Identification of small non-coding RNA targets using computational predictions and high throughput sequencing data

Gumienny, Rafał Wojciech. Identification of small non-coding RNA targets using computational predictions and high throughput sequencing data. 2016, Doctoral Thesis, University of Basel, Faculty of Science.


Official URL: http://edoc.unibas.ch/diss/DissB_12634

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Although non-coding RNAs have been known for a relatively long time, they have
largely been viewed as having a passive role in cellular processes. Ribosomal
RNAs were to thought to serve as a scaffold of the protein production machinery,
tRNAs as transporters for amino acids and even the protein-coding mRNAs were
seen as a passive template for protein synthesis. A sign of a revolution was
perhaps visible with the discovery of small nuclear and nucleolar
RNAs (snRNAs and snoRNAs), but it was not until the year 2000
with the discovery of the let-7 microRNAs that the revolution began.
In microRNAs, a totally new layer of gene regulation was uncovered,
leading to the revision of our understanding of the types of RNA
molecules and their roles in the cell: RNAs are not viewed anymore as
passive, but capable of regulating a vast number of cellular processes.
Most often they serve as guides for ribonucleoprotein complexes that
regulate the processing or expression of target RNAs. Recently developed
high-throughput technologies enabled identification of many
long and small non-coding RNAs. However, the identification of their
targets has remained challenging, in spite of the recently proposed
high-throughput sequencing-based or computational approaches. In
this work, we aimed to identify the targets of two large groups of
RNAs: miRNAs (as well as their exogenous counterparts, the small
interfering RNAs (siRNAs)) and snoRNAs.
MicroRNAs (miRNAs) are ~21 nucleotides long non-coding RNAs
that induce gene expression silencing by guiding Argonaute proteins
to target mRNAs. This pathway is exploited to silence gene expression by means
of siRNAs, that are designed to silence the expression
of specific genes. Functioning similar to miRNAs, siRNAs act not only
on the intended target, but also other transcripts called off-targets.
In a first sub-project we combined the MIRZA biophysical model of
miRNA-target interaction that was previously developed in the group
with structural and sequence features of putative target sites to efficiently
predict both miRNA and siRNA targets on a genome-wide
scale. Starting from the observation that guide RNAs can be captured
bound to their targets in high-throughput data sets, we then revisited
the identification of snoRNA targets. Although snoRNAs are known
for more than 30 years, some of them do not have known targets. To
reveal these, we have developed novel methods to analyse the data
obtained by crosslinking and immunoprecipitation of core snoRNP
proteins as well as by the RiboMeth-seq method that detects 2’-Omethylation sites.
This work provides high-quality sets of miRNA and snoRNA targets and sets the
ground for further analysis of their
complex network of interactions.
Advisors:Zavolan, Mihaela and Filipowicz, Witold
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (Zavolan)
UniBasel Contributors:Gumienny, Rafal Wojciech
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:12634
Thesis status:Complete
Number of Pages:1 Online-Ressource (xv, 122 Seiten)
Identification Number:
edoc DOI:
Last Modified:20 Jun 2018 11:38
Deposited On:20 Jun 2018 11:38

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