Esa Uusipaikka

HIGH LARVAL PREDATION RATE IN NON‐OUTBREAKING POPULATIONS OF A GEOMETRID MOTH

We conducted a two-year predator-exclusion study to assess the magnitude and timing of larval predation in non-outbreaking populations of a geometrid moth, Epirrita autumnata. Laboratory-produced newly hatched larvae were placed on the experimental trees which were assigned to five treatments within two larval densities: (1) all predators, including parasitoids, excluded by mesh bag, (2) birds excluded by cage, (3) ants excluded by glue ring, (4) birds and ants excluded, and (5) control without any predator exclusion. Thereafter, larvae were censused every 3–4 d throughout the five-instar larval period. Mortality of E. autumnata larvae in these populations was high and mostly due to natural enemies. In control trees, only ∼10% of larvae survived, while survival was ∼90% in mesh bags preventing all natural enemies. Bird exclusion significantly improved larval survival, as survival was almost three times higher in trees with cages than in those without cages. On the other hand, ant exclusion did not have any overall effects on larval survival, mostly because ants were only detected in about half of the trees without glue rings. Larvae survived longer in high-density trees from which ants were excluded, but the effect was masked by high mortality, unrelated to ant exclusion, in the late larval season. The results suggest that the effect of ant predation on survival of E. autumnata larvae may be spatially restricted and not important at a larger scale. The same result applies for crab spiders, as they caused high mortality in ∼20% of the study trees. Our results emphasize the importance of considering the spatial scale as well as assessing the impact of multiple predators in order to detect predators affecting survival at the population level. Exclusion of all predation had a significantly stronger effect on larval survival than exclusion of birds alone. Further, mortality was highest during the late larval period, when parasitoids emerge. Thus, a large proportion of larval mortality was most likely due to parasitism. Our results suggest that predation by passerine birds and parasitism may contribute to maintenance of low E. autumnata densities by strong suppression of the number of larvae entering the pupal stage.

Exact Confidence Bands for Linear Regression over Intervals

Abstract Exact simultaneous confidence intervals and bands for the regression line on a set Cx in simple linear regression have been known only when Cx is a finite set with one or two points or when Cx is a finite or infinite interval. Otherwise, conservative bands have been used. In this article we present a method for constructing exact confidence bands on an arbitrary finite union of intervals or points. Also, tables are provided for cases of finite Cx with three points and of Cx an interval.

A statistical score for assessing the quality of multiple sequence alignments.

BackgroundMultiple sequence alignment is the foundation of many important applications in bioinformatics that aim at detecting functionally important regions, predicting protein structures, building phylogenetic trees etc. Although the automatic construction of a multiple sequence alignment for a set of remotely related sequences cause a very challenging and error-prone task, many downstream analyses still rely heavily on the accuracy of the alignments.ResultsTo address the need for an objective evaluation framework, we introduce a statistical score that assesses the quality of a given multiple sequence alignment. The quality assessment is based on counting the number of significantly conserved positions in the alignment using importance sampling method in conjunction with statistical profile analysis framework. We first evaluate a novel objective function used in the alignment quality score for measuring the positional conservation. The results for the Src homology 2 (SH2) domain, Ras-like proteins, peptidase M13, subtilase and β-lactamase families demonstrate that the score can distinguish sequence patterns with different degrees of conservation. Secondly, we evaluate the quality of the alignments produced by several widely used multiple sequence alignment programs using a novel alignment quality score and a commonly used sum of pairs method. According to these results, the Mafft strategy L-INS-i outperforms the other methods, although the difference between the Probcons, TCoffee and Muscle is mostly insignificant. The novel alignment quality score provides similar results than the sum of pairs method.ConclusionThe results indicate that the proposed statistical score is useful in assessing the quality of multiple sequence alignments.

Exact Simultaneous Confidence Intervals for Multiple Comparisons among Three or Four Mean Values

Abstract A procedure for constructing exact simultaneous confidence intervals for a finite set of multiple comparisons among three or four groups is presented. These confidence intervals are hyperbolic and based on an unbiased estimator of the mean values having a normal distribution with covariance matrix σ2V, where V is an arbitrary known positive definite matrix. This procedure is used to study the amount of conservativeness of Tukey—Kramer intervals for pairwise comparisons in an unbalanced one-way analysis of variance design.

Confidence intervals in generalized regression models

Introduction Likelihood-Based Statistical Inference Statistical evidence Statistical inference Likelihood concepts and law of likelihood Likelihood-based methods Profile likelihood-based confidence intervals Likelihood ratio tests (LRTs) Maximum likelihood estimate (MLE) Model selection Generalized Regression Model Examples of regression data Definition of generalized regression models (GRMs) Special cases of GRM Likelihood inference MLE with iterative reweighted least squares Model checking General Linear Model Definition of the general linear model (GLM) Estimate of regression coefficients Test of linear hypotheses Confidence regions and intervals Model checking Nonlinear Regression Model Definition of the nonlinear regression model Estimate of regression parameters Approximate distribution of LRT statistic Profile likelihood-based confidence region Profile likelihood-based confidence interval LRT for a hypothesis on finite set of functions Model checking Generalized Linear Model Definition of generalized linear model (GLIM) MLE of regression coefficients Binomial and Logistic Regression Models Data Binomial distribution Link functions Likelihood inference Logistic regression model Models with other link functions Nonlinear binomial regression model Poisson Regression Model Data Poisson distribution Link functions Likelihood inference Log-linear model Multinomial Regression Data Multinomial distribution Likelihood function Logistic multinomial regression model Proportional odds regression model Other Generalized Linear Regressions Models Negative binomial regression model Gamma regression model Other Generalized Regression Models Weighted GLM Weighted nonlinear regression model Quality design or Taguchi model Lifetime regression model Cox regression model Appendix A: Data Sets Appendix B: Notation Used for Statistical Models Bibliographic notes appear at the end of each chapter.

Model-based prediction of sequence alignment quality

MOTIVATION Multiple sequence alignment (MSA) is an essential prerequisite for many sequence analysis methods and valuable tool itself for describing relationships between protein sequences. Since the success of the sequence analysis is highly dependent on the reliability of alignments, measures for assessing the quality of alignments are highly requisite. RESULTS We present a statistical model-based alignment quality score. Unlike other quality scores, it does not require several parallel alignments for the same set of sequences or additional structural information. Our quality score is based on measuring the conservation level of reference alignments in Homstrad. Reference sequences were realigned with the Mafft, Muscle and Probcons alignment programs, and a sum-of-pairs (SP) score was used to measure the quality of the realignments. Statistical modelling of the SP score as a function of conservation level and other alignment characteristics makes it possible to predict the SP score for any global MSA. The predicted SP scores are highly correlated with the correct SP scores, when tested on the Homstrad and SABmark databases. The results are comparable to that of multiple overlap score (MOS) and better than those of normalized mean distance (NorMD) and normalized iRMSD (NiRMSD) alignment quality criteria. Furthermore, the predicted SP score is able to detect alignments with badly aligned or unrelated sequences. AVAILABILITY The method is freely available at http://www.mtt.fi/AlignmentQuality/.

Parametric methods for estimating covariate-dependent reference limits.

Abstract Age-specific reference limits are required for many clinical laboratory measurements. Statistical assessment of calculated intervals must be performed to obtain reliable reference limits. When parametric, covariate-dependent limits are derived, normal distribution theory usually is applied due to its mathematical simplicity and relative ease of fitting. However, it is not always possible to transform data and achieve a normal distribution. Therefore, models other than those based on normal distribution theory are needed. Generalized linear model theory offers one such alternative. Regardless of the statistical model used, the assumptions behind the model should always be examined.

Cross-Correlation of Spectral Count Ranking to Validate Quantitative Proteome Measurements

The measurement of change in biological systems through protein quantification is a central theme in modern biosciences and medicine. Label-free MS-based methods have greatly increased the ease and throughput in performing this task. Spectral counting is one such method that uses detected MS2 peptide fragmentation ions as a measure of the protein amount. The method is straightforward to use and has gained widespread interest. Additionally reports on new statistical methods for analyzing spectral count data appear at regular intervals, but a systematic evaluation of these is rarely seen. In this work, we studied how similar the results are from different spectral count data analysis methods, given the same biological input data. For this, we chose the algorithms Beta Binomial, PLGEM, QSpec, and PepC to analyze three biological data sets of varying complexity. For analyzing the capability of the methods to detect differences in protein abundance, we also performed controlled experiments by spiking a mixture of 48 human proteins in varying concentrations into a yeast protein digest to mimic biological fold changes. In general, the agreement of the analysis methods was not particularly good on the proteome-wide scale, as considerable differences were found between the different algorithms. However, we observed good agreements between the methods for the top abundance changed proteins, indicating that for a smaller fraction of the proteome changes are measurable, and the methods may be used as valuable tools in the discovery-validation pipeline when applying a cross-validation approach as described here. Performance ranking of the algorithms using samples of known composition showed PLGEM to be superior, followed by Beta Binomial, PepC, and QSpec. Similarly, the normalized versions of the same method, when available, generally outperformed the standard ones. Statistical detection of protein abundance differences was strongly influenced by the number of spectra acquired for the protein and, correspondingly, its molecular mass.

Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment

The assessment of residue conservation in a multiple sequence alignment is a central issue in bioinformatics. Conserved residues and regions are used to determine structural and functional motifs or evolutionary relationships between the sequences of a multiple sequence alignment. For this reason, residue conservation is a valuable measure for database and motif search or for estimating the quality of alignments. In this paper, we present statistical methods for identifying conserved residues in multiple sequence alignments. While most earlier studies examine the positional conservation of the alignment, we focus on the detection of individual conserved residues at a position. The major advantages of multiple comparison methods originate from their ability to select conserved residues simultaneously and to consider the variability of the residue estimates. Large-scale simulations were used for the comparative analysis of the methods. Practical performance was studied by comparing the structurally and functionally important residues of Src homology 2 (SH2) domains to the assignments of the conservation indices. The applicability of the indices was also compared in three additional protein families comprising different degrees of entropy and variability in alignment positions. The results indicate that statistical multiple comparison methods are sensitive and reliable in identifying conserved residues.

Efficient estimation of emission probabilities in profile hidden Markov models.

MOTIVATION Profile hidden Markov models provide a sensitive method for performing sequence database search and aligning multiple sequences. One of the drawbacks of the hidden Markov model is that the conserved amino acids are not emphasized, but signal and noise are treated equally. For this reason, the number of estimated emission parameters is often enormous. Focusing the analysis on conserved residues only should increase the accuracy of sequence database search. RESULTS We address this issue with a new method for efficient emission probability (EEP) estimation, in which amino acids are divided into effective and ineffective residues at each conserved alignment position. A practical study with 20 protein families demonstrated that the EEP method is capable of detecting family members from other proteins with sensitivity of 98% and specificity of 99% on the average, even if the number of free emission parameters was decreased to 15% of the original. In the database search for TIM barrel sequences, EEP recognizes the family members nearly as accurately as HMMER or Blast, but the number of false positive sequences was significantly less than that obtained with the other methods. AVAILABILITY The algorithms written in C language are available on request from the authors.