A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in wellfolded RNAs were identified. Information is encoded in an RNA molecule at two levels: in its primary sequence and in its ability to form higher-order secondary and tertiary structures. Nearly all RNAs can fold to form some secondary structure and, in many RNAs, highly structured regions encode important regulatory motifs. Such structured regulatory elements can be composed of canonical base pairs but may also feature specialized and distinctive RNA structures. Among the best characterized of these specialized structures are RNA pseudoknots. Pseudoknots are relatively rare but occur overwhelmingly in functionally important regions of RNA (2-4). For example, all of the large catalytic RNAs contain pseudoknots (5, 6); roughly two-thirds of the known classes of riboswitches contain pseudoknots that appear to be essential for ligand binding and gene regulatory functions (7); and pseudoknots occur prominently in the regulatory elements that viruses use to usurp cellular metabolism (3). Pseudoknots are thus harbingers of biological function. An important and challenging goal is to identify these structures reliably.Pseudoknots are excluded from the most widely used algorithms that model RNA secondary structure (8). This exclusion is based on the challenge of incorporating the pseudoknot structure into the efficient dynamic programming algorithm used in the most popular secondary structure prediction approaches and because of the additional computational effort required. The prediction of lowest free energy structures with pseudoknots is NP-complete (9), which means that lowest free energy structure cannot be solved as a function of sequence length in polynomial time. In addition, allowing pseudoknots greatly increases the number of (incorrect) hel...
The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions.
The need for comprehensive analysis to compare and combine data across multiple studies in order to validate and extend results is widely recognized. This paper aims to assess the extent of data compatibility in the substance abuse and addiction (SAA) sciences through an examination of measure commonality, defined as the use of similar measures, across grants funded by the National Institute on Drug Abuse (NIDA) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Data were extracted from applications of funded, active grants involving human-subjects research in four scientific areas (epidemiology, prevention, services, and treatment) and six frequently assessed scientific domains. A total of 548 distinct measures were cited across 141 randomly sampled applications. Commonality, as assessed by density (range of 0–1) of shared measurement, was examined. Results showed that commonality was low and varied by domain/area. Commonality was most prominent for (1) diagnostic interviews (structured and semi-structured) for substance use disorders and psychopathology (density of 0.88), followed by (2) scales to assess dimensions of substance use problems and disorders (0.70), (3) scales to assess dimensions of affect and psychopathology (0.69), (4) measures of substance use quantity and frequency (0.62), (5) measures of personality traits (0.40), and (6) assessments of cognitive/neurologic ability (0.22). The areas of prevention (density of 0.41) and treatment (0.42) had greater commonality than epidemiology (0.36) and services (0.32). To address the lack of measure commonality, NIDA and its scientific partners recommend and provide common measures for SAA researchers within the PhenX Toolkit.
BackgroundThe purpose of this manuscript is to describe the PhenX RISING network and the site experiences in the implementation of PhenX measures into ongoing population-based genomic studies.MethodsEighty PhenX measures were implemented across the seven PhenX RISING groups, thirty-three of which were used at more than two sites, allowing for cross-site collaboration. Each site used between four and 37 individual measures and five of the sites are validating the PhenX measures through comparison with other study measures. Self-administered and computer-based administration modes are being evaluated at several sites which required changes to the original PhenX Toolkit protocols. A network-wide data use agreement was developed to facilitate data sharing and collaboration.ResultsPhenX Toolkit measures have been collected for more than 17,000 participants across the PhenX RISING network. The process of implementation provided information that was used to improve the PhenX Toolkit. The Toolkit was revised to allow researchers to select self- or interviewer administration when creating the data collection worksheets and ranges of specimens necessary to run biological assays has been added to the Toolkit.ConclusionsThe PhenX RISING network has demonstrated that the PhenX Toolkit measures can be implemented successfully in ongoing genomic studies. The next step will be to conduct gene/environment studies.
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