Dissecting Multiple Sequence Alignment Methods

194 Pages·2010·2.19 MB·English

Dissecting Multiple Sequence Alignment
Methods
The Analysis, Design and Development of Generic Multiple Sequence
Alignment Components in SeqAn.
Dem Fachbereich Mathematik und Informatik der
Freien Universit(cid:228)t Berlin
zur Erlangung des akademischen Grades eines
Dr. rer. nat.
eingereichte Dissertation
von
Herr Tobias Rausch
Freie Universit(cid:228)t Berlin
Mai 2010
Erstgutachter: Prof. Dr. Knut Reinert
Zweitgutachter: Prof. Dr. Alun Thomas
Tag der Disputation: 11. Mai 2010

Abstract
Multiple sequence alignments are an indispensable tool in bioinformatics. Many
applications rely on accurate multiple alignments, including protein structure pre-
diction, phylogeny and the modeling of binding sites. In this thesis we dissected
and analyzed the crucial algorithms and data structures required to construct such
a multiple alignment. Based upon that dissection, we present a novel graph-based
multiple sequence alignment program and a new method for multi-read alignments
occurring in assembly projects. The advantage of the graph-based alignment is that
a single vertex can represent a single character, a large segment or even an abstract
entity such as a gene. This gives rise to the opportunity to apply the consistency-
based progressive alignment paradigm to alignments of genomic sequences. The
proposed multi-read alignment method outperforms similar methods in terms of
alignment quality and it is apparently one of the (cid:28)rst methods that can readily
be used for insert sequencing. An important aspect of this thesis was the design,
the development and the integration of the essential multiple sequence alignment
components in the SeqAn library. SeqAn is a software library for sequence analysis
that provides the core algorithmic components required to analyze large-scale se-
quence data. SeqAn aims at bridging the current gap between algorithm theory and
available practical implementations in bioinformatics. Hence, we always describe in
conjunction to the theoretical development of the methods, the actual implementa-
tion of the data structures and algorithms in order to strengthen the use of SeqAn as
an experimental platform for rapidly developing and testing applications. All pre-
sented methods are part of the open source SeqAn library that can be downloaded
from our website, www.seqan.de.

Zusammenfassung
Multiple Sequenzvergleiche sind ein entscheidendes Hilfsmittel in der Bioinformatik.
Zahlreiche Anwendungen, wie zum Beispiel Proteinstrukturvorhersagen, Phylogenie
oderdieModellierungvonBindungsstellenberuhenaufdere(cid:30)zientenundbiologisch
korrekten Berechnung von multiplen Sequenzvergleichen. Aufgrund dieser enormen
Bedeutung wurden in der vorliegenden Doktorarbeit Methoden zum multiplen Se-
quenzvergleich detailiert untersucht, analysiert und in elementare Algorithmen und
Datenstrukturen zergliedert. Diese strukturelle Zerlegung bildet die Grundlage f(cid:252)r
unsere eigenen Weiterentwicklungen. Insbesondere diskutieren und beschreiben wir
hier zwei erweiterte Ans(cid:228)tze zum graphbasierten, multiplen Sequenzvergleich und
zum Konsensusalignment. F(cid:252)r beide Methoden zeigen wir die Vorteile unserer Al-
gorithmen gegen(cid:252)ber bisherigen Ans(cid:228)tzen. Ein weiterer zentraler Bestandteil der
Arbeit ist der Entwurf, die Implementierung und die Integration dieser grundle-
genden Algorithmen und Datenstrukturen zum multiplen Sequenzvergleich in der
SeqAn Bibliothek. SeqAn ist eine Softwarebibliothek zur Sequenzanalyse. SeqAn
hat das Ziel die neuesten Erkenntnisse aus der Algorithmentheorie f(cid:252)r praktische
Anwendungen verf(cid:252)gbar zu machen und im Rahmen einer experimentellen Platt-
form anzubieten, in der Algorithmen einfach entworfen, entwickelt und verglichen
werden k(cid:246)nnen. Daher beschreiben wir in der gesamten Arbeit neben der theoreti-
schen Entwicklung der Algorithmen ebenso deren softwaretechnische Umsetzung in
SeqAnundzeigenzumBeispielanhandvonpaarweisenAlignmentalgorithmenderen
(cid:220)berlegenheit in Zeit- und Platzbedarf verglichen mit bisherigen Implementierun-
gen. Am Ende der Arbeit werden Einschr(cid:228)nkungen und m(cid:246)gliche Erweiterungen der
vorgestellten Methoden diskutiert. Alle Algorithmen und Datenstrukturen sind im
Rahmen der SeqAn Bibliothek frei verf(cid:252)gbar: www.seqan.de

Acknowledgement
A big (cid:16)Thank you!(cid:17) to all my advisors, supporters, co-workers, friends and family
members. Doing a PhD and writing a thesis was enriching, exciting, ful(cid:28)lling and
fun and from time to time frustrating, troublesome, stagnant and annoying. The
positive part is no big deal but to be carried and pampered through the tough part
makes the di(cid:27)erence. The support of Knut Reinert, my primary advisor, was truly
remarkable. Knut, I felt that your guidance, your ideas and your encouragements
were extraordinarily valuable. I greatly enjoyed working in your group and I hope,
we are able to stay in touch for future projects. Thanks a lot! I am also very happy
to got to know Cedric Notredame. I have rarely seen anybody, who is as enthusiastic
as you are, Cedric. Your famous aircraft survivability story probably unites all of
your students and I am actually very proud to be one of the guys, who heard about
it, thanks! Alun Thomas, you truly enjoy research. Without having seen someone
like you before, I probably would have never started this PhD project. I am very
grateful for the time I could spend with you, many thanks! Another big (cid:16)Thank
you!(cid:17) to Andreas, Anne-Katrin, David and Marcel for joint projects or helping me
out with SeqAn. Many thanks to the other group and (cid:16)(cid:29)oor(cid:17) members for fun and
thoughts at work and in the free time: thanks Alexandra, Anja, Chris, Christopher,
Clemens, Eva, Gesine, Laura, Markus, Martin, Ole, Sandro, Stephan and all the
others. Many thanks to my best friends Christian (Hyv(cid:228)(cid:228) p(cid:228)iv(cid:228)(cid:228)), Andi, Berni,
Johannes and Holli (Servus, Tirol isch scho was) and Divya, Richard and Sebastian.
I feel very privileged to have friends like you I can rely on. Last but not least I
would like to thank my family and Shan. I know, the past three years were quite
di(cid:30)cult but having people like you by my side is indeed impressive. Vielen Dank!
Contents
I Introduction 1
1 Motivation 3
1.1 Biological Background . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Multiple Sequence Alignment in Computational Biology . . . . . . . . 6
1.2.1 Structure prediction . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.2 Function prediction . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.3 Domain identi(cid:28)cation . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.4 Modeling binding sites . . . . . . . . . . . . . . . . . . . . . . 10
1.2.5 Phylogeny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2.6 Sequence consensus . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 History of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4 Guide to the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.4.2 Graph theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.4.3 The SeqAn library . . . . . . . . . . . . . . . . . . . . . . . . 19
2 Multiple Sequence Alignments 23
2.1 Alignment De(cid:28)nition . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2 Alignment Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.1 Sum of pairs score . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3 Alignment Representation . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.1 Alignment matrices and pro(cid:28)les . . . . . . . . . . . . . . . . . 27
2.3.2 Alignment graphs . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.3 De Bruijn graphs . . . . . . . . . . . . . . . . . . . . . . . . . 29
i