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.
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.
A comprehensive analysis of enriched functional categories in differentially expressed genes is important to extract the underlying biological processes of genome-wide expression profiles. Moreover, identification of the network of significant functional modules in these dynamic processes is an interesting challenge. This study introduces DynaMod, a web-based application that identifies significant functional modules reflecting the change of modularity and differential expressions that are correlated with gene expression profiles under different conditions.
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.
We introduce GO-Elite, a flexible and powerful pathway analysis tool for a wide array of species, identifiers (IDs), pathways, ontologies and gene sets. In addition to the Gene Ontology (GO), GO-Elite allows the user to perform over-representation analysis on any structured ontology annotations, pathway database or biological IDs (e.g. gene, protein or metabolite). GO-Elite exploits the structured nature of biological ontologies to report a minimal set of non-overlapping terms. The results can be visualized on WikiPathways or as networks.
Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference. Moreover, when sample sizes are small, inference is likely to be too liberal when, in a Bayesian setting, applying a non-appropriate prior or to lack power when not carefully borrowing information across features.
In recent times, information on miRNAs and their binding sites is gaining momentum. Therefore, there is interest in the development of tools extracting miRNA related information from known literature. Hence, we describe GeneAFinder and miRAFinder scripts (open source) developed using python programming for the semi-automatic extraction and arrangement of updated information on miRNAs, genes and additional data from published article abstracts in PubMed. The scripts are suitable for custom modification as per requirement.
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.
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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