Multiple hypothesis testing to detect lineages under positive selection that affects only a few sites

Detection of positive Darwinian selection has become ever more important with the rapid growth of genomic data sets. Recent branch-site models of codon substitution account for variation of selective pressure over branches on the tree and across sites in the sequence and provide a means to detect short episodes of molecular adaptation affecting just a few sites. In likelihood ratio tests based on such models, the branches to be tested for positive selection have to be specified a priori. In the absence of a biological hypothesis to designate so-called foreground branches, one may test many branches, but a correction for multiple testing becomes necessary. In this paper, we employ computer simulation to evaluate the performance of 6 multiple test correction procedures when the branch-site models are used to test every branch on the phylogeny for positive selection. Four of the methods control the familywise error rates (FWERs), whereas the other 2 control the false discovery rate (FDR). We found that all correction procedures achieved acceptable FWER except for extremely divergent sequences and serious model violations, when the test may become unreliable. The power of the test to detect positive selection is influenced by the strength of selection and the sequence divergence, with the highest power observed at intermediate divergences. The 4 correction procedures that control the FWER had similar power. We recommend Rom's procedure for its slightly higher power, but the simple Bonferroni correction is useable as well. The 2 correction procedures that control the FDR had slightly more power and also higher FWER. We demonstrate the multiple test procedures by analyzing gene sequences from the extracellular domain of the cluster of differentiation 2 (CD2) gene from 10 mammalian species. Both our simulation and real data analysis suggest that the multiple test procedures are useful when multiple branches have to be tested on the same data set.     

Statistical aspects of van't Hoff analysis: a simulation study

The thermodynamics of biological interactions is frequently studied by the van't Hoff analysis whereby data on variation of the binding constant K(D) with temperature are used to obtain estimates of standard enthalpy (Delta H degrees ), entropy (Delta S degrees ), and heat capacity (Delta C degrees P) of complex formation. A Monte Carlo simulation demonstrates that the absolute error of the above parameters is proportional to the relative error of KD and independent of the actual values of KD and of the way they vary with temperature. The error of Delta H degrees is approximately the same as that of T Delta S degrees (within 14% in the temperature range 5-45 degrees C). The error depends both on the number of temperature points within the experimental temperature range and on the size of the range, but it is more sensitive to the latter. Using the linear form of the van't Hoff equation to fit data with non-zero Delta C degrees P gives erroneous Delta H degrees and DeltaS degrees estimates at standard temperature except for the case when the T points are placed symmetrically with respect to the standard temperature. With the range of Delta C degrees P values usual for protein-protein interactions, the KD error must be very low to confidently infer that Delta C degrees P is non-zero or to claim that two interactions have different Delta C degrees P.     

What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.     

Okazaki fragment maturation involves α-segment error editing by the mammalian FEN1/MutSα functional complex

During nuclear DNA replication, proofreading-deficient DNA polymerase α (Pol α) initiates Okazaki fragment synthesis with lower fidelity than bulk replication by proofreading-proficient Pol δ or Pol ε. Here, we provide evidence that the exonuclease activity of mammalian flap endonuclease (FEN1) excises Pol α replication errors in a MutSα-dependent, MutLα-independent mismatch repair process we call Pol α-segment error editing (AEE). We show that MSH2 interacts with FEN1 and facilitates its nuclease activity to remove mismatches near the 5′ ends of DNA substrates. Mouse cells and mice encoding FEN1 mutations display AEE deficiency, a strong mutator phenotype, enhanced cellular transformation, and increased cancer susceptibility. The results identify a novel role for FEN1 in a specialized mismatch repair pathway and a new cancer etiological mechanism.     

Observer error,dental wear,and the inference of new world sundadonty

Dental morphology provides important information on human evolution and interpopulation relationships. Dental wear is one of the major limitations of morphological data analysis. Wear figures heavily in existing debates about patterns of New World dental variation with some scholars finding evidence for a more generalized dentition in early New World populations (Powell: Doctoral Dissertation, Texas A&M University, TX (1995)) and others questioning these findings based on the probable effects of dental wear on trait scores (Turner, The First Americans: the Pleistocene Colonization of the New World. San Francisco: California Academy of Sciences (2002) 123–158; Turner: Am J Phys Anthropol 130 (2006) 455–461; Turner and Scott, Handbook of paleoanthropology, Vol. III: Phylogeny of Hominids. New York: Springer (2007) 1901–1941). Here we evaluate these competing claims using data from the Early Archaic Windover sample. Results confirm the dental distinctiveness of Windover with respect to other Old World Asian (i.e., sinodont/sundadont) populations. However, comparison of our results to those of Powell (1995) also highlights significant interobserver error. Statistical analysis of matched wear and morphology scores suggests trait downgrading for some traits. Patterns of missing data present a more challenging (and potentially serious) problem. Use of Little's MCAR test for missing data mechanisms indicates a complex process of data collection in which incidental and opportunistic recording of both highly worn and unerupted teeth introduce a “missing not at random” mechanism into our dataset that biases dental trait frequencies. We conclude that patterns of missingness and formal research designs for “planned missingness” are needed to help mitigate this bias. Am J Phys Anthropol 156:349–362, 2015. © 2014 Wiley Periodicals, Inc.     

Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood

We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance–covariance matrices ( G ). Large‐sample theory shows that maximum‐likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G . This suggests that sampling estimates of G from this distribution can be used to assess the variability of estimates of G , and of functions of G . We refer to this as the REML‐MVN method. This has been implemented in the mixed‐model program WOMBAT. Estimates of sampling variances from REML‐MVN were compared to those from the parametric bootstrap and from a Bayesian Markov chain Monte Carlo (MCMC) approach (implemented in the R package MCMCglmm). We apply each approach to evolvability statistics previously estimated for a large, 20‐dimensional data set for Drosophila wings. REML‐MVN and MCMC sampling variances are close to those estimated with the parametric bootstrap. Both slightly underestimate the error in the best‐estimated aspects of the G matrix. REML analysis supports the previous conclusion that the G matrix for this population is full rank. REML‐MVN is computationally very efficient, making it an attractive alternative to both data resampling and MCMC approaches to assessing confidence in parameters of evolutionary interest.     

Measures of precision for dissimilarity‐based multivariate analysis of ecological communities

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity‐based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity‐based standard error (MultSE) as a useful quantity for assessing sample‐size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided.     

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

Background

RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial.

Results

We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site’s coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone.

Conclusions

These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1456-x) contains supplementary material, which is available to authorized users.     

Prediction model of DnBP degradation based on BP neural network in AAO system

A laboratory-scale anaerobic-anoxic-oxic (AAO) system was established to investigate the fate of DnBP. A removal kinetic model including sorption and biodegradation was formulated, and kinetic parameters were evaluated with batch experiments under anaerobic, anoxic, oxic conditions. However, it is highly complex and is difficult to confirm the kinetic parameters using conventional mathematical modeling. To correlate the experimental data with available models or some modified empirical equations, an artificial neural network model based on multilayered partial recurrent back propagation (BP) algorithm was applied for the biodegradation of DnBP from the water quality characteristic parameters. Compared to the kinetic model, the performance of the network for modeling DnBP is found to be more impressive. The results showed that the biggest relative error of BP network prediction model was 9.95%, while the kinetic model was 14.52%, which illustrates BP model predicting effluent DnBP more accurately than kinetic model forecasting.