of miRNAs in a wide range of biological processes makes them excellent candidates for studying gene function or for therapeutic applications. For this purpose, different RNA-based gene silencing techniques have been developed.
High-throughput screening (HTS) is a common technique for both drug discovery and basic research, but researchers often struggle with how best to derive hits from HTS data. While a wide range of hit identification techniques exist, little information is available about their sensitivity and specificity, especially in comparison to each other. To address this, we have developed the open-source NoiseMaker software tool for generation of realistically noisy virtual screens.
MicroRNAs (miRNAs), as a kind of important small endogenous single-stranded non-coding RNA, play critical roles in a large number of human diseases. However, the currently known experimental verifications of the disease-miRNA associations are still rare and experimental identification is time-consuming and labor-intensive. Accordingly, identifying potential disease-related miRNAs to help people understand the pathogenesis of complex diseases has become a hot topic.
Although thousands of pseudogenes have been annotated in the human genome, their transcriptional regulation, expression profiles and functional mechanisms are largely unknown. In this study, we developed dreamBase (http://rna.sysu.edu.cn/dreamBase) to facilitate the investigation of DNA modification, RNA regulation and protein binding of potential expressed pseudogenes from multidimensional high-throughput sequencing data.
We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications.
The cell-specific information of transcriptional regulation on microRNAs (miRNAs) is crucial to the precise understanding of gene regulations in various physiological and pathological processes existed in different tissues and cell types. The database, mirTrans, provides comprehensive information about cell-specific transcription of miRNAs including the transcriptional start sites (TSSs) of miRNAs, transcription factor (TF) to miRNA regulations and miRNA promoter sequences.
MicroRNAs (miRNAs) regulate genes that are associated with various diseases. To better understand miRNAs, the miRNA regulatory mechanism needs to be investigated and the real targets identified. Here, we present miRTDL, a new miRNA target prediction algorithm based on convolutional neural network (CNN). The CNN automatically extracts essential information from the input data rather than completely relying on the input dataset generated artificially when the precise miRNA target mechanisms are poorly known.
PURPOSE: The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer. METHODS: A database was set up by searching the GEO, EGA, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data.
The miRBase database is the central and official repository for miRNAs and the current release is miRBase version 21.0. Name changes in different miRBase releases cause inconsistencies in miRNA names from version to version. When working with only a small number of miRNAs the translation can be done manually. However, with large sets of miRNAs, the necessary correction of such inconsistencies becomes burdensome and error-prone.
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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