Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex
data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in
the statistical methods necessary to analyze such data are following closely behind the advances in data generation
methods. The statistical methods required by bioinformatics present many new and difficult problems for the research
community.
This book provides an introduction to some of these new methods. The main biological topics treated include sequence
analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical
techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov
models, and multiple testing methods.
The second edition features new chapters on microarray analysis and on statistical inference, including a discussion
of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution.
Much material has been clarified and reorganized.
The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical
methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The
earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis
on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should
be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory
courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood
from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the
end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.