Posterior Decoding Methods for Optimization and Accuracy Control of Multiple Alignments

136 Pages·2007·1.27 MB·English

Posterior Decoding Methods for Optimization and
Accuracy Control of Multiple Alignments
Ariel Shaul Schwartz
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2007-39
http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-39.html
March 28, 2007
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Posterior Decoding Methods for Optimization and
Accuracy Control of Multiple Alignments
by
Ariel Shaul Schwartz
B.Sc. (Technion, Israel) 1999
A dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
in
Computer Science
in the
GRADUATE DIVISION
of the
UNIVERSITY OF CALIFORNIA, BERKELEY
Committee in charge:
Professor Lior Pachter, Chair
Professor Dan Klein
Professor Marti Hearst
Spring 2007
The dissertation of Ariel Shaul Schwartz is approved.
Chair Date
Date
Date
University of California, Berkeley
Spring 2007
Posterior Decoding Methods for Optimization and
Accuracy Control of Multiple Alignments
Copyright (cid:13)c 2007
by
Ariel Shaul Schwartz
Abstract
Posterior Decoding Methods for Optimization and
Accuracy Control of Multiple Alignments
by
Ariel Shaul Schwartz
Doctor of Philosophy in Computer Science
University of California, Berkeley
Professor Lior Pachter, Chair
Comparative genomics analyses, such as genome annotation, phylogenetic inference,
protein structure prediction and protein function prediction, rely heavily on multi-
ple sequence alignment (MSA), which is essentially a homology prediction problem
at the character level over multiple sequences. Alignments are also crucial in other
settings, including natural language processing, as seen in word alignment in ma-
chine translation, or in the problem of citation entity alignment that is introduced in
this dissertation. Fundamental as it is, MSA is a hard computational problem, and
producing accurate MSAs remains challenging.
In this thesis we develop new posterior decoding based methods for multiple align-
ments of discrete characters. We begin by discussing biological sequence alignment,
and describe a new alignment accuracy measure (AMA), which is based on a metric
for the space of alignments. In the case of pairwise sequence alignment, it is possible
to find an alignment that maximizes the expected AMA value using posterior decod-
ing. We then describe an extension of the pairwise alignment method to MSA using
1
a novel approach that is based on a poset representation of multiple alignments, and
an efficient online topological ordering algorithm. This leads to a sequence annealing
algorithm, which is an incremental method for building MSAs one match at a time.
The sequence annealing algorithm outperforms all existing algorithms on benchmark
test sets of protein sequence alignments. It simultaneously achieves high recall and
high precision, dramatically reducing the number of incorrectly aligned residues in
comparison to other programs. The method can adjust the recall/precision trade off,
and can reliably identify homologous regions among protein sequences. Finally, we
demonstratethatsimilarposteriordecodingmethodscanbeappliedtotheproblemof
multiple word alignment of citation sentences (citances) in the bioscience literature.
We extend the definition of AMA to many-to-many word alignments, and develop a
posterior decoding algorithm for multiple citance alignment.
Professor Lior Pachter
Dissertation Committee Chair
2
To Abba.
i
Contents
Contents ii
List of Figures iv
List of Tables vii
Acknowledgments x
1 Introduction 1
2 Hidden Markov Models and posterior decoding 8
2.1 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 HMM inference algorithms . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 State and transition posterior decoding . . . . . . . . . . . . . . . . . 12
2.4 Multiple sequence HMMs . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4.1 The alignment problem . . . . . . . . . . . . . . . . . . . . . . 15
2.4.2 Multiple sequences . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Alignment Metric Accuracy 19
3.1 Metric based alignment accuracy . . . . . . . . . . . . . . . . . . . . 23
3.2 AMA based alignments . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.1 Maximal expected accuracy alignments . . . . . . . . . . . . . 28
3.2.2 The AMAP algorithm . . . . . . . . . . . . . . . . . . . . . . 29
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.1 Performance of existing programs on the SABmark datasets . 33
ii
3.3.2 Controls for multiple alignment experiments . . . . . . . . . . 35
3.3.3 Performance of the AMAP algorithm . . . . . . . . . . . . . . 44
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4 Multiple Alignment by Sequence Annealing 50
4.1 Alignment posets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2 Sequence annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.4 Summary and Future Directions . . . . . . . . . . . . . . . . . . . . . 72
5 Multiple Alignment of Citation Sentences 74
5.1 Citances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2 Multiple citance alignments . . . . . . . . . . . . . . . . . . . . . . . 79
5.3 MCA utility and loss functions . . . . . . . . . . . . . . . . . . . . . . 82
5.4 Probabilistic model for MCA . . . . . . . . . . . . . . . . . . . . . . . 92
5.4.1 Conditional random fields for word alignment . . . . . . . . . 93
5.4.2 The posterior decoding algorithm for MCA . . . . . . . . . . . 95
5.5 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.6 Feature engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6 Conclusions 108
Bibliography 110
A Proof that the triangle-inequality holds for the Braun-Blanquet co-
efficient 116
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