Modern biology is undergoing an historical transformation, becoming – among other things – increasingly data driven. A combination of statistical, computational, and biological methods has become the norm in modern genomic research. Of course this is at odds with the standard organization of university curricula, which typically focus on only one of these three subjects. It is hard enough to provide a good synthesis of computer science and statistics, let alone to include molecular biology! Yet, the importance of the algorithms typical of this field can only be appreciated within their biological context, their results can only be interpreted within a statistical framework, and a basic knowledge of all three areas is a necessary condition for any research project.

We believe that users of software should know something about the algorithms behind the results that are presented, and software designers should know something about the problems that will be attacked with their tools. We also believe that scientific ideas need to be understood within their context, and are often best communicated to students by means of examples and case studies.

This book addresses just that need: providing a rigorous yet accessible introduction to this interdisciplinary field, one that can be read by both biologically and computationally minded students, and that is based on case studies.