Probability and Statistics and Related Biomolecular Sequence Analysis

点击翻译按钮获取中文摘要

9704552 Karlin This research centers on developing new probabilistic and statistical tools of relevance to biomolecular sequence analysis with particular emphasis on: (i) The use of scan statistical analysis of marker arrays in multiple correlated sequences. Specifically, to find asymptotic distributions of extremal r-scan lengths for several stochastic models, extending earlier work of the investigators by accommodating dependent and/or transformed data. These asymptotics then serve as benchmarks for interpreting various biological correlated multiple sequence data by providing estimates of probabilities of clustering or over-dispersion of markers along one or more sequences. (ii) To provide a flexible and general framework for characterizing residue clusters and statistical significance of unusual compositions in protein 3D structures as well as the means of assessing statistical significance of strong local similarities among two or more such structures. The proposed framework is based on the study of random models for weighted labeled graphs and as such it generalizes previous work on the probabilistic foundation of general score based methods for use in protein sequence comparison. Since 1995, several prokaryotic genomes and one eukaryotic genome have been reported, and many are forthcoming. These genomes provide opportunities and pose challenges for characterizing genomic inhomogeneities, for detecting significant sequence patterns, and for evolutionary comparisons. Availability of complete genomes and massive amounts of DNA and protein sequences opens a new era in bioinformatics research. Advances in (i) and (ii) above can provide much needed tools for biomolecular data analysis and for comparison of protein sequences and corresponding three-dimensional structures. Software approaches are often used to find relationships between newly discovered sequences and the existing data banks. Similarities detected can be used to predict biologi cal characteristics of new structures, suggest directions for further experimental study, and point to possible medical applications. However, due to the accelerating growth of the data banks, random correlations might at first appear significant and thus mask the true biological information existing in the data. This research aims at putting biomolecular data analysis procedures on a firm mathematical foundation, and concomitantly reducing the masking effect of random correlations.