An MLE-based clustering method on DNA barcode

Ching-Ray Yu

Statistics Department

Rutgers University, USA

chingray@stat.rutgers.edu

07/07/2006

07/07/2006 Rutgers University

Outline

Problem & Motivation Statistical modeling Preliminary result Future work

07/07/2006 Rutgers University

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.

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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.

{ , , , }ijX A T C G

( , , , )A T C Gj j j j j

thithj

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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

ˆ ˆ ˆ ˆˆ ( , , , )A T C Gjk jk jk jk jk jk

1( ,..., )nY Y Y { , , , }iY A T C G

1

ˆargmax i

nyir

r i

k

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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%.

07/07/2006 Rutgers University

Future work The misclassification rate could be

improved if more complicated models are considered.

For example: 1. gaps of the COI sequences.2. Kimura-two-parameter (K2P) model (1980)

considers the biological diversity with different evolution rate.

3. synonymous and non-synonymous substitutions.Remark: Synonymous substitutions will change the encoded amino acid.

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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.

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

07/07/2006 Rutgers University

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.