Characterization of post-transcriptional regulatory network of RNA-binding proteins using computational predictions and deep sequencing data

Mohsen, Khorshid. Characterization of post-transcriptional regulatory network of RNA-binding proteins using computational predictions and deep sequencing data. 2013, Doctoral Thesis, University of Basel, Faculty of Science.


Official URL: http://edoc.unibas.ch/diss/DissB_10635

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This report is divided into three parts: Data Analysis, Mathematical
Modeling and Conclusion and future directions. In the Data Analysis part,
various methods and tools for characterizing the post-transcriptional
regulatory networks of RNA-binding proteins are discussed and applied.
Chapter 2 introduces PAR-CLIP, a method for transcriptomewide
identification of RNA binding proteins at nucleotide resolution.
PAR-CLIP was successfully applied on RNA binding proteins and
their binding specificity was characterized.
Partly due to their vast volume, the data that were so far generated
in CLIP experiments have not been put in a form that enables fast and
interactive exploration of binding sites. To address this need, Chapter
3 presents CLIPZ, which is a database and analysis environment
for various kinds of deep sequencing (and in particular CLIP) data,
that aims to provide an open-access repository of information for
post-transcriptional regulatory elements.
Chapter 4 revisits various CLIP methods. A set of ideas in terms
of both experimental protocols and data analysis are presented to
improve the quality and reproducibility of such experiments. In
general, cytoplasmic RNAs are isolated in CLIP experiments. Like
many high-throughput experiments, CLIP has a certain amount of
isolated RNAs which do not represent regulatory binding sites. To
improve the quality of the obtained RNAs, a set of novel methods for
data analysis are also suggested. These methods are added as new
tools to the CLIPZ analysis platform.
Argonaute CLIP data could in principle be beneficial in improving
the microRNA target site predictions. However, several questions still
remain which cannot be addressed using CLIP methods. For example:
• Argonaute CLIP data by default does not reveal which microRNAs
are more likely to interact to the mRNA binding site at the
time of cross-linking.
• As mentioned earlier, biochemical and structural studies of Thermus
thermophilus Argonaute protein suggest that the
protein-RNA interaction between microRNA and the Argonaute
protein forms a physical structure that only some positions in
the microRNA become accessible to the target binding site. Having
inferred the interacting microRNA, it is also interesting to
predict the most plausible secondary structure of the hybridized
microRNA-mRNA complex.
Mathematical Modeling part of the report contains Chapter 5. This
chapter presents a novel mathematical model called MIRZA to address
the above mentioned questions. An in-depth introduction to
MIRZA is presented and its performance in terms of identifying functionally
relevant targets of microRNAs is discussed.
Finally, Conclusion and future directions part of the report contains
Chapter 6 in which discusses the main findings of the projects and
gives an outlook of where future work could be taken up.
Advisors:Zavolan, Mihaela
Committee Members:Bergmann, Sven
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (Zavolan)
UniBasel Contributors:Zavolan, Mihaela
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:10635
Thesis status:Complete
Number of Pages:191 S.
Identification Number:
edoc DOI:
Last Modified:22 Jan 2018 15:51
Deposited On:03 Feb 2014 07:46

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