Automated bacterial genome analysis and annotation

More than 300 bacterial genome sequences are publicly available, and many more are scheduled to be completed and released in the near future. Converting this raw sequence information into a better understanding of the biology of bacteria involves the identification and annotation of genes, proteins and pathways. This processing is typically done using sequence annotation pipelines comprised of a variety of software modules and, in some cases, human experts. The reference databases, computational methods and knowledge that form the basis of these pipelines are constantly evolving, and thus there is a need to reprocess genome annotations on a regular basis. The combined challenge of revising existing annotations and extracting useful information from the flood of new genome sequences will necessitate more reliance on completely automated systems.     

Water cost savings from soil biochar amendment: A spatial analysis

While a large body of literature exists on the ability of biochar to retain water and nutrients, little research exists connecting these benefits to biochar qualities, water retention mechanisms, and optimal locations for agricultural use. More information is needed for biochar stakeholders to make informed decisions about where deployment should occur. Specifically, we need to know the biochar characteristics that drive changes in soil water properties, how these effects vary geographically, and what financial benefits farmers can expect in their specific region to identify where biochar can be deployed for optimal results. We conducted a meta‐analysis of the relationship between biochar properties, application rates and observed change in water holding capacity (WHC) as a function of soil texture. Then we mapped our results across US counties to determine where biochar application could yield the largest improvements in soil water properties and added an economic model designed to predict how biochar‐driven changes in soil WHC drive irrigation expenses. Limited data drove our focus to sandy soils, and among these locations, our results suggest that biochar application will be especially effective in the southeast, far north and northeast, and western United States. In a prototype application of our model we predict a 37% reduction in irrigated water use for an instrumented site in Nebraska. Our combined statistical and economic models will be useful for future field experiment proposals, farmers purchasing biochar, and decision makers working to incentivize agricultural advances.     

A parametric copula model for analysis of familial binary data

Modeling the joint distribution of a binary trait (disease) within families is a tedious challenge, owing to the lack of a general statistical model with desirable properties such as the multivariate Gaussian model for a quantitative trait. Models have been proposed that either assume the existence of an underlying liability variable, the reality of which cannot be checked, or provide estimates of aggregation parameters that are dependent on the ordering of family members and on family size. We describe how a class of copula models for the analysis of exchangeable categorical data can be incorporated into a familial framework. In this class of models, the joint distribution of binary outcomes is characterized by a function of the given marginals. This function, referred to as a "copula," depends on an aggregation parameter that is weakly dependent on the marginal distributions. We propose to decompose a nuclear family into two sets of equicorrelated data (parents and offspring), each of which is characterized by an aggregation parameter (alphaFM and alphaSS, respectively). The marginal probabilities are modeled through a logistic representation. The advantage of this model is that it provides estimates of the aggregation parameters that are independent of family size and does not require any arbitrary ordering of sibs. It can be incorporated easily into segregation or combined segregation-linkage analysis and does not require extensive computer time. As an illustration, we applied this model to a combined segregation-linkage analysis of levels of plasma angiotensin I-converting enzyme (ACE) dichotomized into two classes according to the median. The conclusions of this analysis were very similar to those we had reported in an earlier familial analysis of quantitative ACE levels.     

Aging, evolution and individual health span: introduction

The effect of autologous and environmental wear and tear on genes and gene products will be considered in systems renewed by self-replicating stem and precursor cells as well as in systems that do not have the capacity for self-renewal. Aging will be discussed in terms of speciation and in terms of health-span differences between individuals of the same species. The analysis of speciation involves early as well as late-acting genes. The intraspecies analysis is primarily concerned with the second half of life and thus is not affected by selective pressures. Analysis of individual aging is concerned with health span, qualified by identification of the particular system that is responsible for the limit of health span in subpopulations; it depends on a subset of allelic products of many genes and their relative functional capacity. The identification of such alleles can provide the starting point for reverse genetics. Results will be presented, which have been obtained by analysis of age-related events in inbred mice, where regulation, involved in degenerative disease of old age, can be studied in groups of individuals with a relatively constant genetic background. It should be possible to identify appropriate probes for degenerative diseases of old age that can be used for detection of corresponding human genes. Worldwide demographic changes have created the need for a new type of public health policy. To respond to this need, we should learn how to identify individuals at risk from degenerative diseases of old age and how to treat them preventively.     

Actions,Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized

Behavioral evidence suggests that instrumental conditioning is governed by two forms of action control: a goal-directed and a habit learning process. Model-based reinforcement learning (RL) has been argued to underlie the goal-directed process; however, the way in which it interacts with habits and the structure of the habitual process has remained unclear. According to a flat architecture, the habitual process corresponds to model-free RL, and its interaction with the goal-directed process is coordinated by an external arbitration mechanism. Alternatively, the interaction between these systems has recently been argued to be hierarchical, such that the formation of action sequences underlies habit learning and a goal-directed process selects between goal-directed actions and habitual sequences of actions to reach the goal. Here we used a two-stage decision-making task to test predictions from these accounts. The hierarchical account predicts that, because they are tied to each other as an action sequence, selecting a habitual action in the first stage will be followed by a habitual action in the second stage, whereas the flat account predicts that the statuses of the first and second stage actions are independent of each other. We found, based on subjects'' choices and reaction times, that human subjects combined single actions to build action sequences and that the formation of such action sequences was sufficient to explain habitual actions. Furthermore, based on Bayesian model comparison, a family of hierarchical RL models, assuming a hierarchical interaction between habit and goal-directed processes, provided a better fit of the subjects'' behavior than a family of flat models. Although these findings do not rule out all possible model-free accounts of instrumental conditioning, they do show such accounts are not necessary to explain habitual actions and provide a new basis for understanding how goal-directed and habitual action control interact.     

Linkage analysis versus association analysis: distinguishing between two models that explain disease-marker associations.

Human genetics researchers have been intrigued for many years by weak-to-moderate associations between markers and diseases. However, in most cases of association, the cause of this phenomenon is still not known. Recently, interest has grown in pursuing association studies for complex diseases, either instead of or in addition to linkage studies. Hence, it is timely to reconsider what a disease-marker association, particularly in the weak-to-moderate range (relative risk < 10), can tell us about disease etiology. To this end, this study accomplishes three aims: (1) It formulates two different models explaining weak-to-moderate associations and derives the relationship between them. One is a linkage disequilibrium model, and the other is a "susceptibility," or pure association, model. The importance of drawing the distinction between these two models and the implications for our understanding of the genetics of human disease will also be discussed. It will be argued that the linkage disequilibrium model represents true linkage but that the susceptibility model does not. (2) It examines two family-based association tests proposed recently by Parsian et al. and Spielman et al. and derives formulas for their behavior under the two models described above. It demonstrates that these tests yield almost identical results under these two models. It shows that, whereas these tests can confirm an association, they cannot determine whether the association is caused by the linkage disequilibrium model or the susceptibility model. The study also characterizes the probabilities yielded by the family association tests in the presence of weak-to-moderate associations, which will aid researchers using these tests. (3) It proposes two approaches, both based on linkage analysis, which can distinguish between the two models described above. One approach involves a straightforward linkage analysis of the data; the other involves a partitioned association-linkage (PAL) test, as suggested by Greenberg. Formulas are derived for testing identity by descent in affected sib pairs by using both approaches. (4) Finally, the formulas and arguments are illustrated with two examples from the literature and one computer-simulated data set.     

Reducing the exome search space for Mendelian diseases using genetic linkage analysis of exome genotypes

Many exome sequencing studies of Mendelian disorders fail to optimally exploit family information. Classical genetic linkage analysis is an effective method for eliminating a large fraction of the candidate causal variants discovered, even in small families that lack a unique linkage peak. We demonstrate that accurate genetic linkage mapping can be performed using SNP genotypes extracted from exome data, removing the need for separate array-based genotyping. We provide software to facilitate such analyses.     

Towards an understanding of the genetics of human male infertility: lessons from flies

It has been argued that about 4–5% of male adults suffer from infertility due to a genetic causation. From studies in the fruitfly Drosophila, there is evidence that up to 1500 recessive genes contribute to male fertility in that species. Here we suggest that the control of human male fertility is of at least comparable genetic complexity. However, because of small family size, conventional positional cloning methods for identifying human genes will have little impact on the dissection of male infertility. A critical selection of well-defined infertility phenotypes in model organisms, combined with identification of the genes involved and their orthologues in man, might reveal the genes that contribute to human male infertility.     

Optimal Prediction of Moving Sound Source Direction in the Owl

Capturing nature’s statistical structure in behavioral responses is at the core of the ability to function adaptively in the environment. Bayesian statistical inference describes how sensory and prior information can be combined optimally to guide behavior. An outstanding open question of how neural coding supports Bayesian inference includes how sensory cues are optimally integrated over time. Here we address what neural response properties allow a neural system to perform Bayesian prediction, i.e., predicting where a source will be in the near future given sensory information and prior assumptions. The work here shows that the population vector decoder will perform Bayesian prediction when the receptive fields of the neurons encode the target dynamics with shifting receptive fields. We test the model using the system that underlies sound localization in barn owls. Neurons in the owl’s midbrain show shifting receptive fields for moving sources that are consistent with the predictions of the model. We predict that neural populations can be specialized to represent the statistics of dynamic stimuli to allow for a vector read-out of Bayes-optimal predictions.     

Rheumatoid arthritis viewed using a headache paradigm

Results and new hypotheses in animal models often stimulate development of new paradigms in how we view rheumatoid arthritis (RA). The complexity of RA does, however, eventually lead to the rejection of these hypotheses. Here, it is argued that the large number of so-far described animal models, when taken together, also reveals a complex disease. Fortunately, detailed study of each of the animal models will reveal this complexity, and may also be helpful in elucidating the complexity of the human disease. Benoist and Mathis [1] recently contributed a new animal model in which an autoimmune response to a ubiquitous antigen leads to an antibody-mediated inflammatory attack in the joints. It is argued that this new model, as with other animal models, is unlikely to explain RA, but it will add to the tools available to reveal the complexity of RA.     

Pathways and Networks-Based Analysis of Candidate Genes Associated with Nicotine Addiction

Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction.     

Biological weapons, genetics and social analysis: emerging responses, emerging issues--I

Recent terrorist attacks in the USA have generated significant attention in many countries to the threats posed by biological weapons. In response to these events and the spectre of future attacks, bioscientists and professional organizations have begun or intensified asking questions about the possible malign applications of their research. As Part I of a two-part article, this paper surveys how genetics might contribute to the development of novel forms of weaponry. It is further argued that the dilemmas and difficulties facing bioscientists pose pressing and thorny questions for the hitherto agendas and orientations of those concerned with the social, ethical and political implications of genetics. Part II will examine the emerging responses initiated by biomedical organizations and spokespersons in the US and the UK. This will be done with a view to asking how scientific and medical research communities are defining and policing notions of professionalism, responsibility and accountability. On the basis of this, suggested lines for future social analysis will be offered.     

水稻SBP基因家族的生物信息学分析(英文)

SQUAMOSA PROMOTER BINDING PROTEIN-LIKE(SBP)转录因子家族是植物特有的一类转录因子。本文确定了20水稻基因组上编码的SBP基因。通过分类,染色体定位,保守区确定,亲缘关系,以及水稻SBP家族中的重复基因及该家族成员形成蛋白二聚体的可能性进行分析,其次利用了Affymetrix水稻基因组芯片数据,对所有这些基因的表达谱进行了分析。结果表明,水稻SBP基因在花和种子的发育过程中可能发挥重要作用,而其对环境胁迫却不敏感。这对进一步研究SBP的功能提供了有价值的线索和思路。     

Perturbations to uncover gene networks

After the major achievements of the DNA sequencing projects, an equally important challenge now is to uncover the functional relationships among genes (i.e. gene networks). It has become increasingly clear that computational algorithms are crucial for extracting meaningful information from the massive amount of data generated by high-throughput genome-wide technologies. Here, we summarise how systems identification algorithms, originating from physics and control theory, have been adapted for use in biology. We also explain how experimental perturbations combined with genome-wide measurements are being used to uncover gene networks. Perturbation techniques could pave the way for identifying gene networks in more complex settings such as multifactorial diseases and for improving the efficacy of drug evaluation.     

Optimal strategies for mapping complex diseases in the presence of multiple loci.

Recent advances in genome technology have led to mapping and subsequent isolation, by positional cloning, of a number of genes for common and/or complex human diseases. It therefore will be possible to utilize information about a known locus in the search for additional, perhaps less penetrant, genes for a particular disease. It is also unclear, under these situations, what the optimal sampling strategy should be. To address these questions, we have calculated the expected LOD score for localizing one locus in a variety of two-locus models of disease, for four different pedigree structures, and under three different scenarios regarding knowledge/testing of one of the two loci. These design considerations are evaluated by use of a cost function that incorporates the costs of ascertaining different family structures, the relative costs of genotyping and mutation testing family members, and the amount of information provided by each family structure and testing scenario. The results indicate that, in most cases, affected sib pairs are a particularly poor strategy, especially when linkage or mutation data are available at the known locus. We also demonstrate that prescreening the sample of families for mutations at known susceptibility loci is, in general, a cost-effective strategy.     

The Arabidopsis Information Resource (TAIR): a comprehensive database and web-based information retrieval, analysis, and visualization system for a model plant

Arabidopsis thaliana, a small annual plant belonging to the mustard family, is the subject of study by an estimated 7000 researchers around the world. In addition to the large body of genetic, physiological and biochemical data gathered for this plant, it will be the first higher plant genome to be completely sequenced, with completion expected at the end of the year 2000. The sequencing effort has been coordinated by an international collaboration, the Arabidopsis Genome Initiative (AGI). The rationale for intensive investigation of Arabidopsis is that it is an excellent model for higher plants. In order to maximize use of the knowledge gained about this plant, there is a need for a comprehensive database and information retrieval and analysis system that will provide user-friendly access to Arabidopsis information. This paper describes the initial steps we have taken toward realizing these goals in a project called The Arabidopsis Information Resource (TAIR) (www.arabidopsis.org).     

The existence conditions for bacterial plasmids: Theory and reality

Bacteria abound with conjugative and nonconjugative plasmids that often carry genes determining a number of environmental adaptations. Plasmids may also encode genes that enable them to transmit themselves infectiously to new host cells, by conjugation or mobilization. The question of whether plasmids can be maintained in a bacterial community as parasitic DNA, that is, while conferring a selective disadvantage to their host, serves as a basic hypothesis in theoretical studies of the population biology of plasmids. The conditions necessary for the establishment and maintenance of plasmids have been determined analytically for the simplest possible models. Based on these a priori conditions, on some reconsiderations and extensions of these models, and on recent estimates of transfer rates of liquid and surface bacterial populations, it will be argued that within a bacterial population, a parasitic lifestyle is unlikely for most naturally occurring plasmids. This result raises anew the problem of how cryptic plasmids are maintained and why plasmids encode costly and elaborate genes for horizontal transfer.     

Hierarchical state-space estimation of leatherback turtle navigation ability

Remotely sensed tracking technology has revealed remarkable migration patterns that were previously unknown; however, models to optimally use such data have developed more slowly. Here, we present a hierarchical Bayes state-space framework that allows us to combine tracking data from a collection of animals and make inferences at both individual and broader levels. We formulate models that allow the navigation ability of animals to be estimated and demonstrate how information can be combined over many animals to allow improved estimation. We also show how formal hypothesis testing regarding navigation ability can easily be accomplished in this framework. Using Argos satellite tracking data from 14 leatherback turtles, 7 males and 7 females, during their southward migration from Nova Scotia, Canada, we find that the circle of confusion (the radius around an animal's location within which it is unable to determine its location precisely) is approximately 96 km. This estimate suggests that the turtles' navigation does not need to be highly accurate, especially if they are able to use more reliable cues as they near their destination. Moreover, for the 14 turtles examined, there is little evidence to suggest that male and female navigation abilities differ. Because of the minimal assumptions made about the movement process, our approach can be used to estimate and compare navigation ability for many migratory species that are able to carry electronic tracking devices.     

New feature subset selection procedures for classification of expression profiles

Methods for extracting useful information from the datasets produced by microarray experiments are at present of much interest. Here we present new methods for finding gene sets that are well suited for distinguishing experiment classes, such as healthy versus diseased tissues. Our methods are based on evaluating genes in pairs and evaluating how well a pair in combination distinguishes two experiment classes. We tested the ability of our pair-based methods to select gene sets that generalize the differences between experiment classes and compared the performance relative to two standard methods. To assess the ability to generalize class differences, we studied how well the gene sets we select are suited for learning a classifier. We show that the gene sets selected by our methods outperform the standard methods, in some cases by a large margin, in terms of cross-validation prediction accuracy of the learned classifier. We show that on two public datasets, accurate diagnoses can be made using only 15-30 genes. Our results have implications for how to select marker genes and how many gene measurements are needed for diagnostic purposes. When looking for differential expression between experiment classes, it may not be sufficient to look at each gene in a separate universe. Evaluating combinations of genes reveals interesting information that will not be discovered otherwise. Our results show that class prediction can be improved by taking advantage of this extra information.