REAPR: a universal tool for genome assembly evaluation.

Methods to reliably assess the accuracy of genome sequence data are lacking. Currently completeness is only described qualitatively and mis-assemblies are overlooked. Here we present REAPR, a tool that precisely identifies errors in genome assemblies without the need for a reference sequence. We have validated REAPR on complete genomes or de novo assemblies from bacteria, malaria and Caenorhabditis elegans, and demonstrate that 86% and 82% of the human and mouse reference genomes are error-free, respectively. When applied to an ongoing genome project, REAPR provides corrected assembly statistics allowing the quantitative comparison of multiple assemblies. REAPR is available at http://www.sanger.ac.uk/resources/software/reapr/

Interpreting the genomic landscape of introgression.

Introgression, the transfer of genetic material between species through hybridisation, occurs in many taxa and has important consequences. Genomic studies allow us to characterise the landscape of introgression across the genome, shedding light on both its adaptive benefits and the incompatibilities that help to maintain species barriers. Studies taking a genome-wide view suggest that adaptive introgression may be common, but that introgressed variation between many species is selected against throughout much of the genome. Confounding factors can complicate interpretations from these data, and computational simulations have proved vital to illustrate expected patterns under different scenarios. Future developments will move beyond correlative evidence to explicit models that account for how selection and genetic drift influence introgressed variation

A comprehensive evaluation of assembly scaffolding tools

Background: Genome assembly is typically a two-stage process: contig assembly followed by the use of paired sequencing reads to join contigs into scaffolds. Scaffolds are usually the focus of reported assembly statistics; longer scaffolds greatly facilitate the use of genome sequences in downstream analyses, and it is appealing to present larger numbers as metrics of assembly performance. However, scaffolds are highly prone to errors, especially when generated using short reads, which can directly result in inflated assembly statistics. Results: Here we provide the first independent evaluation of scaffolding tools for second-generation sequencing data. We find large variations in the quality of results depending on the tool and dataset used. Even extremely simple test cases of perfect input, constructed to elucidate the behaviour of each algorithm, produced some surprising results. We further dissect the performance of the scaffolders using real and simulated sequencing data derived from the genomes of Staphylococcus aureus, Rhodobacter sphaeroides, Plasmodium falciparum and Homo sapiens. The results from simulated data are of high quality, with several of the tools producing perfect output. However, at least 10% of joins remains unidentified when using real data. Conclusions: The scaffolders vary in their usability, speed and number of correct and missed joins made between contigs. Results from real data highlight opportunities for further improvements of the tools. Overall, SGA, SOPRA and SSPACE generally outperform the other tools on our datasets. However, the quality of the results is highly dependent on the read mapper and genome complexity

Microwave properties of an inhomogeneous optically illuminated plasma in a microstrip gap

The optical illumination of a microstrip gap on a thick semiconductor substrate creates an inhomogeneous electron-hole plasma in the gap region. This allows the study of the propagation mechanism through the plasma region. This paper uses a multilayer plasma model to explain the origin of high losses in such structures. Measured results are shown up to 50 GHz and show good agreement with the simulated multilayer model. The model also allows the estimation of certain key parameters of the plasma, such as carrier density and diffusion length, which are difficult to measure by direct means. The detailed model validation performed here will enable the design of more complex microwave structures based on this architecture. While this paper focuses on monocrystalline silicon as the substrate, the model is easily adaptable to other semiconductor materials such as GaAs

The WiggleZ Dark Energy Survey: testing the cosmological model with baryon acoustic oscillations at z = 0.6

We measure the imprint of baryon acoustic oscillations (BAOs) in the galaxy clustering pattern at the highest redshift achieved to date, z= 0.6, using the distribution of N= 132 509 emission-line galaxies in the WiggleZ Dark Energy Survey. We quantify BAOs using three statistics: the galaxy correlation function, power spectrum and the band-filtered estimator introduced by Xu et al. The results are mutually consistent, corresponding to a 4.0 per cent measurement of the cosmic distance–redshift relation at z= 0.6 [in terms of the acoustic parameter ‘A(z)’ introduced by Eisenstein et al., we find A(z= 0.6) = 0.452 ± 0.018]. Both BAOs and power spectrum shape information contribute towards these constraints. The statistical significance of the detection of the acoustic peak in the correlation function, relative to a wiggle-free model, is 3.2σ. The ratios of our distance measurements to those obtained using BAOs in the distribution of luminous red galaxies at redshifts z= 0.2 and 0.35 are consistent with a flat Λ cold dark matter model that also provides a good fit to the pattern of observed fluctuations in the cosmic microwave background radiation. The addition of the current WiggleZ data results in a ≈30 per cent improvement in the measurement accuracy of a constant equation of state, w, using BAO data alone. Based solely on geometric BAO distance ratios, accelerating expansion (w < −1/3) is required with a probability of 99.8 per cent, providing a consistency check of conclusions based on supernovae observations. Further improvements in cosmological constraints will result when the WiggleZ survey data set is complete

Impact ionisation electroluminescence in planar GaAs-based heterostructure Gunn diodes:Spatial distribution and impact of doping nonuniformities

When biased in the negative differential resistance regime, electroluminescence (EL) is emitted from planar GaAs heterostructure Gunn diodes. This EL is due to the recombination of electrons in the device channel with holes that are generated by impact ionisation when the Gunn domains reach the anode edge. The EL forms non-uniform patterns whose intensity shows short-range intensity variations in the direction parallel to the contacts and decreases along the device channel towards the cathode. This paper employs Monte Carlo models, in conjunction with the experimental data, to analyse these non-uniform EL patterns and to study the carrier dynamics responsible for them. It is found that the short-range lateral (i.e., parallel to the device contacts) EL patterns are probably due to non-uniformities in the doping of the anode contact, illustrating the usefulness of EL analysis on the detection of such inhomogeneities. The overall decreasing EL intensity towards the anode is also discussed in terms of the interaction of holes with the time-dependent electric field due to the transit of the Gunn domains. Due to their lower relative mobility and the low electric field outside of the Gunn domain, freshly generated holes remain close to the anode until the arrival of a new domain accelerates them towards the cathode. When the average over the transit of several Gunn domains is considered, this results in a higher hole density, and hence a higher EL intensity, next to the anode

On the analogy of quantum wave-particle duality with bouncing droplets

We explore the hydrodynamic analogues of quantum wave-particle duality in the context of a bouncing droplet system which we model in such a way as to promote comparisons to the de Broglie-Bohm interpretation of quantum mechanics. Through numerical means we obtain single-slit diffraction and double-slit interference patterns that strongly resemble those reported in experiment and that reflect a striking resemblance to quantum diffraction and interference on a phenomenological level. We, however, identify evident differences from quantum mechanics which arise from the governing equations at the fundamental level.Comment: 8 pages, 7 figure

Recombination rate variation shapes barriers to introgression across butterfly genomes.

Hybridisation and introgression can dramatically alter the relationships among groups of species, leading to phylogenetic discordance across the genome and between populations. Introgression can also erode species differences over time, but selection against introgression at certain loci acts to maintain postmating species barriers. Theory predicts that species barriers made up of many loci throughout the genome should lead to a broad correlation between introgression and recombination rate, which determines the extent to which selection on deleterious foreign alleles will affect neutral alleles at physically linked loci. Here, we describe the variation in genealogical relationships across the genome among three species of Heliconius butterflies: H. melpomene (mel), H. cydno (cyd), and H. timareta (tim), using whole genomes of 92 individuals, and ask whether this variation can be explained by heterogeneous barriers to introgression. We find that species relationships vary predictably at the chromosomal scale. By quantifying recombination rate and admixture proportions, we then show that rates of introgression are predicted by variation in recombination rate. This implies that species barriers are highly polygenic, with selection acting against introgressed alleles across most of the genome. In addition, long chromosomes, which have lower recombination rates, produce stronger barriers on average than short chromosomes. Finally, we find a consistent difference between two species pairs on either side of the Andes, which suggests differences in the architecture of the species barriers. Our findings illustrate how the combined effects of hybridisation, recombination, and natural selection, acting at multitudes of loci over long periods, can dramatically sculpt the phylogenetic relationships among species

Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS

A common feature of many magnetic resonance image (MRI) data processing methods is the voxel-by-voxel (a voxel is a volume element) manner in which the processing is performed. In general, however, MRI data are expected to exhibit some level of spatial correlation, rendering an independent-voxels treatment inefficient in its use of the data. Bayesian random effect models are expected to be more efficient owing to their information-borrowing behaviour. To illustrate the Bayesian random effects approach, this paper outlines a Markov chain Monte Carlo (MCMC) analysis of a perfusion MRI dataset, implemented in R using the BRugs package. BRugs provides an interface to WinBUGS and its GeoBUGS add-on. WinBUGS is a widely used programme for performing MCMC analyses, with a focus on Bayesian random effect models. A simultaneous modeling of both voxels (restricted to a region of interest) and multiple subjects is demonstrated. Despite the low signal-to-noise ratio in the magnetic resonance signal intensity data, useful model signal intensity profiles are obtained. The merits of random effects modeling are discussed in comparison with the alternative approaches based on region-of-interest averaging and repeated independent voxels analysis. This paper focuses on perfusion MRI for the purpose of illustration, the main proposition being that random effects modeling is expected to be beneficial in many other MRI applications in which the signal-to-noise ratio is a limiting factor