Abstract
Bioinformatics has evolved to a scientific discipline that is central for discoveries in genetics, molecular biology, biotechnology and medicine. Over the last decades a huge set of databases, data structures, methods and algorithms has been developed in this scientific field.
In this work, we provide a comprehensive overview of the underlying biotechnologies, such as microarrays and next generation sequencing, the data types, approaches, methodology and algorithms. The aim of this master thesis is to provide those topics in the form of a textbook that can serve as a basis of a University lecture.
There are at least two aspects that make this effort challenging:
1. The interdisciplinary nature of Bioinformatics. Bioinformatics can be considered as a science in the intersection of computer science, molecular biology and mathematics. Thus, inhomogeneous prior knowledge about the subject has to be taken into account when content such as algorithms, the programming language R, statistics, biotechnologies, is presented.
2. A high amount of technical details. There is a certain danger that the content loses itself into “not seeing the woods from all the trees”. On the other hand, each of the presented concepts has a wide scope and many details are necessary to get an understanding.
This work uses a pedagogical concept that separates the details as much as possible from the “big picture”. Boxes with explanations, footnotes for further details, code snippets to explain an algorithm, pictures, illustrations and graphics to make it understandable are an integral part of both the script and the slides that are developed from it. Difficult concepts are explained in two or three separate ways, so that they can be understood by as many readers as possible. Things are always put into context (both within this lecture and across others), so that the knowledge that builds up with the reader is not a collection of seemingly unrelated bits and pieces.
Overall, this work presents a new, comprehensive overview of methods from two main bioinformatics areas - genomics and transcriptomics - in the form of a textbook with a focus on pedagogical presentation of the content.
In this work, we provide a comprehensive overview of the underlying biotechnologies, such as microarrays and next generation sequencing, the data types, approaches, methodology and algorithms. The aim of this master thesis is to provide those topics in the form of a textbook that can serve as a basis of a University lecture.
There are at least two aspects that make this effort challenging:
1. The interdisciplinary nature of Bioinformatics. Bioinformatics can be considered as a science in the intersection of computer science, molecular biology and mathematics. Thus, inhomogeneous prior knowledge about the subject has to be taken into account when content such as algorithms, the programming language R, statistics, biotechnologies, is presented.
2. A high amount of technical details. There is a certain danger that the content loses itself into “not seeing the woods from all the trees”. On the other hand, each of the presented concepts has a wide scope and many details are necessary to get an understanding.
This work uses a pedagogical concept that separates the details as much as possible from the “big picture”. Boxes with explanations, footnotes for further details, code snippets to explain an algorithm, pictures, illustrations and graphics to make it understandable are an integral part of both the script and the slides that are developed from it. Difficult concepts are explained in two or three separate ways, so that they can be understood by as many readers as possible. Things are always put into context (both within this lecture and across others), so that the knowledge that builds up with the reader is not a collection of seemingly unrelated bits and pieces.
Overall, this work presents a new, comprehensive overview of methods from two main bioinformatics areas - genomics and transcriptomics - in the form of a textbook with a focus on pedagogical presentation of the content.
| Original language | English |
|---|---|
| Qualification | Master |
| Awarding Institution |
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| Supervisors/Reviewers |
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| Publication status | Published - May 2021 |
Fields of science
- 102019 Machine learning
- 102004 Bioinformatics
- 102032 Computational intelligence
- 101028 Mathematical modelling
- 101016 Optimisation
- 101031 Approximation theory
- 102020 Medical informatics
- 101019 Stochastics
- 102003 Image processing
- 103029 Statistical physics
- 101018 Statistics
- 101017 Game theory
- 102001 Artificial intelligence
- 202017 Embedded systems
- 101015 Operations research
- 101014 Numerical mathematics
- 101029 Mathematical statistics
- 101026 Time series analysis
- 101024 Probability theory
- 102013 Human-computer interaction
- 101027 Dynamical systems
- 305907 Medical statistics
- 101004 Biomathematics
- 305905 Medical informatics
- 102033 Data mining
- 102 Computer Sciences
- 305901 Computer-aided diagnosis and therapy
- 106007 Biostatistics
- 102018 Artificial neural networks
- 106005 Bioinformatics
- 202037 Signal processing
- 202036 Sensor systems
- 202035 Robotics
JKU Focus areas
- Digital Transformation
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