Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Selective antibacterial activity of the cationic peptide PaDBS1R6 against Gram-negative bacteria

Infections caused by Gram-negative bacteria, Escherichia coli and Pseudomonas aeruginosa foremost among them, constitute a major worldwide health problem. Bioinformatics methodologies are being used to rationally design new antimicrobial peptides, a potential alternative for treating these infections. One of the algorithms used to develop antimicrobial peptides is the Joker, which was used to design the peptide PaDBS1R6. This study evaluates the antibacterial activities of PaDBS1R6 in vitro and in vivo, characterizes the peptide interaction to target membranes, and investigates the PaDBS1R6 structure in contact with mimetic vesicles. Moreover, we demonstrate that PaDBS1R6 exhibits selective antimicrobial activity against Gram-negative bacteria. In the presence of negatively charged and zwitterionic lipids the structural arrangement of PaDBS1R6 transits from random coil to α-helix, as characterized by circular dichroism. The tertiary structure of PaDBS1R6 was determined by NMR in zwitterionic dodecylphosphocholine (DPC) micelles. In conclusion, PaDBS1R6 is a candidate for the treatment of nosocomial infections caused by Gram-negative bacteria, as template for producing other antimicrobial agents.     

A varying‐coefficient generalized odds rate model with time‐varying exposure: An application to fitness and cardiovascular disease mortality

Varying‐coefficient models have become a common tool to determine whether and how the association between an exposure and an outcome changes over a continuous measure. These models are complicated when the exposure itself is time‐varying and subjected to measurement error. For example, it is well known that longitudinal physical fitness has an impact on cardiovascular disease (CVD) mortality. It is not known, however, how the effect of longitudinal physical fitness on CVD mortality varies with age. In this paper, we propose a varying‐coefficient generalized odds rate model that allows flexible estimation of age‐modified effects of longitudinal physical fitness on CVD mortality. In our model, the longitudinal physical fitness is measured with error and modeled using a mixed‐effects model, and its associated age‐varying coefficient function is represented by cubic B‐splines. An expectation‐maximization algorithm is developed to estimate the parameters in the joint models of longitudinal physical fitness and CVD mortality. A modified pseudoadaptive Gaussian‐Hermite quadrature method is adopted to compute the integrals with respect to random effects involved in the E‐step. The performance of the proposed method is evaluated through extensive simulation studies and is further illustrated with an application to cohort data from the Aerobic Center Longitudinal Study.     

Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm

The ultimate goal of the Recommender System (RS) is to offer a proposal that is very close to the user's real opinion. Data clustering can be effective in increasing the accuracy of production proposals by the RS. In this paper, single-objective hybrid evolutionary approach is proposed for clustering items in the offline collaborative filtering RS. This method, after generating a population of randomized solutions, at each iteration, improves the population of solutions first by Genetic Algorithm (GA) and then by using the Gravitational Emulation Local Search (GELS) algorithm. Simulation results on standard datasets indicate that although the proposed hybrid meta-heuristic algorithm requires a relatively high run time, it can lead to more appropriate clustering of existing data and thus improvement of the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Coverage criteria.     

Determination of the structural ensemble of the molten globule state of a protein by computer simulations

We have used computer simulations to investigate the structural nature of the molten globule (MG) state of canine milk lysozyme. To sample the conformational space efficiently, we performed replica-exchange umbrella sampling simulations with the radius of gyration as a reaction coordinate. We applied the Weighted Histogram Analysis Method to the trajectory of the simulations to obtain the potential of mean force, from which we identified representative structures corresponding to local minima in the free energy surface. The representative structures obtained in this way are in accord with the characteristics of the MG state reported previously by experimental studies. We conjecture that the MG state comprises a series of partially structured states undergoing relatively fast conformational interchange.     

Modelling time‐varying low‐temperature‐induced mortality rates for pupae of Tuta absoluta (Gelechiidae,Lepidoptera)

In a series of laboratory experiments, acclimated pupae of Tuta absoluta were exposed to various constant low temperatures in order to estimate their maximum survival times (Kaplan–Meier, Lt_99.99). A Weibull function was fitted to the data points, describing maximum survival time as a function of temperature. In another experiment at ?6°C, the progress of mortality increasing with exposure time was identified. These values were fitted by a sigmoidal function converging asymptotically to 100% mortality for very long exposure times. Analysing mortality data from the maximum survival experiment by a generalized linear model showed a significant common slope parameter (p < .001) that reveals parallelism of the survival curves at each temperature if a log time axis is used. These curves appear stretched (time scaled) if plotted with a nonlogarithmic time axis. By combining these mathematical relations, it was possible to calculate a species‐specific ‘mortality surface’ which exhibits mortalities, depending on temperature and duration of exposure. In order to accumulate hourly mortalities for courses of varying temperatures, an algorithm was developed which yields mortality values from that surface taking into account the attained mortality level. In validation experiments, recorded mortalities were compared against modelled mortalities. Prediction of mortality was partially supported by the model, but pupae experiencing intensely fluctuating temperatures showed decreased mortality, probably caused by rapid cold hardening during exposure. Despite this observation, mortality data converged to distinct levels very close to 100% depending on the intensity of temperature fluctuations that were characteristic for different types of experiments. The highest mortality limit occurred at intensely fluctuating temperatures in laboratory experiments. This constituted a benchmark that was not reached under various field conditions. Thus, it was possible to identify temperature limits for the extinction of field populations of Tuta absoluta pupae.