Preface
The intention to write this textbook came from the fact that when I started to deal with NGS
data analysis, I felt like I was abandoned in a jungle. A jungle of databases, scripts in
different programming languages, packages, software tools, repositories, etc. What you
need from all these components for what purpose of NGS analysis, I had to painstakingly
learn from numerous tutorials, publications (and there are plenty of them), books and by
attending courses. I also had to find a lot of information on specific problem solutions in
bioinformatics forums such as Biostars (https://www.biostars.org/). A basic initial structure
regarding the “right” approach to get solid results from the NGS data on a defined problem
would have been more than desirable. This textbook is intended to facilitate these
circumstances and introduce NGS technology, its application and the analysis of the data
obtained. However, it should be kept in mind that this textbook does not cover all
possibilities of NGS data analysis, but rather provides a theoretical understanding, based
on a few practical examples, to give a basic orientation in dealing with biological sequences
and their computer-based analysis.
Computational biology has developed rapidly over the last decades and continues to do
so. Bioinformatics not only deals with NGS data, but also with molecular structures,
enzymatic activity, medical and pharmacological statistics, to name just a few topics.
Nevertheless, the analysis of biological sequences has become a very important part in
bioinformatics, both in the natural sciences and in medicine. In order to enable you to
handle NGS data professionally and to be able to answer specific research questions about
your sequencing data without having to give your data into other hands and to invest a lot
of money for it, this textbook is meant to be a practical guide. In addition, the experimental
procedure (library preparation) required prior to the actual sequencing as well as the most
common sequencing technologies currently available on the market is also illustrated.
Protocols should be viewed as guidelines, not as rules that guarantee success. It is still
necessary to think by yourself—NGS data analysts need to recognize the complexity of
living organisms, respond dynamically to variations, and understand when methods and
protocols are not suited to a data set. Therefore, a detailed documentation of all analysis
steps carried out with a note of the reason why the respective step was taken is absolutely
v
The intention to write this textbook came from the fact that when I started to deal with NGS
data analysis, I felt like I was abandoned in a jungle. A jungle of databases, scripts in
different programming languages, packages, software tools, repositories, etc. What you
need from all these components for what purpose of NGS analysis, I had to painstakingly
learn from numerous tutorials, publications (and there are plenty of them), books and by
attending courses. I also had to find a lot of information on specific problem solutions in
bioinformatics forums such as Biostars (https://www.biostars.org/). A basic initial structure
regarding the “right” approach to get solid results from the NGS data on a defined problem
would have been more than desirable. This textbook is intended to facilitate these
circumstances and introduce NGS technology, its application and the analysis of the data
obtained. However, it should be kept in mind that this textbook does not cover all
possibilities of NGS data analysis, but rather provides a theoretical understanding, based
on a few practical examples, to give a basic orientation in dealing with biological sequences
and their computer-based analysis.
Computational biology has developed rapidly over the last decades and continues to do
so. Bioinformatics not only deals with NGS data, but also with molecular structures,
enzymatic activity, medical and pharmacological statistics, to name just a few topics.
Nevertheless, the analysis of biological sequences has become a very important part in
bioinformatics, both in the natural sciences and in medicine. In order to enable you to
handle NGS data professionally and to be able to answer specific research questions about
your sequencing data without having to give your data into other hands and to invest a lot
of money for it, this textbook is meant to be a practical guide. In addition, the experimental
procedure (library preparation) required prior to the actual sequencing as well as the most
common sequencing technologies currently available on the market is also illustrated.
Protocols should be viewed as guidelines, not as rules that guarantee success. It is still
necessary to think by yourself—NGS data analysts need to recognize the complexity of
living organisms, respond dynamically to variations, and understand when methods and
protocols are not suited to a data set. Therefore, a detailed documentation of all analysis
steps carried out with a note of the reason why the respective step was taken is absolutely
v