The NanoString nCounter Platform is a new and promising technology for measuring nucleic acid abundances. It has several advantages over PCR-based techniques, including avoidance of amplification, direct sequence interrogation and digital detection for absolute quantification. These features minimize aspects of experimental error and hold promise for dealing with challenging experimental conditions such as archival formalin-fixed paraffin-embedded samples.
Growing evidence demonstrates that local well-ordered structures are closely correlated with cis-acting elements in the post-transcriptional regulation of gene expression. The prediction of a well-ordered folding sequence (WFS) in genomic sequences is very helpful in the determination of local RNA elements with structure-dependent functions in mRNAs.
In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific 'active parts' of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical 'themes'-in the form of enriched biologically relevant microRNA-mediated subpathways-that determine the functionality of signaling networks across time.
To provide a set of useful analysis tools for the researchers to explore the microRNA data.
The R language was used for generating the Graphical Users Interface and implementing most functions. Some Practical Extraction and Report Language (Perl) scripts were used for parsing source files.
High-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of these valuable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data.
miRPursuit is a pipeline developed for running end-to-end analyses of high-throughput small RNA (sRNA) sequence data in model and nonmodel plants, from raw data to identified and annotated conserved and novel sequences. It consists of a series of UNIX shell scripts, which connect open-source sRNA analysis software. The involved parameters can be combined with convenient workflow management by users without advanced computational skills.
High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data.
Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNA's differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network.
RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption.
The genome-wide identification of microRNA transcription start sites (miRNA TSSs) is essential for understanding how miRNAs are regulated in development and disease. In this study, we developed mirSTP (mirna transcription Start sites Tracking Program), a probabilistic model for identifying active miRNA TSSs from nascent transcriptomes generated by global run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq).
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:
