A two-phase approach for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We recently evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach in delineating breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

Cryptic vicariance in Gulf of California fishes parallels vicariant patterns found in Baja, California mammals and reptiles

Comparisons across multiple taxa can often clarify the histories of biogeographic regions. In particular, historic barriers to movement should affect multiple species and, thus, result in a pattern of concordant intraspecific genetic divisions among species. A striking example of such comparative phylogeography is the recent observation that populations of many small mammals and reptiles living on the Baja, California peninsula have a large genetic break between northern and southern peninsular populations. In the present study, I demonstrate that five species of near-shore fishes living on the Baja coastline of the Gulf of California share this genetic pattern. The simplest explanation for this concordant genetic division within both terrestrial and marine vertebrates is that the Baja peninsula was fragmented by a Plio-Pleistocene marine seaway and that this seaway posed a substantial barrier to movement for near-shore fishes. The genetic divisions within Gulf of California fishes also coincide with recognized biogeographic regions based on fish community composition and several environmental factors. It is likely that adaptation to regional environments and present-day oceanographic circulation limits gene exchange between biogeographic regions and helps maintain evidence of past vicariance.

Differential patterns of Male and Female mtDNA exchange across the Atlantic Ocean in the blue mussel, Mytilus edulis

Comparisons among loci with differing modes of inheritance can reveal unexpected aspects of population history. We employ a multilocus approach to ask whether two types of independently assorting mitochondrial DNAs (maternally and paternally inherited: F- and M-mtDNA) and a nuclear locus (ITS) yield concordant estimates of gene flow and population divergence. The blue mussel, Mytilus edulis, is distributed on both North American and European coastlines and these populations are separated by the waters of the Atlantic Ocean. Gene flow across the Atlantic Ocean differs among loci, with F-mtDNA and ITS showing an imprint of some genetic interchange and M-mtDNA showing no evidence for gene flow. Gene flow of F-mtDNA and ITS causes trans-Atlantic population divergence times to be greatly underestimated for these loci, although a single trans-Atlantic population divergence time (1.2 MYA) can be accommodated by considering all three loci in combination in a coalescent framework. The apparent lack of gene flow for M-mtDNA is not readily explained by different dispersal capacities of male and female mussels. A genetic barrier to M-mtDNA exchange between North American and European mussel populations is likely to explain the observed pattern, perhaps associated with the double uniparental system of mitochondrial DNA inheritance.

What is the Problem of Ad Hoc Hypotheses?

The received view of an ad hoc hypothesis is that it accounts for only the observation(s) it was designed to account for, and so non-adhocness is generally held to be necessary or important for an introduced hypothesis or modification to a theory. Attempts by Popper and several others to convincingly explicate this view, however, prove to be unsuccessful or of doubtful value, and familiar and firmer criteria for evaluating the hypotheses or modified theories so classified are characteristically available. These points are obscured largely because the received view fails to adequately separate psychology from methodology or to recognise ambiguities in the use of 'ad hoc'.

Population subdivision in marine environments: the contributions of biogeography, geographic distance, and discontinuous habitat to genetic differentiation in a blennioid

The relative importance of factors that may promote genetic differentiation in marine organisms is largely unknown. Here, contributions to population structure from biogeography, habitat distribution, and isolation by distance were investigated in Axoclinus nigricaudus, a small subtidal rock reef fish, throughout its range in the Gulf of California. A 408 basepair fragment of the mitochondrial control region was sequenced from 105 individuals. Variation was significantly partitioned between many pairs of populations. Phylogenetic analyses, hierarchical analyses of variance, and general linear models substantiated a major break between two putative biogeographic regions. This genetic discontinuity coincides with an abrupt change in ecological characteristics (including temperature and salinity) but does not coincide with known oceanographic circulation patterns. Geographic distance and the nature of habitat separating populations (continuous habitat along a shoreline, discontinuous habitat along a shoreline, and open water) also contributed to population structure in general linear model analyses. To verify that local populations are genetically stable over time, one population was resampled on four occasions over eighteen months; it showed no evidence of a temporal component to diversity. These results indicate that having a planktonic life stage does not preclude geographically partitioned genetic variation over relatively small geographic distances in marine environments. Moreover, levels of genetic differentiation among populations of Axoclinus nigricaudus cannot be explained by a single factor, but are due to the combined influences of a biogeographic boundary, habitat, and geographic distance.

Sampling phylogenetic tree space with the generalized Gibbs sampler

The generalized Gibbs sampler (GGS) is a recently developed Markov chain Monte Carlo (MCMC) technique that enables Gibbs-like sampling of state spaces that lack a convenient representation in terms of a fixed coordinate system. This paper describes a new sampler, called the tree sampler, which uses the GGS to sample from a state space consisting of phylogenetic trees. The tree sampler is useful for a wide range of phylogenetic applications, including Bayesian, maximum likelihood, and maximum parsimony methods. A fast new algorithm to search for a maximum parsimony phylogeny is presented, using the tree sampler in the context of simulated annealing. The mathematics underlying the algorithm is explained and its time complexity is analyzed. The method is tested on two large data sets consisting of 123 sequences and 500 sequences, respectively. The new algorithm is shown to compare very favorably in terms of speed and accuracy to the program DNAPARS from the PHYLIP package.

A two-phase strategy for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We previously evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach to delineate breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Likelihood-based estimation of the regression model with scrambled responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator outperforms the classical estimators in almost all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.

A context noise model of episodic word recognition

Item noise models of recognition assert that interference at retrieval is generated by the words from the study list. Context noise models of recognition assert that interference at retrieval is generated by the contexts in which the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate how it can account for data from the item noise and dual-processing approaches to recognition memory. From the item noise perspective, list strength and list length effects, the mirror effect for word frequency and concreteness, and the effects of the similarity of other words in a list are considered. From the dual-processing perspective, process dissociation data on the effects of length, temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise approach to recognition is a viable alternative to existing approaches.

The advantages of theft over toil: The design inference and arguing from ignorance

Intelligent design theorist William Dembski has proposed an explanatory filter for distinguishing between events due to chance, lawful regularity or design. We show that if Dembski's filter were adopted as a scientific heuristic, some classical developments in science would not be rational, and that Dembski's assertion that the filter reliably identifies rarefied design requires ignoring the state of background knowledge. If background information changes even slightly, the filter's conclusion will vary wildly. Dembski fails to overcome Hume's objections to arguments from design.

Phylogeny and evolution of anomalous roots in Daviesia (Fabaceae : Mirbelieae)

The phylogeny of the Australian legume genus Daviesia was estimated using sequences of the internal transcribed spacers of nuclear ribosomal DNA. Partial congruence was found with previous analyses using morphology, including strong support for monophyly of the genus and for a sister group relationship between the clade D. pachyloma and the rest of the genus. A previously unplaced bird-pollinated species, anceps + D. D. epiphyllum, was well supported as sister to the only other bird-pollinated species in the genus, D. speciosa, indicating a single origin of bird pollination in their common ancestor. Other morphological groups within Daviesia were not supported and require reassessment. A strong and previously unreported sister clade of Daviesia consists of the two monotypic genera Erichsenia and Viminaria. These share phyllode-like leaves and indehiscent fruits. The evolutionary history of cord roots, which have anomalous secondary thickening, was explored using parsimony. Cord roots are limited to three separate clades but have a complex history involving a small number of gains (most likely 0-3) and losses (0-5). The anomalous structure of cord roots ( adventitious vascular strands embedded in a parenchymatous matrix) may facilitate nutrient storage, and the roots may be contractile. Both functions may be related to a postfire resprouting adaptation. Alternatively, cord roots may be an adaptation to the low-nutrient lateritic soils of Western Australia. However, tests for association between root type, soil type, and growth habit were equivocal, depending on whether the variables were treated as phylogenetically dependent (insignificant) or independent ( significant).