Prediction of Protein-Protein Interactions Using Protein Signature Profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

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.     

响应面法优化金针菇栽培工艺的研究

以提高金针菇单瓶产量为目的,以常规生产为对照,通过单因素试验、Plackett-Burman试验、响应面优化法、验证试验分析培养基装瓶量、灭菌前pH、接种量、搔菌深度、搔菌补水量对金针菇单瓶平均产量的影响。单因素试验结果表明,单瓶平均产量分别在装瓶量1 000 g、灭菌前pH值 6.80、接种量35 mL、搔菌深度10 mm、搔菌补水量10 mL时达到最大值;Plackett-Burman试验表明装瓶量、灭菌前pH和搔菌补水量是影响金针菇单瓶平均产量的关键因素;响应面优化法预测的最优化条件为培养基装瓶量1 004.05 g、灭菌前pH值6.83、搔菌补水量11.41 mL,金针菇单瓶平均产量为473.81 g;结合单因素试验及响应面预测,将验证试验设置为培养基装瓶量(1 000±5) g、灭菌前pH值(6.80±0.10)、搔菌补水量(11±1) mL、搔菌深度(10±2) mm、接种量(35±5) mL,金针菇单瓶平均产量为466.36 g,比对照组提高11.63 g,与预测值接近,相对误差为1.57%,试验设计符合生产需求。     

An effective method for accurate nymphal-stage delimitation of the cicada Hyalessa fuscata

Subterranean nymphal development in cicadas presents challenges to researchers in accurately estimating the number of their developmental stages, although such information is crucial to understanding and predicting their population dynamics. While most studies have relied on head width as an attribute for life-stage determination to date, such character in cicadas can be highly variable and thus differentiation solely based on such morphology is prone to subjectivity in practice. Here, we propose a reliable method for instar estimation that is applicable to Hyalessa fuscata nymphs. We first obtained morphometrics of nymphs in all stages. Second, we computed logarithm-transformation and principal component analysis to extract a transformed variable that captures most of the variance of morphological characteristics. Third, k-means were computed to divide the dataset into distinct clusters assuming four-, five- and six life-stage scenarios for the best interferences of life stages. Finally, simple linear regression analysis was conducted to compare and select the best fit model. Our result shows that five nymphal stages best fit for H. fuscata nymphs. This method is expected to provide an easy-to-handle ecological tool for the study of life history of cicadas as well as other insects that have long life cycles and multiple developmental stages.     

Development of a novel MATLAB-based framework for implementing mechanical joint stability constraints within OpenSim musculoskeletal models

The Static Optimization (SO) solver in OpenSim estimates muscle activations and forces that only equilibrate applied moments. In this study, SO was enhanced through an open-access MATLAB interface, where calculated muscle activations can additionally satisfy crucial mechanical stability requirements. This Stability-Constrained SO (SCSO) is applicable to many OpenSim models and can potentially produce more biofidelic results than SO alone, especially when antagonistic muscle co-contraction is required to stabilize body joints. This hypothesis was tested using existing models and experimental data in the literature. Muscle activations were calculated by SO and SCSO for a spine model during two series of static trials (i.e. simulation 1 and 2), and also for a lower limb model (supplementary material 2). In simulation 1, symmetric and asymmetric flexion postures were compared, while in simulation 2, various external load heights were compared, where increases in load height did not change the external lumbar flexion moment, but necessitated higher EMG activations. During the tasks in simulation 1, the predicted muscle activations by SCSO demonstrated less average deviation from the EMG data (6.8% −7.5%) compared to those from SO (10.2%). In simulation 2, SO predicts constant muscle activations and forces, while SCSO predicts increases in the average activations of back and abdominal muscles that better match experimental data. Although the SCSO results are sensitive to some parameters (e.g. musculotendon stiffness), when considering the strategy of the central nervous system in distributing muscle forces and in activating antagonistic muscles, the assigned activations by SCSO are more biofidelic than SO.     

A superposition free method for protein conformational ensemble analyses and local clustering based on a differential geometry representation of backbone

Here a differential geometry (DG) representation of protein backbone is explored on the analyses of protein conformational ensembles. The protein backbone is described by curvature, κ, and torsion, τ, values per residue and we propose 1) a new dissimilarity and protein flexibility measurement and 2) a local conformational clustering method. The methods were applied to Ubiquitin and c-Myb-KIX protein conformational ensembles and results show that κ\τ metric space allows to properly judge protein flexibility by avoiding the superposition problem. The d_max measurement presents equally good or superior results when compared to RMSF, especially for the intrinsically unstructured protein. The clustering method is unique as it relates protein global to local dynamics by providing a global clustering solutions per residue. The methods proposed can be especially useful to the analyses of highly flexible proteins. The software written for the analyses presented here is available at https://github.com/AMarinhoSN/FleXgeo for academic usage only.     

The influence of habitat and adults on the spatial distribution of juvenile corals

Population distributions are affected by a variety of spatial processes, including dispersal, intraspecific dynamics and habitat selection. Within reef‐building coral communities, these processes are especially important during the earliest life stages when reproduction provides mobility among sessile organisms and populations experience the greatest mortality bottlenecks both before and immediately after settlement. Here, we used large‐area imaging to create photomosaics that allowed us to identify and map the location of 4681 juvenile (1–5 cm diameter) and 25 902 adult (>5 cm diameter) coral colonies from eight 100‐m² plots across the forereef of Palmyra Atoll. Using metrics of density, percent cover and the relative location of each colony within each plot, we examined abundance and spatial relationships between juvenile and adult coral taxa. Within coral taxa, juvenile density was generally positively related to the numerical density and percent cover of adults. Nearest neighbor analyses showed aggregation of juveniles near adults of the same taxon for two of the focal taxa (Pocillopora and Fungiids), while all other taxa showed random spatial patterning relative to adults. Three taxa had clustered distributions of juveniles overall. Additionally, we found that on a colony level, juveniles for five of nine focal taxa (accounting for >98% of all identified juveniles) associated with a specific habitat type, with four of those five taxa favoring unconsolidated (e.g. rubble) over consolidated substrata. The general lack of clustering in juvenile corals contrasts with consistent clustering patterns seen in adult corals, suggesting that adult spatial patterns are largely driven by processes occurring after maturity such as partial colony mortality, including fission and fragmentation. The association of many taxa with unconsolidated habitat also suggests that corals may play an important role in colonizing natural rubble patches that could contribute to reef stabilization over time.     

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.     

Statistical inference for self-designing clinical trials with a one-sided hypothesis

In the process of monitoring clinical trials, it seems appealing to use the interim findings to determine whether the sample size originally planned will provide adequate power when the alternative hypothesis is true, and to adjust the sample size if necessary. In the present paper, we propose a flexible sequential monitoring method following the work of Fisher (1998), in which the maximum sample size does not have to be specified in advance. The final test statistic is constructed based on a weighted average of the sequentially collected data, where the weight function at each stage is determined by the observed data prior to that stage. Such a weight function is used to maintain the integrity of the variance of the final test statistic so that the overall type I error rate is preserved. Moreover, the weight function plays an implicit role in termination of a trial when a treatment difference exists. Finally, the design allows the trial to be stopped early when the efficacy result is sufficiently negative. Simulation studies confirm the performance of the method.     

Analyzing a randomized trial on breast self-examination with noncompliance and missing outcomes

Recently, instrumental variables methods have been used to address non-compliance in randomized experiments. Complicating such analyses is often the presence of missing data. The standard model for missing data, missing at random (MAR), has some unattractive features in this context. In this paper we compare MAR-based estimates of the complier average causal effect (CACE) with an estimator based on an alternative, nonignorable model for the missing data process, developed by Frangakis and Rubin (1999, Biometrika, 86, 365-379). We also introduce a new missing data model that, like the Frangakis-Rubin model, is specially suited for models with instrumental variables, but makes different substantive assumptions. We analyze these issues in the context of a randomized trial of breast self-examination (BSE). In the study two methods of teaching BSE, consisting of either mailed information about BSE (the standard treatment) or the attendance of a course involving theoretical and practical sessions (the new treatment), were compared with the aim of assessing whether teaching programs could increase BSE practice and improve examination skills. The study was affected by the two sources of bias mentioned above: only 55% of women assigned to receive the new treatment complied with their assignment and 35% of the women did not respond to the post-test questionnaire. Comparing the causal estimand of the new treatment using the MAR, Frangakis-Rubin, and our new approach, the results suggest that for these data the MAR assumption appears least plausible, and that the new model appears most plausible among the three choices.