In plants, various phloem-mobile macromolecules including noncoding RNAs, mRNAs and proteins are suggested to act as important long-distance signals in regulating crucial physiological and morphological transition processes such as flowering, plant growth and stress responses. Given recent advances in high-throughput sequencing technologies, numerous mobile macromolecules have been identified in diverse plant species from different plant families.
Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD.
SNPLogic (http://www.snplogic.org) brings together single nucleotide polymorphism (SNP) information from numerous sources to provide a comprehensive SNP selection, annotation and prioritization system for design and analysis of genotyping projects.
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.
Adenosine-to-inosine (A-to-I) editing by adenosine deaminase acting on the RNA (ADAR) proteins is one of the most frequent modifications during post- and co-transcription. To facilitate the assignment of biological functions to specific editing sites, we designed an automatic online platform to annotate A-to-I RNA editing sites in pre-mRNA splicing signals, microRNAs (miRNAs) and miRNA target untranslated regions (3' UTRs) from human (Homo sapiens) high-throughput sequencing data and predict their effects based on large-scale bioinformatic analysis.
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 MiRNA SNP Disease Database (MSDD, http://www.bio-bigdata.com/msdd/) is a manually curated database that provides comprehensive experimentally supported associations among microRNAs (miRNAs), single nucleotide polymorphisms (SNPs) and human diseases. SNPs in miRNA-related functional regions such as mature miRNAs, promoter regions, pri-miRNAs, pre-miRNAs and target gene 3'-UTRs, collectively called 'miRSNPs', represent a novel category of functional molecules.
Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative examples. Those methods have important practical limitations when they have to be applied to a real prediction task. First, there is the challenge of dealing with a scarce number of positive (well-known) pre-miRNA examples.
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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