Stochastic Models for Biological Patterns

This chapter reviews the prevalent mathematical formalisms for modeling biological sequences and patterns. Underlying theoretical principles for computationally simple IID models is followed by a discussion on the Markov models for sequences. Similarly, discussions on pattern models focused on the theoretical background of Position Specific Scoring Matrices and Profiles for representation of biological sequence patterns. A detailed discussion and illustration of modeling patterns using Hidden Markov Models is presented.

[1]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[2]  M. Gribskov,et al.  [13] Identification of sequence patterns with profile analysis , 1996 .

[3]  A. D. McLachlan,et al.  Profile analysis: detection of distantly related proteins. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[4]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[5]  P. Bucher.  Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. , 1990, Journal of molecular biology.

[6]  Rodger Staden,et al.  Methods for calculating the probabilities of finding patterns in sequences , 1989, Comput. Appl. Biosci..

[7]  M. Borodovsky,et al.  How to interpret an anonymous bacterial genome: machine learning approach to gene identification. , 1998, Genome research.

[8]  Anders Krogh,et al.  Chapter 4 - An introduction to hidden Markov models for biological sequences , 1998 .

[9]  Elmar Nöth,et al.  Interpolated markov chains for eukaryotic promoter recognition , 1999, Bioinform..

[10]  Rodger Staden,et al.  Methods to define and locate patterns of motifs in sequences , 1988, Comput. Appl. Biosci..

[11]  D. Haussler,et al.  Protein modeling using hidden Markov models: analysis of globins , 1993, [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences.