A MOSAIC of methods: Improving ortholog detection through integration of algorithmic diversity

Ortholog detection (OD) is a critical step for comparative genomic analysis of protein-coding sequences. In this paper, we begin with a comprehensive comparison of four popular, methodologically diverse OD methods: MultiParanoid, Blat, Multiz, and OMA. In head-to-head comparisons, these methods are shown to significantly outperform one another 12-30% of the time. This high complementarity motivates the presentation of the first tool for integrating methodologically diverse OD methods. We term this program MOSAIC, or Multiple Orthologous Sequence Analysis and Integration by Cluster optimization. Relative to component and competing methods, we demonstrate that MOSAIC more than quintuples the number of alignments for which all species are present, while simultaneously maintaining or improving functional-, phylogenetic-, and sequence identity-based measures of ortholog quality. Further, we demonstrate that this improvement in alignment quality yields 40-280% more confidently aligned sites. Combined, these factors translate to higher estimated levels of overall conservation, while at the same time allowing for the detection of up to 180% more positively selected sites. MOSAIC is available as python package. MOSAIC alignments, source code, and full documentation are available at http://pythonhosted.org/bio-MOSAIC

Using stock returns to identify government spending shocks

This paper explores a new approach to identifying government spending shocks which avoids many of the shortcomings of existing approaches. The new approach is to identify government spending shocks with statistical innovations to the accumulated excess returns of large US military contractors. This strategy is used to estimate the dynamic responses of output, hours, consumption and real wages to a government spending shock. We find that positive government spending shocks are associated with increases in output, hours, and consumption. Real wages initially decline after a government spending shock and then rise after a year. We estimate the government spending multiplier associated with increases in military spending to be about 0.6 over a horizon of 5 years.Fiscal policy ; Government spending policy ; Stocks

High energy flare physics group summary

The contributions of the High Energy Flare Physics Special Session in the American Astronomical Society Solar Physics Division Meeting are reviewed. Oral and poster papers were presented on observatories and instruments available for the upcoming solar maximum. Among these are the space-based Gamma Ray Observatory, the Solar Flare and Cosmic Burst Gamma Ray Experiment on the Ulysses spacecraft, the Soft X Ray Telescope on the spacecraft Solar-A, and the balloon-based Gamma Ray Imaging Device. Ground based observatories with new capabilities include the BIMA mm-wave interferometer (Univ. of California, Berkeley; Univ. of Illinois; Univ. of Maryland), Owens Valley Radio Observatory and the Very Large Array. The highlights of the various instrument performances are reported and potential data correlations and collaborations are suggested

Design of two-dimensional particle assemblies using isotropic pair interactions with an attractive well

Using ground-state and relative-entropy based inverse design strategies, isotropic interactions with an attractive well are determined to stabilize and promote as- sembly of particles into two-dimensional square, honeycomb, and kagome lattices. The design rules inferred from these results are discussed and validated in the dis- covery of interactions that favor assembly of the highly open truncated-square and truncated-hexagonal lattices.Comment: 11 pages, 5 figures and supplemental materia

Mixtures of Common Skew-t Factor Analyzers

A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data. By assuming common component factor loadings, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well-modelled by the mixtures of factor analyzers model or a variant thereof. Furthermore, assuming that the component densities follow a skew-t distribution allows robust clustering of skewed data. The alternating expectation-conditional maximization algorithm is employed for parameter estimation. We demonstrate excellent clustering performance when our model is applied to real and simulated data.This paper marks the first time that skewed common factors have been used