A STATISTICAL ANALYTICAL APPROACH TO DECIPHER INFORMATION FROM BIOLOGICAL SEQUENCES : APPLICATION TO MURINE SPLICE-SITE ANALYSIS AND PREDICTION

Abstract A simple statistical approach for the analysis of biological sequences, such as splice-sites, promoter regions, helices and extended structure forming regions or any other sequence dependent functional entities in proteins, is presented. The approach has been proved useful to develop a method for prediction of such entities in newly available sequences. We first search for invariant sequence features of each functional entity from the experimentally available sequences and identify a set of ‘like’ sequences with similar sequence features. In the next step, concrete features of sequence entities in terms of occurrences of smaller subsequences are identified at various positions which are used as a knowledge base to select potential functional entities from the identified ‘like’ sequences. The third step consists of refinement of this pattern learning, statistical improvements of the knowledge base weight matrices, and finally its application to predict functional entities in newly available sequen...