Sequence-Structure Alignment Using a Statistical Analysis of Core Models and Dynamic Programming

The expanding availability of protein data enforces the application of empirical methods necessary to recognize protein structures. In this paper a sequence-structure alignment method is described and applied to various Ubiquitin-like folded Ras-binding domains. On the basis of two probability functions that evaluate similarities between the occurrence of amino-acids in the primary and secondary protein structure, different versions of simple scoring functions are proposed. The application of the program ’PLACER’ that uses a dynamic programming approach enables the search for an optimal sequence-structure alignment and the prediction of the secondary structure

Quantitative trait loci mapping in plant genetics by [alpha]-design experiments and molecular genetic marker systems

Research concerning the quantitative trait loci (QTL) mapping in plant genetics usually consists of two stages. The first stage is concerned with collecting data while the second one, based on the data collected, is concerned with a proper QTL study. The final inferences are strictly connected with the quality of the two approaches applied in both stages. Data to be analyzed come from an experiment dealing with offsprings obtained from a crossing system of several lines. The genotypes then are observed in some natural or quasi natural environment. The QTL studies are based on so called genotype adjusted means. In a-designs the adjusted means can be calculated in many ways, which will be presented in this paper. We also give an EM-algorithm for the estimation of genetic parameters and comment on recent biometrical research in molecular plant genetics. Finally we mention some activities in the new field of bioinformatics

Spectral estimation for psycho-physiological data Estimating lower-dimensional representations in frequency space

Two different estimation techniques for the spectrum of a nonstationary time series are compared empirically. Both of them are assuming a time-dependent autoregressive (AR-) model for the data. The fifirst estimation technique used is the Frequency State Dependent Model (FSDM-) technique (Schmitz and Urfer, 1997), a modification of the well known Kalman-filter approach. The FSD-Model is based on Priestleys SD-Models for the analysis of nonstationary time series (e.g.,Priestley, 1988). An alternative approach for estimating AR-parameters of nonstationary time series was proposed by Tsatsannis and Giannkis (1993). The basic idea is to directly decompose the time-dependent autoregressive parameters into their wavelet representation and to select suitable wavelet coefficients for reconstruction. In either case, Kitagawa's (1983) "instantaneous spectrum" is calculated to obtain the actual spectral estimates. Applied to empirical data, both approaches lead to similar spectral estimates. However, simulations show how crucial the selection of wavelet coefficients is when applying the latter technique

Genetical and statistical aspects of polymerase chain reactions

In this paper we describe the principles of polymerase chain reaction (PCR) and its expanding use in molecular genetic research and molecular medicine. A short introduction of exemplary applications of the PCR is connected with a discussion of the lack of PCR accuracy. We give a statistical model for the PCR and discuss estimation methods in order to quantify the lack of PCR accuracy

Application of Hidden Markov Models for Identification of Short Protein Repeats

In this paper, hidden Markov models (HMMs) are discussed in the context of molecular biological sequence analysis. The statistics relevant in the HMM approach are described in detail. An HMM based method is used to analyze two proteins that contain short protein repeats (SPRs). As a benchmark, a state-of-the-art program for the detection of SPRs is also used for both proteins. Finally, an outlook for combination possibilities of HMMs with phylogenetic approaches is given

Secondary structure classification of amino-acid sequences using state-space modeling

The secondary structure classification of amino acid sequences can be carried out by a statistical analysis of sequence and structure data using state-space models. Aiming at this classification, a modified filter algorithm programmed in S is applied to data of three proteins. The application leads to correct classifications of two proteins even when using relatively simple estimation methods for the parameters of the state-space models. Furthermore, it has been shown that the assumed initial distribution strongly influences the classification results referring to two proteins

Application of the disposition model to breast cancer data

In this paper, we have presented the second level nesting of Bonney's disposition model (Bonney, 1998) and examined the implications of higher level nesting of the disposition model in relation to the dimension of the parameter space. We have also compared the performance of the disposition model with Cox's regression model (Cox, 1972). It has been observed that the disposition model has a very large number of unknown parameters, and is therefore limited by the method of estimation used. In the case of the maximum likelihood method, reasonable estimates are obtained if the number of parameters in the model is at most nine. This corresponds to about four to seven covariates. Since each covariate in Cox's model provides a parameter, it is possible to include more covariates in the regression analysis. On the other hand, as opposed to Cox's model, the disposition model is fitted with parameters to capture aggregation in families, if there should be any. The choice of a particular model should therefore depend on the available data set and the purpose of the statistical analysis. --Second level nesting,Proportional hazards model,Quadratic exponential form,Partial likelihood,Familial aggregation,Second-order methods,Marginal models,Conditional models

Analysis of Spatial Structure of Latent Effects Governing Hydrogeological Phenomena

In the present study we investigate the data provided by the karstwater level monitoring system set up in the Transdanubian Mountains, more precisely in the Bakony, the Keszthelyi Mountains and the Balaton-Highland. (Here, like in the sequel, the term karstwater is used for groundwater in karstic areas.) The detailed description of the monitoring system itself and the geological and hydrogeological situation in which the system was planned to function and collect data about the water level can be found in Markus et al (1997) as well as the results of our previous study in determining the underlying (called also latent or background) effects driving the karstwater fluctuations

Statistical analysis of Sequence-Structure Alignment Scores

The structural analysis of proteins is fundamental to the analysis of protein functions. In this context, sequence-structure alignment methods are important among the different empirical methods. In order to assess the quality of sequence-structure alignments, a statistical method using a Bayesian approach proposed by Lathrop et al. (1998) will be presented. Finally, the results of a developed statistical analysis of scores of RDP(recursive dynamic programming)-sequence-structure alignments (Thiele et al., 1999) according to data of six proteins will be described