Dirichlet Mixtures

Specific papers of interest

• Sjolander, K., Karplus, K., Brown, M., Hughey, R., Krogh, A., Mian, I.S., and Haussler, D. Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology. CABIOS, 12(4): 327-345, Aug 1996.

  Abstract

  This paper presents the mathematical foundations of Dirichlet mixtures, which have been used to improve database search results for homologous sequences, when a variable number of sequences from a protein family or domain are known. We present a method for condensing the information in a protein database into a mixture of Dirichlet densities. These mixtures are designed to be combined with observed amino acid frequencies, to form estimates of expected amino acid probabilities at each position in a profile, hidden Markov model, or other statistical model. These estimates give a statistical model greater generalization capacity, such that remotely related family members can be more reliably recognized by the model. Dirichlet mixtures have been shown to outperform substitution matrices and other methods for computing these expected amino acid distributions in database search, resulting in fewer false positives and false negatives for the families tested. This paper corrects a previously published formula for estimating these expected probabilities, and contains complete derivations of the Dirichlet mixture formulas, methods for optimizing the mixtures to match particular databases, and suggestions for efficient implementation.
• Sjolander, K, Karplus, K., Brown, M., Hughey, R., Krogh, A., Mian, I.S., and Haussler, D. Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology.

  This paper is the tech-report version of the CABIOS paper, and has very similar content.

• A more in-depth explanation of Dirichlet mixtures is available. A nine-component mixture estimated from the Blocks database, this paper, and other documentation are also available from our FTP directory. If you have any problems, or are new to ftp-ing, see FTP instructions.

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  See computational biology research at UCSC for other interesting papers.