main.tex

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\title{Introduction to \\ Bioinformatics}
\date{First Edition}
\author[Bla\v{z} Zupan and Toma\v{z} Curk]{B. Zupan and T. Curk}
\publisher{University of Ljubljana}

%%
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%\usepackage{microtype}

\usepackage{lipsum} %todo sample text, remove when ready
\usepackage{booktabs} % For nicely typeset tabular material

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% Prints an asterisk that takes up no horizontal space.
% Useful in tabular environments.
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%%
% Prints a trailing space in a smart way.
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% Some shortcuts for Tufte's book titles.  The lowercase commands will
% produce the initials of the book title in italics.  The all-caps commands
% will print out the full title of the book in italics.
% todo: remove
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% Prints the month name (e.g., January) and the year (e.g., 2008)
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% Macros for typesetting the documentation
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% Generates the index
\usepackage{makeidx}
\makeindex

%%%% Kevin Godny's code for title page and contents from https://groups.google.com/forum/#!topic/tufte-latex/ujdzrktC1BQ
\makeatletter
\renewcommand{\maketitlepage}{%
\begingroup%
\setlength{\parindent}{0pt}

{\fontsize{24}{24}\selectfont\textit{\@author}\par}

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\vfill{\fontsize{14}{14}\selectfont\textit{\@publisher}\par}

\thispagestyle{empty}
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}
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\titlecontents{part}%
    [0pt]% distance from left margin
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    {\allcaps{Part~\thecontentslabel}\allcaps}% before w/ label (label = ``Part I'')
    {\allcaps{Part~\thecontentslabel}\allcaps}% before w/o label
    {}% filler and page (leaders and page num)
    [\vspace*{0.5\baselineskip}]% after

\titlecontents{chapter}%
    [4em]% distance from left margin
    {}% above (global formatting of entry)
    {\contentslabel{2em}\textit}% before w/ label (label = ``Chapter 1'')
    {\hspace{0em}\textit}% before w/o label
    {\qquad\thecontentspage}% filler and page (leaders and page num)
    [\vspace*{0.5\baselineskip}]% after
%%%% End additional code by Kevin Godby

\usepackage{listings}
\lstset{
    language=Python,
    basicstyle=\ttfamily\small,          % Code font style
    keywordstyle=\color{blue}\bfseries,  % Keywords in blue
    commentstyle=\color{gray},           % Comments in gray
    stringstyle=\color{red},             % Strings in red
    frame=lines,                         % Adds frame around the code
    numbers=none,                        % No line numbers
    showstringspaces=false,              % Don't show spaces in strings
    backgroundcolor=\color{white},       % White background (default)
}

\usepackage{multirow}
\usepackage{array}

\begin{document}

% Front matter
\frontmatter

% r.3 full title page
\maketitle


% v.4 copyright page
\newpage
\begin{fullwidth}
~\vfill
\thispagestyle{empty}
\setlength{\parindent}{0pt}
\setlength{\parskip}{\baselineskip}
Copyright \copyright\ \the\year\ \thanklessauthor

\par\smallcaps{Published by \thanklesspublisher}

\par This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

\par\textit{Draft Edition, \monthyear}
\end{fullwidth}

% r.5 contents
\tableofcontents


% r.9 introduction
\cleardoublepage
\chapter*{Introduction}

This book, {\em Introduction to Bioinformatics}, is designed for computer scientists who want a practical, data-driven approach to understanding biological data. It assumes no prior knowledge of biology, but does require a basic understanding of algorithms, computational thinking, and some familiarity with Python programming. The goal is to provide a gradual learning curve that introduces bioinformatics concepts in a way that is both accessible and focused on computational methods.

In recent years, biotechnology has advanced to the point where laboratories worldwide are able to collect vast amounts of molecular data. Coupled with the wealth of data already available online and numerous annotated datasets, computational approaches have become essential. Today, almost all biological research relies heavily on data, and the sheer volume requires experts skilled in data science.

In this course, we aim to equip students with the tools and knowledge necessary to address the challenges posed by this influx of biological data. Through the use of algorithms, models and computational strategies, students will be able to derive meaningful insights. As biological research increasingly relies on large-scale data analysis, skills in statistics, data visualization, and machine learning are critical. Statistical methods help extract meaningful patterns from noisy data, while machine learning and artificial intelligence provide powerful techniques for predictive modeling and pattern recognition. Data visualization is key to interpreting complex data sets, allowing scientists to effectively explore trends and relationships. By integrating these approaches, this book and course prepare readers to navigate the computational landscape of modern biology and contribute to cutting-edge bioinformatics research.

%%
% Start the main matter (normal chapters)
\mainmatter

\part{Molecular Biology}
\input{c-molbio.tex}
\input{c-history.tex}
\input{c-genome.tex}
\input{c-genes.tex}
\input{c-alignment.tex}

\backmatter

% \bibliography{main}
% \bibliographystyle{plainnat}


\printindex

\end{document}