DSpace at VNU: An improving method for estimating amino acid replacement models

An improving method for estimating amino acid replacement models Lê Văn Đạt Trường Đại học Công nghệ Chuyên ngành: Khoa học máy tính; Mã số: 60 48 01 Người hướng dẫn: TS.. Lê Sỹ Vinh

An improving method for estimating amino acid

replacement models

Lê Văn Đạt

Trường Đại học Công nghệ Chuyên ngành: Khoa học máy tính; Mã số: 60 48 01

Người hướng dẫn: TS Lê Sỹ Vinh

Năm bảo vệ: 2012

Abstract: Amino acid replacement models (amino acid substitution models or

ma-trices) play important roles in protein phylogenetics analysis and protein sequence alignment Dayhoff was the fi rst person who proposed a method to build amino acid models in 1972 Currently, maximum likelihood (ML) methods are widely used to estimate popular models such as WAG, LG, FLU, etc However, ML methods are slow and not applicable to large datasets The most time consuming step in estimating matrices is build-ingphylogenetics trees from protein alignments In this thesis, we propose new methods to overcome the obstacle by splitting large alignments into small ones which still contain enough evolutionary information for esti-mating matrices Experiments with both Pfam and FLU data sets show that proposed meth-ods are about three to nine times faster than the best current method while the quality of estimated matrices are nearly the same Thus, our methods will enable researchers to estimate matrices from very large datasets

List of Figures v

1.1 Motivation 1

1.2 Outline of thesis 5

2.1 Amino and amino substitutions 6

2.2 Markov model 8

2.3 Amino substitution models 9

2.4 Methods to estimate amino substitution models 10

2.4.1 Counting methods 10

2.4.2 Maximum likelihood methods 12

2.4.2.1 Intro 12

2.4.2.2 Steps to build an amino substitution

model by maximum likelihood method 13

2.4.3 15

3 Alignment splitting methods for estimating amino

3.1 The multiple alignment 17

3.2 Steps to build an amino substitution model by align-ment splitting method 19

3.3 Random alignment splitting 20

3.4 Tree-based alignment splitting 20

4 Results 25 4.1 Compare methods on Pfam data set 26

4.1.1 Data preparation 26

4.1.2 Time 26

4.1.3 P 26

4.1.4 Robustness of model 29

4.2 Compare methods on FLU data set 30

4.2.1 Data preparation 30

4.2.2 Time 31

4.2.3 P 31

4.2.4 Robustness of model 33

36

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