Pattern-based phylogenetic distance estimation and tree reconstruction

We have developed an alignment-free method that calculates phylogenetic distances using a maximum-likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive comparison of existing alignment-free methods (freely available as Python package decaf+py at http://www.bioinformatics.org.au), we have created a data set of reference trees covering a wide range of phylogenetic distances. Amino acid sequences were evolved along the trees and input to the tested methods; from their calculated distances we infered trees whose topologies we compared to the reference trees. We find our pattern-based method statistically superior to all other tested alignment-free methods. We also demonstrate the general advantage of alignment-free methods over an approach based on automated alignments when sequences violate the assumption of collinearity. Similarly, we compare methods on empirical data from an existing alignment benchmark set that we used to derive reference distances and trees. Our pattern-based approach yields distances that show a linear relationship to reference distances over a substantially longer range than other alignment-free methods. The pattern-based approach outperforms alignment-free methods and its phylogenetic accuracy is statistically indistinguishable from alignment-based distances.

"mind the gap": The size-distance dissociation in visual neglect is a cueing effect

There is a growing body of evidence that the processes mediating the allocation of spatial attention within objects may be separable from those governing attentional distribution between objects. In the neglect literature, a related proposal has been made regarding the perception of (within-object) sizes and (between-object) distances. This proposal follows observations that, in size-matching and bisection tasks, neglect is more strongly expressed when patients are required to attend to the sizes of discrete objects than to the (unfilled) distances between objects. These findings are consistent with a partial dissociation between size and distance processing, but a simpler alternative must also be considered. Whilst a neglect patient may fail to explore the full extent of a solid stimulus, the estimation of an unfilled distance requires that both endpoints be inspected before the task can be attempted at all. The attentional cueing implicit in distance estimation tasks might thus account for their superior performance by neglect patients. We report two bisection studies that address this issue. The first confirmed, amongst patients with left visual neglect, a reliable reduction of rightward error for unfilled gap stimuli as compared with solid lines. The second study assessed the cause of this reduction, deconfounding the effects of stimulus type (lines vs. gaps) and attentional cueing, by applying an explicit cueing manipulation to line and gap bisection tasks. Under these matched cueing conditions, all patients performed similarly on line and gap bisection tasks, suggesting that the reduction of neglect typically observed for gap stimuli may be attributable entirely to cueing effects. We found no evidence that a spatial extent, once fully attended, is judged any differently according to whether it is filled or unfilled.

Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F-0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F-0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (D-LR) appeared to be an effective way to predict whether F-0 immigrants could be identified for a particular pair of populations using a given set of markers.