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At least 1,405 records · Page 78Linked to original sources
PubMed1994

Parametric analysis of rank transformed data for statistical assessment of genotoxicity data with examples from cultured mammalian cells.

The utility of rank transformation followed by parametric analysis of the ranks has been assessed for determination of the statistical significance of genotoxicity data. Both nonparametric and parametric analytical methods have defects when used to assess the significance of results from routine regulatory tests. Superficially, the rank transformation method followed by parametric analysis of ranks appears to be an ideal solution. However, we considered that such a test might suffer a substantial loss of power when used to analyse normally distributed data with very low sample replication. To test this hypothesis we took 22 data sets from five 'borderline' positive compounds in mouse lymphoma treat-and-plate assays where treatment-related increases were between 1.5- and 3-fold the control and analysed these results by Dunnett's t-test using rank transformed data and weighted, untransformed data. In theory these mouse lymphoma data should show the rank transformation system at its worst in comparison with parametric methodology using weighted data. Surprisingly, the rank transformation methodology showed no loss of power and, overall, performed more consistently than the weighted data methodology. Based on this limited number of data sets, rank transformation followed by parametric analysis of ranks seems to be an approach very suitable for genotoxicity assays in general, particularly where distributions are non-normal or of uncertain form. It combines the general applicability of non-parametric methods with the power of parametric analyses. However, the methodology still requires to be further validated in use and by computer simulation.

Animals↗
PubMed2000

Stochastic gene expression: density of defects frozen into permanent Turing patterns.

We estimate density of defects frozen into a biological Turing pattern which was turned on at a finite rate. Self-locking of gene expression in individual cells, which makes the Turing transition discontinuous, stabilizes the pattern together with its defects. Defects frozen into the pattern are a permanent record of the transition-they give an animal its own characteristic lifelong "fingerprints" or, as for vital organ formation, they can be fatal. Density of defects scales like the fourth root of the transition rate. This dependence is so weak that there is not enough time during morphogenesis to get rid of defects simply by slowing down the rate. A defect-free pattern can be obtained by spatially inhomogeneous activation of the genes. If the supercritical density of activator spreads slower than certain threshold velocity, then the Turing pattern is expressed without any defects.

Animals↗
PubMed2005

Coevolutionary dynamics: from finite to infinite populations.

Traditionally, frequency dependent evolutionary dynamics is described by deterministic replicator dynamics assuming implicitly infinite population sizes. Only recently have stochastic processes been introduced to study evolutionary dynamics in finite populations. However, the relationship between deterministic and stochastic approaches remained unclear. Here we solve this problem by explicitly considering large populations. In particular, we identify different microscopic stochastic processes that lead to the standard or the adjusted replicator dynamics. Moreover, differences on the individual level can lead to qualitatively different dynamics in asymmetric conflicts and, depending on the population size, can even invert the direction of the evolutionary process.

Animals↗
PubMed2000

The fitness effects of spontaneous mutations in Caenorhabditis elegans.

Spontaneous mutation to mildly deleterious alleles has emerged as a potentially unifying component of a variety of observations in evolutionary genetics and molecular evolution. However, the biological significance of hypotheses based on mildly deleterious mutation depends critically on the rate at which new mutations arise and on their average effects. A long-term mutation-accumulation experiment with replicate lines of the nematode Caenorhabditis elegans maintained by single-progeny descent indicates that recurrent spontaneous mutation causes approximately 0.1% decline in fitness per generation, which is about an order of magnitude less than that suggested by previous studies with Drosophila. Two rather different approaches, Bateman-Mukai and maximum likelihood, suggest that this observation, along with the observed rate of increase in the variance of fitness among lines, is consistent with a genomic deleterious mutation rate for fitness of approximately 0.03 per generation and with an average homozygous effect of approximately 12%. The distribution of mutational effects for fitness appears to have a relatively low coefficient of variation, being no more extreme than expected for a negative exponential, and for one composite fitness measure (total progeny production) approaches constancy of effects. These results are derived from assays in a benign environment. At stressful temperatures, estimates of the genomic deleterious mutation rate (for genes expressed at such temperatures) is sixfold lower, whereas those for the average homozygous effect is approximately eightfold higher. Our results are reasonably compatible with existing estimates for flies, when one considers the differences between these species in the number of germ-line cell divisions per generation and the magnitude of transposable element activity.

Animals↗
PubMed1995

Population subdivision and gene flow in Danish house mice.

Genetic subdivision in local populations of the European house mice, Mus musculus domesticus and M. m. musculus, was analysed to study patterns of gene flow. The data consisted of frequencies of microsatellite alleles in 16 samples (250 individuals) from a total of 11 sites in Jutland, which included successive samples from three sites. Sequences of the control region of mitochondrial DNA in three successive samples from one site were also analysed. Microsatellite genotype frequencies within samples were close to Hardy-Weinberg expectations. Levels of microsatellite differentiation among samples (theta = 0.05-0.21) corresponded to limited gene flow at migration-drift equilibrium (Nm = 1-5). Weak isolation by distance for microsatellites in M.m. musculus suggested that gene flow tends to occur among neighbouring sites. Estimates of effective population size over a few generations were much lower than those corresponding to the long periods needed for arrival at mutation-drift equilibrium. This suggested that subpopulations had been influenced by gene flow since formation, or had originated recently from genetically diverse founders.

Analysis of Variance↗
PubMed2005

Local genetic differentiation among populations of the mass-flowering tropical shrub Erythroxylum havanense (Erythroxylaceae).

This study is aimed to understand the role of life-history traits in determining the genetic structure of populations. We used Erythroxylum havanense, a distylous shrub with synchronous and massive patterns of flowering and fruiting. We suggest that the high concentration of ephemeral resources produced by mass flowering satiates both pollinators and frugivores, restricting gene flow and leading to genetic differentiation among populations. Using random amplified polymorphic DNAs as genetic markers, we estimated genetic diversity and structure statistics to quantify the amount and distribution of genetic variation within and among five populations from the Pacific coast of Mexico. High levels of genetic variation within populations and significant differentiation among populations located very near to each other were found. Furthermore, spatial autocorrelation analysis indicated the presence of significant genetic structure at short spatial distances. We suggest that by influencing the foraging behavior of pollinators and frugivores, mass flowering may produce the observed patterns of genetic structure, while small differences in flowering or fruiting phenology could further reinforce the isolation of nearby populations.

Animals↗
PubMed2005

Use of principal component analysis and the GE-biplot for the graphical exploration of gene expression data.

This note is in response to Wouters et al. (2003, Biometrics 59, 1131-1139) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to determine principal component analyses that are useful for exploratory analysis of microarray data. We also present another biplot representation, the GE-biplot (Gene Expression biplot), that is a useful method for exploring gene expression data with the major advantage of being able to aid interpretation of both the samples and the genes relative to each other.

Algorithms↗
PubMed2002

Developmental robustness.

Developmental robustness, the capacity to stay "on track" despite the myriad vicissitudes that inevitably plague a developing organism, is, I argue, a prerequisite for natural selection and key to our understanding of the evolution of developmental processes. But how is such robustness achieved? And how can we reconcile this property with the delicate precision that seems to characterize so many developmental mechanisms, with what Michael Behe calls "irreducible complexity"? By looking at context, I argue. Developmental mechanisms must be robust with respect to the kinds of insults they are most likely to face, but with respect to less likely vicissitudes, they can be fragile. More specifically, I examine the relative absence of reaction-diffusion mechanisms in development and suggest that such mechanisms, theoretically attractive though they may be, have been judged by evolution to be ill suited for providing protection against the kinds of vicissitudes developing organisms are most likely to face, and have been supplanted by more intricate mechanisms that are protected from insult by structural design.

Adaptation, Physiological↗
PubMed2001

Genealogical and evolutionary inference with the human Y chromosome.

Population genetics has emerged as a powerful tool for unraveling human history. In addition to the study of mitochondrial and autosomal DNA, attention has recently focused on Y-chromosome variation. Ambiguities and inaccuracies in data analysis, however, pose an important obstacle to further development of the field. Here we review the methods available for genealogical inference using Y-chromosome data. Approaches can be divided into those that do and those that do not use an explicit population model in genealogical inference. We describe the strengths and weaknesses of these model-based and model-free approaches, as well as difficulties associated with the mutation process that affect both methods. In the case of genealogical inference using microsatellite loci, we use coalescent simulations to show that relatively simple generalizations of the mutation process can greatly increase the accuracy of genealogical inference. Because model-free and model-based approaches have different biases and limitations, we conclude that there is considerable benefit in the continued use of both types of approaches.

Biological Evolution↗
PubMed2005

Systematic variation normalization in microarray data to get gene expression comparison unbiased.

Normalization removes or minimizes the biases of systematic variation that exists in experimental data sets. This study presents a systematic variation normalization (SVN) procedure for removing systematic variation in two channel microarray gene expression data. Based on an analysis of how systematic variation contributes to variability in microarray data sets, our normalization procedure includes background subtraction determined from the distribution of pixel intensity values from each data acquisition channel and log conversion, linear or non-linear regression, restoration or transformation, and multiarray normalization. In the case when a non-linear regression is required, an empirical polynomial approximation approach is used. Either the high terminated points or their averaged values in the distributions of the pixel intensity values observed in control channels may be used for rescaling multiarray datasets. These pre-processing steps remove systematic variation in the data attributable to variability in microarray slides, assay-batches, the array process, or experimenters. Biologically meaningful comparisons of gene expression patterns between control and test channels or among multiple arrays are therefore unbiased using normalized but not unnormalized datasets.

Algorithms↗
PubMed2005

A dimensionless fit measure for phylogenetic distance trees.

We present a dimensionless fit index for phylogenetic trees that have been constructed from distance matrices. It is designed to measure the quality of the fit of the data to a tree in absolute terms, independent of linear transformations on the distance matrix. The index can be used as an absolute measure to evaluate how well a set of data fits to a tree, or as a relative measure to compare different methods that are expected to produce the same tree. The usefulness of the index is demonstrated in three examples.

Algorithms↗
PubMed2005

Genotype and haplotype association study of the STRK1 region on 5q12 among Japanese: a case-control study.

BACKGROUND AND PURPOSE: Cerebral infarction is thought to be a multifactorial disease that is affected by several environmental factors and genetic variants. Gretarsdottir et al identified a candidate locus (STRK1) for cerebral infarction with a significant logarithm of odds score at 5q12 in whites in 2002 and subsequently identified the PDE4D gene as a susceptibility gene at this locus in 2003. The aims of this haplotype-based case-control study were to confirm, using microsatellite markers and single-nucleotide polymorphisms (SNPs), whether PDE4D is also a susceptibility gene for cerebral infarction in Japanese subjects. METHODS: Cerebral infarction was defined as noncardiogenic ischemic stroke with signs and symptoms lasting >1 month in duration. We genotyped 208 Japanese cerebral infarction patients and 270 non-cerebral infarction controls for 31 SNPs, 3 dinucleotide microsatellites, and 1 tetranucleotide variable number of tandem repeat. Haplotypes were constructed and their frequencies compared between the cerebral infarction patients and the controls. RESULTS: The haplotype-based case-control study revealed that in addition to the region of the PDE4D gene (P=0.002), another region (P<0.001) also existed within the STRK1 locus. CONCLUSIONS: The region of the PDE4D gene and the other newly detected region within the STRK1 locus were associated with cerebral infarction.

3',5'-Cyclic-AMP Phosphodiesterases↗
PubMed2005

Generalizations of Markov model to characterize biological sequences.

BACKGROUND: The currently used kth order Markov models estimate the probability of generating a single nucleotide conditional upon the immediately preceding (gap = 0) k units. However, this neither takes into account the joint dependency of multiple neighboring nucleotides, nor does it consider the long range dependency with gap > 0. RESULT: We describe a configurable tool to explore generalizations of the standard Markov model. We evaluated whether the sequence classification accuracy can be improved by using an alternative set of model parameters. The evaluation was done on four classes of biological sequences--CpG-poor promoters, all promoters, exons and nucleosome positioning sequences. Using di- and tri-nucleotide as the model unit significantly improved the sequence classification accuracy relative to the standard single nucleotide model. In the case of nucleosome positioning sequences, optimal accuracy was achieved at a gap length of 4. Furthermore in the plot of classification accuracy versus the gap, a periodicity of 10-11 bps was observed which might indicate structural preferences in the nucleosome positioning sequence. The tool is implemented in Java and is available for download at ftp://ftp.pcbi.upenn.edu/GMM/. CONCLUSION: Markov modeling is an important component of many sequence analysis tools. We have extended the standard Markov model to incorporate joint and long range dependencies between the sequence elements. The proposed generalizations of the Markov model are likely to improve the overall accuracy of sequence analysis tools.

Base Sequence↗
PubMed2006

Statistical inference of chromosomal homology based on gene colinearity and applications to Arabidopsis and rice.

BACKGROUND: The identification of chromosomal homology will shed light on such mysteries of genome evolution as DNA duplication, rearrangement and loss. Several approaches have been developed to detect chromosomal homology based on gene synteny or colinearity. However, the previously reported implementations lack statistical inferences which are essential to reveal actual homologies. RESULTS: In this study, we present a statistical approach to detect homologous chromosomal segments based on gene colinearity. We implement this approach in a software package ColinearScan to detect putative colinear regions using a dynamic programming algorithm. Statistical models are proposed to estimate proper parameter values and evaluate the significance of putative homologous regions. Statistical inference, high computational efficiency and flexibility of input data type are three key features of our approach. CONCLUSION: We apply ColinearScan to the Arabidopsis and rice genomes to detect duplicated regions within each species and homologous fragments between these two species. We find many more homologous chromosomal segments in the rice genome than previously reported. We also find many small colinear segments between rice and Arabidopsis genomes.

Algorithms↗
PubMed2006

Assessing stability of gene selection in microarray data analysis.

BACKGROUND: The number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data. RESULTS: Using resampling techniques we have found that some genes are selected much less frequently (across sub-samples) than other genes with the same adjusted p-values. The extent to which this type of instability manifests itself can be assessed by a method introduced in this paper. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations. CONCLUSION: Resampling represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using resampling techniques it is also possible to assess variability of different performance indicators. Stability properties of several multiple testing procedures are described at length in the present paper.

Biomarkers, Tumor↗
PubMed2006

The PowerAtlas: a power and sample size atlas for microarray experimental design and research.

BACKGROUND: Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies. RESULTS: To address this challenge, we have developed a Microrarray PowerAtlas. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO). The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC). CONCLUSION: This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes.

Algorithms↗
PubMed2005

Using equilibrium frequencies in models of sequence evolution.

BACKGROUND: The f factor is a new parameter for accommodating the influence of both the starting and ending states in the rate matrices of "generalized weighted frequencies" (+gwF) models for sequence evolution. In this study, we derive an expected value for f, starting from a nearly neutral model of weak selection, and then assess the biological interpretation of this factor with evolutionary simulations. RESULTS: An expected value of f = 0.5 (i.e., equal dependency on the starting and ending states) is derived for sequences that are evolving under the nearly neutral model of this study. However, this expectation is sensitive to violations of its underlying assumptions as illustrated with the evolutionary simulations. CONCLUSION: This study illustrates how selection, drift, and mutation at the population level can be linked to the rate matrices of models for sequence evolution to derive an expected value of f. However, as f is affected by a number of factors that limit its biological interpretation, this factor should normally be estimated as a free parameter rather than fixed a priori in a +gwF analysis.

Biological Evolution↗