Circular RNAs are a large class of animal RNAs with regulatory potency.

Circular RNAs (circRNAs) in animals are an enigmatic class of RNA with unknown function. To explore circRNAs systematically, we sequenced and computationally analysed human, mouse and nematode RNA. We detected thousands of well-expressed, stable circRNAs, often showing tissue/developmental-stage-specific expression. Sequence analysis indicated important regulatory functions for circRNAs. We found that a human circRNA, antisense to the cerebellar degeneration-related protein 1 transcript (CDR1as), is densely bound by microRNA (miRNA) effector complexes and harbours 63 conserved binding sites for the ancient miRNA miR-7. Further analyses indicated that CDR1as functions to bind miR-7 in neuronal tissues. Human CDR1as expression in zebrafish impaired midbrain development, similar to knocking down miR-7, suggesting that CDR1as is a miRNA antagonist with a miRNA-binding capacity ten times higher than any other known transcript. Together, our data provide evidence that circRNAs form a large class of post-transcriptional regulators. Numerous circRNAs form by head-to-tail splicing of exons, suggesting previously unrecognized regulatory potential of coding sequences.

Large-scale de novo prediction of physical protein-protein association.

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org.

The GNB3 C825T polymorphism and essential hypertension: a meta-analysis of 34 studies including 14,094 cases and 17,760 controls.

OBJECTIVES: The C825T single nucleotide polymorphism of the G-protein beta3 (GNB3) has been implicated in susceptibility to essential hypertension, through the expression of an alternatively spliced truncated variant. In an effort to clarify earlier inconclusive results, we performed a meta-analysis of population-based case-control genetic association studies. METHODS: Random-effects methods were applied on summary data in order to combine the results of the individual studies. RESULTS: We identified in total 34 studies, including 14,094 hypertensive cases and 17,760 controls. The TT versus CC + CT contrast yielded an overall odds ratio (OR) of 1.08 [95% confidence interval (CI): 1.01, 1.15], the contrast of TT + CT versus CC, an OR of 1.17 (95% CI: 1.06, 1.29), whereas that of the T allele versus C allele yielded a non-significant OR of 1.05 (95% CI: 0.98, 1.13). There was moderate evidence for a publication bias in the latter two contrasts, which was eliminated after excluding studies not in Hardy-Weinberg equilibrium and those performed on non-normal populations (those with a diagnosis of diabetes, obesity and myocardial infarction). Subgroup analyses revealed that non-significant estimates arose from studies on Asian populations, as opposed to the Caucasian ones. Furthermore, the frequency of the T allele was lower in Caucasians and these populations were found to inhabit higher latitudes. CONCLUSIONS: The findings are in agreement with a recently proposed causal model for systolic blood pressure, which correlates it with the T allele and the absolute latitude. Further studies are needed in order to fully address questions about the aetiological mechanism of the particular association, as well as to study the effect in populations of African descent.

Key factors for successful data integration in biomarker research.

Integrating a wide range of biomedical data such as that rapidly emerging from the use of next-generation sequencing is expected to have a key role in identifying and qualifying new biomarkers to support precision medicine. Here, we highlight some of the challenges for biomedical data integration and approaches to address them.

A database for G proteins and their interaction with GPCRs.

BACKGROUND: G protein-coupled receptors (GPCRs) transduce signals from extracellular space into the cell, through their interaction with G proteins, which act as switches forming hetero-trimers composed of different subunits (alpha,beta,gamma). The alpha subunit of the G protein is responsible for the recognition of a given GPCR. Whereas specialised resources for GPCRs, and other groups of receptors, are already available, currently, there is no publicly available database focusing on G Proteins and containing information about their coupling specificity with their respective receptors. DESCRIPTION: gpDB is a publicly accessible G proteins/GPCRs relational database. Including species homologs, the database contains detailed information for 418 G protein monomers (272 Galpha, 87 Gbeta and 59 Ggamma) and 2782 GPCRs sequences belonging to families with known coupling to G proteins. The GPCRs and the G proteins are classified according to a hierarchy of different classes, families and sub-families, based on extensive literature searchs. The main innovation besides the classification of both G proteins and GPCRs is the relational model of the database, describing the known coupling specificity of the GPCRs to their respective alpha subunit of G proteins, a unique feature not available in any other database. There is full sequence information with cross-references to publicly available databases, references to the literature concerning the coupling specificity and the dimerization of GPCRs and the user may submit advanced queries for text search. Furthermore, we provide a pattern search tool, an interface for running BLAST against the database and interconnectivity with PRED-TMR, PRED-GPCR and TMRPres2D. CONCLUSIONS: The database will be very useful, for both experimentalists and bioinformaticians, for the study of G protein/GPCR interactions and for future development of predictive algorithms. It is available for academics, via a web browser at the URL: http://bioinformatics.biol.uoa.gr/gpDB.

Accounting for redundancy when integrating gene interaction databases.

During the last years gene interaction networks are increasingly being used for the assessment and interpretation of biological measurements. Knowledge of the interaction partners of an unknown protein allows scientists to understand the complex relationships between genetic products, helps to reveal unknown biological functions and pathways, and get a more detailed picture of an organism's complexity. Being able to measure all protein interactions under all relevant conditions is virtually impossible. Hence, computational methods integrating different datasets for predicting gene interactions are needed. However, when integrating different sources one has to account for the fact that some parts of the information may be redundant, which may lead to an overestimation of the true likelihood of an interaction. Our method integrates information derived from three different databases (Bioverse, HiMAP and STRING) for predicting human gene interactions. A Bayesian approach was implemented in order to integrate the different data sources on a common quantitative scale. An important assumption of the Bayesian integration is independence of the input data (features). Our study shows that the conditional dependency cannot be ignored when combining gene interaction databases that rely on partially overlapping input data. In addition, we show how the correlation structure between the databases can be detected and we propose a linear model to correct for this bias. Benchmarking the results against two independent reference data sets shows that the integrated model outperforms the individual datasets. Our method provides an intuitive strategy for weighting the different features while accounting for their conditional dependencies.