Bioinformatics for Evolutionary Biologists: A Problems Approach - Tapa dura

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Bernhard Haubold studied Natural Sciences at Cambridge University, UK and completed his DPhil in bBacterial population genetics at Oxford University, UK in 1998. After training as a Post-Doc at the Max-Planck-Institute for Chemical Ecology in Jena, Germany he worked in the biotech industry before taking on a professorship in Bioinformatics at the University of Applied Sciences, Weihenstephan, Germany in 2003. In 2008, he moved to his current post as a researcher at the Max-Planck-Institute for Evolutionary Biology in Plön, Germany. He also teaches Bioinformatics at Lübeck University. Together with Thomas Wiehe he is the author of Introduction to Computational Biology - An Evolutionary Approach, which was published by Birkhäuser in 2006.

Angelika Börsch-Haubold studied Pharmacy at Freiburg University, Germany and Pharmacology at Cambridge University, UK. She completed her DPhil in Pharmacology at Oxford University, UK in 1997, and was a junior research fellow at Linacre College, Oxford between 1997 and 1998. From 2005 to 2008, she taught various courses at the University of Applied Sciences, Weihenstephan, Germany. From 2008 to 2014 she worked as a researcher at the Max-Planck-Institute for Evolutionary Biology in Plön, Germany. She has contributed teaching material to Science in School and was involved in translating the fifth edition of The Molecular Biology of the Cell into German.

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This self-contained textbook covers fundamental aspects of sequence analysis with special emphasis on evolutionary biology, including sequence alignment, exact matching, phylogeny reconstruction, and coalescent simulation. It addresses these topics through a series of over 800 computer problems, ranging from elementary to research level, to enable learning by doing. Students solve the problems in the same computational environment used for decades in science - the Unix command line. This is available on all four major operating systems for PCs: Windows, macOS, chromeOS, and Linux. To learn using this powerful system, students analyze sample sequence data by applying generic tools, bioinformatics software, and over 50 programs specifically written for this course and available via GitHub. The solutions for all problems are included, making the book ideal for self-study. Problems are grouped into sections headed by an introduction and a list of new terms. By using practical computingto explore sequence data in an evolutionary context, the book enables readers to tackle their own computational problems.