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

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

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

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

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