Identification of the most informative regions of the mitochondrial genome for phylogenetic and coalescent analyses

Analysis of complete mitochondrial genome sequences is becoming increasingly common in genetic studies. The availability of full genome datasets enables an analysis of the information content distributed throughout the mitochondrial genome in order to optimize the research design of future evolutionary studies. The goal of our study was to identify informative regions of the human mitochondrial genome using two criteria: (1) accurate reconstruction of a phylogeny and (2) consistent estimates of time to most recent common ancestor (TMRCA). We created two series of datasets by deleting individual genes of varied length and by deleting 10 equal-size fragments throughout the coding region. Phylogenies were statistically compared to the full-coding-region tree, while coalescent methods were used to estimate the TMRCA and associated credible intervals. Individual fragments important for maintaining a phylogeny similar to the full-coding-region tree encompassed bp 577-2122 and 11,399-16,023, including all or part of 12S rRNA, 16S rRNA, ND4, ND5, ND6, and cytb. The control region only tree was the most poorly resolved with the majority of the tree manifest as an unresolved polytomy. Coalescent estimates of TMRCA were less sensitive to removal of any particular fragment(s) than reconstruction of a consistent phylogeny. Overall, we discovered that half the genome, i.e., bp 3669-11,398, could be removed with no significant change in the phylogeny (p(AU)=0.077) while still maintaining overlap of TMRCA 95% credible intervals. Thus, sequencing a contiguous fragment from bp 11,399 through the control region to bp 3668 would create a dataset that optimizes the information necessary for phylogenetic and coalescent analyses and also takes advantage of the wealth of data already available on the control region.     

John Rex's main mistake

Science progresses by the discovery and correction of mistakes in the interpretation of data. John Rex sought ‘to distinguish among the various studies made by sociologists those which are distinguishable as race relations studies’. He examined them from the standpoint of classical sociological theory. His main mistake was his continued but unsuccessful attempt to synthesize two quite different kinds of sociological knowledge. He underestimated the importance of differences in the criteria for the validation of causal explanation and for the projection of historical trends.     

Two-dimensional informative array testing

Summary Array-based group-testing algorithms for case identification are widely used in infectious disease testing, drug discovery, and genetics. In this article, we generalize previous statistical work in array testing to account for heterogeneity among individuals being tested. We first derive closed-form expressions for the expected number of tests (efficiency) and misclassification probabilities (sensitivity, specificity, predictive values) for two-dimensional array testing in a heterogeneous population. We then propose two "informative" array construction techniques which exploit population heterogeneity in ways that can substantially improve testing efficiency when compared to classical approaches that regard the population as homogeneous. Furthermore, a useful byproduct of our methodology is that misclassification probabilities can be estimated on a per-individual basis. We illustrate our new procedures using chlamydia and gonorrhea testing data collected in Nebraska as part of the Infertility Prevention Project.     

Methods of selecting informative variables

We propose a new method for selection of the most informative variables from the set of variables which can be measured directly. The information is measured by metrics similar to those used in experimental design theory, such as determinant of the dispersion matrix of prediction or various functions of its eigenvalues. The basic model admits both population variability and observational errors, which allows us to introduce algorithms based on ideas of optimal experimental design. Moreover, we can take into account cost of measuring various variables which makes the approach more practical. It is shown that the selection of optimal subsets of variables is invariant to scale transformations unlike other methods of dimension reduction, such as principal components analysis or methods based on direct selection of variables, for instance principal variables and battery reduction. The performance of different approaches is compared using the clinical data.     

A frailty model for informative censoring

To account for the correlation between failure and censoring, we propose a new frailty model for clustered data. In this model, the risk to be censored is affected by the risk of failure. This model allows flexibility in the direction and degree of dependence between failure and censoring. It includes the traditional frailty model as a special case. It allows censoring by some causes to be analyzed as informative while treating censoring by other causes as noninformative. It can also analyze data for competing risks. To fit the model, the EM algorithm is used with Markov chain Monte Carlo simulations in the E-steps. Simulation studies and analysis of data for kidney disease patients are provided. Consequences of incorrectly assuming noninformative censoring are investigated.     

我的驯鹿,我的梦想——玛丽亚·索的诉说

一个不足200人的群体,一群不足800头的驯鹿,得到了政府的大力扶持,却依然在传统与现代的夹缝中徘徊。让我们倾听他们从山林里发出的微弱声音,跟他们一起思考:传统和现代,先进和落后……     

A DSRPCL-SVM approach to informative gene analysis

Microarray data based tumor diagnosis is a very interesting topic in bioinformatics. One of the key problems is the discovery and analysis of informative genes of a tumor. Although there are many elaborate approaches to this problem, it is still difficult to select a reasonable set of informative genes for tumor diagnosis only with microarray data. In this paper, we classify the genes expressed through microarray data into a number of clusters via the distance sensitive rival penalized competitive learning （DSRPCL） algorithm and then detect the informative gene cluster or set with the help of support vector machine （SVM）. Moreover, the critical or powerful informative genes can be found through further classifications and detections on the obtained informative gene clusters. It is well demonstrated by experiments on the colon, leukemia, and breast cancer datasets that our proposed DSRPCL-SVM approach leads to a reasonable selection of informative genes for tumor diagnosis.     

Reflections provoked by Michael Banton's John Rex's main mistake

Banton's article provoked a number of reflections on working with John Rex in the 1960s. The idea of race has a long history both in human relations and sociological theory. But the possibility of there being a specific field of ‘race relations’ remains contested. Ideas of race and ethnicity are elided in public discourse, making the resolution of linguistic issues in the field especially difficult and as yet unresolved. These reflections touch upon the political environment in which John Rex's earlier writing on race was conducted and upon the breadth of influences upon a sociologist rooted in the classical tradition, especially the sociology of Max Weber.