Galina V. Glazko

Overcredibility of molecular phylogenies obtained by Bayesian phylogenetics

Bayesian phylogenetics has recently been proposed as a powerful method for inferring molecular phylogenies, and it has been reported that the mammalian and some plant phylogenies were resolved by using this method. The statistical confidence of interior branches as judged by posterior probabilities in Bayesian analysis is generally higher than that as judged by bootstrap probabilities in maximum likelihood analysis, and this difference has been interpreted as an indication that bootstrap support may be too conservative. However, it is possible that the posterior probabilities are too high or too liberal instead. Here, we show by computer simulation that posterior probabilities in Bayesian analysis can be excessively liberal when concatenated gene sequences are used, whereas bootstrap probabilities in neighbor-joining and maximum likelihood analyses are generally slightly conservative. These results indicate that bootstrap probabilities are more suitable for assessing the reliability of phylogenetic trees than posterior probabilities and that the mammalian and plant phylogenies may not have been fully resolved.

Diversity and Function of Adaptive Immune Receptors in a Jawless Vertebrate

Instead of the immunoglobulin-type antigen receptors of jawed vertebrates, jawless fish have variable lymphocyte receptors (VLRs), which consist of leucine-rich repeat (LRR) modules. Somatic diversification of the VLR gene is shown here to occur through a multistep assembly of LRR modules randomly selected from a large bank of flanking cassettes. The predicted concave surface of the VLR is lined with hypervariable positively selected residues, and computational analysis suggests a repertoire of about 1014 unique receptors. Lamprey immunized with anthrax spores responded with the production of soluble antigen-specific VLRs. These findings reveal that two strikingly different modes of antigen recognition through rearranged lymphocyte receptors have evolved in the jawless and jawed vertebrates.

Estimation of divergence times from multiprotein sequences for a few mammalian species and several distantly related organisms

When many protein sequences are available for estimating the time of divergence between two species, it is customary to estimate the time for each protein separately and then use the average for all proteins as the final estimate. However, it can be shown that this estimate generally has an upward bias, and that an unbiased estimate is obtained by using distances based on concatenated sequences. We have shown that two concatenation-based distances, i.e., average gamma distance weighted with sequence length (d2) and multiprotein gamma distance (d3), generally give more satisfactory results than other concatenation-based distances. Using these two distance measures for 104 protein sequences, we estimated the time of divergence between mice and rats to be approximately 33 million years ago. Similarly, the time of divergence between humans and rodents was estimated to be approximately 96 million years ago. We also investigated the dependency of time estimates on statistical methods and various assumptions made by using sequence data from eubacteria, protists, plants, fungi, and animals. Our best estimates of the times of divergence between eubacteria and eukaryotes, between protists and other eukaryotes, and between plants, fungi, and animals were 3, 1.7, and 1.3 billion years ago, respectively. However, estimates of ancient divergence times are subject to a substantial amount of error caused by uncertainty of the molecular clock, horizontal gene transfer, errors in sequence alignments, etc.

Evolution and diversification of lamprey antigen receptors: evidence for involvement of an AID-APOBEC family cytosine deaminase

The variable lymphocyte receptors (VLRs) of jawless vertebrates such as lamprey and hagfish are composed of highly diverse modular leucine-rich repeats. Each lymphocyte assembles a unique VLR by rearrangement of the germline gene. In the lamprey genome, we identify here about 850 distinct cassettes encoding leucine-rich repeat modules that serve as sequence templates for the hypervariable VLR repertoires. The data indicate a gene conversion–like process in VLR diversification. Genomic analysis suggested a link between the VLR and platelet glycoprotein receptors. Lamprey lymphocytes express two putative deaminases of the AID-APOBEC family that may be involved in VLR diversification, as indicated by in vitro mutagenesis and recombination assays. Vertebrate acquired immunity could have therefore originated from lymphocyte receptor diversification by an ancestral AID-like DNA cytosine deaminase.

Control of the mean number of false discoveries, Bonferroni and stability of multiple testing

The Bonferroni multiple testing procedure is commonly perceived as being overly conservative in large-scale simultaneous testing situations such as those that arise in microarray data analysis. The objective of the present study is to show that this popular belief is due to overly stringent requirements that are typically imposed on the procedure rather than to its conservative nature. To get over its notorious conservatism, we advocate using the Bonferroni selection rule as a procedure that controls the per family error rate (PFER). The present paper reports the first study of stability properties of the Bonferroni and Benjamini--Hochberg procedures. The Bonferroni procedure shows a superior stability in terms of the variance of both the number of true discoveries and the total number of discoveries, a property that is especially important in the presence of correlations between individual

The Sad1-UNC-84 homology domain in Mps3 interacts with Mps2 to connect the spindle pole body with the nuclear envelope

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Pathway analysis of expression data: deciphering functional building blocks of complex diseases.

-values. Its stability and the ability to provide strong control of the PFER make the Bonferroni procedure an attractive choice in microarray studies.

Unite and conquer

The spindle pole body (SPB) is the sole site of microtubule nucleation in Saccharomyces cerevisiae; yet, details of its assembly are poorly understood. Integral membrane proteins including Mps2 anchor the soluble core SPB in the nuclear envelope. Adjacent to the core SPB is a membrane-associated SPB substructure known as the half-bridge, where SPB duplication and microtubule nucleation during G1 occurs. We found that the half-bridge component Mps3 is the budding yeast member of the SUN protein family (Sad1-UNC-84 homology) and provide evidence that it interacts with the Mps2 C terminus to tether the half-bridge to the core SPB. Mutants in the Mps3 SUN domain or Mps2 C terminus have SPB duplication and karyogamy defects that are consistent with the aberrant half-bridge structures we observe cytologically. The interaction between the Mps3 SUN domain and Mps2 C terminus is the first biochemical link known to connect the half-bridge with the core SPB. Association with Mps3 also defines a novel function for Mps2 during SPB duplication.

Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

Identification of differentially expressed pathways from expression data is an important problem because it allows us to gain insights into the functional working mechanism of cells beyond the detection of differentially expressed genes. In this paper we present a brief guide to methods for the pathway analysis of expression data. Despite the vast amount of different statistical methods that have been developed so far, there is a considerable similarity among them, allowing a systematic classification and a reduction to a few null hypotheses that are effectively tested. Systems biology aims to find emergent phenomena by the integration of heterogeneous data. In general, data integration itself is a part of any scientific inference: its elementary steps are the integration of observations (measurements) into the context of biological knowledge. However, in the case of systems biology, the scale of integration is many folds higher, resulting in a prodigious number of new computational approaches for the simultaneous analyses of heterogeneous data. In this paper we discuss one popular way of integrating biological knowledge into large-scale genome-wide measurements, namely the identification of functionally related genes (pathways) enriched or differentially expressed in gene expression data [1]. It should be noted that the approaches discussed are also applicable to the analyses of, e.g., RNA-seq, metabolomics or proteomics data and, generally, different types of biological measurements when preexisting biological knowledge is available. In the early stages of methodological developments for gene expression data analyses, most approaches were focused on producing so-called gene lists. This is a set of individual genes called differentially expressed as identified by univariate test statistics (e.g., a t-test) [2]–[4]. Instead, more recent approaches clearly reflect systems biologys trend of data integration and interpretation [5]–[7], focusing on sets of functionally related genes (e.g., from the same signaling or metabolic pathway) rather than individual genes. The purpose of this paper is to provide a brief guide to methods for the analysis of differentially expressed pathways or gene sets, which we simply call pathway-based methods. For this reason, we emphasize an illustration of the methods rather than their technical description. The reader is encouraged to follow the cited literature for technical details.

Detecting intergene correlation changes in microarray analysis: a new approach to gene selection

MOTIVATION Recently, many univariate and several multivariate approaches have been suggested for testing differential expression of gene sets between different phenotypes. However, despite a wealth of literature studying their performance on simulated and real biological data, still there is a need to quantify their relative performance when they are testing different null hypotheses. RESULTS In this article, we compare the performance of univariate and multivariate tests on both simulated and biological data. In the simulation study we demonstrate that high correlations equally affect the power of both, univariate as well as multivariate tests. In addition, for most of them the power is similarly affected by the dimensionality of the gene set and by the percentage of genes in the set, for which expression is changing between two phenotypes. The application of different test statistics to biological data reveals that three statistics (sum of squared t-tests, Hotellings T(2), N-statistic), testing different null hypotheses, find some common but also some complementing differentially expressed gene sets under specific settings. This demonstrates that due to complementing null hypotheses each test projects on different aspects of the data and for the analysis of biological data it is beneficial to use all three tests simultaneously instead of focusing exclusively on just one.