Dataset Information

Rapid likelihood analysis on large phylogenies using partial sampling of substitution histories.

ABSTRACT: Likelihood-based approaches can reconstruct evolutionary processes in greater detail and with better precision from larger data sets. The extremely large comparative genomic data sets that are now being generated thus create new opportunities for understanding molecular evolution, but analysis of such large quantities of data poses escalating computational challenges. Recently developed Markov chain Monte Carlo methods that augment substitution histories are a promising approach to alleviate these computational costs. We analyzed the computational costs of several such approaches, considering how they scale with model and data set complexity. This provided a theoretical framework to understand the most important computational bottlenecks, leading us to combine novel variations of our conditional pathway integration approach with recent advances made by others. The resulting technique ("partial sampling" of substitution histories) is considerably faster than all other approaches we considered. It is accurate, simple to implement, and scales exceptionally well with dimensions of model complexity and data set size. In particular, the time complexity of sampling unobserved substitution histories using the new method is much faster than previously existing methods, and model parameter and branch length updates are independent of data set size. We compared the performance of methods on a 224-taxon set of mammalian cytochrome-b sequences. For a simple nucleotide substitution model, partial sampling was at least 10 times faster than the PhyloBayes program, which samples substitutions in continuous time, and about 100 times faster than when using fully integrated substitution histories. Under a general reversible model of amino acid substitution, the partial sampling method was 1,600 times faster than when using fully integrated substitution histories, confirming significantly improved scaling with model state-space complexity. Partial sampling of substitutions thus dramatically improves the utility of likelihood approaches for analyzing complex evolutionary processes on large data sets.

SUBMITTER: de Koning AP

PROVIDER: S-EPMC2877550 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Rapid likelihood analysis on large phylogenies using partial sampling of substitution histories.

de Koning A P Jason AP Gu Wanjun W Pollock David D DD

Molecular biology and evolution 20090925 2

Likelihood-based approaches can reconstruct evolutionary processes in greater detail and with better precision from larger data sets. The extremely large comparative genomic data sets that are now being generated thus create new opportunities for understanding molecular evolution, but analysis of such large quantities of data poses escalating computational challenges. Recently developed Markov chain Monte Carlo methods that augment substitution histories are a promising approach to alleviate the ...[more]

PMID: 19783593

Similar Datasets

Project description:The Canadian Study of Health and Aging (CSHA) employed a prevalent cohort design to study survival after onset of dementia, where patients with dementia were sampled and the onset time of dementia was determined retrospectively. The prevalent cohort sampling scheme favors individuals who survive longer. Thus, the observed survival times are subject to length bias. In recent years, there has been a rising interest in developing estimation procedures for prevalent cohort survival data that not only account for length bias but also actually exploit the incidence distribution of the disease to improve efficiency. This article considers semiparametric estimation of the Cox model for the time from dementia onset to death under a stationarity assumption with respect to the disease incidence. Under the stationarity condition, the semiparametric maximum likelihood estimation is expected to be fully efficient yet difficult to perform for statistical practitioners, as the likelihood depends on the baseline hazard function in a complicated way. Moreover, the asymptotic properties of the semiparametric maximum likelihood estimator are not well-studied. Motivated by the composite likelihood method (Besag 1974), we develop a composite partial likelihood method that retains the simplicity of the popular partial likelihood estimator and can be easily performed using standard statistical software. When applied to the CSHA data, the proposed method estimates a significant difference in survival between the vascular dementia group and the possible Alzheimer's disease group, while the partial likelihood method for left-truncated and right-censored data yields a greater standard error and a 95% confidence interval covering 0, thus highlighting the practical value of employing a more efficient methodology. To check the assumption of stable disease for the CSHA data, we also present new graphical and numerical tests in the article. The R code used to obtain the maximum composite partial likelihood estimator for the CSHA data is available in the online Supplementary Material, posted on the journal web site.

Project description:A new monotonicity-constrained maximum likelihood approach, called Partial Order Optimum Likelihood (POOL), is presented and applied to the problem of functional site prediction in protein 3D structures, an important current challenge in genomics. The input consists of electrostatic and geometric properties derived from the 3D structure of the query protein alone. Sequence-based conservation information, where available, may also be incorporated. Electrostatics features from THEMATICS are combined with multidimensional isotonic regression to form maximum likelihood estimates of probabilities that specific residues belong to an active site. This allows likelihood ranking of all ionizable residues in a given protein based on THEMATICS features. The corresponding ROC curves and statistical significance tests demonstrate that this method outperforms prior THEMATICS-based methods, which in turn have been shown previously to outperform other 3D-structure-based methods for identifying active site residues. Then it is shown that the addition of one simple geometric property, the size rank of the cleft in which a given residue is contained, yields improved performance. Extension of the method to include predictions of non-ionizable residues is achieved through the introduction of environment variables. This extension results in even better performance than THEMATICS alone and constitutes to date the best functional site predictor based on 3D structure only, achieving nearly the same level of performance as methods that use both 3D structure and sequence alignment data. Finally, the method also easily incorporates such sequence alignment data, and when this information is included, the resulting method is shown to outperform the best current methods using any combination of sequence alignments and 3D structures. Included is an analysis demonstrating that when THEMATICS features, cleft size rank, and alignment-based conservation scores are used individually or in combination THEMATICS features represent the single most important component of such classifiers.

Dataset Information

Rapid likelihood analysis on large phylogenies using partial sampling of substitution histories.

Publications

Rapid likelihood analysis on large phylogenies using partial sampling of substitution histories.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets