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@PHDTHESIS{Rahman:282308,
author = {Rahman, Raza-ur},
title = {{D}evelopment of novel analysis and data integration
systems to understand human gene regulation},
school = {Georg-August-Universität Göttingen},
type = {Dissertation},
reportid = {DZNE-2025-01278},
pages = {123 p.},
year = {2018},
note = {Dissertation, Georg-August-Universität Göttingen, 2018},
abstract = {Abstract: This thesis covers a very broad range of
bioinformatics methods ranging from the development of the
analysis pipeline to the data integration and development of
an expression atlas (database and web application
development). In addition, an in silico method was developed
to annotate genome with novel features, and predicting
diseases based on the expression profiles. Development of
online analysis of small RNA sequencing data: Small RNA
(sRNA) are biomolecules that play important roles in
organismal health and disease; as such, sRNA dysregulation
can cause severe diseases. The modern method of choice for
sRNA expression profiling is sRNA sequencing (sRNA-seq).
There are several sRNA-seq analysis platforms available that
differ in their analysis portfolio, performance, and
user-friendliness. However, these analysis platforms lack
one or more important features such as disease biomarkers
identification, detection of viral and bacterial infections
in sRNA-seq samples, storage of novel predicted miRNAs,
multivariate differential expression(DE) analysis and
automated submission of jobs via an application programming
interface (API). To this end, we developed an online
analysis tool called as Oasis 2, a fast and flexible web
application which provide many different sRNA-seq analysis
options on a single platform. Its major functionalities
include quantification of different sRNA species,
multivariate differential expression (DE), identification of
biomarkers for disease, prediction and storage of novel
miRNAs with proper universally accepted nomenclature,
identification of infection or contamination,
functional/enrichment analysis. Additionally Oasis2 enables
users to perform all these different analysis over the web
application, as well as over API for automatic submission.
Oasis 2 generates downloadable interactive web reports for
easy visualization, exploration, and analysis of data on a
local system. In future, small RNA editing, modification,
and mutation events can be implemented in Oasis 2.
Additionally the reported output for bacterial and viral
infections and contaminations can be enhanced. Development
of small RNA expression atlas (SEA) : As discussed in
Section 2 that sRNAs have crucial role in organismal health
and disease, yet the number and scope of the currently
available sRNA-seq expression repositories are very limited.
For example, most of the sRNA-seq repositories support one
or two organisms and none of these databases provide search
by ontological terms. Considering these shortcomings, we
developed sRNA expression atlas (SEA), a data repository to
store sRNA expression profiles along with the experimental
details such as organism, tissue, cell type, disease, age,
gender and technical details like sequencer, kit and barcode
etc. Additionally we built a web application that allows end
users to query and visualize sRNA expression profiles in an
interactive manner. SEA allows users to search for
ontology-based queries, supporting single or combined
searches for five pre-defined terms such as organism,
tissue, disease, cell type, and cell line across different
experiments. Currently it contains expression and
meta-information of over 2,500 sRNA-seq samples across 10
organisms. As far as we are aware, SEA is the only sRNA-seq
database that supports ontology-based queries. In the
future, additional available meta-information such as age,
gender, developmental stage, genotype as well as technical
experimental details can standardized (connect to
ontologies) and the search could be enhanced to allow users
to query sRNA expression profiles based on them. Moreover,
further sRNA-seq datasets should be incorporated into SEA.
Lastly, one can store DE and biomarker prediction results
for all the sRNA-seq datasets having at-least two groups
(such control and diseased) and make them query-able and
comparable across different datasets. Prediction and
validation of mutually exclusive splicing of exons :
Mutually exclusive splicing of exons (MXEs) is a mechanism
of functional gene and protein diversification with
important roles in organismal development and diseases, such
as in SNAP-25 as part of the neuroexocytosis machinery.
Additionally mutations in MXEs have been shown to cause
diseases such as Timothy syndrome (missense mutation in the
CACNA1C gene). Despite their important roles, the current
knowledge of human MXEs is very limited, that is to say,
that the human genome annotation (Gen-Bank v. 37.3) contains
only 158 MXEs in 79 protein-coding genes. To this end, an in
silco method was developed to predict MXEs based on sequence
similarity, similar lengths, and reading frame conservation;
predicted MXEs were validated using the publicly available
billions of RNA-seq reads. Based on this method the current
knowledge of human MXEs is increased by almost an order of
magnitude from 158 to 1,399 MXEs. These MXEs shows tissue
and developmental stage specific expression and also have
potential roles in diseases. As a heuristic approach was
used for the prediction of MXEs in this thesis, in the
future a machine learning approach can be used for the
prediction of MXEs, which may increase the predicting power
of the method and could result in further novel MXEs.},
cin = {AG Bonn 1},
cid = {I:(DE-2719)1410003},
pnm = {899 - ohne Topic (POF4-899)},
pid = {G:(DE-HGF)POF4-899},
typ = {PUB:(DE-HGF)11},
url = {https://pub.dzne.de/record/282308},
}
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