PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

This is a PLOS Computational Biology Software Article

     

植物microRNA的生物信息学预测与分析

microRNAs(miRNAs)是一类广泛存在于真核生物中调控基因转录后表达的非编码小分子RNA。大量研究表明,miRNA在调节多种生物途径中起着重要的作用,采用生物信息学方法预测与分析miRNA是当前发现和鉴定植物miRNA的重要策略之一。研究内容总结了生物信息学预测植物miRNA及其靶基因的方法策略,阐述了生物信息学在植物miRNA研究中的重要作用,为今后的研究奠定了基础。     

基于物理化学性质优化的蛋白质相互作用预测研究

蛋白质相互作用预测是生物信息学研究的重要问题之一,提出了一种基于物理化学性质优化的蛋白质相互作用预测方法,与现有方法的显著不同就是,并未使用已知的氨基酸残基的物理化学性质,而是通过粒子群算法优化得到有益于相互作用预测的物理化学性质数值.对真实的数据集测试表明,优化得到的物理化学性质比现有的物理化学性质更有益于提高蛋白质相互作用的预测性能,与其它方法相比,也具有一定的优势,说明该方法是一种有效的蛋白质相互作用预测方法.     

The Prediction of Cardiovascular Disease Based on Trace Element Contents in Hair and a Classifier of Boosting Decision Stumps

The early discovery of cardiovascular disease (CVD) is crucial for performing successful treatments. This study aims at exploring the feasibility of Adaboost (ensemble from machining learning) using decision stumps as weak classifier, combined with trace element analysis of hair, for accurately predicting early CVD. A total of 124 hair samples composed of two groups of samples (one is healthy group from 100 healthy persons aged 24–72 while the other is patient group from 24 cardiovascular disease patients aged 36–81) were used. Nine kinds of trace elements, i.e., chromium (Cr), manganese (Mn), cadmium (Cd), copper (Cu), zinc (Zn), selenium (Se), iron (Fe), aluminum (Al), and nickel (Ni), were selected. In a preliminary analysis, no obvious linear correlations between elements can be observed and the concentration of Cr, Fe, Al, Cd, Ni, or Se for healthy group is higher than that for patient group while the opposite is true for Mn, Cu, or Zn, indicating that both low Se/Fe and high Mn/Cu can be identified as major risk factors. Based on the proposed approach, the final ensemble classifier, constructed on the training set and contained only four decision stumps, achieved an overall identification accuracy of 95.2%, a sensitivity of 100% and a specificity of 94% on the independent test set. The results suggested that integrating Adaboost and trace element analysis of hair sample can serve as a useful tool of diagnosing CVD in clinical practice.     

Interactions between Financial and Environmental Networks in OECD Countries

We analysed a multiplex of financial and environmental networks between OECD countries from 2002 to 2010. Foreign direct investments and portfolio investment showing the flows in equity securities, short-term, long-term and total debt, these securities represent the financial layers; emissions of NO_x, PM10, SO₂, CO₂equivalent and the water footprint associated with international trade represent the environmental layers. We present a new measure of cross-layer correlations between flows in different layers based on reciprocity. For the assessment of results, we implement a null model for this measure based on the exponential random graph theory. We find that short-term financial flows are more correlated with environmental flows than long-term investments. Moreover, the correlations between reverse financial and environmental flows (i.e. the flows of different layers going in opposite directions) are generally stronger than correlations between synergic flows (flows going in the same direction). This suggests a trade-off between financial and environmental layers, where, more financialised countries display higher correlations between outgoing financial flows and incoming environmental flows than from lower financialised countries. Five countries are identified as hubs in this finance-environment multiplex: The United States, France, Germany, Belgium-Luxembourg and United Kingdom.     

转录因子与microRNA在基因表达调控中的功能联系及差异

转录因子和微RNA(microRNA)是最大的两类反式作用因子,它们是基因表达调控的重要调控因子.它们协调发挥调控作用,精细调控基因的表达,在细胞分化和动物生长发育过程中发挥重要的作用.随着对转录因子和microRNA研究的深入,人们发现转录因子和microRNA在基因表达调控网络中关系紧密,它们的分子作用机制有许多相似之处,两者都通过各自的顺式作用元件调控基因表达,且作用的方式类似.但转录因子和microRNA也存在不同之处,转录因子既可以激活基因表达,也可抑制基因表达,而microRNA主要是抑制基因表达.另外,转录因子调控区的复杂性一般高于microRNA的调控区域.本文综述了转录因子和microRNA的异同点,并提出了未来转录因子和microRNA的研究方向.     

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype.     

Identification of New Interactions between Endolysosomal Tethering Factors

Proper functioning of the precisely controlled endolysosomal system is essential for maintaining the homeostasis of the entire cell. Tethering factors play pivotal roles in mediating the fusion of different transport vesicles, such as endosomes or autophagosomes with each other or with lysosomes. In this work, we uncover several new interactions between the endolysosomal tethering factors Rabenosyn-5 (Rbsn) and the HOPS and CORVET complexes. We find that Rbsn binds to the HOPS/CORVET complexes mainly via their shared subunit Vps18 and we mapped this interaction to the 773–854 region of Vps18. Based on genetic rescue experiments, the binding between Rbsn and Vps18 is required for endosomal transport and is dispensable for autophagy. Moreover, Vps18 seems to be important for β1 integrin recycling by binding to Rbsn and its known partner Vps45.     

Use of a Fractional Factorial Design to Evaluate Interactions of Environmental Factors Affecting Biodegradation Rates

For investigation of main and interactive effects of six experimentally controlled environmental factors on phenol biodegradation in a shake-flask system, a largely neglected statistical procedure was applied. A major benefit resulting from the application of the orthogonal, fractional factorial design is that the number of experiments necessary to evaluate multifactor interactions is limited. In our investigation, the required number of experiments was reduced to 81 from the 324 necessary with conventional factorial designs; information was sacrificed for only 3 of 15 possible two-factor interactions. Six experimentally controlled factors were investigated at two or three treatment levels each; the six factors were (1) amount of phenol substrate, (2) amount of bacterial inoculum, (3) filtration of inoculum, (4) type of basal salts medium, (5) initial pH of basal salts medium, and (6) flask closure. Significant main effects were found for factors 1, 2, and 4; whereas significant interactive effects were found only for factor 2 with factor 3 and for factor 2 with factor 5. Our results suggest that the application of these statistical designs will greatly reduce the number of experiments necessary to evaluate multifactor effects on degradation rates during optimization of both hazard screening systems and waste treatment systems.     

microRNAs对脂肪细胞分化相关因子的作用及预测

microRNAs是一类调节靶基因的转录后翻译的小型非编码单链RNA,研究已发现microRNAs在癌症、心血管疾病及糖尿病中显示极为重要的生物学功能。糖尿病目前已成为威胁人类健康的最主要疾病,尤其是II型糖尿病的发病机制成为研究热点。脂肪细胞分化异常是导致II型糖尿病以及胰岛素抵抗的主要因素。进一步阐明microRNAs对脂肪细胞分化过程的作用机制,可能为糖尿病治疗找到新的靶点。本综述将从microRNAs与脂肪细胞分化基因、核激素受体以及相关信号通路相互作用三方面阐述和预测microRNAs对脂肪细胞分化的潜在作用。     

Environmental Effects on Agonistic Interactions between Males of the Cockroach Nauphoeta cinerea

The effects of different environments on the agonistic behavior of males of the cockroach, Nauphoeta cinerea were investigated. We compared the social interactions between pairs of males that had been reared during the period of sexual maturation, when social behavior develops, under one of four environmental treatments: (1) control (28°C with ad libitum food and water) (2) heat stress (35°C, ad libitum food and water) (3) water deprivation (28°C), or (4) food and water deprivation (28°C). Different environments affected the structure of the interactions between males and the behavior of both dominant and subordinate individuals. The mean number of agonistic acts per minute was similar for all treatment groups except the water-deprived group, which was significantly lower. Water-deprived, food- and water-deprived, and heat-stress rearing conditions reduced the stability of agonistic interactions relative to the control group. When reared under stressful conditions, dominant-scored males decreased the number of aggressive acts and increased the number of submissive acts, and subordinate-scored males decreased the number of submissive acts and increased the number of aggressive acts. Thus, stressful environmental conditions can disrupt agonistic interactions and cause both dominant and subordinate male N. cinerea to adopt different behavioral strategies during male-male competition.     

Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints

Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods.     

Interactions between Environmental Factors and Melatonin Receptor Type 1A Polymorphism in Relation to Oral Cancer Susceptibility and Clinicopathologic Development

Background

The purpose of this study was to explore the combined effect of melatonin receptor type 1A (MTNR1A) gene polymorphisms and exposure to environmental carcinogens on the susceptibility and clinicopathological characteristics of oral cancer.

Methodology and Principal Findings

Three polymorphisms of the MTNR1A gene from 618 patients with oral cancer and 560 non-cancer controls were analyzed by real-time polymerase chain reaction (PCR). The CTA haplotype of the studied MTNR1A polymorphisms (rs2119882, rs13140012, rs6553010) was related to a higher risk of oral cancer. Moreover, MTNR1A gene polymorphisms exhibited synergistic effects of environmental factors (betel quid and tobacco use) on the susceptibility of oral cancer. Finally, oral-cancer patients with betel quid-chewing habit who had T/T allele of MTNR1A rs13140012 were at higher risk for developing an advanced clinical stage and lymph node metastasis.

Conclusion

These results support gene-environment interactions of MTNR1A polymorphisms with smoking and betel quid-chewing habits possibly altering oral-cancer susceptibility and metastasis.     

Multivariate Approach for Studying Interactions between Environmental Variables and Microbial Communities

To understand the role of human microbiota in health and disease, we need to study effects of environmental and other epidemiological variables on the composition of microbial communities. The composition of a microbial community may depend on multiple factors simultaneously. Therefore we need multivariate methods for detecting, analyzing and visualizing the interactions between environmental variables and microbial communities. We provide two different approaches for multivariate analysis of these complex combined datasets: (i) We select variables that correlate with overall microbiota composition and microbiota members that correlate with the metadata using canonical correlation analysis, determine independency of the observed correlations in a multivariate regression analysis, and visualize the effect size and direction of the observed correlations using heatmaps; (ii) We select variables and microbiota members using univariate or bivariate regression analysis, followed by multivariate regression analysis, and visualize the effect size and direction of the observed correlations using heatmaps. We illustrate the results of both approaches using a dataset containing respiratory microbiota composition and accompanying metadata. The two different approaches provide slightly different results; with approach (i) using canonical correlation analysis to select determinants and microbiota members detecting fewer and stronger correlations only and approach (ii) using univariate or bivariate analyses to select determinants and microbiota members detecting a similar but broader pattern of correlations. The proposed approaches both detect and visualize independent correlations between multiple environmental variables and members of the microbial community. Depending on the size of the datasets and the hypothesis tested one can select the method of preference.     

Study of a Bionic Pattern Classifier Based on Olfactory Neural System

Simulating biological olfactory neural system, KIII network, which is a high-dimensional chaotic neural network, is designed in this paper. Different from conventional artificial neural network, the KⅢ network works in its chaotic trajectory. It can simulate not only the output EEG waveform observed in electrophysiological experiments, but also the biological intelligence for pattern classification. The simulation analysis and application to the recognition of handwriting numerals are presented here. The classification performance of the KⅢ network at different noise levels was also investigated.     

Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.     

水稻MicroRNA的预测及实验验证

根据已报道水稻pre-miRNA的序列与结构信息,利用支持向量机（support vector machine, SVM）方法在miRNA前体上预测成熟区,产生一个模型——mature-SVM.它预测水稻成熟区的敏感性和特异性分别为86.7% 和100%;然后,用这个模型对从水稻基因组中筛选出的46.501条pre-miRNA进行成熟链预测,此外再根据miRNA的作用原理用blast程序所进一步的筛选,得到了127条pre-miRNA及成熟miRNA;除去其中已知的21条,最后得到106条候选的新的水稻miRNA. 从中随机挑取10条进行Northern验证,结果有4条miRNA得到确认.