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An MLE-based clustering method on DNA barcode

An MLE-based clustering method on DNA barcode. Ching-Ray Yu Statistics Department Rutgers University, USA chingray@stat.rutgers.edu 07/07/2006. Outline. Problem & Motivation Statistical modeling Preliminary result Future work. Problem & Motivation.

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An MLE-based clustering method on DNA barcode

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  1. An MLE-based clustering method on DNA barcode Ching-Ray Yu Statistics Department Rutgers University, USA chingray@stat.rutgers.edu 07/07/2006

  2. Outline • Problem & Motivation • Statistical modeling • Preliminary result • Future work Rutgers University

  3. Problem & Motivation • Species clustering problem is a very important issue on DNA barcode project. • MEGA3 by S. Kumar, K. Tamura, and M. Nei (2004) has limited success when applying the true data set. • A good method to cluster new biological specimens to the proper species is very important. This is also one of the goals of this DNA initiative. Rutgers University

  4. Statistical modeling • Statistical model. • Assumption: • Nucleotides are iid. random variables which are multinomial distributed with parameter , where i stands for the DNA (COI) sequence and j indicates the position. Rutgers University

  5. MLE-based clustering method • Maximum likelihood estimator (MLE) • Suppose are the MLE of in species k. • The clustering rule : A new unidentified specimen , where , is assigned to species k if Rutgers University

  6. Preliminary result • There are 150 species with 1623 COI sequences in the training datasets provide by DIMACS. • In order to performance the misclassification rate of the MLE-based clustering method, I randomly select 200 COI sequences as testing set. The MLE of parameters is estimated from the rest 1423 COI sequences. The misclassification rate is about 16%. Rutgers University

  7. Future work • The misclassification rate could be improved if more complicated models are considered. • For example: • gaps of the COI sequences. • Kimura-two-parameter (K2P) model (1980) considers the biological diversity with different evolution rate. • synonymous and non-synonymous substitutions. • Remark:Synonymous substitutions will change the encoded amino acid. Rutgers University

  8. Proposed deliverables • If the MLE-based clustering method works well on barcode sequences, it can be written as a paper with R-code for public users of barcode data. Rutgers University

  9. Acknowledgement • NSF grant • Data Analysis Working Group • DIMACS at Rutgers • My advisor: Dr. Javier Cabrera Rutgers University

  10. References • M. Kimura (1980) A simple method for estimating evolutionary rate of base substitution through comparative studies of nucleotide sequences. J. Mol. Evol. 16: 111-120. • Sudhir Kumar, Koichior Tamura and Masatoshi Nei (2004) MEGA 3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment. Briefings in Bioinformatics. Vol 5. No 2. 150-163. Rutgers University

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