Comparing multiple RNA secondary structures using tree comparisons

In a previous paper, an algorithm was presented for analyzingmultiple RNA secondary structures utilizing a multiple stringalignment algorithm. In this paper we present another approachto the problem of comparing many secondary structures by utilizinga very efficient tree-matching algorithm that will compare twotrees in O(|T₁|x|T₂|x L₁ x L₂) in the worst case and very closeto O(|T₁|x|T₂|) for average trees representing secondary structures.The result of the pairwise comparison algorithm is then usedwith acluster algorithm to produce a multiple structure clusteringwhich can be displayed in ataxonomy tree to show related structures. Received on September 15, 1989; accepted on June 12, 1990     

Rethinking experiments that explore multiple global change factors

Our current capacity to predict the responses of ecosystem functions under global change factors is limited. We propose new and more efficient approaches to experimental design and modeling that utilize interactions between ecosystem functions to improve our understanding of their sensitivity to global change factors.     

A permutation-based multiple testing method for time-course microarray experiments

Background  

Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation.     

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.     

Decomposing multiple dimensions of stability in global change experiments

Ecological stability is the central framework to understand an ecosystem's ability to absorb or recover from environmental change. Recent modelling and conceptual work suggests that stability is a multidimensional construct comprising different response aspects. Using two freshwater mesocosm experiments as case studies, we show how the response to single perturbations can be decomposed in different stability aspects (resistance, resilience, recovery, temporal stability) for both ecosystem functions and community composition. We find that extended community recovery is tightly connected to a nearly complete recovery of the function (biomass production), whereas systems with incomplete recovery of the species composition ranged widely in their biomass compared to controls. Moreover, recovery was most complete when either resistance or resilience was high, the latter associated with low temporal stability around the recovery trend. In summary, no single aspect of stability was sufficient to reflect the overall stability of the system.     

Comparing G: multivariate analysis of genetic variation in multiple populations

The additive genetic variance–covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design.     

Comparing terracotta and earthenware for multiple functionalities in microbial fuel cells

The properties of earthenware and terracotta were investigated in terms of structural integrity and ion conductivity, in two microbial fuel cell (MFC) designs. Parameters such as wall thickness (4, 8, 18 mm), porosity and cathode hydration were analysed. During the early stages of operation (2 weeks), the more porous earthenware lost anolyte quickly and was unstable between feeding compared to terracotta. Three weeks later MFCs of all thicknesses were more stable and could sustain longer periods of power production without maintenance. In all cases, the denser terracotta produced higher open circuit voltage; however, earthenware the more porous and less iron-rich of the two, proved to be the better material for power production, to the extent that the thickest wall (18 mm) MFC produced 15 % higher power than the thinnest wall (4 mm) terracotta. After 6 weeks of operation, the influence of wall thickness was less exaggerated and power output was comparable between the 4 and 8 mm ceramic membranes. Cylindrical earthenware MFCs produced significantly higher current (75 %) and power (33 %) than terracotta MFCs. A continuous dripping mode of cathode hydration produced threefold higher power than when MFCs were submerged in water, perhaps because of a short-circuiting effect through the material. This shows a significant improvement in terms of biosystems engineering, since a previously high-maintenance half-cell, is now shown to be virtually self-sufficient.     

Comparing food limitation among three stages of nesting: supplementation experiments with the burrowing owl

Food availability is an important limiting factor for avian reproduction. In altricial birds, food limitation is assumed to be more severe during the nestling stage than during laying or incubation, but this has yet to be adequately tested. Using food‐supplementation experiments over a 5‐year period, we determined the degree and timing of food limitation for burrowing owls (Athene cunicularia) breeding in Canada. Burrowing owls are an endangered species and food limitation during the nestling stage could influence reproductive performance of this species at the northern extent of their range. Supplemented pairs fledged on average 47% more owlets than unfed pairs, except during a year when natural food was not limiting (i.e., a prey irruption year). The difference in fledgling production resulted from high nestling mortality in unfed broods, with 96% of all nestling deaths being attributed to food shortage. Supplemental feeding during the nestling period also increased fledgling structural size. Pairs fed from the start of laying produced the same number of hatchlings as pairs that received no supplemental food before hatch. Furthermore, pairs supplemented from egg laying to fledging and pairs supplemented during the nestling period alone had the same patterns of nestling survival, equal numbers of fledglings, and similar fledgling mass and structural size. Our results provide empirical support for the hypothesis that the nestling period is the most food‐limited phase of the breeding cycle. The experimental design we introduce here could be used with other altricial species to examine how the timing of food limitation differs among birds with a variety of life‐history strategies. For burrowing owls, and other species with similar life histories, long‐term, large‐scale, and appropriately timed habitat management increasing prey abundance or availability is critical for conservation.     

A fast and efficient gene-network reconstruction method from multiple over-expression experiments

Background  

Reverse engineering of gene regulatory networks presents one of the big challenges in systems biology. Gene regulatory networks are usually inferred from a set of single-gene over-expressions and/or knockout experiments. Functional relationships between genes are retrieved either from the steady state gene expressions or from respective time series.     

Remote controlled equipment for multiple blood withdrawal in gravitational physiology experiments

Electro-mechanical equipment for multiple blood withdrawal from small experimental animals applied to a centrifuge with maximal 6g gravitational overloading has been developed and tested. The equipment consists of a transmitter and receiver equipped by microcomputers. Active rotor stepping motors are driving four pairs of syringes. It is also possible to measure the instantaneous gravitational force using an accelerometric transducer. This telemetrically regulated blood sampling allows studying selective effects of hypergravity during centrifugation. It can be also used for study of microgravity effects in the animal organism during space flights for the understanding of the mechanism of the changes of the activity of neuroendocrine system and metabolic processes.     

Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments

MOTIVATION: The parametric F-test has been widely used in the analysis of factorial microarray experiments to assess treatment effects. However, the normality assumption is often untenable for microarray experiments with small replications. Therefore, permutation-based methods are called for help to assess the statistical significance. The distribution of the F-statistics across all the genes on the array can be regarded as a mixture distribution with a proportion of statistics generated from the null distribution of no differential gene expression whereas the other proportion of statistics generated from the alternative distribution of genes differentially expressed. This results in the fact that the permutation distribution of the F-statistics may not approximate well to the true null distribution of the F-statistics. Therefore, the construction of a proper null statistic to better approximate the null distribution of F-statistic is of great importance to the permutation-based multiple testing in microarray data analysis. RESULTS: In this paper, we extend the ideas of constructing null statistics based on pairwise differences to neglect the treatment effects from the two-sample comparison problem to the multifactorial balanced or unbalanced microarray experiments. A null statistic based on a subpartition method is proposed and its distribution is employed to approximate the null distribution of the F-statistic. The proposed null statistic is able to accommodate unbalance in the design and is also corrected for the undue correlation between its numerator and denominator. In the simulation studies and real biological data analysis, the number of true positives and the false discovery rate (FDR) of the proposed null statistic are compared with those of the permutated version of the F-statistic. It has been shown that our proposed method has a better control of the FDRs and a higher power than the standard permutation method to detect differentially expressed genes because of the better approximated tail probabilities.     

Spin-locked multiple quantum coherence for signal enhancement in heteronuclear multidimensional NMR experiments

Summary For methine sites the relaxation rate of ¹³C-¹H two-spin coherence is generally slower than the relaxation rate of the individual ¹³C and ¹H single spin coherences. The slower decay of two-spin coherence can be used to increase the sensitivity and resolution in heteronuclear experiments, particularly those that require correlation of H and C chemical shifts. To avoid dephasing of the two-spin coherence caused by ¹H-¹H J-couplings, the ¹H spin is locked by the application of a weak rf field, resulting in a spin-locked multiple quantum coherence. For a sample of calcium-free calmodulin, use of the multiple quantum approach yields significant signal enhancement over the conventional constant-time 2D HSQC experiment. The approach is applicable to many multidimensional NMR experiments, as demonstrated for a 3D ¹³C-separated ROESY CT-HMQC spectrum.     

Multiple-bond kinetics from single-molecule pulling experiments: evidence for multiple NCAM bonds

The kinetic parameters of single bonds between neural cell adhesion molecules were determined from atomic force microscope measurements of the forced dissociation of the homophilic protein-protein bonds. The analytical approach described provides a systematic procedure for obtaining rupture kinetics for single protein bonds from bond breakage frequency distributions obtained from single-molecule pulling experiments. For these studies, we used the neural cell adhesion molecule (NCAM), which was recently shown to form two independent protein bonds. The analysis of the bond rupture data at different loading rates, using the single-bond full microscopic model, indicates that the breakage frequency distribution is most sensitive to the distance to the transition state and least sensitive to the molecular spring constant. The analysis of bond failure data, however, motivates the use of a double-bond microscopic model that requires an additional kinetic parameter. This double-bond microscopic model assumes two independent NCAM-NCAM bonds, and more accurately describes the breakage frequency distribution, particularly at high loading rates. This finding agrees with recent surface-force measurements, which showed that NCAM forms two spatially distinct bonds between opposed proteins.