Expanding the diversity of mycobacteriophages: Insights into genome architecture and evolution

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists. © 2011 Hatfull et al

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Optimal allocation of high dimensional assets through canonical vines

Abstract. The widely used mean-variance criteria is actually not the optimal solution for asset allocation as the joint distribution of asset returns are distributed in asymmetric ways rather than in the assumed normal distribution. It is a computationally challenging task to model the asymmetries and skewness of joint distributions of returns in high dimensional space due to their own complicated structural complexities. This paper proposes to use a new form of canonical vine to produce the complex joint distribution of asset returns. Then, we use the utility function of Constant Relative Risk Aversion to determine the optimal allocation of the assets. The importance of using the asymmetries information is assessed by comparing the performance of a portfolio based on the mean-variance criteria and that of a portfolio based on the new canonical vine. The results show that the investors using the forecasts of these asymmetries can make better portfolio decisions than those who ignore the asymmetries information. © Springer-Verlag 2013

Strategic asset allocation by mixing shrinkage, vine copula and market equilibrium

We propose a new portfolio optimization method combining the merits of the shrinkage estimation, vine copula structure, and Black–Litterman model. It is useful for many investors to satisfy simultaneously the three investment objectives: estimation sensitivity, asymmetric risks appreciation, and portfolio stability. A typical investor with such objectives is a sovereign wealth fund (SWF). We use China's SWF as an example to empirically test the method based on a 15-asset strategic asset allocation problem. Robustness tests using subsamples not only show the method's overall effectiveness but also manifest that the function of each component is as expected

Canonical Vine Copulas in the Context of Modern Portfolio Management: Are They Worth it?

In the context of managing downside correlations, we examine the use of multi-dimensional elliptical and asymmetric copula models to forecast returns for portfolios with 3-12 constituents. Our analysis assumes that investors have no short-sales constraints and a utility function characterized by the min imization of Conditional Value-at-Risk (CVaR). We examine the efficient frontiers produced by each model and focus on comparing two methods for incorporating scalable asymmetric dependence (AD) structures across asset returns using the Archimedean Clayton copula in an out-of-sample, long-run multi-period setting. For portfolios of higher dimensions, we find that modelling asymmetries within the marginals and the dependence structure with the Clayton canonical vine copula (CVC) consistently produces the highest-ranked outcomes across a range of statistical and economic metrics when com pared to other models incorporating elliptical or symmetric dependence structures. Accordingly, we conclude that CVC copulas are 'worth it' when managing larger portfolios

Tail dependence functions and vine copulas

Tail dependence and conditional tail dependence functions describe, respectively, the tail probabilities and conditional tail probabilities of a copula at various relative scales. The properties as well as the interplay of these two functions are established based upon their homogeneous structures. The extremal dependence of a copula, as described by its extreme value copulas, is shown to be completely determined by its tail dependence functions. For a vine copula built from a set of bivariate copulas, its tail dependence function can be expressed recursively by the tail dependence and conditional tail dependence functions of lower-dimensional margins. The effect of tail dependence of bivariate linking copulas on that of a vine copula is also investigated