A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.     

A Periodically-Forced Mathematical Model for the Seasonal Dynamics of Malaria in Mosquitoes

We describe and analyze a periodically-forced difference equation model for malaria in mosquitoes that captures the effects of seasonality and allows the mosquitoes to feed on a heterogeneous population of hosts. We numerically show the existence of a unique globally asymptotically stable periodic orbit and calculate periodic orbits of field-measurable quantities that measure malaria transmission. We integrate this model with an individual-based stochastic simulation model for malaria in humans to compare the effects of insecticide-treated nets (ITNs) and indoor residual spraying (IRS) in reducing malaria transmission, prevalence, and incidence. We show that ITNs are more effective than IRS in reducing transmission and prevalence though IRS would achieve its maximal effects within 2 years while ITNs would need two mass distribution campaigns over several years to do so. Furthermore, the combination of both interventions is more effective than either intervention alone. However, although these interventions reduce transmission and prevalence, they can lead to increased clinical malaria; and all three malaria indicators return to preintervention levels within 3 years after the interventions are withdrawn.     

Mapping Risk of Malaria Transmission in Mainland Portugal Using a Mathematical Modelling Approach

Malaria is currently one of the world´s major health problems. About a half-million deaths are recorded every year. In Portugal, malaria cases were significantly high until the end of the 1950s but the disease was considered eliminated in 1973. In the past few years, endemic malaria cases have been recorded in some European countries. With the increasing human mobility from countries with endemic malaria to Portugal, there is concern about the resurgence of this disease in the country. Here, we model and map the risk of malaria transmission for mainland Portugal, considering 3 different scenarios of existing imported infections. This risk assessment resulted from entomological studies on An. atroparvus, the only known mosquito capable of transmitting malaria in the study area. We used the malariogenic potential (determined by receptivity, infectivity and vulnerability) applied over geospatial data sets to estimate spatial variation in malaria risk. The results suggest that the risk exists, and the hotspots are concentrated in the northeast region of the country and in the upper and lower Alentejo regions.     

A Framework for Assessment of Ecological Risks from Multiple Activities

The standard framework for ecological risk assessment does not explicitly address multiple activities. Although this has not prevented its use for assessments of risks from multiple agents, the routine assessment of complex programs or of multiple agents acting on a site, watershed or region would be aided by use of a framework that is designed for that purpose. The framework proposed in this paper is modular with respect to the individual activities which makes the assessment more manageable and more efficient when the same activities are addressed in multiple programs or at multiple sites. It explicitly allows for analysis of indirect effects in terms of causal chains. It includes links to other risk assessments for which changes in ecological conditions are the hazardous agent. For example, changes in ecological condition may create risks to agricultural economies or to the cultural resource values of a site. Finally, the framework includes a standard approach to estimating the combined effects of the multiple agents acting on a receptor.     

A Framework for Adaptive Cluster Computing Using JavaSpaces

Heterogeneous networked clusters are being increasingly used as platforms for resource-intensive parallel and distributed applications. The fundamental underlying idea is to provide large amounts of processing capacity over extended periods of time by harnessing the idle and available resources on the network in an opportunistic manner. In this paper we present the design, implementation and evaluation of a framework that uses JavaSpaces to support this type of opportunistic adaptive parallel/distributed computing over networked clusters in a non-intrusive manner. The framework targets applications exhibiting coarse grained parallelism and has three key features: (1) portability across heterogeneous platforms, (2) minimal configuration overheads for participating nodes, and (3) automated system state monitoring (using SNMP) to ensure non-intrusive behavior. Experimental results presented in this paper demonstrate that for applications that can be broken into coarse-grained, relatively independent tasks, the opportunistic adaptive parallel computing framework can provide performance gains. Furthermore, the results indicate that monitoring and reacting to the current system state minimizes the intrusiveness of the framework.     

QTL形态标记定位的一种数学方法

根据家蚕中位于Z染色体上的伴性遗传的双形态标记和假定与其有连锁关系的一个具有一对主基因差异的数量性状在测交世代中,所作的理论分布,本文建立了QTL形态标记定位的数学方法,即频数分布面积法,并给出了相应的检测一对主基因在测交世代中的同分离比例及其与形态标记是否有连锁关系的X2统计量．这种定位方法亦适应于非伴性遗传方式的QTL形态标记定位．与单标记定位的极大似然方法相比,我们的方法所作的双标记定位能显示QTL与形态标记发生重组的交叉干步作用,并且定位结果不受作用于数量性状的环境效应所影响．     

A Pooled Response Strategy for Combining Multiple Lines of Evidence to Quantitatively Estimate Impact

The impacts of sediment contaminants can be evaluated by different lines of evidence, including toxicity tests and ecological community studies. Responses from 10 different toxicity assays/tests were combined to arrive at a “site score.” We employed a relatively simple summary measure, pooled P-values where we quantify a potential decrement in response in a contaminated site relative to nominally clean reference sites. The response-specific P-values were defined relative to a “null” distribution of responses in reference sites, and were then pooled using standard meta-analytic methods. Ecological community data were also evaluated using an analogous strategy. A distribution of distances of the reference sites from thecentroid of the reference sites was obtained. The distance from each of the test sites from the centroid of the reference sites was then calculated, and the proportion of reference distances that exceed the test site difference was used to define an empirical P-value for that test site. A plot of the toxicity P-value versus the community P-value was used to identify sites based on both alteration in community structure and toxicity, that is, by weight-of-evidence. This approach provides a useful strategy for examining multiple lines of evidence that should be accessible to the broader scientific community. The use of a large collection of reference sites to empirically define P-values is appealing in that parametric distribution assumptions are avoided, although this does come at the cost of assuming the reference sites provide an appropriate comparison group for test sites.     

A Method for Accurate Reconstructions of the Upper Airway Using Magnetic Resonance Images

Objective

The purpose of this study is to provide an optimized method to reconstruct the structure of the upper airway (UA) based on magnetic resonance imaging (MRI) that can faithfully show the anatomical structure with a smooth surface without artificial modifications.

Methods

MRI was performed on the head and neck of a healthy young male participant in the axial, coronal and sagittal planes to acquire images of the UA. The level set method was used to segment the boundary of the UA. The boundaries in the three scanning planes were registered according to the positions of crossing points and anatomical characteristics using a Matlab program. Finally, the three-dimensional (3D) NURBS (Non-Uniform Rational B-Splines) surface of the UA was constructed using the registered boundaries in all three different planes.

Results

A smooth 3D structure of the UA was constructed, which captured the anatomical features from the three anatomical planes, particularly the location of the anterior wall of the nasopharynx. The volume and area of every cross section of the UA can be calculated from the constructed 3D model of UA.

Conclusions

A complete scheme of reconstruction of the UA was proposed, which can be used to measure and evaluate the 3D upper airway accurately.     

PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data

Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns''s disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn''s disease; and one novel association implicating a gene involved in apoptosis pathways in rheumatoid arthritis. We provide software for applying our PUMA analysis framework.     

SCGPred： A Score-based Method for Gene Structure Prediction by Combining Multiple Sources of Evidence

Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly se- quenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http：//bio.scu.edu.cn/SCGPred/.     

A Mathematical Framework for the Selection of an Optimal Set of Peptides for Epitope-Based Vaccines

Epitope-based vaccines (EVs) have a wide range of applications: from therapeuticto prophylactic approaches, from infectious diseases to cancer. The developmentof an EV is based on the knowledge of target-specific antigens from whichimmunogenic peptides, so-called epitopes, are derived. Such epitopes form thekey components of the EV. Due to regulatory, economic, and practical concernsthe number of epitopes that can be included in an EV is limited. Furthermore, asthe major histocompatibility complex (MHC) binding these epitopes is highlypolymorphic, every patient possesses a set of MHC class I and class II moleculesof differing specificities. A peptide combination effective for one person canthus be completely ineffective for another. This renders the optimal selectionof these epitopes an important and interesting optimization problem. In thiswork we present a mathematical framework based on integer linear programming(ILP) that allows the formulation of various flavors of the vaccine designproblem and the efficient identification of optimal sets of epitopes. Out of auser-defined set of predicted or experimentally determined epitopes, theframework selects the set with the maximum likelihood of eliciting a broad andpotent immune response. Our ILP approach allows an elegant and flexibleformulation of numerous variants of the EV design problem. In order todemonstrate this, we show how common immunological requirements for a good EV(e.g., coverage of epitopes from each antigen, coverage of all MHC alleles in aset, or avoidance of epitopes with high mutation rates) can be translated intoconstraints or modifications of the objective function within the ILP framework.An implementation of the algorithm outperforms a simple greedy strategy as wellas a previously suggested evolutionary algorithm and has runtimes on the orderof seconds for typical problem sizes.     

A Mathematical Model of Sickle Cell Genome Frequency in Response to Selective Pressure from Malaria

Because of the relative prevalence of hereditary sickle cell disease and the auxiliary role of the sickle cell gene in reducing the mortality of malaria, it is believed that P. falciparum has exerted selection pressure on human populations to increase the prevalence of this otherwise detrimental gene. A model incorporating three genotypes and two age cohorts is used to test the hypothesis that higher death rates due to malaria can exert selective pressure to increase the prevalence of the sickle cell gene. The model displays selection pressure for the carrier gene in the presence of increasing malaria death rates for either adults or children, showing both higher final frequencies of the gene as well as shortened time to reach these frequencies.     

A Conceptual Framework for Using Mussels as Biomonitors in Whole Effluent Toxicity

As a main source of direct and continuous input of pollutants in the aquatic ecosystem, studying the effects of effluents on receiving ecosystems has a high ecological relevance. While ecological risk assessment procedures are usually based on a chemical-based single component approach, their application for complex mixtures and effluents is less straightforward. A chemical-based approach has to rely on the knowledge of what chemicals are present in effluents, their potential toxicity, how all of these individual chemicals interact and what their individual and combined contribution to the mixture is. Whole effluent toxicity (WET) testing, however, is an integrative tool that measures the toxic effect of an effluent as a whole and accounts for uncharacterized sources of toxicity and for interactions. This paper addresses the use of transplanted bivalves, i.e., caged mussels, as a biomonitoring tool in WET testing with special reference to field situations in both freshwater and marine environments. We indicate how endpoints at different levels of biological organization within exposed mussels can give an integrative overview of effects. Finally, we will provide a framework for future research using mussels and discuss a multitude of instream responses for routine, efficient and cost-effective active biomonitoring applications.     

恶性疟原虫多抗原表位基因的融合与表达

本研究以霍乱毒素B亚基(CT-B)基因为载体,构建了含不同抗原表位的恶性疟原虫的融合基因CTB/ATE和CTB/AWTE。前者除含有恶性疟原虫裂殖子表面主要抗原表位杂合多肽基因SPf66外,还含有很强的T辅助细胞表位CST3和Tc细胞表位;后者在此基础上将我国发现的B细胞表位NKNDD基因经8次串联后融合其中、两种形式的融合基因经测序正确后转入大肠杆菌TK1046中,产量分别为10mg/L及5mg/L。表达产物CTB/AWTE经亲和层析纯化的双抗夹心ELISA测定表明,该融合蛋白在保留了与抗CTB抗体结合的同时,与抗NKNDD单抗的结合效价达1∶8000。     

Fast and Accurate Resonance Assignment of Small-to-Large Proteins by Combining Automated and Manual Approaches

The process of resonance assignment is fundamental to most NMR studies of protein structure and dynamics. Unfortunately, the manual assignment of residues is tedious and time-consuming, and can represent a significant bottleneck for further characterization. Furthermore, while automated approaches have been developed, they are often limited in their accuracy, particularly for larger proteins. Here, we address this by introducing the software COMPASS, which, by combining automated resonance assignment with manual intervention, is able to achieve accuracy approaching that from manual assignments at greatly accelerated speeds. Moreover, by including the option to compensate for isotope shift effects in deuterated proteins, COMPASS is far more accurate for larger proteins than existing automated methods. COMPASS is an open-source project licensed under GNU General Public License and is available for download from http://www.liu.se/forskning/foass/tidigare-foass/patrik-lundstrom/software?l=en. Source code and binaries for Linux, Mac OS X and Microsoft Windows are available.

This is a PLOS Computational Biology Software article.