MicroRNAs, by regulating the expression of hundreds of target genes, play critical roles in developmental biology and the etiology of numerous diseases, including cancer. As a vast amount of microRNA expression profile data are now publicly available, the integration of microRNA expression data sets with gene expression profiles is a key research problem in life science research.
We used a machine learning method, the nearest neighbor algorithm (NNA), to learn the relationship between miRNAs and their target proteins, generating a predictor which can then judge whether a new miRNA-target pair is true or not. We acquired 198 positive (true) miRNA-target pairs from Tarbase and the literature, and generated 4,888 negative (false) pairs through random combination. A 0/1 system and the frequencies of single nucleotides and di-nucleotides were used to encode miRNAs into vectors while various physicochemical parameters were used to encode the targets.
High-throughput RNA interference (RNAi) screening has opened up a path to investigating functional genomics in a genome-wide pattern. However, such studies are often restricted to assays that have a single readout format. Recently, advanced image technologies have been coupled with high-throughput RNAi screening to develop high-content screening, in which one or more cell image(s), instead of a single readout, were generated from each well.
High-throughput measurement technologies have triggered a rise in large-scale cancer studies containing multiple levels of molecular data. While there are a number of efficient methods to analyze individual data types, there are far less that enhance data interpretation after analysis. We present the R package Director, a dynamic visualization approach to linking and interrogating multiple levels of molecular data after analysis for clinically meaningful, actionable insights.
The computational identification of non-coding RNA regions on the genome is currently receiving much attention. However, it is essentially harder than gene-finding problems for protein-coding regions because non-coding RNA sequences do not have strong statistical signals. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignment of RNA sequences.
Deep sequencing of small RNAs has become a routine process in recent years, but no dedicated viewer is as yet available to explore the sequence features simultaneously along with secondary structure and gene expression of microRNA (miRNA). We present a highly interactive application that visualizes the sequence alignment, secondary structure and normalized read counts in synchronous multipanel windows.
Real-time quantitative PCR (RT-qPCR) has become a widely used method for accurate expression profiling of targeted mRNA and ncRNA. Selection of appropriate internal control genes for RT-qPCR normalization is an elementary prerequisite for reliable expression measurement. Here, we present ICG (http://icg.big.ac.cn), a wiki-driven knowledgebase for community curation of experimentally validated internal control genes as well as their associated experimental conditions.
MiRNAs are short, non-coding molecules that negatively regulate gene expression and thereby play several important roles in living organisms. Dozens of computational methods for miRNA-related research have been developed, which greatly differ in various aspects. The substantial availability of difficult-to-compare approaches makes it challenging for the user to select a proper tool and prompts the need for a solution that will collect and categorize all the methods.
The continuous increase of available biological data as consequence of modern high-throughput technologies poses new challenges for analysis techniques and database applications. Especially for miRNAs, one class of small non-coding RNAs, many algorithms have been developed to predict new candidates from next-generation sequencing data. While the amount of publications describing novel miRNA candidates keeps steadily increasing, the current gold standard database for miRNAs - miRBase - has not been updated since June 2014.
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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