Identification and analysis of chromodomain-containing proteins encoded in the mouse transcriptome

The chromodomain is 40-50 amino acids in length and is conserved in a wide range of chromatic and regulatory proteins involved in chromatin remodeling. Chromodomain-containing proteins can be classified into families based on their broader characteristics, in particular the presence of other types of domains, and which correlate with different subclasses of the chromodomains themselves. Hidden Markov model (HMM)-generated profiles of different subclasses of chromodomains were used here to identify sequences encoding chromodomain-containing proteins in the mouse transcriptome and genome. A total of 36 different loci encoding proteins containing chromodomains, including 17 novel loci, were identified. Six of these loci (including three apparent pseudogenes, a novel HP1 ortholog, and two novel Msl-3 transcription factor-like proteins) are not present in the human genome, whereas the human genome contains four loci (two CDY orthologs and two apparent CDY pseuclogenes) that are not present in mouse. A number of these loci exhibit alternative splicing to produce different isoforms, including 43 novel variants, some of which lack the chromodomain. The likely functions of these proteins are discussed in relation to the known functions of other chromodomain-containing proteins within the same family.

Cost-effectiveness of combination peginterferon alpha-2a and ribavirin compared with interferon alpha-2b and ribavirin in patients with chronic hepatitis C

BACKGROUND: Sustained virological response (SVR) is the primary objective in the treatment of chronic hepatitis C (CHC). Results from a recent clinical trial of patients with previously untreated CHC demonstrate that the combination of peginterferon alpha-2a and ribavirin produces a greater SVR than interferon alpha-2b and ribavirin combination therapy. However, the cost-effectiveness of peginterferon alpha-2a plus ribavirin in the U.S. setting has not been investigated. METHODS: A Markov model was developed to investigate cost-effectiveness in patients with CHC using genotype to guide treatment duration. SVR and disease progression parameters were derived from the clinical trials and epidemiologic studies. The impact of treatment on life expectancy and costs were projected for a lifetime. Patients who had an SVR were assumed to remain virus-free for the rest of their lives. In genotype 1 patients, the SVRs were 46% for peginterferon alpha-2a plus ribavirin and 36% for interferon alpha-2b plus ribavirin. In genotype 2/3 patients, the SVRs were 76% for peginterferon alpha-2a plus ribavirin and 61% for interferon alpha-2b plus ribavirin. Quality of life and costs were based on estimates from the literature. All costs were based on published U.S. medical care costs and were adjusted to 2003 U.S. dollars. Costs and benefits beyond the first year were discounted at 3%. RESULTS: In genotype 1, peginterferon alpha-2a plus ribavirin increases quality-adjusted life expectancy (QALY) by 0.70 yr compared to interferon alpha-2b plus ribavirin, producing a cost-effectiveness ratio of $2,600 per QALY gained. In genotype 2/3 patients, peginterferon alpha-2a plus ribavirin increases QALY by 1.05 yr in comparison to interferon alpha-2b plus ribavirin. Peginterferon alpha-2a combination therapy in patients with HCV genotype 2 or 3 is dominant (more effective and cost saving) compared to interferon alpha-2b plus ribavirin. Results weighted by genotype prevalence (75% genotype 1; 25% genotype 2 or 3) also show that peginterferon alpha-2a plus ribavirin is dominant. Peginterferon alpha-2a and ribavirin remained cost-effective (below $16,500 per QALY gained) under sensitivity analyses on key clinical and cost parameters. CONCLUSION: Peginterferon alpha-2a in combination with ribavirin with duration of therapy based on genotype, is cost-effective compared with conventional interferon alpha-2b in combination with ribavirin when given to treatment-naive adults with CHC.

A cost-effectiveness analysis of fluvastatin in patients with diabetes after successful percutaneous coronary intervention

Background: The Lescol Intervention Prevention Study (LIPS) was a multinational randomized controlled trial that showed a 47% reduction in the relative risk of cardiac death and a 22% reduction in major adverse cardiac events (MACEs) from the routine use of fluvastatin, compared with controls, in patients undergoing percutaneous coronary intervention (PCI, defined as angioplasty with or without stents). In this study, MACEs included cardiac death, nonfatal myocardial infarction, and subsequent PCI and coronary artery bypass graft. Diabetes was the greatest risk factor for MACEs. Objective: This study estimated the cost-effectiveness of fluvastatin when used for secondary prevention of MACEs after PCI in people with diabetes. Methods: A post hoc subgroup analysis of patients with diabetes from the LIPS was used to estimate the effectiveness of fluvastatin in reducing myocardial infarction, revascularization, and cardiac death. A probabilistic Markov model was developed using United Kingdom resource and cost data to estimate the additional costs and quality-adjusted life-years (QALYs) gained over 10 years from the perspective of the British National Health Service. The model contained 6 health states, and the transition probabilities were derived from the LIPS data. Crossover from fluvastatin to other lipid-lowering drugs, withdrawal from fluvastatin, and the use of lipid-lowering drugs in the control group were included. Results: In the subgroup of 202 patients with diabetes in the LIPS trial, 18 (15.0%) of 120 fluvastatin patients and 21 (25.6%) of 82 control participants were insulin dependent (P = NS). Compared with the control group, patients treated with fluvastatin can expect to gain an additional mean (SD) of 0.196 (0.139) QALY per patient over 10 years (P < 0.001) and will cost the health service an additional mean (SD) of 10 (E448) (P = NS) (mean [SD] US $16 [$689]). The additional cost per QALY gained was;(51 (US $78). The key determinants of cost-effectiveness included the probabilities of repeat interventions, cardiac death, the cost of fluvastatin, and the time horizon used for the evaluation. Conclusion: Fluvastatin was an economically efficient treatment to prevent MACEs in these patients with diabetes undergoing PCI.

Predicting the solvent accessibility of transmembrane residues from protein sequence

In this study, we propose a novel method to predict the solvent accessible surface areas of transmembrane residues. For both transmembrane alpha-helix and beta-barrel residues, the correlation coefficients between the predicted and observed accessible surface areas are around 0.65. On the basis of predicted accessible surface areas, residues exposed to the lipid environment or buried inside a protein can be identified by using certain cutoff thresholds. We have extensively examined our approach based on different definitions of accessible surface areas and a variety of sets of control parameters. Given that experimentally determining the structures of membrane proteins is very difficult and membrane proteins are actually abundant in nature, our approach is useful for theoretically modeling membrane protein tertiary structures, particularly for modeling the assembly of transmembrane domains. This approach can be used to annotate the membrane proteins in proteomes to provide extra structural and functional information.

Optimizing Presence–Absence Surveys For Detecting Population Trends

Presence-absence surveys are a commonly used method for monitoring broad-scale changes in wildlife distributions. However, the lack of power of these surveys for detecting population trends is problematic for their application in wildlife management. Options for improving power include increasing the sampling effort or arbitrarily relaxing the type I error rate. We present an alternative, whereby targeted sampling of particular habitats in the landscape using information from a habitat model increases power. The advantage of this approach is that it does not require a trade-off with either cost or the Pr(type I error) to achieve greater power. We use a demographic model of koala (Phascolarctos cinereus) population dynamics and simulations of the monitoring process to estimate the power to detect a trend in occupancy for a range of strategies, thereby demonstrating that targeting particular habitat qualities can improve power substantially. If the objective is to detect a decline in occupancy, the optimal strategy is to sample high-quality habitats. Alternatively, if the objective is to detect an increase in occupancy, the optimal strategy is to sample intermediate-quality habitats. The strategies with the highest power remained the same under a range of parameter assumptions, although observation error had a strong influence on the optimal strategy. Our approach specifically applies to monitoring for detecting long-term trends in occupancy or abundance. This is a common and important monitoring objective for wildlife managers, and we provide guidelines for more effectively achieving it.

A generalized Markov sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis-Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Subgeometric rates of convergence for a class of continuous-time Markov process

Let (Phi(t))(t is an element of R+) be a Harris ergodic continuous-time Markov process on a general state space, with invariant probability measure pi. We investigate the rates of convergence of the transition function P-t(x, (.)) to pi; specifically, we find conditions under which r(t) vertical bar vertical bar P-t (x, (.)) - pi vertical bar vertical bar -> 0 as t -> infinity, for suitable subgeometric rate functions r(t), where vertical bar vertical bar - vertical bar vertical bar denotes the usual total variation norm for a signed measure. We derive sufficient conditions for the convergence to hold, in terms of the existence of suitable points on which the first hitting time moments are bounded. In particular, for stochastically ordered Markov processes, explicit bounds on subgeometric rates of convergence are obtained. These results are illustrated in several examples.

A stochastic metapopulation model accounting for habitat dynamics

A stochastic metapopulation model accounting for habitat dynamics is presented. This is the stochastic SIS logistic model with the novel aspect that it incorporates varying carrying capacity. We present results of Kurtz and Barbour, that provide deterministic and diffusion approximations for a wide class of stochastic models, in a form that most easily allows their direct application to population models. These results are used to show that a suitably scaled version of the metapopulation model converges, uniformly in probability over finite time intervals, to a deterministic model previously studied in the ecological literature. Additionally, they allow us to establish a bivariate normal approximation to the quasi-stationary distribution of the process. This allows us to consider the effects of habitat dynamics on metapopulation modelling through a comparison with the stochastic SIS logistic model and provides an effective means for modelling metapopulations inhabiting dynamic landscapes.

Searching for convergence in phylogenetic Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) is a methodology that is gaining widespread use in the phylogenetics community and is central to phylogenetic software packages such as MrBayes. An important issue for users of MCMC methods is how to select appropriate values for adjustable parameters such as the length of the Markov chain or chains, the sampling density, the proposal mechanism, and, if Metropolis-coupled MCMC is being used, the number of heated chains and their temperatures. Although some parameter settings have been examined in detail in the literature, others are frequently chosen with more regard to computational time or personal experience with other data sets. Such choices may lead to inadequate sampling of tree space or an inefficient use of computational resources. We performed a detailed study of convergence and mixing for 70 randomly selected, putatively orthologous protein sets with different sizes and taxonomic compositions. Replicated runs from multiple random starting points permit a more rigorous assessment of convergence, and we developed two novel statistics, delta and epsilon, for this purpose. Although likelihood values invariably stabilized quickly, adequate sampling of the posterior distribution of tree topologies took considerably longer. Our results suggest that multimodality is common for data sets with 30 or more taxa and that this results in slow convergence and mixing. However, we also found that the pragmatic approach of combining data from several short, replicated runs into a metachain to estimate bipartition posterior probabilities provided good approximations, and that such estimates were no worse in approximating a reference posterior distribution than those obtained using a single long run of the same length as the metachain. Precision appears to be best when heated Markov chains have low temperatures, whereas chains with high temperatures appear to sample trees with high posterior probabilities only rarely. [Bayesian phylogenetic inference; heating parameter; Markov chain Monte Carlo; replicated chains.]