Designed for a one or two semester senior undergraduate or graduate bioinformatics course, "Statistical Bioinformatics" takes a broad view of the subject - not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications. The inclusion of R code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the important foundation needed to conduct bioinformatics. This book integrates biological, statistical and computational concepts. It includes R & SAS code. It provides coverage of complex statistical methods in context with applications in bioinformatics. Exercises and examples aid teaching and learning presented at the right level. Bayesian methods and the modern multiple testing principles are presented in this one convenient book.