You searched for subject:(RNA secondary structure).
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Boston College
1.
Senter, Evan Andrew.
On the Use of Coarse-Grained Thermodynamic Landscapes to
Efficiently Estimate Folding Kinetics for RNA Molecules.
Degree: PhD, Biology, 2015, Boston College
URL: http://dlib.bc.edu/islandora/object/bc-ir:104881 
► RNA folding pathways play an important role in various biological processes, such as 1) the conformational switch in spliced leader RNA from Leptomonas collosoma, which…
(more)
▼ RNA folding pathways play an important role in various
biological processes, such as 1) the conformational switch in
spliced leader
RNA from Leptomonas collosoma, which controls
transsplicing of a portion of the 5’ exon, and 2)
riboswitches–portions of the 5’ untranslated region of mRNA that
regulate genes by allostery. Since
RNA folding pathways are
determined by the thermodynamic landscape, we have developed a
number of novel algorithms—including FFTbor and FFTbor2D—which
efficiently compute the coarse-grained energy landscape for a given
RNA sequence. These energy landscapes can then be used to produce a
model for
RNA folding kinetics that can compute both the mean first
passage time (MFPT) and equilibrium time in a deterministic and
efficient manner, using a new software package we call Hermes. The
speed of the software provided within Hermes—namely FFTmfpt and
FFTeq—present what we believe to be the first suite of kinetic
analysis tools for
RNA sequences that are suitable for high
throughput usage, something we believe to be of interest in the
field of synthetic design.
Advisors/Committee Members: Peter Clote (Thesis advisor).
Subjects/Keywords: Kinetics; RNA; Secondary Structure; Thermodynamics
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APA (6th Edition):
Senter, E. A. (2015). On the Use of Coarse-Grained Thermodynamic Landscapes to
Efficiently Estimate Folding Kinetics for RNA Molecules. (Doctoral Dissertation). Boston College. Retrieved from http://dlib.bc.edu/islandora/object/bc-ir:104881
Chicago Manual of Style (16th Edition):
Senter, Evan Andrew. “On the Use of Coarse-Grained Thermodynamic Landscapes to
Efficiently Estimate Folding Kinetics for RNA Molecules.” 2015. Doctoral Dissertation, Boston College. Accessed March 06, 2021.
http://dlib.bc.edu/islandora/object/bc-ir:104881.
MLA Handbook (7th Edition):
Senter, Evan Andrew. “On the Use of Coarse-Grained Thermodynamic Landscapes to
Efficiently Estimate Folding Kinetics for RNA Molecules.” 2015. Web. 06 Mar 2021.
Vancouver:
Senter EA. On the Use of Coarse-Grained Thermodynamic Landscapes to
Efficiently Estimate Folding Kinetics for RNA Molecules. [Internet] [Doctoral dissertation]. Boston College; 2015. [cited 2021 Mar 06].
Available from: http://dlib.bc.edu/islandora/object/bc-ir:104881.
Council of Science Editors:
Senter EA. On the Use of Coarse-Grained Thermodynamic Landscapes to
Efficiently Estimate Folding Kinetics for RNA Molecules. [Doctoral Dissertation]. Boston College; 2015. Available from: http://dlib.bc.edu/islandora/object/bc-ir:104881
University of California – San Francisco
2.
Rouskin, Silvia.
Investigating RNA structure and function, transcriptome-wide.
Degree: Biochemistry and Molecular Biology, 2014, University of California – San Francisco
URL: http://www.escholarship.org/uc/item/2f6600pg
► RNA plays a dual role as an informational molecule and a direct effector of biological tasks. The latter function is enabled by RNA's ability to…
(more)
▼ RNA plays a dual role as an informational molecule and a direct effector of biological tasks. The latter function is enabled by RNA's ability to adopt complex secondary and tertiary folds and thus has motivated extensive computational and experimental efforts for determining RNA structures. Existing approaches for evaluating RNA structure have been largely limited to in vitro systems, yet the thermodynamic forces which drive RNA folding in vitro may not be sufficient to predict stable RNA structures in vivo. Indeed, the presence of RNA binding proteins and ATP-dependent helicases can influence which structures are present inside cells. Here we present an approach for globally monitoring RNA structure in native conditions in vivo with single nucleotide precision. This method is based on in vivo modification with dimethyl sulfate (DMS), which reacts with unpaired adenine and cytosine residues9, followed by deep sequencing to monitor modifications. Our data from yeast and mammalian cells are in excellent agreement with known mRNA structures and with the high-resolution crystal structure of the Saccharomyces cerevisiae ribosome. Comparison between in vivo and in vitro data reveals that in rapidly dividing cells there are vastly fewer structured mRNA regions in vivo than in vitro. Even thermostable RNA structures are often denatured in cells, highlighting the importance of cellular processes in regulating RNA structure. Indeed, analysis of mRNA structure under ATP-depleted conditions in yeast reveals that energy-dependent processes strongly contribute to the predominantly unfolded state of mRNAs inside cells. Our studies broadly enable the functional analysis of physiological RNA structures and reveal that, in contrast to the Anfinsen view of protein folding, thermodynamics play an incomplete role in determining mRNA structure in vivo.
Subjects/Keywords: Biochemistry; RNA; secondary structure; transcriptome
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APA (6th Edition):
Rouskin, S. (2014). Investigating RNA structure and function, transcriptome-wide. (Thesis). University of California – San Francisco. Retrieved from http://www.escholarship.org/uc/item/2f6600pg
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Rouskin, Silvia. “Investigating RNA structure and function, transcriptome-wide.” 2014. Thesis, University of California – San Francisco. Accessed March 06, 2021.
http://www.escholarship.org/uc/item/2f6600pg.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Rouskin, Silvia. “Investigating RNA structure and function, transcriptome-wide.” 2014. Web. 06 Mar 2021.
Vancouver:
Rouskin S. Investigating RNA structure and function, transcriptome-wide. [Internet] [Thesis]. University of California – San Francisco; 2014. [cited 2021 Mar 06].
Available from: http://www.escholarship.org/uc/item/2f6600pg.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Rouskin S. Investigating RNA structure and function, transcriptome-wide. [Thesis]. University of California – San Francisco; 2014. Available from: http://www.escholarship.org/uc/item/2f6600pg
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Rochester
3.
Tan, Zhen.
Modeling RNA Secondary Structure Using Structure
Conservation.
Degree: PhD, 2021, University of Rochester
URL: http://hdl.handle.net/1802/36184
► With increasing number of non-coding RNA families being identified, there is strong interest in developing computational methods to estimate sequence alignment and secondary structure. I…
(more)
▼ With increasing number of non-coding RNA families
being identified, there
is strong interest in developing
computational methods to estimate sequence
alignment and secondary
structure.
I developed TurboFold II, an algorithm that takes
multiple, unaligned
homologous RNA sequences, and outputs the
predicted secondary structures and
the structural alignment of the
sequences. Secondary structure conservation
information is
incorporated in the alignment using a match score, calculated from
estimated base pairing probabilities, to represent the secondary
structural
similarity between nucleotide positions in the two
sequences. TurboFold II
computes a multiple sequence alignment,
based on a probabilistic consistency
transformation and a
hierarchically computed guide tree. TurboFold II has
comparable
alignment accuracy with MAFFT and higher accuracy than other
tools.
TurboFold II also has comparable structure prediction
accuracy as the original
TurboFold algorithm, which is one of the
most accurate methods.
I adapted the TurboFold II algorithm for
prediction of RNA secondary
structures to utilize base pairing
probabilities guided by SHAPE experimental data.
Results
demonstrate that the SHAPE mapping data for a sequence improves
structure prediction accuracy for other homologous sequences beyond
the
accuracy obtained by sequence comparison alone.
I also
developed TurboHomology, a method for secondary structure
modeling
and alignment for a newly discovered sequence of an RNA family with
a known secondary structure and an existing multiple sequence
alignment. TurboHomology achieves greater accuracy than TurboFold
II by taking advantage of the known structure and
alignment.
Subjects/Keywords: RNA secondary structure; RNA sequence alignment; RNAstructure; Turbofold; RNA structure probing
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APA (6th Edition):
Tan, Z. (2021). Modeling RNA Secondary Structure Using Structure
Conservation. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/36184
Chicago Manual of Style (16th Edition):
Tan, Zhen. “Modeling RNA Secondary Structure Using Structure
Conservation.” 2021. Doctoral Dissertation, University of Rochester. Accessed March 06, 2021.
http://hdl.handle.net/1802/36184.
MLA Handbook (7th Edition):
Tan, Zhen. “Modeling RNA Secondary Structure Using Structure
Conservation.” 2021. Web. 06 Mar 2021.
Vancouver:
Tan Z. Modeling RNA Secondary Structure Using Structure
Conservation. [Internet] [Doctoral dissertation]. University of Rochester; 2021. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1802/36184.
Council of Science Editors:
Tan Z. Modeling RNA Secondary Structure Using Structure
Conservation. [Doctoral Dissertation]. University of Rochester; 2021. Available from: http://hdl.handle.net/1802/36184
University of Rochester
4.
Mathews, David H. (1971 - ).
RNA Secondary Structure Comparative Analysis: Method
Development and Application to Genomics.
Degree: PhD, 2016, University of Rochester
URL: http://hdl.handle.net/1802/31383
► RNA not only codes protein sequences, it also functions by having specific structures. Because RNA secondary structure is more stable than tertiary structure, it is…
(more)
▼ RNA not only codes protein sequences, it also
functions by having specific structures.
Because RNA secondary
structure is more stable than tertiary structure, it is feasible
to
study secondary structure independently. To improve secondary
structure prediction
accuracy, comparative analysis can be used.
It assumes RNAs that conserve function
usually evolve under
structural constraints.
Previous computational methods that use
comparative analysis did not accommodate
domain insertions, where
structural motifs are inserted in a sequence with respect to its
homologs. For this work, domain insertion was introduced into the
program Dynalign,
which takes two sequences as input and outputs
their conserved structures. This update,
Dynalign II,
significantly improves prediction accuracy upon Dynalign,
especially over
base pairs in inserted domains.
Computational
comparative analysis methods for RNA structure prediction require
parameters that quantify evolutionary constraints on RNA secondary
structure and
sequence alignment, e.g., parameters for base pairs
and nucleotides deletion, insertion
and mutation. A machine
learning method called a log linear model was used to
quantify
these parameters using structural alignments of homologous RNAs as
the
training set. It was found that evolution favors structural
conservation and disfavors
structural mutation between homologous
RNAs.
Comparative analysis also helps identify functional
non-coding RNAs (ncRNA). Since
homologous ncRNAs evolve under
structural constraints, predicted structural conservation of
homologous genomic sequences can be utilized to identify ncRNAs. A
new program, Multifind, was developed. It takes multiple sequences
as input and
assesses the probability that they are homologous
ncRNAs using a support vector
machine (SVM). One input to SVM is
based on the conservation of the input sequence
structures
predicted by Multilign, a multiple sequence conserved structure
prediction
program based on Dynalign. Multifind performs better
than competing programs on
testing sets constructed from a RNA
database Rfam and detects unique ncRNAs on
genomic data
sets.
Subjects/Keywords: Comparative sequence analysis; Non-coding RNA; RNA; RNA secondary structure
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APA (6th Edition):
Mathews, D. H. (. -. ). (2016). RNA Secondary Structure Comparative Analysis: Method
Development and Application to Genomics. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/31383
Chicago Manual of Style (16th Edition):
Mathews, David H (1971 - ). “RNA Secondary Structure Comparative Analysis: Method
Development and Application to Genomics.” 2016. Doctoral Dissertation, University of Rochester. Accessed March 06, 2021.
http://hdl.handle.net/1802/31383.
MLA Handbook (7th Edition):
Mathews, David H (1971 - ). “RNA Secondary Structure Comparative Analysis: Method
Development and Application to Genomics.” 2016. Web. 06 Mar 2021.
Vancouver:
Mathews DH(-). RNA Secondary Structure Comparative Analysis: Method
Development and Application to Genomics. [Internet] [Doctoral dissertation]. University of Rochester; 2016. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1802/31383.
Council of Science Editors:
Mathews DH(-). RNA Secondary Structure Comparative Analysis: Method
Development and Application to Genomics. [Doctoral Dissertation]. University of Rochester; 2016. Available from: http://hdl.handle.net/1802/31383
5.
Mizoguchi, Nobuyoshi.
A Grammar-Based Approach to RNA Pseudoknotted Structure Prediction for Aligned Sequences : 形式文法と比較解析法に基づくＲＮＡのシュードノット2次構造予測; ケイシキ ブンポウ ト ヒカク カイセキホウ ニ モトヅク RNA ノ シュードノット 2 ジ コウゾウ ヨソク.
Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学
URL: http://hdl.handle.net/10061/6249
Subjects/Keywords: RNA secondary structure
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APA ·
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APA (6th Edition):
Mizoguchi, N. (n.d.). A Grammar-Based Approach to RNA Pseudoknotted Structure Prediction for Aligned Sequences : 形式文法と比較解析法に基づくＲＮＡのシュードノット2次構造予測; ケイシキ ブンポウ ト ヒカク カイセキホウ ニ モトヅク RNA ノ シュードノット 2 ジ コウゾウ ヨソク. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/6249
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Mizoguchi, Nobuyoshi. “A Grammar-Based Approach to RNA Pseudoknotted Structure Prediction for Aligned Sequences : 形式文法と比較解析法に基づくＲＮＡのシュードノット2次構造予測; ケイシキ ブンポウ ト ヒカク カイセキホウ ニ モトヅク RNA ノ シュードノット 2 ジ コウゾウ ヨソク.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed March 06, 2021.
http://hdl.handle.net/10061/6249.
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Mizoguchi, Nobuyoshi. “A Grammar-Based Approach to RNA Pseudoknotted Structure Prediction for Aligned Sequences : 形式文法と比較解析法に基づくＲＮＡのシュードノット2次構造予測; ケイシキ ブンポウ ト ヒカク カイセキホウ ニ モトヅク RNA ノ シュードノット 2 ジ コウゾウ ヨソク.” Web. 06 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Vancouver:
Mizoguchi N. A Grammar-Based Approach to RNA Pseudoknotted Structure Prediction for Aligned Sequences : 形式文法と比較解析法に基づくＲＮＡのシュードノット2次構造予測; ケイシキ ブンポウ ト ヒカク カイセキホウ ニ モトヅク RNA ノ シュードノット 2 ジ コウゾウ ヨソク. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2021 Mar 06].
Available from: http://hdl.handle.net/10061/6249.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.
Council of Science Editors:
Mizoguchi N. A Grammar-Based Approach to RNA Pseudoknotted Structure Prediction for Aligned Sequences : 形式文法と比較解析法に基づくＲＮＡのシュードノット2次構造予測; ケイシキ ブンポウ ト ヒカク カイセキホウ ニ モトヅク RNA ノ シュードノット 2 ジ コウゾウ ヨソク. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/6249
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.
University of Rochester
6.
Ling, Clarence.
Regulation of Ribosome Structural Dynamics by
Antibiotics, Translation Factors and mRNA Secondary
Structure.
Degree: PhD, 2020, University of Rochester
URL: http://hdl.handle.net/1802/35487
► The structural dynamics of the ribosome underlie the mechanism by which information encoded in messenger RNAs is translated into a polypeptide chain. During protein synthesis,…
(more)
▼ The structural dynamics of the ribosome underlie
the mechanism by which information encoded in messenger RNAs is
translated into a polypeptide chain. During protein synthesis, the
small and large ribosome subunits rotate relative to each other
while the L1 stalk, a mobile domain of large subunit, moves
inward/outward relative to the core of the large subunit.
Translation factors, antibiotics and structured RNAs are able to
regulate or perturb translation by modulating ribosome structural
dynamics. Furthermore, translation factors also undergo large
structural rearrangements while interacting with the ribosome.
Using single-molecule Förster resonance energy transfer (smFRET) to
follow the structural rearrangements of the ribosome and
translation factors, I have found (a) that the antibiotic,
blastidicin S (BlaS), slows down intersubunit rotation, (b) the HIV
and dnaX frameshift stimulating stemloops inhibit ribosome
translocation by blocking tRNA binding to the A site of the
ribosome, and (c) bacterial initiation factor 2 (IF2) positions
ribosome subunits in a semi-rotated orientation during the
subunit-joining step of translation initiation.
Many small
molecule antibiotics that target translation perturb ribosome
structural dynamics. BlaS is a potent translation inhibitor in both
eukaryotes and bacteria; however, the mechanism of BlaS action was
not clear. I found that BlaS interacts with the P-site tRNA and
inhibits spontaneous intersubunit rotation in bacterial ribosomes.
Further studies by our collaborators from UMASS School of Medicine
revealed that BlaS targets proteins synthesis via a unique
mechanism hampering translation termination.
During translation
elongation, the ribosome moves along mRNA in a codon-by- codon
manner and unwinds mRNA secondary structure. However, specific mRNA
stemloops, such as the frameshift stimulating signal (FSS) from the
E. coli dnaX gene, induce ribosome stalling and frameshifting. We
sought to determine how FSS RNA stemloops stall ribosome
translocation despite efficient helicase activity of the ribosome.
Surprisingly, we found that the FSSs from HIV and dnaX mRNAs block
tRNA binding to the A site of the ribosome, thereby inhibiting
translocation mediated by the bacterial ribosome translocase,
elongation factor-G. Our future studies will identify properties of
mRNA secondary structure that inhibit A-site tRNA binding. Thus,
our studies will provide fundamental insights into the regulation
of translation elongation by mRNA structure and the mechanism of
programmed ribosome frameshifting.
We also extended our studies
beyond translation elongation to elucidate the molecular mechanism
of the subunit-joining step of translation initiation in bacteria.
Initiation of protein synthesis is the key regulatory step of
translation. In bacteria, translation initiation is controlled by
initiation factors (IFs) 1, 2 and 3, which ensure selection of the
initiator tRNA and promote joining of the large and small ribosomal
subunits. Using smFRET, we find that IF2, a translational GTPase,…
Subjects/Keywords: Ribsome; smFRET; initiation; antibiotics; RNA secondary structure
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APA ·
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MLA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ling, C. (2020). Regulation of Ribosome Structural Dynamics by
Antibiotics, Translation Factors and mRNA Secondary
Structure. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/35487
Chicago Manual of Style (16th Edition):
Ling, Clarence. “Regulation of Ribosome Structural Dynamics by
Antibiotics, Translation Factors and mRNA Secondary
Structure.” 2020. Doctoral Dissertation, University of Rochester. Accessed March 06, 2021.
http://hdl.handle.net/1802/35487.
MLA Handbook (7th Edition):
Ling, Clarence. “Regulation of Ribosome Structural Dynamics by
Antibiotics, Translation Factors and mRNA Secondary
Structure.” 2020. Web. 06 Mar 2021.
Vancouver:
Ling C. Regulation of Ribosome Structural Dynamics by
Antibiotics, Translation Factors and mRNA Secondary
Structure. [Internet] [Doctoral dissertation]. University of Rochester; 2020. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1802/35487.
Council of Science Editors:
Ling C. Regulation of Ribosome Structural Dynamics by
Antibiotics, Translation Factors and mRNA Secondary
Structure. [Doctoral Dissertation]. University of Rochester; 2020. Available from: http://hdl.handle.net/1802/35487
University of Georgia
7.
Shaw, Timothy Isham.
Exploring HIV RNA structure diversity.
Degree: 2014, University of Georgia
URL: http://hdl.handle.net/10724/30630
► HIV-1 has several mechanisms that can significantly impact its genetic diversity. A high rate of mutation coupled with a strong tolerance for sequence change has…
(more)
▼ HIV-1 has several mechanisms that can significantly impact its genetic diversity. A high rate of mutation coupled with a strong tolerance for sequence change has allowed HIV-1 to evolve a number of machineries to evade immune control. RNA
secondary structure was recently found critical in directing recombination and replication mechanisms. In our work, we assessed existing RNA structure modeling technologies for HIV application and developed a novel RNA structure prediction pipeline. In
our pipeline, to compliment different prediction strategies, we combined experimental and computational methods to optimize HIV RNA structure prediction accuracy. The pipeline was applied to examine recombination and replication mechanisms. Based on the
comparison of B subtype derived from complete genomes and from the recombinants CRF07-08, we found RNA structure variations at VPR-ENV splice donor/acceptor sites and at the NEF/LTR region. To quantify the RNA structure space, we further developed a
measurement that uses Shannon Entropy to capture the distribution of Boltzmann un-pairing probabilities. Through our quantification, we were able to estimate the ribosomal frameshift efficiency across various HIV subtypes. Our work revealed that the
frameshift element can be clustered according to different subtypes, and recombinants of the two subtypes tend to have identical frameshift elements in both HIV populations. Potential association between frameshift efficiency and disease progression was
observed. This work can help us address existing knowledge gaps on replication and recombination mechanisms that could lead to novel antiviral therapies and HIV/AIDS vaccines development.
Subjects/Keywords: RNA secondary structure; HIV; evolutionary diversity
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APA ·
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APA (6th Edition):
Shaw, T. I. (2014). Exploring HIV RNA structure diversity. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/30630
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shaw, Timothy Isham. “Exploring HIV RNA structure diversity.” 2014. Thesis, University of Georgia. Accessed March 06, 2021.
http://hdl.handle.net/10724/30630.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shaw, Timothy Isham. “Exploring HIV RNA structure diversity.” 2014. Web. 06 Mar 2021.
Vancouver:
Shaw TI. Exploring HIV RNA structure diversity. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/10724/30630.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shaw TI. Exploring HIV RNA structure diversity. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/30630
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Boston College
8.
Pei, Shermin.
Identification of functional RNA structures in sequence
data.
Degree: PhD, Biology, 2016, Boston College
URL: http://dlib.bc.edu/islandora/object/bc-ir:107275
► Structured RNAs have many biological functions ranging from catalysis of chemical reactions to gene regulation. Many of these homologous structured RNAs display most of their…
(more)
▼ Structured RNAs have many biological functions ranging
from catalysis of chemical reactions to gene regulation. Many of
these homologous structured RNAs display most of their conservation
at the
secondary or tertiary
structure level. As a result,
strategies for natural structured
RNA discovery rely heavily on
identification of sequences sharing a common stable
secondary
structure. However, correctly identifying the functional elements
of the
structure continues to be challenging. In addition to
studying natural RNAs, we improve our ability to distinguish
functional elements by studying sequences derived from in vitro
selection experiments to select structured RNAs that bind specific
proteins. In this thesis, we seek to improve methods for
distinguishing functional
RNA structures from arbitrarily predicted
structures in sequencing data. To do so, we developed novel
algorithms that prioritize the structural properties of the
RNA
that are under selection. In order to identify natural structured
ncRNAs, we bring concepts from evolutionary biology to bear on the
de novo
RNA discovery process. Since there is selective pressure to
maintain the
structure, we apply molecular evolution concepts such
as neutrality to identify functional
RNA structures. We hypothesize
that alignments corresponding to structured RNAs should consist of
neutral sequences. During the course of this work, we developed a
novel measure of neutrality, the
structure ensemble neutrality
(SEN), which calculates neutrality by averaging the magnitude of
structure retained over all single point mutations to a given
sequence. In order to analyze in vitro selection data for
RNA-protein binding motifs, we developed a novel framework that
identifies enriched substructures in the sequence pool. Our method
accounts for both sequence and
structure components by abstracting
the overall
secondary structure into smaller substructures composed
of a single base-pair stack. Unlike many current tools, our
algorithm is designed to deal with the large data sets coming from
high-throughput sequencing. In conclusion, our algorithms have
similar performance to existing programs. However, unlike previous
methods, our algorithms are designed to leverage the evolutionary
selective pressures in order to emphasize functional
structure
conservation.
Advisors/Committee Members: Michelle M. Meyer (Thesis advisor), Peter Clote (Thesis advisor).
Subjects/Keywords: motif; neutrality; RNA; robustness; secondary structure; SELEX
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Pei, S. (2016). Identification of functional RNA structures in sequence
data. (Doctoral Dissertation). Boston College. Retrieved from http://dlib.bc.edu/islandora/object/bc-ir:107275
Chicago Manual of Style (16th Edition):
Pei, Shermin. “Identification of functional RNA structures in sequence
data.” 2016. Doctoral Dissertation, Boston College. Accessed March 06, 2021.
http://dlib.bc.edu/islandora/object/bc-ir:107275.
MLA Handbook (7th Edition):
Pei, Shermin. “Identification of functional RNA structures in sequence
data.” 2016. Web. 06 Mar 2021.
Vancouver:
Pei S. Identification of functional RNA structures in sequence
data. [Internet] [Doctoral dissertation]. Boston College; 2016. [cited 2021 Mar 06].
Available from: http://dlib.bc.edu/islandora/object/bc-ir:107275.
Council of Science Editors:
Pei S. Identification of functional RNA structures in sequence
data. [Doctoral Dissertation]. Boston College; 2016. Available from: http://dlib.bc.edu/islandora/object/bc-ir:107275
Boston College
9.
Bayegan, Amir Hossein.
Novel algorithms to analyze RNA secondary structure
evolution and folding kinetics.
Degree: PhD, Biology, 2018, Boston College
URL: http://dlib.bc.edu/islandora/object/bc-ir:108256
► RNA molecules play important roles in living organisms, such as protein translation, gene regulation, and RNA processing. It is known that RNA secondary structure is…
(more)
▼ RNA molecules play important roles in living
organisms, such as protein translation, gene regulation, and
RNA
processing. It is known that
RNA secondary structure is a scaffold
for tertiary
structure leading to extensive amount of interest in
RNA secondary structure. This thesis is primarily focused on the
development of novel algorithms for the analysis of
RNA secondary
structure evolution and folding kinetics. We describe a software
RNAsampleCDS to generate mRNA sequences coding user-specified
peptides overlapping in up to six open reading frames. Sampled
mRNAs are then analyzed with other tools to provide an estimate of
their
secondary structure properties. We investigate homology of
RNAs with respect to both sequence and
secondary structure
information as well. RNAmountAlign an efficient software package
for multiple global, local, and semiglobal alignment of RNAs using
a weighted combination of sequence and structural similarity with
statistical support is presented. Furthermore, we approach
RNA
folding kinetics from a novel network perspective, presenting
algorithms for the shortest path and expected degree of nodes in
the network of all
secondary structures of an
RNA. In these
algorithms we consider move set MS2 , allowing addition, removal
and shift of base pairs used by several widely-used
RNA secondary
structure folding kinetics software that implement Gillespie’s
algorithm. We describe MS2distance software to compute the shortest
MS2 folding trajectory between any two given
RNA secondary
structures. Moreover, RNAdegree software implements the first
algorithm to efficiently compute the expected degree of an
RNA MS2
network of
secondary structures. The source code for all the
software and webservers for RNAmountAlign, MS2distance, and
RNAdegree are publicly available at
http://bioinformatics.bc.edu/clotelab/.
Advisors/Committee Members: Peter Clote (Thesis advisor).
Subjects/Keywords: alignment; evolution; folding kinetics; RNA; secondary structure
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APA (6th Edition):
Bayegan, A. H. (2018). Novel algorithms to analyze RNA secondary structure
evolution and folding kinetics. (Doctoral Dissertation). Boston College. Retrieved from http://dlib.bc.edu/islandora/object/bc-ir:108256
Chicago Manual of Style (16th Edition):
Bayegan, Amir Hossein. “Novel algorithms to analyze RNA secondary structure
evolution and folding kinetics.” 2018. Doctoral Dissertation, Boston College. Accessed March 06, 2021.
http://dlib.bc.edu/islandora/object/bc-ir:108256.
MLA Handbook (7th Edition):
Bayegan, Amir Hossein. “Novel algorithms to analyze RNA secondary structure
evolution and folding kinetics.” 2018. Web. 06 Mar 2021.
Vancouver:
Bayegan AH. Novel algorithms to analyze RNA secondary structure
evolution and folding kinetics. [Internet] [Doctoral dissertation]. Boston College; 2018. [cited 2021 Mar 06].
Available from: http://dlib.bc.edu/islandora/object/bc-ir:108256.
Council of Science Editors:
Bayegan AH. Novel algorithms to analyze RNA secondary structure
evolution and folding kinetics. [Doctoral Dissertation]. Boston College; 2018. Available from: http://dlib.bc.edu/islandora/object/bc-ir:108256
University of Rochester
10.
Seetin, Matthew G.
RNA Structure Prediction:Advancing Both Secondary and
Tertiary Structure Prediction.
Degree: PhD, 2011, University of Rochester
URL: http://hdl.handle.net/1802/17694
► RNAs can function without being translated into proteins. These RNAs adopt a structure or structures to perform these functions, and accurate prediction of structure is…
(more)
▼ RNAs can function without being translated into
proteins. These RNAs adopt a structure or structures to perform
these functions, and accurate prediction of structure is a valuable
tool for understanding these functions. RNA structure is
hierarchical, beginning with the primary sequence, then the
secondary structure, i.e. the set of canonical pairs, and
ultimately the tertiary structure, i.e. the three-dimensional
structure.
One significant tool for prediction of secondary
structure is the nearest neighbor model. This assumes the free
energy change of forming a base pair depends on the identities of
the pair and the adjacent pairs. Parameters were previously derived
from optical melting on RNA duplexes where it was assumed all
strands would be completely duplex or single-stranded. When
individual base pairs are allowed to break as a function of
temperature, the model does not agree with experiment. A new
treatment of the data is presented. The probabilities of individual
base pairs are calculated using a partition function, allowing
internal loops and frayed ends. The parameters of the nearest
neighbor model are recalculated using a nonlinear fit to the
original data. These new parameters better fit the data and should
provide improved structure prediction.
Homologous RNAs adopt
similar structures. One important structural motif is the
pseudoknot, a structure difficult to predict and often found near
functionally important regions. Combining information from
thermodynamics and homology, the
TurboKnot algorithm presented
here finds ~80% of known base pairs, and ~75% of predicted pairs
were found in the known structures. Pseudoknots are found with half
or better of the false-positive rate of other methods.
Finally, a
novel protocol for RNA tertiary structure prediction employing
restrained molecular mechanics and simulated annealing is
presented. The restraints are from secondary structure,
co-variation analysis, coaxial stacking predictions, and, when
available, cross-linking data. Results are demonstrated on five
different RNAs. The predicted structure is selected from a pool of
decoy structures by maximizing radius of gyration and base-base
contacts. This approach is sufficient to accurately predict the
structure of RNAs compared to current crystal structures, as
evaluated by root mean square deviation and the accuracy of
base-base contacts.
Subjects/Keywords: RNA Structure; Secondary Structure Prediction; Tertiary Structure Prediction; Pseudoknots
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Seetin, M. G. (2011). RNA Structure Prediction:Advancing Both Secondary and
Tertiary Structure Prediction. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/17694
Chicago Manual of Style (16th Edition):
Seetin, Matthew G. “RNA Structure Prediction:Advancing Both Secondary and
Tertiary Structure Prediction.” 2011. Doctoral Dissertation, University of Rochester. Accessed March 06, 2021.
http://hdl.handle.net/1802/17694.
MLA Handbook (7th Edition):
Seetin, Matthew G. “RNA Structure Prediction:Advancing Both Secondary and
Tertiary Structure Prediction.” 2011. Web. 06 Mar 2021.
Vancouver:
Seetin MG. RNA Structure Prediction:Advancing Both Secondary and
Tertiary Structure Prediction. [Internet] [Doctoral dissertation]. University of Rochester; 2011. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1802/17694.
Council of Science Editors:
Seetin MG. RNA Structure Prediction:Advancing Both Secondary and
Tertiary Structure Prediction. [Doctoral Dissertation]. University of Rochester; 2011. Available from: http://hdl.handle.net/1802/17694
University of Rochester
11.
Harmancı, Arif Özgün (1982 - ).
Probabilistic computational methods for structural
alignment of RNA sequences.
Degree: PhD, 2011, University of Rochester
URL: http://hdl.handle.net/1802/13722
► In this thesis, the problem of structural alignment of homologous RNA sequences is addressed. The structural alignment of a given set of RNA sequences is…
(more)
▼ In this thesis, the problem of structural alignment
of homologous RNA sequences is addressed.
The structural alignment
of a given set of RNA sequences is a secondary structure
for each
sequence, such that the structures are similar to each other, and a
sequence alignment
between the sequences that is conforming with
the secondary structures. A solution
to this problem was proposed
by Sankoff as a dynamic programming algorithm whose time
and
memory complexities are polynomial in the length of shortest
sequence and exponential
in the number of input sequences,
respectively. Variants of Sankoff’s method employ
constraints that
reduce the computation by restricting the allowed alignments or
structures.
In the first part of the thesis, a new methodology is
presented for the purpose of
establishing alignment constraints
based on nucleotide alignment and insertion posterior
probabilities. Using a hidden Markov model, posterior probabilities
of alignment and insertion
are computed and these probabilities
are additively combined to obtain probabilities
of co-incidence.
The constraints on alignments are computed by adaptively
thresholding
these probabilities to determine co-incidence
constraints for pruning of computations that
hold with high
probability. The proposed constraints are implemented into
Dynalign, a free
energy minimization algorithm for structural
alignment. Compared with prior non-adaptive
approaches, the
probabilistic constraints offer a significant reduction in
computation time
along with a marginal increase in base pair
prediction accuracy.
Next, a novel algorithm for structural
alignment of two RNA sequences is presented.
The similarity of the
structures is imposed by matched helical regions in the structural
alignnment.
A matched helical region represents a conserved helix
in each structure. Compared
to the structural alignment models of
previous methods, the matched helical regions extend
the
possibilities for alignment of paired nucleotides, which enables
the new structural
alignment space to better accomodate the
structural variability within a sequence family. A probability
distribution over the space of structural alignments is proposed
based on
pseudo-free energy changes, that account for both
stability of structures and plausibility of
the sequence
alignment. Three different problems are addressed for structural
alignment
prediction as inferences from the distribution: 1.
Estimation of the maximum a posteriori
(MAP) structural alignment,
i.e., the structural alignment with highest probability in
the
space, 2. Computation of base pairing probabilities of nucleotides
in each sequence,
3. Sampling the structural alignment space for
analysis of modes of the distribution over
structural alignments.
Finally, the problem of structure prediction for an arbitrary
number of homologous
sequences is addressed. To circumvent the
computational complexity, the prediction is
broken down into an
iterative computation of base pairing probabilities for each
sequence
using extrinsic…
Subjects/Keywords: Non-coding RNA; Secondary structure; RNA structural alignment
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Harmancı, A. . (. -. ). (2011). Probabilistic computational methods for structural
alignment of RNA sequences. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/13722
Chicago Manual of Style (16th Edition):
Harmancı, Arif Özgün (1982 - ). “Probabilistic computational methods for structural
alignment of RNA sequences.” 2011. Doctoral Dissertation, University of Rochester. Accessed March 06, 2021.
http://hdl.handle.net/1802/13722.
MLA Handbook (7th Edition):
Harmancı, Arif Özgün (1982 - ). “Probabilistic computational methods for structural
alignment of RNA sequences.” 2011. Web. 06 Mar 2021.
Vancouver:
Harmancı A(-). Probabilistic computational methods for structural
alignment of RNA sequences. [Internet] [Doctoral dissertation]. University of Rochester; 2011. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1802/13722.
Council of Science Editors:
Harmancı A(-). Probabilistic computational methods for structural
alignment of RNA sequences. [Doctoral Dissertation]. University of Rochester; 2011. Available from: http://hdl.handle.net/1802/13722
University of Rochester
12.
Xu, Zhenjiang.
Non-Coding RNA: From Structure Prediction to Discovery in
Genomes.
Degree: PhD, 2013, University of Rochester
URL: http://hdl.handle.net/1802/26787
► RNA plays remarkably diverse roles in organisms, such as maintaining telomeres, regulating gene expression, and catalyzing reactions. With current techniques, it is often slow and…
(more)
▼ RNA plays remarkably diverse roles in organisms,
such as maintaining telomeres,
regulating gene expression, and
catalyzing reactions. With current techniques, it is often
slow
and expensive to solve the majority of those RNA structures
experimentally. Thus
computational RNA analysis remains an
attractive tool.
RNA secondary structure, the sum of canonical
base pairs (A-U, G-U and G-C),
can be predicted by free energy
minimization using a nearest neighbor model. The
prediction
accuracy, however, is limited. Dynalign improves prediction by
finding
conserved structures of two homologous RNA sequences. A
novel algorithm, Multilign,
was developed to compute conserved
structures for more than two sequences
progressively using
multiple Dynalign calculations. It keeps base pairs in low free
energy
structures predicted by all the Dynalign calculations and
removes false competing base
pairs. The benchmark on various RNA
families showed that Multilign performs better
than Dynalign.
Traditionally, the averages of structure prediction accuracies are
tabulated to
compare the performance of different RNA secondary
prediction algorithms without
statistical testing. It was
demonstrated here that the prediction accuracies of methods
correlate with each other. The paired two-sample t-test was
introduced to rigorously
evaluate whether one method outperforms
another. A pipeline of statistical analyses was
proposed to guide
the choice of data set size and performance assessment for
benchmarks.
Functional RNA motifs tend to have stable and
conserved secondary structure and
can be identified from genomes
with algorithms derived from secondary structure
prediction. The
Streptomyces coelicolor genome was scanned using a Dynalign-based
method to search ncRNA. The prediction result was compared with the
results from
RNAz (another program finding RNA genes) and 454
sequencing data, showing the three
sets of data overlap little.
Untranslated regions of human hypoxia-related genes were also
scanned and many candidates of conserved, structural cis-regulatory
motifs were found.
Collaborating with Butler Lab, bioinformatics
analysis was performed on RNA
deep sequencing data generated from
four S. cerevisiae genotypes, BY4724, rrp6-Δ ,
air1-Δ rrp6-Δ and
air2-Δ rrp6-Δ to study the RNA exosome substrate specificity. The
differentially expressed genes were identified in these genotypes,
revealing that Air1p
and Air2p convey substrate specificities
during RNA degradation.
Subjects/Keywords: RNA; Secondary Structure; Prediction; Non-Coding RNA; Genomes
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Xu, Z. (2013). Non-Coding RNA: From Structure Prediction to Discovery in
Genomes. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/26787
Chicago Manual of Style (16th Edition):
Xu, Zhenjiang. “Non-Coding RNA: From Structure Prediction to Discovery in
Genomes.” 2013. Doctoral Dissertation, University of Rochester. Accessed March 06, 2021.
http://hdl.handle.net/1802/26787.
MLA Handbook (7th Edition):
Xu, Zhenjiang. “Non-Coding RNA: From Structure Prediction to Discovery in
Genomes.” 2013. Web. 06 Mar 2021.
Vancouver:
Xu Z. Non-Coding RNA: From Structure Prediction to Discovery in
Genomes. [Internet] [Doctoral dissertation]. University of Rochester; 2013. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1802/26787.
Council of Science Editors:
Xu Z. Non-Coding RNA: From Structure Prediction to Discovery in
Genomes. [Doctoral Dissertation]. University of Rochester; 2013. Available from: http://hdl.handle.net/1802/26787
University of Toronto
13.
Li, Xiao.
Computational Analysis of RNA-binding Protein Target-site Selection and Function.
Degree: 2013, University of Toronto
URL: http://hdl.handle.net/1807/70100
► Gene expression is extensively regulated by the binding of RNA-binding proteins (RBPs) to cis-regulatory elements encoded in mRNA. A robust literature has emerged regarding the…
(more)
▼ Gene expression is extensively regulated by the binding of RNA-binding proteins (RBPs) to cis-regulatory elements encoded in mRNA. A robust literature has emerged regarding the stability and intracellular distribution of transcripts genome-wide. However, clear binding preferences have only been reported for a few RBPs, and these binding data collectively explain only a small portion of these post-transcriptional events. I developed the RNA Regulatory Element Analysis and Discovery (RNA-READ) pipeline, which takes as input positive (e.g., regulated transcripts) and negative gene lists (e.g., the co-expressed transcripts not affected by the same post-transcriptional event), and outputs the RNA cis-regulatory element that distinguishes the positive from the negative transcripts. First, RNA-READ tests for enrichment of previously reported RNA motifs, then it performs novel RNA motif discovery to identify the consensus RNA motif that best discriminates between the positive and negative transcripts. An important innovation of the RNA-READ pipeline is that it considers both sequence and structural constraints on binding of RBPs. I consider two binding classes: (1) the RBP binds to mRNAs with primary sequence-specificities; (2) the RBP binds to elements in mRNAs defined completely by structure (i.e., shape recognition). I have shown that computationally estimated target-site accessibility improves prediction of sequence-specific binding for various RBPs, with >22% average relative decrease in error versus using only sequence information. The predictive power is further increased with the introduction of structural-context constraints to the single-stranded target sites. Furthermore, I showed that computationally estimated intrinsic mRNA secondary structure is also helpful for determining RBP binding via shape recognition. I identified specific structural elements enriched in dsRBP Staufen targets versus non-targets. I applied the RNA-READ pipeline to the datasets that measured translation, stability and localization of transcripts in the Drosophila early embryo and identified numerous significant associations between the presence of motif matches and specific regulatory outcomes.
PhD
Advisors/Committee Members: Morris, Quaid, Lipshitz, Howard, Molecular and Medical Genetics.
Subjects/Keywords: RNA-binding protein; RNA cis-regulatory element; RNA secondary structure; Post-transcriptional regulation; 0307; 0715
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Li, X. (2013). Computational Analysis of RNA-binding Protein Target-site Selection and Function. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/70100
Chicago Manual of Style (16th Edition):
Li, Xiao. “Computational Analysis of RNA-binding Protein Target-site Selection and Function.” 2013. Doctoral Dissertation, University of Toronto. Accessed March 06, 2021.
http://hdl.handle.net/1807/70100.
MLA Handbook (7th Edition):
Li, Xiao. “Computational Analysis of RNA-binding Protein Target-site Selection and Function.” 2013. Web. 06 Mar 2021.
Vancouver:
Li X. Computational Analysis of RNA-binding Protein Target-site Selection and Function. [Internet] [Doctoral dissertation]. University of Toronto; 2013. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1807/70100.
Council of Science Editors:
Li X. Computational Analysis of RNA-binding Protein Target-site Selection and Function. [Doctoral Dissertation]. University of Toronto; 2013. Available from: http://hdl.handle.net/1807/70100
University of Pennsylvania
14.
Berkowitz, Nathan Daniel.
Genome-Wide Approaches To Study Rna Secondary Structure.
Degree: 2016, University of Pennsylvania
URL: https://repository.upenn.edu/edissertations/2189
► The central hypothesis of molecular biology depicts RNA as an intermediary conveyor of genetic information. RNA is transcribed from DNA and translated to proteins, the…
(more)
▼ The central hypothesis of molecular biology depicts RNA as an intermediary conveyor of genetic information. RNA is transcribed from DNA and translated to proteins, the molecular machines of the cell. However, many RNAs do not encode protein and instead function as molecular machines themselves. The most famous examples are ribosomal RNAs and transfer RNAs, which together form the core translational machinery of the cell. Many other non-coding RNAs have been discovered including catalytic and regulatory RNAs. In many cases RNA function is tightly linked to its secondary structure, which is the collection of hydrogen bonds between complimentary RNA sequences that drives these molecules into their three dimensional structure.
Over the last decade, technology for determining the sequence of DNA and RNA has advanced rapidly, making transcriptome-wide expression profiling fast and widely available. In this dissertation, I discuss recent efforts to leverage this powerful technology to study, not just RNA expression, but several other aspects of RNA function. In particular, I focus on three tightly linked aspects of RNA biology: RNA-secondary structure, RNA cleavage, and regulatory small RNAs. I introduce a database for integrating, comparing, and contrasting techniques for determining RNA secondary structure including a technique developed in my dissertation laboratory. Additionally, I discuss a newly improved technology capable of detecting RNA cleavage events. Finally, I integrate RNA secondary structure probing and RNA cleavage detection to interrogate a family of genes important for eukaryotic small RNA-mediated silencing. These diverse analyses are just a few examples of the vast promises offered by adapting RNA-sequencing technology to probe RNA function across many cellular processes.
Subjects/Keywords: miRNA; RNA; RNA Dependent RNA Polymerase; Secondary Structure; Sequencing; siRNA; Bioinformatics; Molecular Biology
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Berkowitz, N. D. (2016). Genome-Wide Approaches To Study Rna Secondary Structure. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/2189
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Berkowitz, Nathan Daniel. “Genome-Wide Approaches To Study Rna Secondary Structure.” 2016. Thesis, University of Pennsylvania. Accessed March 06, 2021.
https://repository.upenn.edu/edissertations/2189.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Berkowitz, Nathan Daniel. “Genome-Wide Approaches To Study Rna Secondary Structure.” 2016. Web. 06 Mar 2021.
Vancouver:
Berkowitz ND. Genome-Wide Approaches To Study Rna Secondary Structure. [Internet] [Thesis]. University of Pennsylvania; 2016. [cited 2021 Mar 06].
Available from: https://repository.upenn.edu/edissertations/2189.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Berkowitz ND. Genome-Wide Approaches To Study Rna Secondary Structure. [Thesis]. University of Pennsylvania; 2016. Available from: https://repository.upenn.edu/edissertations/2189
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
The Ohio State University
15.
Lin, Yi-Hsuan.
The interplay between single-stranded binding proteins on
RNA secondary structure.
Degree: PhD, Physics, 2015, The Ohio State University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1429098303
► Interactions between RNAs and RNA-binding proteins (RBPs) are significant in post-transcriptional regulation. In this process, an mRNA molecule is bound by many proteins and/or microRNAs…
(more)
▼ Interactions between RNAs and
RNA-binding proteins
(RBPs) are significant in post-transcriptional regulation. In this
process, an mRNA molecule is bound by many proteins and/or
microRNAs to modulate its function. It is therefore an interesting
question how these multiple RBPs collaborate to enable
combinatorial gene regulation. Here, we propose a possible
mechanism which can support this RBP-RBP collaboration, termed
"cooperativity". Such a cooperativity can exist merely based on
fundamental principles of statistical physics and thermodynamics of
RNA structure folding, without considering any further details of
RNA and RBP properties. The theory is based on the idea that a
successfully binding RBP will prohibit the formation of some
originally allowed
RNA structures, thus changing the statistical
properties of the
RNA structure ensemble, as well as the binding
probabilities of other RBPs on the same
RNA. In addition, this
mechanism does not require direct physical interactions between
RBPs, and thus supports the long-range characteristic of the
cooperativity. Focusing on an
RNA with two binding sites, we first
calculate the correlation function between the RBPs on the
RNA-RBP
complex, verifying that this cooperativity exists. We then derive a
characteristic difference of free energy differences, i.e. delta
delta G, as a quantitative measure of this
structure-mediated
cooperativity. We apply this measure to a large number of human
mRNAs, and discover that this cooperativity is a generic feature.
Interestingly, this cooperativity not only affects binding sites in
close proximity along the sequence but also configurations in which
one binding site is located in the 5’UTR and the other is located
in the 3’UTR of the mRNA. Some intriguing interplays between RBPs,
microRNA binding sites, and UTR sequences are also disclosed. In
the last chapter, we extend our model to handle multiple
sequence-specified protein binding sites. We apply this extended
model to the binding reaction between the protein HuR and several
RNA sequences, theoretically calculating their dissociation
constants and comparing with experimental results. We discover that
RNA secondary structures are crucial in the interplay between HuR
and
RNA sequences, verifying the importance of the
structure-mediated cooperativity in realistic
RNA-protein binding
reactions.
Advisors/Committee Members: Bundschuh, Ralf (Advisor).
Subjects/Keywords: Molecular Biology; Theoretical Physics; Biophysics; RNA; RNA secondary structure; RNA-protein binding; cooperativity; untranslated region
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lin, Y. (2015). The interplay between single-stranded binding proteins on
RNA secondary structure. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1429098303
Chicago Manual of Style (16th Edition):
Lin, Yi-Hsuan. “The interplay between single-stranded binding proteins on
RNA secondary structure.” 2015. Doctoral Dissertation, The Ohio State University. Accessed March 06, 2021.
http://rave.ohiolink.edu/etdc/view?acc_num=osu1429098303.
MLA Handbook (7th Edition):
Lin, Yi-Hsuan. “The interplay between single-stranded binding proteins on
RNA secondary structure.” 2015. Web. 06 Mar 2021.
Vancouver:
Lin Y. The interplay between single-stranded binding proteins on
RNA secondary structure. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2021 Mar 06].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1429098303.
Council of Science Editors:
Lin Y. The interplay between single-stranded binding proteins on
RNA secondary structure. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1429098303
New Jersey Institute of Technology
16.
Wen, Dongrong.
Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.
Degree: PhD, Computer Science, 2012, New Jersey Institute of Technology
URL: https://digitalcommons.njit.edu/dissertations/335
► RNA secondary and tertiary structure motifs play important roles in cells. However, very few web servers are available for RNA motif search and prediction.…
(more)
▼ RNA secondary and tertiary
structure motifs play important roles in cells. However, very few web servers are available for
RNA motif search and prediction. In this dissertation, a cyberinfrastructure, named RNAcyber, capable of performing
RNA motif search and prediction, is proposed, designed and implemented.
The first component of RNAcyber is a web-based search engine, named RmotifDB. This web-based tool integrates an
RNA secondary structure comparison algorithm with the
secondary structure motifs stored in the Rfam database. With a user-friendly interface, RmotifDB provides the ability to search for ncRNA
structure motifs in both structural and sequential ways. The second component of RNAcyber is an enhanced version of RmotifDB. This enhanced version combines data from multiple sources, incorporates a variety of well-established
structure-based search methods, and is integrated with the Gene Ontology. To display RmotifDB’s search results, a software tool, called RSview, is developed. RSview is able to display the search results in a graphical manner.
Finally, RNAcyber contains a web-based tool called Junction-Explorer, which employs a data mining method for predicting tertiary motifs in
RNA junctions. Specifically, the tool is trained on solved
RNA tertiary structures obtained from the Protein Data Bank, and is able to predict the configuration of coaxial helical stacks and families (topologies) in
RNA junctions at the
secondary structure level. Junction-Explorer employs several algorithms for motif prediction, including a random forest classification algorithm, a pseudoknot removal algorithm, and a feature ranking algorithm based on the gini impurity measure. A series of experiments including 10-fold cross- validation has been conducted to evaluate the performance of the Junction-Explorer tool. Experimental results demonstrate the effectiveness of the proposed algorithms and the superiority of the tool over existing methods. The RNAcyber infrastructure is fully operational, with all of its components accessible on the Internet.
Advisors/Committee Members: Jason T. L. Wang, James A. McHugh, David Nassimi.
Subjects/Keywords: RNA motif; RNA motif search; Secondary structure; Motif prediction; Tertiary structure; Computer Sciences
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APA (6th Edition):
Wen, D. (2012). Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/335
Chicago Manual of Style (16th Edition):
Wen, Dongrong. “Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.” 2012. Doctoral Dissertation, New Jersey Institute of Technology. Accessed March 06, 2021.
https://digitalcommons.njit.edu/dissertations/335.
MLA Handbook (7th Edition):
Wen, Dongrong. “Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.” 2012. Web. 06 Mar 2021.
Vancouver:
Wen D. Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2012. [cited 2021 Mar 06].
Available from: https://digitalcommons.njit.edu/dissertations/335.
Council of Science Editors:
Wen D. Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. [Doctoral Dissertation]. New Jersey Institute of Technology; 2012. Available from: https://digitalcommons.njit.edu/dissertations/335
University of Rochester
17.
Priore, Salvatore F. (1983 - ).
Discovery and characterization of influenza virus RNA
secondary structures.
Degree: PhD, 2013, University of Rochester
URL: http://hdl.handle.net/1802/27156
► PART I: Influenza virus is a significant public health threat, partially because of its capacity to readily exchange gene segments between different host species to…
(more)
▼ PART I: Influenza virus is a significant public
health threat, partially
because of its capacity to readily
exchange gene segments between different
host species to form
novel pandemic strains. Even though influenza virus uses
RNA
exclusively throughout its entire life cycle, little is known about
the structure
of the Influenza A genome and transcriptome. This
works presents bioinformatics
efforts to discover and characterize
RNA secondary structures in both the (+) and
(−)RNA orientations
of influenza A virus. All available unique influenza A
sequences
from the NCBI database were used to identify 20 likely structured
regions, primarily in the (+)RNA. One of these predictions is
verified and refined
using small molecule mapping and isoenergetic
microarrays for a conserved
region of the Segment 8 (NS1/NEP)
intron.
Part II: In addition to locally
conserved structure, this work examines the
global RNA secondary
structure in coding regions of both Influenza A and B. For
Influenza A, segments 1, 5, 7 and 8 show evidence of wide-spread
structure
conservation. This phenomenon is referred to as Global
Ordered RNA Structure,
or GORS. On average the predicted
thermodynamic stability of each coding
region segregated based on
the host species with avian having the most stable
folding free
energies followed by swine and then human. Similarly, a study of
Influenza B virus, which infects primarily humans, showed GORS in
Segments 1,
2, 5 and 8. In silico codon mutations that maintained
the amino acid sequence for
each segment demonstrate the
relatively unstable folding free energies of
influenza B coding
regions. Together these results highlight some of the
molecular
similarities and differences between influenza A and B and
demonstrate the evolutionary adaptation of
Influenza
Subjects/Keywords: GORS; Influenza; RNA; Secondary structure; Structure prediction; Virology
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APA (6th Edition):
Priore, S. F. (. -. ). (2013). Discovery and characterization of influenza virus RNA
secondary structures. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/27156
Chicago Manual of Style (16th Edition):
Priore, Salvatore F (1983 - ). “Discovery and characterization of influenza virus RNA
secondary structures.” 2013. Doctoral Dissertation, University of Rochester. Accessed March 06, 2021.
http://hdl.handle.net/1802/27156.
MLA Handbook (7th Edition):
Priore, Salvatore F (1983 - ). “Discovery and characterization of influenza virus RNA
secondary structures.” 2013. Web. 06 Mar 2021.
Vancouver:
Priore SF(-). Discovery and characterization of influenza virus RNA
secondary structures. [Internet] [Doctoral dissertation]. University of Rochester; 2013. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1802/27156.
Council of Science Editors:
Priore SF(-). Discovery and characterization of influenza virus RNA
secondary structures. [Doctoral Dissertation]. University of Rochester; 2013. Available from: http://hdl.handle.net/1802/27156
University of Rochester
18.
Sloma, Michael F.
Computational Tools for RNA Structure
Prediction.
Degree: PhD, 2018, University of Rochester
URL: http://hdl.handle.net/1802/33903
► RNA is a versatile biomolecule that functions in many cellular processes. In addition to acting as a template for protein synthesis, RNA plays a direct…
(more)
▼ RNA is a versatile biomolecule that functions in
many cellular processes. In
addition to acting as a template for
protein synthesis, RNA plays a direct catalytic role in
formation
of the peptide bond and in pre-mRNA splicing. Further, RNA acts in
regulation
of transcription and translation, in genome maintenance
at the telomere, and in the
maintenance of epigenetic marks. The
ENCODE project identified thousands of
expressed RNA sequences of
unknown function, so new roles are likely still to be
discovered.
Although a vast number of RNA sequences are now known,
experimental
methods to determine RNA structure, such as X-ray
crystallography, NMR, and electron
microscopy, remain expensive
and difficult. Computational tools, therefore, play a crucial
role
in making sense of RNA sequences with unknown structure. In this
work, three
computational methods were developed that take an RNA
sequence as input and predict
properties of the molecule's
thermodynamic ensemble of structures. These methods
include
ProbScan, a method for identifying loop motifs in the structural
ensemble;
CycleFold, a method for identifying non-canonical base
pairs in the structural ensemble;
and a re-implementation and
refinement of prior work in the lab that predicts tertiary
structures using all-atom molecular dynamics
simulation
Subjects/Keywords: RNA; Secondary structure; Structure prediction; Bioinformatics; Computational biology; Structural biology.
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APA (6th Edition):
Sloma, M. F. (2018). Computational Tools for RNA Structure
Prediction. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/33903
Chicago Manual of Style (16th Edition):
Sloma, Michael F. “Computational Tools for RNA Structure
Prediction.” 2018. Doctoral Dissertation, University of Rochester. Accessed March 06, 2021.
http://hdl.handle.net/1802/33903.
MLA Handbook (7th Edition):
Sloma, Michael F. “Computational Tools for RNA Structure
Prediction.” 2018. Web. 06 Mar 2021.
Vancouver:
Sloma MF. Computational Tools for RNA Structure
Prediction. [Internet] [Doctoral dissertation]. University of Rochester; 2018. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1802/33903.
Council of Science Editors:
Sloma MF. Computational Tools for RNA Structure
Prediction. [Doctoral Dissertation]. University of Rochester; 2018. Available from: http://hdl.handle.net/1802/33903
University of Michigan
19.
Mustoe, Anthony M.
The Role of Topological Constraints in RNA Tertiary Folding and Dynamics.
Degree: PhD, Biophysics, 2014, University of Michigan
URL: http://hdl.handle.net/2027.42/110505
► Functional RNA molecules must fold into highly complex three-dimensional (3D) structures and undergo precise structural dynamics in order to carry out their biological functions. However,…
(more)
▼ Functional
RNA molecules must fold into highly complex three-dimensional (3D) structures and undergo precise structural dynamics in order to carry out their biological functions. However, the principles that govern
RNA 3D folding and dynamics remain poorly understood. Recent studies have proposed that topological constraints arising from the basic connectivity and steric properties of
RNA secondary structure strongly confine the 3D conformation of
RNA junctions and thus may contribute to the specificity of
RNA 3D folding and dynamics. Herein, this hypothesis is explored in quantitative detail using a combination of computational heuristic models and the specially developed coarse-grained molecular dynamics model TOPRNA.
First, studies of two-way junctions provide new insight into the significance and mechanism of action of topological constraints. It is demonstrated that topological constraints explain the directionality and amplitude of bulge-induced bends, and that long-range tertiary interactions can modify topological constraints by disrupting non-canonical pairing in internal loops. Furthermore, topological constraints are shown to define free energy landscapes that coincide with the distribution of bulge conformations in structural databases and reproduce solution NMR measurements made on bulges.
Next, TOPRNA is used to investigate the contributions of topological constraints to tRNA folding and dynamics. Topological constraints strongly constrain tRNA 3D conformation and notably discriminate against formation of non-native tertiary contacts, providing a sequence-independent source of folding specificity. Furthermore, topological constraints are observed to give rise to thermodynamic cooperativity between distinct tRNA tertiary interactions and encode functionally important 3D dynamics. Mutant tRNAs with unnatural
secondary structures are shown to lack these favorable characteristics, suggesting that topological constraints underlie the evolutionary conservation of tRNA
secondary structure. Additional studies of a non-canonical mitochondrial tRNA show that increased topological constraints can reduce the entropic cost of tertiary folding, and that disruptions of topological constraints explain the pathogenicity of a insertion mutation in this tRNA. UV melting experiments verify these findings.
Finally, TOPRNA is used to study the topological constraints of the 197 nucleotide Azoarcus Group I ribozyme. It is shown that topological constraints strongly confine this
RNA and provide a mechanism for encoding tertiary
structure specificity and cooperative hierarchical folding behavior.
Advisors/Committee Members: Brooks Iii, Charles L. (committee member), Al-Hashimi, Hashim (committee member), Sept, David Samuel (committee member), Fierke, Carol (committee member).
Subjects/Keywords: RNA folding; RNA dynamics; Secondary structure; tRNA; Biological Chemistry; Chemistry; Physics; Science
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APA (6th Edition):
Mustoe, A. M. (2014). The Role of Topological Constraints in RNA Tertiary Folding and Dynamics. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/110505
Chicago Manual of Style (16th Edition):
Mustoe, Anthony M. “The Role of Topological Constraints in RNA Tertiary Folding and Dynamics.” 2014. Doctoral Dissertation, University of Michigan. Accessed March 06, 2021.
http://hdl.handle.net/2027.42/110505.
MLA Handbook (7th Edition):
Mustoe, Anthony M. “The Role of Topological Constraints in RNA Tertiary Folding and Dynamics.” 2014. Web. 06 Mar 2021.
Vancouver:
Mustoe AM. The Role of Topological Constraints in RNA Tertiary Folding and Dynamics. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/2027.42/110505.
Council of Science Editors:
Mustoe AM. The Role of Topological Constraints in RNA Tertiary Folding and Dynamics. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/110505
University of Toronto
20.
Cook, Kathleen Bolton.
Targeting and Specificity of RNA-binding Proteins.
Degree: PhD, 2015, University of Toronto
URL: http://hdl.handle.net/1807/70852
► RNA-binding proteins (RBPs) are critical parts of the gene regulatory framework, but the vast majority have unknown specificity for RNA. In this thesis I undertook…
(more)
▼ RNA-binding proteins (RBPs) are critical parts of the gene regulatory framework, but the vast majority have unknown specificity for
RNA. In this thesis I undertook three projects aimed at identifying the determinants of
RNA specificity.
First, I describe the creation of a database of experimental
RNA-binding data that catalogues metazoan RBPs and published measurements of
RNA binding. This catalogue allowed me to identify that the
RNA-binding specificity of only a small fraction of RBPs was known.
In collaboration with other members of the Hughes and Morris labs, I employed RNAcompete, an in vitro microarray-based method for determining the sequence specificity of RBPs, to over 200 eukaryotic RBPs. In order to determine the optimal modelling framework, I performed a systematic comparison of motif-finding methods on the RNAcompete data. The resulting motifs capture a wide range of
RNA-binding specificities and allow for the inference of motifs for 20% of metazoan RBPs.
In the last data chapter I describe a methodology, termed RNAcompete-S, that I developed for experimentally testing and computationally identifying the sequence and
secondary structure preferences of
RNA-binding proteins. Applying this method to a panel of RBPs with diverse
RNA-binding domains and strategies allowed me to identify the known sequence and structural specificities of these proteins. Specifically, I was able to identify de novo an 18-nt motif matching the conserved SLBP binding site. I also developed an extension of the position weight matrix (PWM) motif model called sequence-
structure motifs (SSMs), which allow both visualization of sequence and structural preferences in a way similar to standard sequence logos, and allow scanning of genomic and mRNA sequences.
The work described here represents a small step toward understanding of protein specificity for
RNA sequences and structures. Further refinements of the methods and models I present are described in the concluding chapter. The
RNA-binding specificities discovered, and the experimental and computational methods developed, should help identify binding sites in targets of RBPs to understand their function in living cells.
Advisors/Committee Members: Hughes, Timothy R, Molecular and Medical Genetics.
Subjects/Keywords: Computational biology; RNA-binding proteins; RNA secondary structure; Stem-loop binding protein; 0307
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APA (6th Edition):
Cook, K. B. (2015). Targeting and Specificity of RNA-binding Proteins. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/70852
Chicago Manual of Style (16th Edition):
Cook, Kathleen Bolton. “Targeting and Specificity of RNA-binding Proteins.” 2015. Doctoral Dissertation, University of Toronto. Accessed March 06, 2021.
http://hdl.handle.net/1807/70852.
MLA Handbook (7th Edition):
Cook, Kathleen Bolton. “Targeting and Specificity of RNA-binding Proteins.” 2015. Web. 06 Mar 2021.
Vancouver:
Cook KB. Targeting and Specificity of RNA-binding Proteins. [Internet] [Doctoral dissertation]. University of Toronto; 2015. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1807/70852.
Council of Science Editors:
Cook KB. Targeting and Specificity of RNA-binding Proteins. [Doctoral Dissertation]. University of Toronto; 2015. Available from: http://hdl.handle.net/1807/70852
Georgia Tech
21.
Rogers, Emily.
A novel method for cluster analysis of RNA structural data.
Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/60232
► Functional RNA is known to contribute to a host of important biological pathways, with new discoveries being made daily. Because function is dependent on structure,…
(more)
▼ Functional
RNA is known to contribute to a host of important biological pathways, with new discoveries being made daily. Because function is dependent on
structure, computational tools that predict
secondary structure of
RNA are crucial to researchers. By far the most popular method is to predict the minimum free energy
structure as the native. However, well-known limitations of this method have led the computational
RNA community to move on to Boltzmann sampling. This method predicts an ensemble of structures sampled from the Boltzmann distribution under the Nearest Neighbor Thermodynamic Model (NNTM). Although providing a more thorough view of the folding landscape of a sequence, the Boltzmann sampling method also has the drawback of needing post-processing (i.e. data mining) in order to be meaningful. This dissertation presents a novel method of representing and clustering
secondary structures of a Boltzmann sample. In addition, it demonstrates its ability to extract the meaningful structural signal of a Boltzmann sample by identifying significant commonalities and differences. Applications include
two outstanding problems in the computational
RNA community: investigating the ill-conditioning of thermodynamic optimization under the NNTM, and predicting a consensus
structure for a set of sequences. Finally, this dissertation concludes with research performed as an intern for the Department of Defense's Defense Forensic Science Center. This work concerns analyzing the results of a DNA mixture interpretation study, highlighting the current state of forensic interpretation today.
Advisors/Committee Members: Heitsch, Christine E. (advisor), Bader, David (advisor), Aluru, Srinivas (committee member), Wartell, Roger (committee member), Aranda, Roman (committee member).
Subjects/Keywords: Computational biology; Structural biology; RNA folding; Boltzmann sampling; Cluster analysis; RNA secondary structure prediction
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rogers, E. (2018). A novel method for cluster analysis of RNA structural data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60232
Chicago Manual of Style (16th Edition):
Rogers, Emily. “A novel method for cluster analysis of RNA structural data.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 06, 2021.
http://hdl.handle.net/1853/60232.
MLA Handbook (7th Edition):
Rogers, Emily. “A novel method for cluster analysis of RNA structural data.” 2018. Web. 06 Mar 2021.
Vancouver:
Rogers E. A novel method for cluster analysis of RNA structural data. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/1853/60232.
Council of Science Editors:
Rogers E. A novel method for cluster analysis of RNA structural data. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60232
NSYSU
22.
Chang, Chia-Hung.
Accuracy Improvement for RNA Secondary Structure Prediction with SVM.
Degree: Master, Computer Science and Engineering, 2008, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319
► Ribonucleic acid (RNA) sometimes occurs in a complex structure called pseudoknots. Prediction of RNA secondary structures has drawn much attention from both biologists and computer…
(more)
▼ Ribonucleic acid (
RNA) sometimes occurs in a complex
structure called pseudoknots. Prediction of
RNA secondary structures has drawn much attention from both biologists and computer scientists. Consequently, many useful tools have been developed for
RNA secondary structure prediction, with or without pseudoknots. These tools have their individual strength and weakness. As a result, we propose a hybrid feature extraction method which integrates two prediction tools pknotsRG and NUPACK with a support vector machine (SVM). We first extract some useful features from the target
RNA sequence, and then decide its prediction tool preference with SVM classification. Our test data set contains 723
RNA sequences, where 202 pseudoknotted
RNA sequences are obtained from PseudoBase, and 521 nested
RNA sequences are obtained from
RNA SSTRAND. Experimental results show that our method improves not only the overall accuracy but also the sensitivity and the selectivity of the target sequences. Our method serves as a preprocessing process in analyzing
RNA sequences before employing the
RNA secondary structure prediction tools. The ability to combine the existing methods and make the prediction tools more accurate is our main contribution.
Advisors/Committee Members: Yow-Ling Shiue (chair), Chia-Ning Yang (chair), Yue-Li Wang (chair), Chang-Biau Yang (committee member), Shih-Chung Chen (chair).
Subjects/Keywords: RNA; secondary structure; support vector machine; machine learning; classification
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APA ·
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MLA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Chang, C. (2008). Accuracy Improvement for RNA Secondary Structure Prediction with SVM. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Chang, Chia-Hung. “Accuracy Improvement for RNA Secondary Structure Prediction with SVM.” 2008. Thesis, NSYSU. Accessed March 06, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Chang, Chia-Hung. “Accuracy Improvement for RNA Secondary Structure Prediction with SVM.” 2008. Web. 06 Mar 2021.
Vancouver:
Chang C. Accuracy Improvement for RNA Secondary Structure Prediction with SVM. [Internet] [Thesis]. NSYSU; 2008. [cited 2021 Mar 06].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Chang C. Accuracy Improvement for RNA Secondary Structure Prediction with SVM. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
NSYSU
23.
Liu, Chu-Kai.
Prediction for the Domain of RNA with Support Vector Machine.
Degree: Master, Computer Science and Engineering, 2011, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100
► The three-domain system is a biological classification of RNA. In bioinformatics, predicting the domain of RNA is helpful in the research of DNA and protein.…
(more)
▼ The three-domain system is a biological classification of
RNA. In bioinformatics, predicting the domain of
RNA is helpful in the research of DNA and protein. By reviewing the related literatures, we notice that many researches are conducted for domain prediction with only the primary
structure. However, compared with the primary
structure, the
secondary structure of an
RNA contains more discriminative information. Therefore, we propose an SVM-based prediction algorithm that considers both the features of primary and
secondary structures.
In our experiment, we adopt 1606
RNA sequences from RNase P, 5S ribosomal
RNA and snoRNA databases. The experimental results show that our algorithm achieves 96.39%, 95.70%, and 95.46% accuracies by combining three softwares of
secondary structure prediction, pknotsRG, NUPACK, and RNAstructure, respectively. Thus, our method is a new effective approach for predicting the domain of an
RNA sequence. The software implementation of our method, named RDP (
RNA Domain Prediction), is available on the Web http://bio.cse.nsysu.edu.tw/RDP/.
Advisors/Committee Members: Kuo-Tsung Tseng (chair), Chia-Ning Yang (chair), Chang-Biau Yang (committee member), Chia-Ping Chen (chair), Kuo-Si Huang (chair).
Subjects/Keywords: secondary structure; SVM; prediction; RNA; three-domain system
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Liu, C. (2011). Prediction for the Domain of RNA with Support Vector Machine. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Liu, Chu-Kai. “Prediction for the Domain of RNA with Support Vector Machine.” 2011. Thesis, NSYSU. Accessed March 06, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Liu, Chu-Kai. “Prediction for the Domain of RNA with Support Vector Machine.” 2011. Web. 06 Mar 2021.
Vancouver:
Liu C. Prediction for the Domain of RNA with Support Vector Machine. [Internet] [Thesis]. NSYSU; 2011. [cited 2021 Mar 06].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Liu C. Prediction for the Domain of RNA with Support Vector Machine. [Thesis]. NSYSU; 2011. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Pretoria
24.
[No author].
Using SetPSO to determine RNA secondary
structure
.
Degree: 2009, University of Pretoria
URL: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/
► RNA secondary structure prediction is an important field in Bioinformatics. A number of different approaches have been developed to simplify the determination of RNA molecule…
(more)
▼ RNA secondary structure prediction is an important
field in Bioinformatics. A number of different approaches have been
developed to simplify the determination of
RNA molecule structures.
RNA is a nucleic acid found in living organisms which fulfils a
number of important roles in living cells. Knowledge of its
structure is crucial in the understanding of its function.
Determining
RNA secondary structure computationally, rather than by
physical means, has the advantage of being a quicker and cheaper
method. This dissertation introduces a new Set-based Particle Swarm
Optimisation algorithm, known as SetPSO for short, to optimise the
structure of an
RNA molecule, using an advanced thermodynamic
model.
Structure prediction is modelled as an energy minimisation
problem. Particle swarm optimisation is a simple but effective
stochastic optimisation technique developed by Kennedy and
Eberhart. This simple technique was adapted to work with variable
length particles which consist of a set of elements rather than a
vector of real numbers. The effectiveness of this
structure
prediction approach was compared to that of a dynamic programming
algorithm called mfold. It was found that SetPSO can be used as a
combinatorial optimisation technique which can be applied to the
problem of
RNA secondary structure prediction. This research also
included an investigation into the behaviour of the new SetPSO
optimisation algorithm. Further study needs to be conducted to
evaluate the performance of SetPSO on different combinatorial and
set-based optimisation problems.
Advisors/Committee Members: Engelbrecht, Andries P (advisor).
Subjects/Keywords: Rna;
Secondary structure;
Setpso;
Combinatorial;
Computational intelligence;
Particle swarm optimiser;
UCTD
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Manager
APA (6th Edition):
author], [. (2009). Using SetPSO to determine RNA secondary
structure
. (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-02162009-112429/
Chicago Manual of Style (16th Edition):
author], [No. “Using SetPSO to determine RNA secondary
structure
.” 2009. Masters Thesis, University of Pretoria. Accessed March 06, 2021.
http://upetd.up.ac.za/thesis/available/etd-02162009-112429/.
MLA Handbook (7th Edition):
author], [No. “Using SetPSO to determine RNA secondary
structure
.” 2009. Web. 06 Mar 2021.
Vancouver:
author] [. Using SetPSO to determine RNA secondary
structure
. [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2021 Mar 06].
Available from: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/.
Council of Science Editors:
author] [. Using SetPSO to determine RNA secondary
structure
. [Masters Thesis]. University of Pretoria; 2009. Available from: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/
San Jose State University
25.
Mali, Meenakshee.
RNA SECONDARY STRUCTURE PREDICTION TOOL.
Degree: MS, Computer Science, 2011, San Jose State University
URL: https://doi.org/10.31979/etd.v9y6-uzac
;
https://scholarworks.sjsu.edu/etd_projects/164
► Ribonucleic Acid (RNA) is one of the major macromolecules essential to all forms of life. Apart from the important role played in protein synthesis,…
(more)
▼ Ribonucleic Acid (
RNA) is one of the major macromolecules essential to all forms of life. Apart from the important role played in protein synthesis, it performs several important functions such as gene regulation, catalyst of biochemical reactions and modification of other RNAs. In some viruses, instead of DNA,
RNA serves as the carrier of genetic information.
RNA is an interesting
subject of research in the scientific community. It has lead to important biological discoveries. One of the major problems researchers are trying to solve is the
RNA structure prediction problem. It has been found that the
structure of
RNA is evolutionary conserved and it can help to determine the functions served by them. In this project, I will be developing a tool to predict the
secondary structure of
RNA using simulated annealing. The aim of this project is to understand in detail the simulated annealing algorithm and implement it to find solutions to
RNA secondary structure. The results will be compared with the very famous tool Mfold, developed by Michael Zuker, using the minimum free energy approach.
Advisors/Committee Members: Sami Khuri, Chris Pollett, Robert Fowler.
Subjects/Keywords: RNA Secondary Structure Prediction; Bioinformatics; Other Computer Sciences
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APA (6th Edition):
Mali, M. (2011). RNA SECONDARY STRUCTURE PREDICTION TOOL. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164
Chicago Manual of Style (16th Edition):
Mali, Meenakshee. “RNA SECONDARY STRUCTURE PREDICTION TOOL.” 2011. Masters Thesis, San Jose State University. Accessed March 06, 2021.
https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164.
MLA Handbook (7th Edition):
Mali, Meenakshee. “RNA SECONDARY STRUCTURE PREDICTION TOOL.” 2011. Web. 06 Mar 2021.
Vancouver:
Mali M. RNA SECONDARY STRUCTURE PREDICTION TOOL. [Internet] [Masters thesis]. San Jose State University; 2011. [cited 2021 Mar 06].
Available from: https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164.
Council of Science Editors:
Mali M. RNA SECONDARY STRUCTURE PREDICTION TOOL. [Masters Thesis]. San Jose State University; 2011. Available from: https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164
University of Pretoria
26.
Neethling, Charles
Marais.
Using SetPSO to
determine RNA secondary structure.
Degree: Computer Science, 2009, University of Pretoria
URL: http://hdl.handle.net/2263/29202
► RNA secondary structure prediction is an important field in Bioinformatics. A number of different approaches have been developed to simplify the determination of RNA molecule…
(more)
▼ RNA secondary structure prediction is an important field
in Bioinformatics. A number of different approaches have been
developed to simplify the determination of
RNA molecule structures.
RNA is a nucleic acid found in living organisms which fulfils a
number of important roles in living cells. Knowledge of its
structure is crucial in the understanding of its function.
Determining
RNA secondary structure computationally, rather than by
physical means, has the advantage of being a quicker and cheaper
method. This dissertation introduces a new Set-based Particle Swarm
Optimisation algorithm, known as SetPSO for short, to optimise the
structure of an
RNA molecule, using an advanced thermodynamic
model.
Structure prediction is modelled as an energy minimisation
problem. Particle swarm optimisation is a simple but effective
stochastic optimisation technique developed by Kennedy and
Eberhart. This simple technique was adapted to work with variable
length particles which consist of a set of elements rather than a
vector of real numbers. The effectiveness of this
structure
prediction approach was compared to that of a dynamic programming
algorithm called mfold. It was found that SetPSO can be used as a
combinatorial optimisation technique which can be applied to the
problem of
RNA secondary structure prediction. This research also
included an investigation into the behaviour of the new SetPSO
optimisation algorithm. Further study needs to be conducted to
evaluate the performance of SetPSO on different combinatorial and
set-based optimisation problems.
Advisors/Committee Members: Engelbrecht, Andries P. (advisor).
Subjects/Keywords: Rna; Secondary
structure;
Setpso;
Combinatorial; Computational
intelligence; Particle swarm
optimiser;
UCTD
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APA (6th Edition):
Neethling, C. (2009). Using SetPSO to
determine RNA secondary structure. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/29202
Chicago Manual of Style (16th Edition):
Neethling, Charles. “Using SetPSO to
determine RNA secondary structure.” 2009. Masters Thesis, University of Pretoria. Accessed March 06, 2021.
http://hdl.handle.net/2263/29202.
MLA Handbook (7th Edition):
Neethling, Charles. “Using SetPSO to
determine RNA secondary structure.” 2009. Web. 06 Mar 2021.
Vancouver:
Neethling C. Using SetPSO to
determine RNA secondary structure. [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/2263/29202.
Council of Science Editors:
Neethling C. Using SetPSO to
determine RNA secondary structure. [Masters Thesis]. University of Pretoria; 2009. Available from: http://hdl.handle.net/2263/29202
Boston College
27.
Ding, Yang.
Parametric RNA Partition Function Algorithms.
Degree: MS, Biology, 2010, Boston College
URL: http://dlib.bc.edu/islandora/object/bc-ir:101567
► In addition to the well-characterized messenger RNA, transfer RNA and ribosomal RNA, many new classes of noncoding RNA(ncRNA) have been discovered in the past few…
(more)
▼ In addition to the well-characterized messenger
RNA,
transfer
RNA and ribosomal
RNA, many new classes of noncoding
RNA(ncRNA) have been discovered in the past few years. ncRNA has
been shown to play important roles in multiple regulation and
development processes. The increasing needs for
RNA structural
analysis software provide great opportunities on computational
biologists. In this thesis I present three highly non-trivial
RNA
parametric structural analysis algorithms: 1) RNAhairpin and
RNAmultiloop, which calculate parition functions with respect to
hairpin number, multiloop number and multiloop order, 2)
RNAshapeEval, which is based upon partition function calculation
with respect to a fixed abstract shape, and 3) RNAprofileZ, which
calculates the expected partition function and ensemble free energy
given an
RNA position weight matrix.I also describe the application
of these software in biological problems, including evaluating
purine riboswitch aptamer full alignment sequences to adopt their
consensus shape, building hairpin and multiloop profiles for
certain Rfam families, tRNA and pseudoknotted
RNA secondary
structure predictions. These algorithms hold the promise to be
useful in a broad range of biological problems such as structural
motifs search, ncRNA gene finders, canonical and pseudoknotted
secondary structure predictions.
Advisors/Committee Members: Peter Clote (Thesis advisor).
Subjects/Keywords: Dynamic Programming; Partition Function; RNA; Secondary Structure; Thermodynamics
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APA (6th Edition):
Ding, Y. (2010). Parametric RNA Partition Function Algorithms. (Masters Thesis). Boston College. Retrieved from http://dlib.bc.edu/islandora/object/bc-ir:101567
Chicago Manual of Style (16th Edition):
Ding, Yang. “Parametric RNA Partition Function Algorithms.” 2010. Masters Thesis, Boston College. Accessed March 06, 2021.
http://dlib.bc.edu/islandora/object/bc-ir:101567.
MLA Handbook (7th Edition):
Ding, Yang. “Parametric RNA Partition Function Algorithms.” 2010. Web. 06 Mar 2021.
Vancouver:
Ding Y. Parametric RNA Partition Function Algorithms. [Internet] [Masters thesis]. Boston College; 2010. [cited 2021 Mar 06].
Available from: http://dlib.bc.edu/islandora/object/bc-ir:101567.
Council of Science Editors:
Ding Y. Parametric RNA Partition Function Algorithms. [Masters Thesis]. Boston College; 2010. Available from: http://dlib.bc.edu/islandora/object/bc-ir:101567
University of Cambridge
28.
Sanford, Thomas James.
Mechanistic analysis of the Zika virus translation-replication switch.
Degree: PhD, 2020, University of Cambridge
URL: https://www.repository.cam.ac.uk/handle/1810/298760
► The genomes of positive-sense RNA viruses are required for both translation and replication during infection. These two processes are antagonistic in nature, each requiring the…
(more)
▼ The genomes of positive-sense RNA viruses are required for both translation and replication during infection. These two processes are antagonistic in nature, each requiring the RNA template in opposite directions. Thus, the balance between these processes during infection must be tightly regulated. Zika virus (ZIKV) is a capped, positive-sense RNA virus of the Flavivirus genus, which contains several notable human pathogens including Dengue virus (DENV) and Japanese encephalitis virus. Infection with ZIKV has been linked to congenital microcephaly as well as Guillain-Barré syndrome in infected adults. During infection, flavivirus replication is known to require genome circularisation, mediated by long-range RNA- RNA interactions between cis-acting elements at the 5′ and 3′ ends of the genome. This facilitates the translocation of the viral polymerase NS5 from its site of recruitment at the 5′ end of the genome to the 3′ end in order to begin negative-strand RNA synthesis. However, it is unknown how the switch from genome translation to genome replication during flavivirus infection is regulated. The work presented for this thesis uses an in vitro reconstitution approach to study translation initiation on ZIKV, in which purified components of the translational machinery are added to the RNA and 48S complex formation upon initiating codons assayed using toeprinting. This technique allows the examination of both recruitment and scanning of the 40S ribosomal subunit, as well as the influence of RNA secondary structure on this process. It is shown that ZIKV translation is cap-dependent using the canonical set of initiation factors and that, under these conditions, recruitment of the viral polymerase to the 5′ proximal stem- loop specifically inhibits translation initiation. Furthermore, circularisation of ZIKV and DENV genomes abrogates viral translation initiation through inhibition of ribosome scanning, subsequently re-directing the ribosome towards upstream near-cognate initiation codons. Conversely, the linear form of the viral genome, which predominates during infection, is shown to be translation-competent. This thesis therefore proposes a model by which the viral polymerase and dynamic viral genome conformations together prime a genome for replication by ensuring ribosome clearance, thus allowing replication to occur unimpeded.
Subjects/Keywords: Zika; Translation; RNA secondary structure; Replication; Flavivirus; Dengue
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APA ·
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APA (6th Edition):
Sanford, T. J. (2020). Mechanistic analysis of the Zika virus translation-replication switch. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/298760
Chicago Manual of Style (16th Edition):
Sanford, Thomas James. “Mechanistic analysis of the Zika virus translation-replication switch.” 2020. Doctoral Dissertation, University of Cambridge. Accessed March 06, 2021.
https://www.repository.cam.ac.uk/handle/1810/298760.
MLA Handbook (7th Edition):
Sanford, Thomas James. “Mechanistic analysis of the Zika virus translation-replication switch.” 2020. Web. 06 Mar 2021.
Vancouver:
Sanford TJ. Mechanistic analysis of the Zika virus translation-replication switch. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Mar 06].
Available from: https://www.repository.cam.ac.uk/handle/1810/298760.
Council of Science Editors:
Sanford TJ. Mechanistic analysis of the Zika virus translation-replication switch. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/298760
University of Oxford
29.
Anderson, James William Justin.
Probabilistic models of RNA secondary structure.
Degree: PhD, 2013, University of Oxford
URL: http://ora.ox.ac.uk/objects/uuid:3e58e9d9-c58d-4616-8e88-4082d1ca0e2a
;
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.581305
► This thesis develops probabilistic models of RNA secondary structure. The first chapter introduces RNA secondary structure prediction, in particular stochastic context-free grammars (SCFGs), and considers…
(more)
▼ This thesis develops probabilistic models of RNA secondary structure. The first chapter introduces RNA secondary structure prediction, in particular stochastic context-free grammars (SCFGs), and considers a novel method for automated design of SCFGs. Many SCFGs are found with a similar predictive quality as those commonly used for RNA secondary structure prediction. The second chapter discusses the effect alignment quality, evolutionary distance between sequences, and number of sequences in an alignment have on RNA secondary structure prediction. By combining statistical alignment and SCFG models we can, in a statistically sound setting, average structure predictions over the space of alignments to decrease loss created by poor alignments. The third chapter incorporates additional biological information about RNA secondary structure formation into the decoding of the SCFG posterior distribution. Combining iterative helix formation, phylogenetic modelling, and a distance function between alignment columns leads to the an improvement in the accuracy of comparative RNA secondary structure prediction. Finally, appendices briefly discuss further work concerning probabilistic models of RNA secondary structure which may be of interest to the reader.
Subjects/Keywords: 572.88; Mathematical genetics and bioinformatics (statistics); bioinformatics; RNA secondary structure
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APA ·
Chicago ·
MLA ·
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Export
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Manager
APA (6th Edition):
Anderson, J. W. J. (2013). Probabilistic models of RNA secondary structure. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:3e58e9d9-c58d-4616-8e88-4082d1ca0e2a ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.581305
Chicago Manual of Style (16th Edition):
Anderson, James William Justin. “Probabilistic models of RNA secondary structure.” 2013. Doctoral Dissertation, University of Oxford. Accessed March 06, 2021.
http://ora.ox.ac.uk/objects/uuid:3e58e9d9-c58d-4616-8e88-4082d1ca0e2a ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.581305.
MLA Handbook (7th Edition):
Anderson, James William Justin. “Probabilistic models of RNA secondary structure.” 2013. Web. 06 Mar 2021.
Vancouver:
Anderson JWJ. Probabilistic models of RNA secondary structure. [Internet] [Doctoral dissertation]. University of Oxford; 2013. [cited 2021 Mar 06].
Available from: http://ora.ox.ac.uk/objects/uuid:3e58e9d9-c58d-4616-8e88-4082d1ca0e2a ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.581305.
Council of Science Editors:
Anderson JWJ. Probabilistic models of RNA secondary structure. [Doctoral Dissertation]. University of Oxford; 2013. Available from: http://ora.ox.ac.uk/objects/uuid:3e58e9d9-c58d-4616-8e88-4082d1ca0e2a ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.581305
University of the Western Cape
30.
Tanov, Emil Pavlov.
The identification of biologically important secondary structures in disease-causing RNA viruses.
Degree: 2012, University of the Western Cape
URL: http://hdl.handle.net/11394/4562
► Viral genomes consist of either deoxyribonucleic acid (DNA) or ribonucleic acid (RNA). The viral RNA molecules are responsible for two functions, firstly, their sequences contain…
(more)
▼ Viral genomes consist of either deoxyribonucleic acid (DNA) or ribonucleic acid (
RNA). The viral
RNA molecules are responsible for two functions, firstly, their sequences contain the genetic code, which encodes the viral proteins, and secondly, they may form structural elements important in the regulation of the viral life-cycle. Using a host of computational and bioinformatics techniques we investigated how predicted
secondary structure may influence the evolutionary dynamics of a group of single-stranded
RNA viruses from the Picornaviridae family. We detected significant and marginally significant correlations between regions predicted to be structured and synonymous substitution constraints in these regions, suggesting that selection may be acting on those sites to maintain the integrity of certain structures. Additionally, coevolution analysis showed that nucleotides predicted to be base paired, tended to co-evolve with one another in a complimentary fashion in four out of the eleven species examined. Our analyses were then focused on individual structural elements within the genome-wide predicted structures. We ranked the predicted
secondary structural elements according to their degree of evolutionary conservation, their associated synonymous substitution rates and the degree to which nucleotides predicted to be base paired coevolved with one another. Top ranking structures coincided with well characterized
secondary structures that have been previously described in the literature. We also assessed the impact that genomic
secondary structures had on the recombinational dynamics of picornavirus genomes, observing a strong tendency for recombination breakpoints to occur in non-coding regions. However, convincing evidence for the association between the distribution of predicted
RNA structural elements and breakpoint clustering was not detected.
Advisors/Committee Members: Harkins, Gordon W (advisor), Christoffels, Alan (advisor).
Subjects/Keywords: Bioinformatics techniques;
Viral genomes;
Genomic secondary structure;
RNA viruses
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Tanov, E. P. (2012). The identification of biologically important secondary structures in disease-causing RNA viruses.
(Thesis). University of the Western Cape. Retrieved from http://hdl.handle.net/11394/4562
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Tanov, Emil Pavlov. “The identification of biologically important secondary structures in disease-causing RNA viruses.
” 2012. Thesis, University of the Western Cape. Accessed March 06, 2021.
http://hdl.handle.net/11394/4562.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Tanov, Emil Pavlov. “The identification of biologically important secondary structures in disease-causing RNA viruses.
” 2012. Web. 06 Mar 2021.
Vancouver:
Tanov EP. The identification of biologically important secondary structures in disease-causing RNA viruses.
[Internet] [Thesis]. University of the Western Cape; 2012. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/11394/4562.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Tanov EP. The identification of biologically important secondary structures in disease-causing RNA viruses.
[Thesis]. University of the Western Cape; 2012. Available from: http://hdl.handle.net/11394/4562
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
◁ [1] [2] [3] [4] ▶
. 
Open Access Theses and Dissertations