Sequence weighting methods have been used to reduce redundancy and emphasize diversity in multiple sequence alignment and searching applications. Each of these methods is based on a notion of distance between a sequence and an ancestral or generalized sequence. We describe a different approach, which bases weights on the diversity observed at each position in the alignment, rather than on a sequence distance measure. These position-based weights make minimal assumptions, are simple to compute, and perform well in comprehensive evaluations. Redundancy is a common feature of sequence databanks, where a typical gene or protein family is represented by a highly non-random sample of sequences. For example, an ancient protein family might be represented by a few highly diverged microbial and invertebrate sequences plus many mammalian sequences that form a closely related subgroup. This situation can be detrimental in sequence alignment and searching applications, where it is usually desirable to represent the diversity among related sequences. Since closely related sequences are largely redundant, they provide less information in a multiple sequence alignment than their distant cousins. Sequence weighting methods have been introduced to compensate for over-representation

SUMMARY. We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cophenetic matrix form suggests a simple and effective proposal distribution for selecting candidate trees close to the current tree in the chain. We illustrate the algorithm with restriction site data on 9 plant species, then extend to DNA sequences from 32 species of fish. The algorithm mixes well in both examples from random starting trees, generating reproducible estimates and credible sets for the path of evolution.

Over the past two decades, comparative biological analyses have undergone profound changes with the incorporation of rigorous evolutionary perspectives and phylogenetic information. This change followed in large part from the realization that traditional methods of statistical analysis tacitly assumed independence of all observations, when in fact biological groups such as species are differentially related to each other according to their evolutionary history. New phylogenetically based analytical methods were then rapidly developed, incorporated into ‘the comparative method’, and applied to many physiological, biochemical, morphological and behavioral investigations. We now review the rationale for including phylogenetic information in comparative studies and briefly discuss three methods for doing this (independent contrasts, generalized least-squares models, Summary and Monte Carlo computer simulations). We discuss when and how to use phylogenetic information in comparative studies and provide several examples in which it has been helpful, or even crucial, to a comparative analysis. We also consider some difficulties with phylogenetically based statistical methods, and of comparative approaches in general, both practical and theoretical. It is our personal opinion that the incorporation of phylogeny information into comparative studies has been highly beneficial, not only because it can improve the reliability of statistical inferences, but also because it continually emphasizes the potential importance of past evolutionary history in determining current form and function.

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of carotenoid- versus melanin-based coloration

Making accurate functional predictions for genes is a key step in this era of high throughput gene and genome sequencing. While most functional prediction methods are comparative in nature, many do not take advantage of the power that an evolutionary perspective provides to any comparative biology analysis. Here we review how evolutionary analysis can greatly benefit both homology-based and nonhomology-based functional prediction methods. Examples that are discussed include phylogenetic determination of orthology, the use of character state reconstruction analysis of gene function, and evolutionary analysis of rates and patterns of gene evolution. & 2002 Elsevier Science (USA)

Abstract.—We examined the effect of soft polytomies on the performance (Type I error rate and bias) of Felsenstein’s (1985; Am. Nat. 125:1–15) method of phylogenetically independent contrasts for estimating a bivariate correlation. We speci�cally tested the adequacy of bounding degrees of freedom, as suggested by Purvis and Garland (1993; Syst. Biol. 42:569–575). We simulated bivariate character evolution under Brownian motion (assumed by independent contrasts) and eight other models on �ve phylogenetic trees. For non-Brownian motion simulations, the adequacy of branchlength standardization was checked with a simple diagnostic (Garland et al., 1992; Syst. Biol. 41: 18–32), and transformations were applied as indicated. Surprisingly, soft polytomies tended to have negligible effects on Type I error rates when models other than Brownian motion were used. Overall, and irrespective of evolutionary model, degrees of freedom were appropriately bounded for hypothesis testing, and unbiased estimates of the correlation coef�cient were obtained. Our results, along with those of previous simulation studies, suggest that independent contrasts can reliably be applied to real data, even with phylogenetic uncertainty. [Comparative method; computer simulation; hypothesis testing; polytomies.] Many available phylogenetic trees include

We measured sprint performance of phrynosomatid lizards and selected outgroups (n = 27 species). Maximal sprint running speeds were obtained with a new measurement technique, a high-speed treadmill (H.S.T.). Animals were measured at their approximate ®eld-active body temperatures once on both of 2 consecutive days. Within species, individual variation in speed measurements was consistent between trial days and repeatabilities were similar to values reported previously for photocell-timed racetrack measurements. Multiple regression with phylogenetically independent contrasts indicates that interspeci®c variation in maximal speed is positively correlated with hindlimb span, but not signi®cantly related to either body mass or body temperature. Among the three phrynosomatid subclades, sand lizards (Uma, Callisaurus, Cophosaurus, Holbrookia) have the highest sprint speeds and longest hindlimbs, horned lizards (Phrynosoma) exhibit the lowest speeds and shortest limbs, and the Sceloporus group (including Uta and Urosaurus) is intermediate in both speed and hindlimb span.

Variation in DNA content has been largely ignored as a factor in evolution, particularly following the advent of sequence-based approaches to genomic analysis. The significant genome size diversity among organisms (more than 200000-fold among eukaryotes) bears no relationship to organismal complexity and both the origins and reasons for the clearly non-random distribution of this variation remain unclear. Several theories have been proposed to explain this ` C-value enigma ' (heretofore known as the ` C-value paradox '), each of which can be described as either a ` mutation pressure ' or ` optimal DNA ' theory. Mutation pressure theories consider the large portion of non-coding DNA in eukaryotic genomes as either ` junk ' or ` selfish ' DNA and are important primarily in considerations of the origin of secondary DNA. Optimal DNA theories di#er from mutation pressure theories by emphasizing the strong link between DNA content and cell and nuclear volumes. While mutation pressure theories generally explain this association with cell size as coincidental, the nucleoskeletal theory proposes a coevolutionary interaction between nuclear and cell volume, with DNA content adjusted adaptively following shifts in cell size. Each of these approaches to the C-value enigma is problematic for a variety of reasons and the preponderance of the available evidence instead favours the nucleotypic theory which postulates a causal link between bulk DNA amount and cell volume. Under this view, variation in DNA content is under direct selection via its impacts on cellular and organismal parameters.

Most phylogenetically based statistical methods for the analysis of quantitative or continuously varying phenotypic traits assume that variation within species is absent or at least negligible, which is unrealistic for many traits. Within-species variation has several components. Differences among populations of the same species may represent either phylogenetic divergence or direct effects of environmental factors that differ among populations (phenotypic plasticity). Within-population variation also contributes to within-species variation and includes sampling variation, instrument-related error, low repeatability caused by fluctuations in behavioral or physiological state, variation related to age, sex, season, or time of day, and individual variation within such categories. Here we develop techniques for analyzing phylogenetically correlated data to include within-species variation, or “measurement error ” as it is often termed in the statistical literature. We derive methods for (i) univariate analyses, including measurement of “phylogenetic signal, ” (ii) correlation and principal components analysis for multiple traits, (iii) multiple regression, and (iv) inference of “functional relations, ” such as reduced major axis (RMA) regression. The methods are capable of incorporating measurement error that differs for each data point (mean value for a species or population), but they can be modified for special cases in which less is known about measurement error (e.g., when one is willing to assume something about the ratio of measurement error in two traits). We show that failure to incorporate measurement error can lead to both biased and imprecise (unduly uncertain) parameter estimates. Even previous methods that are thought to account for measurement error, such as conventional RMA regression,