Structural RNA Homology Search and Alignment Using Covariance Models

282 Pages·2016·4.47 MB·English

WWaasshhiinnggttoonn UUnniivveerrssiittyy iinn SStt.. LLoouuiiss
WWaasshhiinnggttoonn UUnniivveerrssiittyy OOppeenn SScchhoollaarrsshhiipp
All Theses and Dissertations (ETDs)
January 2009
SSttrruuccttuurraall RRNNAA HHoommoollooggyy SSeeaarrcchh aanndd AAlliiggnnmmeenntt UUssiinngg
CCoovvaarriiaannccee MMooddeellss
Eric Nawrocki
Washington University in St. Louis
Follow this and additional works at: https://openscholarship.wustl.edu/etd
RReeccoommmmeennddeedd CCiittaattiioonn
Nawrocki, Eric, "Structural RNA Homology Search and Alignment Using Covariance Models" (2009). All
Theses and Dissertations (ETDs). 256.
https://openscholarship.wustl.edu/etd/256
This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has
been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington
University Open Scholarship. For more information, please contact [email protected].
WASHINGTON UNIVERSITY IN ST. LOUIS
Division of Biology and Biomedical Sciences
(Computational Biology)
Dissertation Examination Committee:
Sean Eddy, Chair
Michael Brent
Jeremy Buhler
Justin Fay
Jeff Gordon
Rob Mitra
Gary Stormo
STRUCTURAL RNA HOMOLOGY SEARCH AND ALIGNMENT
USING COVARIANCE MODELS
by
Eric Paul Nawrocki
A dissertation presented to the
Graduate School of Arts and Sciences
of Washington University in
partial fulfillment of the
requirements for the degree
of Doctor of Philosophy
December 2009
Saint Louis, Missouri
copyright by
Eric Paul Nawrocki
2009
ABSTRACT OF THE DISSERTATION
Structural RNA
Homology Search and Alignment
Using Covariance Models
by
Eric Paul Nawrocki
Doctor of Philosophy in Biology and Biomedical Sciences
(Computational Biology)
Washington University in St. Louis, 2009
Sean R. Eddy, Chairman
Functional RNAelements donotencode proteins, but rather functiondirectlyas RNAs.
Many different types of RNAs play important roles in a wide range of cellular processes,
including protein synthesis, gene regulation, protein transport, splicing, and more. Because
important sequence and structural features tend to be evolutionarily conserved, one way to
learn about functional RNAs is through comparative sequence analysis - by collecting and
aligning examples of homologous RNAs and comparing them.
Covariance models (CMs) are powerful computational tools for homology search and
alignment that score both the conserved sequence and secondary structure of an RNA
family. However, due to the high computational complexity of their search and alignment
algorithms, searchesagainstlargedatabasesandalignmentoflargeRNAslikesmallsubunit
ribosomal RNA (SSU rRNA) are prohibitively slow. Large-scale alignment of SSU rRNA is
of particular utility for environmental survey studies of microbial diversity which often use
the rRNA as a phylogenetic marker of microorganisms.
In this work, we improve CM methods by making them faster and more sensitive to
remote homology. To accelerate searches, we introduce a query-dependent banding (QDB)
technique that makes scoring sequences more efficient by restricting the possible lengths of
structural elements based on their probability given the model. We combine QDB with a
ii
complementary filtering method that quickly prunes away database subsequences deemed
unlikely to receive high CM scores based on sequence conservation alone. To increase
search sensitivity, we apply two model parameterization strategies from protein homology
search tools to CMs. As judged by our benchmark, these combined approaches yield about
a 250-fold speedup and significant increase in search sensitivity compared with previous
implementations. To accelerate alignment, we apply a method that uses a fast sequence-
based alignment of a target sequence to determine constraints for the more expensive CM
sequence- and structure-based alignment. This technique reduces the time required to
align one SSU rRNA sequence from about 15 minutes to 1 second with a negligible effect
on alignment accuracy. Collectively, these improvements make CMs more powerful and
practical tools for RNA homology search and alignment.
iii
Acknowledgements
IgratefullyacknowledgefinancialsupportfromtheHowardHughesMedicalInstitute(HHMI)
and a National Institutes of Health National Human Genome Research Initiative (NIH
NHGRI) Institutional Training Grant in Genomic Science, T32-HG000045. Sean Eddy’s
lab in Washington University at St. Louis, where this work took place from 2004 to 2006,
was supported by NIH NHGRI grant RO1-HG01363, HHMI, and Alvin Goldfarb. Since
then, my research in the Eddy lab at Janelia Farm Research Campus in Ashburn, Virginia
has been financially supported by HHMI.
This dissertation is based on and benefits from important research from many other
scientists. Zasha Weinberg and Larry Ruzzo’s excellent work on RNA homology search,
Michael Brown and David Haussler’s clever methods for accelerating structural RNA align-
ment, and the wealth of freely available information at Robin Gutell’s Comparative RNA
Website (crw) have been particularly crucial.
In addition, I have been extremely fortunate to work in the lab of Sean Eddy. Sean’s
tireless work ethic and depth of knowledge in the biological and computational sciences are
staggering. The past and present members of his lab bring dedication, skill and enthusiasm
to their work that is unmatched. It has been an honor to learn from and work with each of
them.
Finally, I want to thank my wife, Michael Hopkins, for her unwavering support and
confidence, and for adding so much fun to the last six years of my life - none of this would
have been possible without her.
iv
This work is dedicated to my parents, Kathy and Ed Nawrocki.
Thank you for all the love, support, and opportunities that you have provided for me.
v
Contents
Acknowledgements iv
Part 1: RNA homology search 1
1 Introduction to RNA homology search 2
1.1 Functional RNA elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Comparative sequence analysis . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3 Computational methods for sequence homology search . . . . . . . . . . . . 15
1.4 Exploiting conserved structure in RNA homology searches . . . . . . . . . . 34
1.5 Stochastic context-free grammars for RNA sequence and structure modeling 41
1.6 Covariance models: profile stochastic context-free grammars . . . . . . . . . 46
1.7 Addressing the limitations of covariance models . . . . . . . . . . . . . . . . 57
1.8 Outline of Part 1 of this work . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2 QDB for faster RNA similarity searches 65
2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
2.5 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3 Infernal 1.0: inference of RNA alignments 96
vi
3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.3 Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.4 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4 A filter pipeline for accelerating covariance model searches 102
4.1 Previous work on accelerating CM searches using filters . . . . . . . . . . . 103
4.2 Optimized implementations of the CYK and Inside algorithms . . . . . . . 104
4.3 Infernal’s two-stage filter acceleration pipeline . . . . . . . . . . . . . . . . . 106
4.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.6 Conclusion and future directions . . . . . . . . . . . . . . . . . . . . . . . . 130
5 Computational identification of functional RNA homologs in metage-
nomic data 132
5.1 Exploiting conserved structure in RNA similarity searches . . . . . . . . . . 134
5.2 Infernal: software for RNA homology search and alignment . . . . . . . . . 138
5.3 Rfam: high-throughput RNA homology search and annotation . . . . . . . 141
5.4 Limitations of CMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
6 Conclusion to RNA homology search 149
Part 2: RNA alignment 151
7 Introduction to small subunit ribosomal RNA alignment 152
7.1 SSU and the tree of life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
7.2 Rapid culture-independent SSU sequence determination . . . . . . . . . . . 154
7.3 Environmental sequencing surveys . . . . . . . . . . . . . . . . . . . . . . . 155
7.4 Comparative SSU analysis for environmental surveys . . . . . . . . . . . . . 161
vii
7.5 SSU alignment methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
7.6 Dedicated SSU databases and alignment tools . . . . . . . . . . . . . . . . . 175
7.7 Developing a new SSU alignment tool . . . . . . . . . . . . . . . . . . . . . 181
8 HMM banding for faster structural RNA alignment 184
8.1 Faster alignment using banded dynamic programming . . . . . . . . . . . . 184
8.2 HMM banded alignment in Infernal . . . . . . . . . . . . . . . . . . . . . . . 186
8.3 Comparison of HMM and query-dependent banding . . . . . . . . . . . . . 194
8.4 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
8.5 Constructing an HMM that is maximally similar to a CM . . . . . . . . . . 201
9 SSU-align: a tool for structural alignment of SSU rRNA sequences 206
9.1 Aligning SSU sequences with SSU-align . . . . . . . . . . . . . . . . . . . . 207
9.2 Automated probabilistic alignment masking . . . . . . . . . . . . . . . . . . 208
9.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
9.4 SSU-align’s SSU rRNA sequence and structure models . . . . . . . . . . . . 217
Bibliography 236
Vita 265
viii