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

Population Genetics of Rare Variants and Complex Diseases

Identifying drivers of complex traits from the noisy signals of genetic variation obtained from high throughput genome sequencing technologies is a central challenge faced by human geneticists today. We hypothesize that the variants involved in complex diseases are likely to exhibit non-neutral evolutionary signatures. Uncovering the evolutionary history of all variants is therefore of intrinsic interest for complex disease research. However, doing so necessitates the simultaneous elucidation of the targets of natural selection and population-specific demographic history. Here we characterize the action of natural selection operating across complex disease categories, and use population genetic simulations to evaluate the expected patterns of genetic variation in large samples. We focus on populations that have experienced historical bottlenecks followed by explosive growth (consistent with most human populations), and describe the differences between evolutionarily deleterious mutations and those that are neutral. Genes associated with several complex disease categories exhibit stronger signatures of purifying selection than non-disease genes. In addition, loci identified through genome-wide association studies of complex traits also exhibit signatures consistent with being in regions recurrently targeted by purifying selection. Through simulations, we show that population bottlenecks and rapid growth enables deleterious rare variants to persist at low frequencies just as long as neutral variants, but low frequency and common variants tend to be much younger than neutral variants. This has resulted in a large proportion of modern-day rare alleles that have a deleterious effect on function, and that potentially contribute to disease susceptibility.Comment: 36 pages, 7 figure

Exact Level And Power Of Permutation, Bootstrap, And Asymptotic Tests Of Trend

We develop computational tools that can evaluate the exact size and power of three tests of trend (e.g., permutation, bootstrap and asymptotic) without resorting to large-sample theory or simulations. We then use these tools to compare the operating characteristics of the three tests. It is seen that the bootstrap test is ultra-conservative relative to the other two tests and as a result suffers from a severe deterioration in power. The power of the asymptotic test is uniformly larger than that of the other two tests, but it fails to preserve the Type I error for most of the range of the baseline response probability. The permutation test, being exact, is guaranteed to preserve the Type I error throughout the range of the baseline response probability. The price paid for this guarantee is a loss of power relative to the asymptotic test. The power loss is, however, small in most situations

Frame, metric and geodesic evolution in shape-changing nematic shells.

Non-uniform director fields in flat, responsive, glassy nematic sheets lead to the induction of shells with non-trivial topography on the application of light or heat. Contraction along the director causes metric change, with, in general, the induction of Gaussian curvature, that drives the topography change. We describe the metric change, the evolution of the director field, and the transformation of reference state material curves, e.g. spirals into radii, as curvature develops. The non-isometric deformations associated with heat or light change the geodesics of the surface, intriguingly even in regions where no Gaussian curvature results

Rod photoreceptors drive circadian photoentrainment across a wide range of light intensities.

In mammals, synchronization of the circadian pacemaker in the hypothalamus is achieved through direct input from the eyes conveyed by intrinsically photosensitive retinal ganglion cells (ipRGCs). Circadian photoentrainment can be maintained by rod and cone photoreceptors, but their functional contributions and their retinal circuits that impinge on ipRGCs are not well understood. Using mice that lack functional rods or in which rods are the only functional photoreceptors, we found that rods were solely responsible for photoentrainment at scotopic light intensities. Rods were also capable of driving circadian photoentrainment at photopic intensities at which they were incapable of supporting a visually guided behavior. Using mice in which cone photoreceptors were ablated, we found that rods signal through cones at high light intensities, but not at low light intensities. Thus, rods use two distinct retinal circuits to drive ipRGC function to support circadian photoentrainment across a wide range of light intensities

Efficiency of free energy calculations of spin lattices by spectral quantum algorithms

Quantum algorithms are well-suited to calculate estimates of the energy spectra for spin lattice systems. These algorithms are based on the efficient calculation of the discrete Fourier components of the density of states. The efficiency of these algorithms in calculating the free energy per spin of general spin lattices to bounded error is examined. We find that the number of Fourier components required to bound the error in the free energy due to the broadening of the density of states scales polynomially with the number of spins in the lattice. However, the precision with which the Fourier components must be calculated is found to be an exponential function of the system size.Comment: 9 pages, 4 figures; corrected typographical and minor mathematical error