A Bayesian method for analysing spotted microarray data

In the decade since their invention, spotted microarrays have been undergoing technical advances that have increased the utility, scope and precision of their ability to measure gene expression. At the same time, more researchers are taking advantage of the fundamentally quantitative nature of these tools with refined experimental designs and sophisticated statistical analyses. These new approaches utilise the power of microarrays to estimate differences in gene expression levels, rather than just categorising genes as up- or down-regulated, and allow the comparison of expression data across multiple samples. In this review, some of the technical aspects of spotted microarrays that can affect statistical inference are highlighted, and a discussion is provided of how several methods for estimating gene expression level across multiple samples deal with these challenges. The focus is on a Bayesian analysis method, BAGEL, which is easy to implement and produces easily interpreted results.     

Parametric spatial cure rate models for interval-censored time-to-relapse data

Several recent papers (e.g., Chen, Ibrahim, and Sinha, 1999, Journal of the American Statistical Association 94, 909-919; Ibrahim, Chen, and Sinha, 2001a, Biometrics 57, 383-388) have described statistical methods for use with time-to-event data featuring a surviving fraction (i.e., a proportion of the population that never experiences the event). Such cure rate models and their multivariate generalizations are quite useful in studies of multiple diseases to which an individual may never succumb, or from which an individual may reasonably be expected to recover following treatment (e.g., various types of cancer). In this article we extend these models to allow for spatial correlation (estimable via zip code identifiers for the subjects) as well as interval censoring. Our approach is Bayesian, where posterior summaries are obtained via a hybrid Markov chain Monte Carlo algorithm. We compare across a broad collection of rather high-dimensional hierarchical models using the deviance information criterion, a tool recently developed for just this purpose. We apply our approach to the analysis of a smoking cessation study where the subjects reside in 53 southeastern Minnesota zip codes. In addition to the usual posterior estimates, our approach yields smoothed zip code level maps of model parameters related to the relapse rates over time and the ultimate proportion of quitters (the cure rates).     

A combined transmembrane topology and signal peptide prediction method

An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at     

Are residues in a protein folding nucleus evolutionarily conserved?

Protein is the working molecule of the cell, and evolution is the hallmark of life. It is important to understand how protein folding and evolution influence each other. Several studies correlating experimental measurement of residue participation in folding nucleus and sequence conservation have reached different conclusions. These studies are based on assessment of sequence conservation at folding nucleus sites using entropy or relative entropy measurement derived from multiple sequence alignment. Here we report analysis of conservation of folding nucleus using an evolutionary model alternative to entropy-based approaches. We employ a continuous time Markov model of codon substitution to distinguish mutation fixed by evolution and mutation fixed by chance. This model takes into account bias in codon frequency, bias-favoring transition over transversion, as well as explicit phylogenetic information. We measure selection pressure using the ratio omega of synonymous versus non-synonymous substitution at individual residue site. The omega-values are estimated using the PAML method, a maximum-likelihood estimator. Our results show that there is little correlation between the extent of kinetic participation in protein folding nucleus as measured by experimental phi-value and selection pressure as measured by omega-value. In addition, two randomization tests failed to show that folding nucleus residues are significantly more conserved than the whole protein, or the median omega value of all residues in the protein. These results suggest that at the level of codon substitution, there is no indication that folding nucleus residues are significantly more conserved than other residues. We further reconstruct candidate ancestral residues of the folding nucleus and suggest possible test tube mutation studies for testing folding behavior of ancient folding nucleus.     

Bayesian Estimation of Positively Selected Sites

In protein-coding DNA sequences, historical patterns of selection can be inferred from amino acid substitution patterns. High relative rates of nonsynonymous to synonymous changes (=d _N /d _S ) are a clear indicator of positive, or directional, selection, and several recently developed methods attempt to distinguish these sites from those under neutral or purifying selection. One method uses an empirical Bayesian framework that accounts for varying selective pressures across sites while conditioning on the parameters of the model of DNA evolution and on the phylogenetic history. We describe a method that identifies sites under diversifying selection using a fully Bayesian framework. Similar to earlier work, the method presented here allows the rate of nonsynonymous to synonymous changes to vary among sites. The significant difference in using a fully Bayesian approach lies in our ability to account for uncertainty in parameters including the tree topology, branch lengths, and the codon model of DNA substitution. We demonstrate the utility of the fully Bayesian approach by applying our method to a data set of the vertebrate -globin gene. Compared to a previous analysis of this data set, the hierarchical model found most of the same sites to be in the positive selection class, but with a few striking exceptions.     

Modeling DNA Base Substitution in Large Genomic Regions from Two Organisms

We studied the substitution patterns in 7661 well-conserved human–mouse alignments corresponding to the intergenic regions of human chromosome 22. Alignments with a high average GC content tend to have a higher human GC content than mouse GC content, indicating a lack of stationarity. Segmenting the alignments into four groups of GC content and fitting the general reversible substitution model (REV) separately gave significantly better fits than the overall fit and the levels of fit are close to that expected under an REV model. In addition, most of the fitted rate matrices are not of the HKY type but are remarkably strand-symmetric, and we constructed a number of substitution matrices that should be useful for genomic DNA sequence alignment. We did not find obvious signs of temporal inhomogeneity in the substitution rates and concluded that the conserved intergenic regions in human chromosome 22 and mouse appear to have evolved from their common ancestors via a process that is approximately reversible and strand-symmetric, assuming site homogeneity and independence.     

Evolutionary computer programming of protein folding and structure predictions

In order to understand the mechanism of protein folding and to assist the rational de-novo design of fast-folding, non-aggregating and stable artificial enzymes it is very helpful to be able to simulate protein folding reactions and to predict the structures of proteins and other biomacromolecules. Here, we use a method of computer programming called "evolutionary computer programming" in which a program evolves depending on the evolutionary pressure exerted on the program. In the case of the presented application of this method on a computer program for folding simulations, the evolutionary pressure exerted was towards faster finding deep minima in the energy landscape of protein folding. Already after 20 evolution steps, the evolved program was able to find deep minima in the energy landscape more than 10 times faster than the original program prior to the evolution process.     

长白落叶松（Larix olgensis）工业人工林的模拟间伐

应用马尔柯夫过程理论,在获得林分直径转移概率的基础上,采用间伐最小径阶林木,最大径阶林木,中间径阶林木三种间伐方式,在保留不同密度情况下对长白落叶松工业人工林进行模拟间伐,提出了适宜的保留密度和相应的抚育间伐对象。结果表明：马尔柯夫过程确能反映长白落叶松工业人工林的直径转移过程,利用马尔可夫过程理论对长白落叶松工业人工林进行模拟间伐实现了依据培育时间来确定间伐方法和措施,提高了长白落叶松工业人工林经营管理的精准性;长白落叶松工业人工林成林后的间伐无论从培育森林方面,还是从取得木材、加大林分收益方面考虑,都应该以间伐小径阶的林木为主,注重培育I、II级木,间伐III, IV级木;20～25 a长白落叶松工业人工林间伐后的保留经营密度以0.7为宜。     

Elucidation of T cell signalling models

A potential mechanism that allows T cells to reliably discriminate pMHC ligands involves an interplay between kinetic proofreading, negative feedback and a destruction of this negative feedback. We analyse a detailed model of these mechanisms which involves the TCR, SHP1 and ERK. We discover that the behaviour of pSHP1 negative feedback is of primary importance, and particularly the influence of a kinetic proofreading base negative feedback state on pSHP1 dynamics. The CD8 co-receptor is shown to benefit from a kinetic proofreading locking mechanism and is able to overcome pSHP1 negative influences to sensitise a T cell.