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Statistical Bioinformatics: with R

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Bioinformatics is an emerging field in which statistical and computational techniques are used extensively to analyze and interpret biological data obtained from high-throughput genomic technologies. Genomic technologies allow us to monitor thousands of biological processes going on inside living organisms in one snapshot, and are rapidly growing as driving forces of research, particularly in the genetics, biomedical, biotechnology, and pharmaceutical industries.

The success of genome technologies and related techniques, however, heavily depends on correct statistical analyses of genomic data. Through statistical analyses and the graphical displays of genomic data, genomic experiments allow biologists to assimilate and explore the data in a natural and intuitive manner. The storage, retrieval, interpretation, and integration of large volumes of data generated by genomic technologies demand increasing dependence on sophisticated computer and statistical inference techniques. New statistical tools have been developed to make inferences from the genomic data obtained through genomic studies in a more meaningful way.

This textbook is of an interdisciplinary nature, and material presented here can be covered in a one- or two-semester course. It is written to give a solid base in statistics while emphasizing applications in genomics. It is my sincere attempt to integrate different fields to understand the high-throughput biological data and describe various statistical techniques to analyze data. In this textbook, new methods based on Bayesian techniques,MCMCmethods, likelihood functions, design of experiments, and nonparametric methods, along with traditional methods, are discussed. Insights into some useful software such as BAMarray, ORIGEN, and SAM are provided.

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