单分子PCR产物错误率分析

碱基错配致使PCR扩增产物中存在突变序列.大量模板PCR扩增时突变序列所占的比例较低,对随后进行的PCR产物分析影响不大,但当对微量甚至单个模板DNA扩增时,情况则完全不同.对单分子PCR产物的错误率进行了理论分析,结果表明：根据实验目的和条件,选择忠实性不同的聚合酶是十分关键的.     

Substitution Rate Heterogeneity and the Male Mutation Bias

Germline mutation rates have been found to be higher in males than in females in many organisms, a likely consequence of cell division being more frequent in spermatogenesis than in oogenesis. If the majority of mutations are due to DNA replication error, the male-to-female mutation rate ratio (α_m) is expected to be similar to the ratio of the number of germ line cell divisions in males and females (c), an assumption that can be tested with proper estimates of α_m and c. α_m is usually estimated by comparing substitution rates in putatively neutral sequences on the sex chromosomes. However, substantial regional variation in substitution rates across chromosomes may bias estimates of α_m based on the substitution rates of short sequences. To investigate regional substitution rate variation, we estimated sequence divergence in 16 gametologous introns located on the Z and W chromosomes of five bird species of the order Galliformes. Intron ends and potentially conserved blocks were excluded to reduce the effect of using sequences subject to negative selection. We found significant substitution rate variation within Z chromosome (G₁₅ = 37.6, p = 0.0010) as well as within W chromosome introns (G₁₅ = 44.0, p = 0.0001). This heterogeneity also affected the estimates of α_m, which varied significantly, from 1.53 to 3.51, among the introns (ANOVA: F_13,14 =2.68, p = 0.04). Our results suggest the importance of using extensive data sets from several genomic regions to avoid the effects of regional mutation rate variation and to ensure accurate estimates of α_m. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. [Reviewing Editor: Mr. Martin Kreitman] Nick G.C. Smith Deceased     

Codon Bias and Noncoding GC Content Correlate Negatively with Recombination Rate on the Drosophila X Chromosome

The patterns and processes of molecular evolution may differ between the X chromosome and the autosomes in Drosophila melanogaster. This may in part be due to differences in the effective population size between the two chromosome sets and in part to the hemizygosity of the X chromosome in Drosophila males. These and other factors may lead to differences both in the gene complements of the X and the autosomes and in the properties of the genes residing on those chromosomes. Here we show that codon bias and recombination rate are correlated strongly and negatively on the X chromosome, and that this correlation cannot be explained by indirect relationships with other known determinants of codon bias. This is in dramatic contrast to the weak positive correlation found on the autosomes. We explored possible explanations for these patterns, which required a comprehensive analysis of the relationships among multiple genetic properties such as protein length and expression level. This analysis highlights conserved features of coding sequence evolution on the X and the autosomes and illuminates interesting differences between these two chromosome sets.[Reviewing editor: Dr. Richard Kliman]     

Codon Usage Decreases the Error Minimization Within the Genetic Code

The genetic code is not random but instead is organized in such a way that single nucleotide substitutions are more likely to result in changes between similar amino acids. This fidelity, or error minimization, has been proposed to be an adaptation within the genetic code. Many models have been proposed to measure this adaptation within the genetic code. However, we find that none of these consider codon usage differences between species. Furthermore, use of different indices of amino acid physicochemical characteristics leads to different estimations of this adaptation within the code. In this study, we try to establish a more accurate model to address this problem. In our model, a weighting scheme is established for mistranslation biases of the three different codon positions, transition/transversion biases, and codon usage. Different indices of amino acids physicochemical characteristics are also considered. In contrast to pervious work, our results show that the natural genetic code is not fully optimized for error minimization. The genetic code, therefore, is not the most optimized one for error minimization, but one that balances between flexibility and fidelity for different species.     

The Correlation Between Recombination Rate and Dinucleotide Bias in Drosophila melanogaster

Revealing how recombination affects genomic sequence is of great significance to our understanding of genome evolution. The present paper focuses on the correlation between recombination rate and dinucleotide bias in Drosophila melanogaster genome. Our results show that the overall dinucleotide bias is positively correlated with recombination rate for genomic sequences including untranslated regions, introns, intergenic regions, and coding sequences. The correlation patterns of individual dinucleotide biases with recombination rate are presented. Possible mechanisms of interaction between recombination and dinucleotide bias are discussed. Our data indicate that there may be a genome-wide universal mechanism acting between recombination rate and dinucleotide bias, which is likely to be neighbor-dependent biased gene conversion.     

Prediction Equations Do Not Eliminate Systematic Error in Self-Reported Body Mass Index

PLANKEY MICHAEL W, JUNE STEVENS, KATHERINE M FLEGAL, PHILIP F RUST. Prediction equations do not eliminate systematic error in self-reported body mass index. Epidemiological studies of the risks of obesity often use body mass index (BMI) calculated from self-reported height and weight. The purpose of this study was to examine the pattern of reporting error associated with self-reported values of BMI and to evaluate the extent to which linear regression models predict measured BMI from self-reported data and whether these models could compensate for this reporting error. We examined measured and self-reported weight and height on 5079 adults aged 30 years to 64 years from the second National Health and Nutrition Examination Survey. Measured and self-reported BMI (kg/m2) was calculated, and multiple linear regression techniques were used to predict measured BMI from self-reported BMI. The error in self-reported BMI (self-reported BMI minus measured BMI) was not constant but varied systematically with BMI. The correlation between measured BMI and the error in self-reported BMI was ?0.37 for men and ?0.38 for women. The pattern of reporting error was only weakly associated with self-reported BMI, with the correlation being 0.05 for men and ?0.001 for women. Error in predicted BMI (predicted BMI minus measured BMI) also varied systematically with measured BMI, but less consistently with self-reported BMI. More complex models only slightly improved the ability to predict measured BMI compared with self-reported BMI alone. None of the equations were able to eliminate the systematic reporting error in determining measured BMI values from self-reported data. The characteristic pattern of error associated with self-reported BMI is difficult or impossible to correct by the use of linear regression models.     

The Influence of Relatives on the Efficiency and Error Rate of Familial Searching

We investigate the consequences of adopting the criteria used by the state of California, as described by Myers et al. (2011), for conducting familial searches. We carried out a simulation study of randomly generated profiles of related and unrelated individuals with 13-locus CODIS genotypes and YFiler® Y-chromosome haplotypes, on which the Myers protocol for relative identification was carried out. For Y-chromosome sharing first degree relatives, the Myers protocol has a high probability () of identifying their relationship. For unrelated individuals, there is a low probability that an unrelated person in the database will be identified as a first-degree relative. For more distant Y-haplotype sharing relatives (half-siblings, first cousins, half-first cousins or second cousins) there is a substantial probability that the more distant relative will be incorrectly identified as a first-degree relative. For example, there is a probability that a first cousin will be identified as a full sibling, with the probability depending on the population background. Although the California familial search policy is likely to identify a first degree relative if his profile is in the database, and it poses little risk of falsely identifying an unrelated individual in a database as a first-degree relative, there is a substantial risk of falsely identifying a more distant Y-haplotype sharing relative in the database as a first-degree relative, with the consequence that their immediate family may become the target for further investigation. This risk falls disproportionately on those ethnic groups that are currently overrepresented in state and federal databases.     

A Comparison of Nonparametric Error Rate Estimation Methods in Classification Problems

Assessment of the misclassification error rate is of high practical relevance in many biomedical applications. As it is a complex problem, theoretical results on estimator performance are few. The origin of most findings are Monte Carlo simulations, which take place in the “normal setting”: The covariables of two groups have a multivariate normal distribution; The groups differ in location, but have the same covariance matrix and the linear discriminant function LDF is used for prediction. We perform a new simulation to compare existing nonparametric estimators in a more complex situation. The underlying distribution is based on a logistic model with six binary as well as continuous covariables. To study estimator performance for varying true error rates, three prediction rules including nonparametric classification trees and parametric logistic regression and sample sizes ranging from 100‐1,000 are considered. In contrast to most published papers we turn our attention to estimator performance based on simple, even inappropriate prediction rules and relatively large training sets. For the major part, results are in agreement with usual findings. The most strikingly behavior was seen in applying (simple) classification trees for prediction: Since the apparent error rate Êrr.app is biased, linear combinations incorporating Êrr.app underestimate the true error rate even for large sample sizes. The .632+ estimator, which was designed to correct for the overoptimism of Efron's .632 estimator for nonparametric prediction rules, performs best of all such linear combinations. The bootstrap estimator Êrr.B0 and the crossvalidation estimator Êrr.cv, which do not depend on Êrr.app, seem to track the true error rate. Although the disadvantages of both estimators – pessimism of Êrr.B0 and high variability of Êrr.cv – shrink with increased sample sizes, they are still visible. We conclude that for the choice of a particular estimator the asymptotic behavior of the apparent error rate is important. For the assessment of estimator performance the variance of the true error rate is crucial, where in general the stability of prediction procedures is essential for the application of estimators based on resampling methods. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

On Error Minimization in a Sequential Origin of the Standard Genetic Code

Distances between amino acids were derived from the polar requirement measure of amino acid polarity and Benner and co-workers' (1994) 74-100 PAM matrix. These distances were used to examine the average effects of amino acid substitutions due to single-base errors in the standard genetic code and equally degenerate randomized variants of the standard code. Second-position transitions conserved all distances on average, an order of magnitude more than did second-position transversions. In contrast, first-position transitions and transversions were about equally conservative. In comparison with randomized codes, second-position transitions in the standard code significantly conserved mean square differences in polar requirement and mean Benner matrix-based distances, but mean absolute value differences in polar requirement were not significantly conserved. The discrepancy suggests that these commonly used distance measures may be insufficient for strict hypothesis testing without more information. The translational consequences of single-base errors were then examined in different codon contexts, and similarities between these contexts explored with a hierarchical cluster analysis. In one cluster of codon contexts corresponding to the RNY and GNR codons, second-position transversions between C and G and transitions between C and U were most conservative of both polar requirement and the matrix-based distance. In another cluster of codon contexts, second-position transitions between A and G were most conservative. Despite the claims of previous authors to the contrary, it is shown theoretically that the standard code may have been shaped by position-invariant forces such as mutation and base content. These forces may have left heterogeneous signatures in the code because of differences in translational fidelity by codon position. A scenario for the origin of the code is presented wherein selection for error minimization could have occurred multiple times in disjoint parts of the code through a phyletic process of competition between lineages. This process permits error minimization without the disruption of previously useful messages, and does not predict that the code is optimally error-minimizing with respect to modern error. Instead, the code may be a record of genetic process and patterns of mutation before the radiation of modern organisms and organelles. Received: 28 July 1997 / Accepted: 23 January 1998     

Crow-Kimura模型中位点突变率对误差阈的随机效应

为了补充Eigen模型和Crow-Kimura模型的随机效应研究,Crow-Kimura模型中的位点突变率被处理成高斯分布随机变量,从而研究误差阈值的特征以及误差阈值的扩展与随机突变率涨落强度之间的关系. 准物种浓度和群体序参数分析表明,在位点突变率涨落较大时,误差阈值不再是相变点,而是平滑的转变区域. 定量分析表明,随机Crow-Kimura模型中转变区宽度与涨落强度之间的关系是非线性的. 将Crow-Kimura模型与Eigen模型的随机特征进行比较发现,在两个模型中适应值随机化使得转变区域的宽度和随机变量涨落强度之间的关系是线性的,而位点突变率随机化中两者的关系是非线性的（指数）. 对于随机化的Crow-Kimura模型,适应值随机化与位点突变率随机化引起的误差阈扩展效应相当. 对于随机Eigen模型,误差阈的扩展效应则主要是由位点突变率的随机化引起的. 之后,本文概述了Eigen模型和Crow Kimura模型中适应值和位点突变率随机化对误差阈值随机效应的影响,并讨论了上述结果对抗病毒策略、癌症治疗和动植物育种的重要意义.     

A Double-Observer Method to Estimate Detection Rate During Aerial Waterfowl Surveys

Abstract We evaluated double-observer methods for aerial surveys as a means to adjust counts of waterfowl for incomplete detection. We conducted our study in eastern Canada and the northeast United States utilizing 3 aerial-survey crews flying 3 different types of fixed-wing aircraft. We reconciled counts of front- and rear-seat observers immediately following an observation by the rear-seat observer (i.e., on-the-fly reconciliation). We evaluated 6 a priori models containing a combination of several factors thought to influence detection probability including observer, seat position, aircraft type, and group size. We analyzed data for American black ducks (Anas rubripes) and mallards (A. platyrhynchos), which are among the most abundant duck species in this region. The best-supported model for both black ducks and mallards included observer effects. Sample sizes of black ducks were sufficient to estimate observer-specific detection rates for each crew. Estimated detection rates for black ducks were 0.62 (SE = 0.10), 0.63 (SE = 0.06), and 0.74 (SE = 0.07) for pilot-observers, 0.61 (SE = 0.08), 0.62 (SE = 0.06), and 0.81 (SE = 0.07) for other front-seat observers, and 0.43 (SE = 0.05), 0.58 (SE = 0.06), and 0.73 (SE = 0.04) for rear-seat observers. For mallards, sample sizes were adequate to generate stable maximum-likelihood estimates of observer-specific detection rates for only one aerial crew. Estimated observer-specific detection rates for that crew were 0.84 (SE = 0.04) for the pilot-observer, 0.74 (SE = 0.05) for the other front-seat observer, and 0.47 (SE = 0.03) for the rear-seat observer. Estimated observer detection rates were confounded by the position of the seat occupied by an observer, because observers did not switch seats, and by land-cover because vegetation and landform varied among crew areas. Double-observer methods with on-the-fly reconciliation, although not without challenges, offer one viable option to account for detection bias in aerial waterfowl surveys where birds are distributed at low density in remote areas that are inaccessible by ground crews. Double-observer methods, however, estimate only detection rate of animals that are potentially observable given the survey method applied. Auxiliary data and methods must be considered to estimate overall detection rate.     

Asymptotic Expansions and Estimation of the Expected Error Rate for Equal-Mean Discrimination with Uniform Covariance Structure

The asymptotic error rate of the equal-mean, uniform-covariance-matrix classification rule is approximated by a first order asymptotic expansion. The approximation is compared for accuracy with a Monte Carlo simulation. Finally, an estimator of the error rate and an estimator of the variance of the error rate estimator are derived and applied to a classical example.     

Auscultation of Foetal Heart Rate: an Assessment of its Error and Significance

Auscultation of foetal heart rate was shown to be subject to three types of error: a random error, an error biased towards normality when the heart rate is fast or slow, and an error based on the inability to count the heart rate during contractions. In spite of these limitations a clinically observed foetal heart rate of more than 160 beats per minute was shown to be associated with significantly lower Apgar scores at birth. In contrast, a steady foetal heart rate of 100–120 beats per minute was not.     

The Error Rate of Tests for Regression Coincidence and Parallelism Under Unknown Multiplicative Heteroscedasticity

The usual F test of regression coincidence, which is appropriate under a homoscedastic model, is examined under a multiplicatively heteroscedastic model. The departure of the test from its nominal level is slight when the sample of explanatory variables is symmetric, but may be substantially inflated when the sample has positive skew. Conversely, the nominal level may be slightly depressed when the sample has negative skew. The size of the perturbation from the nominal level depends on the degree of heteroscedasticity, however its effect is more pronounced with positively skewed samples. Similar trends are evident for the usual F test of regression parallelism. There is no apparent pattern to the discrepancy of the level of the test with regard to the data which would permit empirical researchers to adjust their results.     

Expected Relationship Between the Silent Substitution Rate and the GC Content: Implications for the Evolution of Isochores

The relationship between the silent substitution rate (K _s) and the GC content along the genome is a focal point of the debate about the origin of the isochore structure in vertebrates. Recent estimation of the silent substitution rate showed a positive correlation between K _s and GC content, in contradiction with the predictions of both the regional mutation bias model and the selection or biased gene conversion model. The aim of this paper is to help resolve this contradiction between theoretical studies and data. We analyzed the relationship between K _s and GC content under (1) uniform mutation bias, (2) a regional mutation bias, and (3) mutation bias and selection. We report that an increase in K _s with GC content is expected under mutation bias because of either nonequilibrium of the isochore structure or an increasing mutation rate from AT toward GC nucleotides in GC-richer isochores. We show by simulations that CpG deamination tends to increase the mutation rate with GC content in a regional mutation bias model. We also demonstrate that the relationship between K _s and GC under the selectionist or biased gene conversion model is positive under weak selection if the mutation selection equilibrium GC frequency is less than 0.5. Received: 28 March 2001 / Accepted: 16 May 2001     

Error in Estimation of Rate and Time Inferred from the Early Amniote Fossil Record and Avian Molecular Clocks

The best reconstructions of the history of life will use both molecular time estimates and fossil data. Errors in molecular rate estimation typically are unaccounted for and no attempts have been made to quantify this uncertainty comprehensively. Here, focus is primarily on fossil calibration error because this error is least well understood and nearly universally disregarded. Our quantification of errors in the synapsid–diapsid calibration illustrates that although some error can derive from geological dating of sedimentary rocks, the absence of good stem fossils makes phylogenetic error the most critical. We therefore propose the use of calibration ages that are based on the first undisputed synapsid and diapsid. This approach yields minimum age estimates and standard errors of 306.1±8.5 MYR for the divergence leading to birds and mammals. Because this upper bound overlaps with the recent use of 310 MYR, we do not support the notion that several metazoan divergence times are significantly overestimated because of serious miscalibration (sensu Lee 1999). However, the propagation of relevant errors reduces the statistical significance of the pre-K–T boundary diversification of many bird lineages despite retaining similar point time estimates. Our results demand renewed investigation into suitable loci and fossil calibrations for constructing evolutionary timescales.[Reviewing Editor: Martin Kreitman]     

Yeast Spontaneous Mutation Rate and Spectrum Vary with Environment

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New insights into the interplay between codon bias determinants in plants

Codon bias is the non-random use of synonymous codons, a phenomenon that has been observed in species as diverse as bacteria, plants and mammals. The preferential use of particular synonymous codons may reflect neutral mechanisms (e.g. mutational bias, G|C-biased gene conversion, genetic drift) and/or selection for mRNA stability, translational efficiency and accuracy. The extent to which these different factors influence codon usage is unknown, so we dissected the contribution of mutational bias and selection towards codon bias in genes from 15 eudicots, 4 monocots and 2 mosses. We analysed the frequency of mononucleotides, dinucleotides and trinucleotides and investigated whether the compositional genomic background could account for the observed codon usage profiles. Neutral forces such as mutational pressure and G|C-biased gene conversion appeared to underlie most of the observed codon bias, although there was also evidence for the selection of optimal translational efficiency and mRNA folding. Our data confirmed the compositional differences between monocots and dicots, with the former featuring in general a lower background compositional bias but a higher overall codon bias.     

Long-term monitoring data meet freshwater species distribution models: Lessons from an LTER-site

Long-term monitoring datasets provide a solid framework for ecological research. Such a dataset from the German long-term ecological research (LTER) site Rhine-Main-Observatory was used to set up a species distribution model (SDM) for the Kinzig catchment. The extensive knowledge on the monitoring data provided by the LTER-site framework allowed to calibrate a robust model for 175 taxa of stream macroinvertebrates and to project their distributions on the Kinzig River stream network using bioclimatic, topographical, hydrological, land use and geological predictors. On average, model performance was good, with a TSS of 0.83 (±0.09 SD) and a ROC of 0.95 (±0.03 SD). The model delivered valuable insights on three sources of bias that plague SDMs in general: (a) level of taxonomic identification of the modeled organisms, (b) the spatial arrangement of sampling sites, and (c) the sampling intensity at each sampling site. Taxonomic resolution did not affect SDM performance. The distribution of high predicted probabilities of occurrence in the stream network coincided with those segments in the stream network most densely and frequently sampled, indicating both a spatial and temporal sampling bias. Species richness curves confirmed the temporal sampling bias. Next to spatial bias, sampling frequency also plays an important role in data collection, affecting further analysis and modeling procedures. Results indicate an underrepresentation of low order streams, an important aspect that should be addressed by both monitoring schemes and modeling approaches.